1 Number one is not a surprise because you may hardly remember an annual top list that was not headed by the 7 Habits. Its decades of popularity sometimes make you wonder: Is anyone left on the planet who doesn’t know (or at least downloaded) the summary? If so – here’s the link. Book Summary To be…
The IEC is pleased to welcome Uruguay as its newest Associate Member. The Instituto Uruguayo de Normas Tecnicas (UNIT) will now host the IEC National Committee of Uruguay.
Learning and Development leaders, particularly within large enterprises, are often at the forefront of ushering in change. Whether it’s in the form of new learning technologies, evolving content strategies, or shifts in educational paradigms, change demands acknowledgment and adept management. The challenge? Effectively navigating the intricate process of change management.
We’re not just talking about any change but transformational change—the kind that reshapes how your organization approaches learning and development at its core. A thoughtful, systematic approach is critical for success, and too often, companies spend too little time socializing and evangelizing why change is necessary, let alone how it will happen.
Before embarking on your change-management journey, you need to:
Develop an understanding of the common challenges you may encounter
Learn from real-world case studies about the successful—and not-so-successful—change-management efforts.
But at the heart of all this is a trio of essentials – empathy, communication, and the ability to listen. These are the three pillars of successful change management.
Understanding the Need for Change in L&D
Change in the learning and development sector, especially within large enterprises, is inevitable and essential. The L&D landscape is continually evolving, driven by technological advancements, changing workforce demographics, and the ever-increasing need for upskilling and reskilling.
Recognizing and responding to these shifts is about more than just staying relevant. L&D is leading the charge in shaping a future-ready workforce.
There’s an increasing emphasis on personalized learning experiences tailored to diverse employee needs and learning styles. Rapid industry transformation is another crucial driver, pushing organizations to adopt more tech-centric approaches to training and development. And then there’s the changing nature of work itself – the shift to remote or hybrid models, the rise of gig work, and the continuous evolution of job roles.
Aligning these changes with your organization’s overarching goals and culture is more than introducing new tools or content. Understanding the unique dynamics of your organization and its people is the only way to craft a change strategy that resonates and sticks successfully. This alignment is crucial because management in L&D is about people – helping them adapt, grow, and thrive in the face of change.
Critical Strategies for Effective Change Management
A strategic approach to change is paramount. Two renowned change management models are adaptable to the unique needs of the L&D sector.
Embed the changes into your organizational culture.
Source: Kotter, Inc.
These steps can be tailored to address specific L&D goals, such as integrating new learning technologies or methodologies, enacting content governance, or shifting how and when you deliver learning content.
The “unfreeze” stage in an L&D context, could involve challenging the current state of your L&D programs and preparing for a shift. The “change” stage is where the actual implementation of new methods or technologies happens. It’s crucial to support and guide your team through this transition.
Finally, the “refreeze” stage involves weaving the changes into the company’s standard operating procedures, ensuring they are sustainable and embedded into the organizational culture.
Successful change management is rooted in customization and alignment. It’s about adapting these models to fit your organization’s unique context. A one-size-fits-all approach rarely works in complex and nuanced environments.
As you put these strategies into use, remember the importance of empathy, communication, and listening. The most well-planned change initiatives can falter without a human-centric approach. Understanding the concerns and needs of those affected by the change—and maintaining open communication channels throughout the process—is vital for success.
Brad Swingruber, CRO
I’ve always been told the best time to make critical company change and digital transformation was “YESTERDAY!” We hear people say this tongue and cheek, but the reality is most organizations are paralyzed with the fear of change.
Four Common Change Management Challenges and Solutions
Change, particularly in large and complex organizations, has challenges. In the realm of learning and development, these challenges can be quite specific. Recognizing them is the first step towards devising effective solutions.
Successful change management requires patience, persistence, and a willingness to adapt.
Resistance to Change: One of the most common challenges is employee resistance. This can stem from a fear of the unknown, comfort with the status quo, or concerns about the impact of change on their roles.
To counter this, involving employees early in the change process is crucial. Communicate the reasons for the change, and how it benefits them and the broader organization. Encourage feedback and involve employees in decision-making where possible. Inclusivity can significantly reduce resistance and increase buy-in.
Technological Hurdles: Implementing new technologies or digital platforms for L&D can be daunting. Challenges can range from technical issues to a lack of digital literacy among employees.
To navigate this, provide comprehensive training and support. Ensure that employees understand how to use the new technology and its relevance and benefits. Gradual implementation, rather than a sudden shift, can also help ease the transition.
The solution lies in continuous alignment and evaluation. Regularly review your L&D strategies to ensure they are in sync with the company’s goals and make adjustments as needed.
View the Missing Piece of Your Learning Tech Stack webinar to modernize your tech stack to drive employee & customer engagement, improve learner experience & outcomes, and build a culture of continuous learning.
Measuring Impact: Another challenge is quantifying the impact of L&D changes. Without clear metrics, it’s difficult to gauge success or identify areas for improvement.
Establish clear, measurable goals at the outset. Whether it’s improved employee performance, higher engagement rates, or increased knowledge retention, having concrete metrics helps in evaluating the effectiveness of your L&D initiatives.
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Deborah Hardison, Allina Health
Empathy, Communication, and Listening: The Pillars of Successful Change
Successful change management initiatives share three fundamental elements: empathy, communication, and active listening. These the essential building blocks that determine the success or failure of your change management efforts.
Display genuine understanding of concerns and address them directly. Empathy is about putting yourself in the shoes of your employees and understanding their perspective. In the context of change management, it means recognizing how changes might impact their learning experience, work routine, and overall job satisfaction.
By showing empathy, you can anticipate potential resistance and address concerns proactively. It also involves being sensitive to the varying learning needs and preferences of a diverse workforce. This empathetic approach can foster a supportive environment conducive to successful change.
Communication is the bridge between vision and reality. Effective communication is the bridge that connects your change vision to its successful implementation.
Nothing tanks a change-management initiative faster than an executive webcast followed by the cascade of a slide deck in a follow-up email. You need to foster a two-way dialogue where feedback is encouraged and valued.
Transparent, clear, and consistent communication helps demystify the change process, aligns everyone’s understanding, and builds trust. Don’t just explaining the “what” and the “how” of the changes but also the “why” – the rationale behind the change and its benefits.
Active listening is a tool for engagement and improvement. Active listening goes hand-in-hand with effective communication. It involves genuinely listening to your employees’ feedback, concerns, and suggestions.
This practice helps identify potential issues early on and makes employees feel valued and involved. Active listening can lead to valuable insights that can further refine and improve your change management strategies. These three pillars can help you create a more inclusive, responsive environment for change. This human-centric approach is what ultimately drives success and ensures the sustained, engaged embrace of change.
Change Is A Journey, Not A Destination
Change management, particularly within large enterprises, is a multifaceted and dynamic process. Guiding an entire organization through a transformation journey is neither quick nor simple. However, it is possible to achieve success if you communicate the need for change, strategically apply robust change management models, effectively tackle the inevitable challenges, and learn from the successes of others.
The true cornerstone of any successful change initiative in L&D is the human element – empathy, communication, and active listening that connects leaders with their teams.
Change is inevitable, but how you manage that change sets you apart. By embracing these principles and strategies, you can ensure that your L&D initiatives are effective in the short term and sustainable and impactful in the long run.
“The ignorance of how to use new knowledge stockpiles exponentially.” —Marshall McLuhan
For the past decade I have promoted the idea that a job is not the same as meaningful work. Most jobs are refillable and replaceable. One worker leaves, another one fills the job position. Our work can help to define us, but our jobs should never define us.
I discuss sensemaking in my PKM framework, though meaning-making is much more important and is related to self-determination theory that states there are three universal human drivers — autonomy, competence, and relatedness. We need some control over our lives, we want to be good at something, and we want to feel that we can relate to other people. These three drivers are what make us do what we do. They support meaning-making.
Dave Gurteen says that, “Meaning-making and sense-making are often used synonymously, but they are different — Meaning-making is the process by which we interpret situations or events in the light of our previous knowledge and experience. It is a matter of identity: it is who we understand ourselves to be in relation to the world around us.”
Even workplaces that support sensemaking often ignore meaning-making. Why are we doing this work in the first place? — is a question that is seldom asked. Even more antithetical to the capitalist, number-crunching workplace is that work should be playful. As Albert Einstein stated, “Combinatory play seems to be the essential feature in productive thought.”
Sometimes play requires time away. It often requires reflection, in which agency is born. We require agency, that part of us that makes us human, that allows us to direct ourselves in solid decision, in order to guide our natural playful selves into the whatever work we deem meaningful. —Kourosh Dini, 2022
Instead of making sense of our lives, our world, and our work, we will be auto-tuned for the ‘correct’ perspective. As we get inundated with new knowledge and information regurgitated by large language models and generative pre-trained transformers — time for meaning-making becomes critical.
I will leave the final thought to the folks at Gaping Void — “You aren’t here to find meaning. You are here to create meaning.”
Similar-sounding terms which are conceptually similar are tricky to deal with when you’re new to them. So this post is to help me tease apart the differences between systemic change, systematic change, system change, and situational change.
Briefly:
Systemic change refers to deep, fundamental change in the nature or operation of a system.
Systematic change pertains to ordered, methodical change following a specific process or protocol.
System change is a broad term for any change affecting a system, including systemic or systematic changes.
Situational change relates to changes in response to specific circumstances or environments; these are often more immediate and reactive.
Let’s dig in a bit more:
Systemic change
Systemic change involves altering the underlying principles, relationships, and processes that define how the system operates. In the context of Systems Thinking in Practice (STiP), systemic change is about shifting the way a system behaves or functions at a deep level, often in response to complex issues or emergent properties. It’s not just a change within the system, but a change of the system itself.
Systematic change
As I explained in my very first post about this module, systemic and systematic change sound very similar. However, the latter is about working step-by-step in a more methodical and planned way to try and effect change. Working systematically often involves following a specific methodology or set of procedures to achieve a desired outcome. With STiP, this might involve methodical approaches to problem-solving or implementing changes within a system.
System change
This is a general term which can encompass both systemic and systematic changes. ‘System change’ is a popular term at the moment, especially in relation to the climate emergency but it can be used from everything from minor adjustments to major transformation in the components, structure, or functioning of a system.
Situational change
If system change is general, then situational change is more context-specific, often referring to changes in particular situations or environments. The aim is less about changing the overall system and more about adapting or responding to specific circumstances or events. As a result, situational change is often reactive, dealing with immediate issues or problems as they arise.
A 2010 research report by The Work Foundation entitled Exceeding Expectation: the principles of outstanding leadership outlined differences between good and outstanding leaders. For the purposes of this post, I’ve stuck to the executive summary (Tamkin, et al., 2010) which outlines three principles of outstanding leadership:
They think and act systemically: they see things as a whole rather than compartmentalising. They connect the parts by a guiding sense of purpose. They understand how action follows reaction, how climate is bound and unravelled by acts, how mutual gains create loyalty and commitment, how confidence provides a springboard to motivation and creativity and how trust speeds interactions and enables people to take personal risks and succeed.
They see people as the route to performance: they are deeply people and relationship centred rather than just people-oriented. They give significant amounts of time and focus to people. For good leaders, people are one group among many that need attention. For outstanding leaders, they are the only route to sustainable performance. They not only like and care about people, but have come to understand at a deep level that the capability and engagement of people is how they achieve exceptional performance
They are self-confident without being arrogant: self-awareness is one of their fundamental attributes. They are highly motivated to achieve excellence and are focused on organisational outcomes, vision and purpose. But they understand they cannot create performance themselves. Rather, they are conduits to performance through their influence on others. The key tool they have to do this is not systems and processes, but themselves and the ways they interact with and impact on those around them. This sense of self is not ego-driven. It is to serve a goal, creating a combination of humility and self-confidence. This is why they watch themselves carefully and act consistently to achieve excellence through their interactions and through their embodiment of the leadership role.
Or, more briefly:
Systemic thinking — outstanding leaders view organisations holistically, understanding the interconnectivity of components and actions. They create an environment of shared purpose, recognising how mutual gains and trust help motivation and creativity.
Focus on people — outstanding leaders place the utmost importance on people and relationships as the key to sustainable performance. They invest a lot of time in nurturing team capabilities and engagement, recognising that people are central to achieving excellence.
Appropriate self-confidence — outstanding leaders leaders balance self-confidence with humility, focusing on organisational goals while understanding their role as facilitators. This approach involves self-awareness and a commitment to influencing others positively, avoiding arrogance.
Thinking about my own career history, perhaps like most people I’ve had the misfortune of experiencing more poor and average leadership than good and outstanding. However, I can think of a couple of examples of outstanding leadership which would certainly back up these three points. In both cases, the people involved were understanding of the differences between the people they led, meaning that they had to help create an environment where all could flourish.
At the same time, in each case there was very much a ‘team’ ethos with an understanding of how we both related to one another and to the bigger picture. With one of the examples, the outstanding leader made us very aware of some of the politics involved and how they were representing and positioning us (as a team) in relation to this. I think that is a good example of systemic thinking.
A search of both the academic and popular literature around systems thinking in relation to leadership brings back a whole range of results. I was struck by the number of links to GOV.UK web page there were, which took me to a list of National Leadership Centre research publications. Of the 19 listed, eight mention ‘systems’ in the title, including one entitled Systems Leadership: How systems thinking enhances systems leadership by Catherine Hobbs and Gerald Midgley, both from the Centre for Systems Studies at the University of Hull.
The authors set the scene by talking about “systemic leadership” which they define as “systems leadership + systemic thinking” (Hobbs & Midgley, 2020, p.1). This is required because of the ‘wicked problems’ facing society:
Systems leadership views organisations as composed of interrelated parts, and it focuses on coordination of these parts to achieve a given purpose. When the issue being addressed is too complex for a single organisation to deal with alone, multiple organisations can become involved. Nevertheless, the idea is the same: constituent parts of an existing system must be ‘joined up’ into a greater whole.
(Hobbs & Midgley, 2020, p.1)
It’s a short paper at only four pages, but I was still surprised not to see any mention of anything resembling ‘B-ball’ and the role of the practitioner. Instead, a range of approaches is discussed with the focus on the importance of joined-up action. ‘System change’ here seems to be used systemically but the focus seems to be on changing the way (i.e. systematic) way that ‘delivery’ is done by public-sector bodies. Instead, argue the authors, we need an “exploratory, design-led, participative, facilitative, and adaptive” future (Hobbs & Midgley, 2020, p.4).
Although I’ve never worked directly in government, as an informed (and often concerned) British citizen I have a keen interest in how it works. I’m also connected with a lot of people who work in various government departments. So I was interested to stumble across guidance on the GOV.UK site for civil servants entitled Systems Leadership Guide: how to be a systems leader. Although the word ‘systemic’ is mentioned six times in the overview, the approach outlined seems to be more systematic in nature.
For example, the following diagram seems like quite a standard circular diagram that you would see on ‘leadership’ slides in every sector around the world:
Although there is discussion of systems being both things you can see and things you can’t, the assumption still seems to be that systems are ‘out there’ in the world, and that systems thinking is an approach to increase performance or outcomes. It seems to be just another approach:
Systems thinking can be used alongside existing project management and stakeholder management techniques like Agile, P3M and Prince2 to strengthen them for dealing with complexity, uncertainty, multiple perspectives and broader interdependencies.
(The civil servant’s systems thinking journey, 2023)
The thought of using Prince2 alongside systemic approaches actually blows my mind.
One of the realisations I’ve had since starting this module is how pernicious the provision of pretty diagrams is. As with the GOV.UK example above, with systems thinking it’s problematic not to start with the individual practitioner reflecting on their own role in the world.
So how do we define what ‘systems thinking’ is. Can we use a systems thinking approach to define it? Now, given that I wrote my doctoral thesis explicitly trying to avoid ‘one definition to rule them all’, you’d expect me to appreciate an approach (Arnold & Wade, 2015) which uses a systemigram instead of simply presenting a contextless word-based definition.
Although potentially ‘scarier’ for those new to systems thinking (like me!) than the GOV.UK diagram, it’s so much richer.The resulting definition of systems thinking is: “The capability of identifying and understanding systems, predicting their behaviors, and devising modifications to them in order to produce desired effects.”
This may not be exactly the definition I would choose, but I appreciate being able to see how they arrived at it. It’s the kind of thing I’ve called for with frameworks for years. Just as with learning a new language, developing a systemic sensibility involves understanding what is and what is not useful when it comes to resources and discussion of systems practice.
References
Arnold, R.D. and Wade, J.P. (2015) ‘A definition of systems thinking: a systems approach’, Procedia Computer Science, 44, pp. 669–678. Available at: https://doi.org/10.1016/j.procs.2015.03.050.
Critical thinking is often cited as one of the most valuable future work skills. How do critical thinking skills manifest themselves in the workplace? Learn about the mindsets, habits and demeanors that make critical thinkers stand out. Table of Contents Intellectual empathy involves the capacity to understand and appreciate the perspectives, thoughts and feelings of others…
International Data Corp. estimated that US $118 billion was spent globally in 2022 to purchase artificial intelligence hardware, software, and data services. IDC has predicted the figure will nearly triple, to $300 billion, by 2026. But public procurement systems are not ready for the challenges of procuring AI systems, which bring with them new risks to citizens.
To help address this challenge IEEE Standards Association has introduced a pioneering standard for AI procurement. The standard, which is in development, can help government agencies be more responsible about how they acquire AI that serves the public interest.
The multilayered complexity of AI systems, and the datasets they’re built on, challenge people responsible for procurement—who rarely understand the systems they’re purchasing and deploying. The vast majority of government procurement models worldwide have yet to adapt their acquisition processes and laws to the systems’ complexity.
The new, voluntary standard is designed to help strengthen AI procurement approaches with due-diligence processes to ensure that agencies are critically evaluating the kinds of AI services and tools they acquire. The standard can provide agencies with a method to require transparency from AI vendors about associated risks.
IEEE P3119 also can help governments use their procuring power to shape the market—which could increase demand for more responsible AI solutions.
A how-to guide
The standard aims to help government agencies strengthen their requirements for AI procurement. Added to existing regulations, it offers complementary how-to guidance that can be applied to a variety of processes including pre-solicitation and contract monitoring.
Existing AI procurement guidelines such as the ones from the U.S. Government Accountability Office, the World Economic Forum, and the Ford Foundation cover AI literacy, best practices, and red flags for vetting technology vendors. The IEEE P3119 standard goes further by providing guidance, for example, on determining whether a problem requires an AI solution. It also can help identify an agency’s risk tolerance, assess a vendor’s answers to questions about AI, recommend curated AI-specific contract language, and evaluate an AI solution across multiple criteria.
IEEE is currently developing such an AI procurement guidance, one that moves beyond principles and best practices to detailed process recommendations. IEEE P3119 explicitly addresses the technical complexity of most AI models and the potential risks to society while also considering the systems’ capacity to scale for deployment in much larger populations.
Discussions in the standards working group centered around ways to identify and evaluate AI risks, how to mitigate risks within procurement needs, and how to provoke transparency about AI governance from vendors, with AI-specific best practices for solicitations and contracts.
The IEEE P3119 processes are meant to complement and optimize existing procurement requirements. The primary goal for the standard is to offer government agencies and AI vendors ways to adapt their procurement practices and solicited proposals to maximize the benefits of AI while minimizing the risks.
The standard is meant to become part of the “request for proposals” stage, integrated with solicitations in order to raise the bar for AI procurement so that the public interest and citizens’ civil rights are proactively protected.
Putting the standard into practice, however, could be challenging for some governments that are dealing with historical regulatory regimes and limited institutional capacity.
A future article will describe the need to test the standard against existing regulations, known as regulatory sandboxes.
The following is excerpted from Watkins, K.E. and Marsick, V.J., 2023. Chapter 4. Learning informally at work: Reframing learning and development. In Rethinking Workplace Learning and Development. Edward Elgar Publishing.
This chapter’s final example illustrates the way in which organically arising IIL (informal and incidental learning) is paired with opportunities to build knowledge through a combination of structured education and informal learning by peers working in frequently complex circumstances.
Reda Sadki, president of The Geneva Learning Foundation (TGLF), rethought L&D for immunization workers in many roles in low- and middle-income countries (LMICs).
Adapting to technology available to participants from the countries that joined this effort, Sadki designed a mix of experiences that broke out of the limits of “training” as it was often designed.
He addressed, the inability to scale up to reach large audiences; difficulty to transfer what is learned; inability to accommodate different learners’ starting places; the need to teach learners to solve complex problems; and the inability to develop sufficient expertise in a timely way. (Marsick et al., 2021, p. 15)
This led his organization, to invite front-line staff from all levels of immunization systems in low- and middle-income countries (LMICs) to create and share new learning in response to the social and behavioral challenges they faced.
Sadki designed L&D for “in-depth engagement on priority topics,” insights into “the raw, unfiltered perspectives of frontline staff,” and peer dialogue that “gives a voice to front-line workers” (The Geneva Learning Foundation, 2022).
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RSS (real simple syndication) is faster and less time consuming than using a search engine, surfing the web, and then creating a huge list of favourites or bookmarks. It’s been around for a long time and many websites support it. Several of the large platforms, like Facebook, do not, because RSS is an open standard and it is difficult to track users with it. If you listen to podcasts on a ‘podcatcher’ then you are likely using RSS.
Big tech have ignored RSS and are not keen on helping people use it, but many news sites still have RSS feeds to which anyone can subscribe. The Canadian Broadcasting Corporation (CBC ) has a multitude of RSS feeds but does not advertise these or help readers use a feed reader [see image below]. Instead, for the past decade CBC has relied on the platform monopolists at Meta, Alphabet, and X, and these are now biting them in their media butts as a result of Canada’s Bill C18 which big media supported.
Michael Geist, Canada Research Chair in Internet and E-commerce Law, has written many posts on Bill C18. Basically it requires large platforms, like Meta and Google, to pay for Canadian news feeds. But Meta has decided that it will no longer allow Canadian news links, including The Beaverton satirical news site.
Many Canadians access news content through digital intermediaries. Bill C-18 would enact the Online News Act (the Act), which proposes a regime to regulate digital platforms that act as intermediaries in Canada’s news media ecosystem in order to enhance fairness in the Canadian digital news market. The Bill introduces a new bargaining framework intended to support news businesses to secure fair compensation when their news content is made available by dominant digital news intermediaries and generates economic gain. It seeks to support balanced negotiations between the businesses that operate dominant digital news intermediaries and the businesses responsible for the news outlets that produce this news content. —Government of Canada
Canadian news outlets are not happy with this situation and are calling for ‘fairness’, as the lack of eyeballs from Facebook is hurting their business. It goes to show that we should be careful who we get in bed with to distribute our work. Meta and Google can change the rules at any time. Cory Doctorow explained the situation on CBC’s The Current [19 minutes]— the problem is not that these platforms are stealing content or linking to it, but rather that they collude to defraud publishers by owning the entire ad-supported ecosystem because they represent the buyers, the sellers, the marketplace, and they are publishers and advertisers in their own right.
Our national broadcaster should not even be involved with this marketplace. Profit-sharing with Meta only gets the CBC more deeply involved with this corporate giant.
So maybe it’s time we all got back to the basics and curated our own news, instead of having it pushed to us by an algorithm.
Most blogs and many news sites have an RSS feed. An aggregator — AKA feed reader — can help keep track of other blogs so you no longer have to visit each site. New posts will appear in the feed reader. I use Inoreader, but there are many others. Mac users can get a free desktop reader — ViennaRSS
For example, to find if a site has an RSS feed, enter it on Inoreader (add new), or you can use a tool like the Get RSS Extension for Chrome or Firefox. Many people have recently left Twitter and upwards of half of those have shifted to Mastodon, an open source federation of servers sharing the same protocols. I am active there — @harold. One can also subscribe to any Mastodon feed by adding .rss to the address, e.g. mastodon.social/@harold.rss — these open protocols support each other!
The former Pearson OPM unit – Pearson Online Learning Services (POLS), soon to be renamed as Boundless Learning – lays off half its staff with no severance and no pay for paid time off while also unilaterally ending non-profitable program partnerships. A case study in how cuts are made can be more important that what cuts are made. [Full-page audio link]
The reason that I believe Pearson is complicit in how the changes have been made is that the company still sells to higher education institutions, including the ones that were its OPM partners. And because Pearson agreed to a deal where its payment for the sale is directly based on these cuts: 27.5% of adjusted EBITDA – with little or no offset for the cost of the layoffs.
I feel I owe a deeper explanation, however, to back up these claims. After all, these layoffs occurred after the acquisition was complete, so why blame the previous owner? [Note: in this post I am referring to the top leadership of Pearson and the decision-making apparatus involved in the sale of POLS. I am not referring to the vast majority of people working at the company.]
I realize I am jumping deep into this topic, but the reason is that otherwise some strategic changes in the OPM market and more generally in the EdTech industry will be missed or misunderstood. Without an explanation, marketing will take over and people will hear the sale went through, we had to do some belt-tightening, and here we are with a new name, better than ever. I believe the true nature of company behavior is important to understand in this case.
Dealing With The Obvious
Before getting into Pearson’s role, let’s deal with the obvious situation. As I described on Friday, the primary actors at fault in this debacle are the new owners Regent LP and the top management team of POLS / Boundless.
Based on TheLayoff discussions, it appears that Regent made similar moves at another portfolio company, Zulily. No severance, no payout for paid time off, no insurance coverage, mass meeting notification. These moves are deliberate and, I believe, part of the reason for the acquisition.
I understand the need for controlling costs and layoffs, but how a company handles these moves matters. And doing so with no notice and no severance is the wrong thing to do and reflects poorly on the character of leadership and ownership.
Looking at other Regent LP portfolio companies, it is not obvious that they handle most acquisitions in the same way, but it is clear that this summer they have with two companies – Zulily and POLS / Boundless. And they are responsible for the terms of the layoffs.
Regent LP has no other presence in education, and they (thus far) have kept the entire POLS executive leadership team in place. This is not a situation of new owners bringing in new leadership and discovering what needs to happen. The POLS / Boundless exec team is also responsible for the terms of the layoffs as well as the misleading manner of communicating with staff.
Pearson Complicity
There are three primary reasons behind my argument – Pearson understood that mass layoffs were needed, Pearson knew how the layoffs would happen (or should have known), and Pearson was and still is actively supporting POLS / Boundless.
Pearson Understood
When Pearson announced the end of the ASU contract – one that contributed nearly 40% of POLS revenue, or roughly $118 million out of $307 million – leadership claimed that the:
profit impact of the contract termination will be modest in 2022 and 2023 and will be offset thereafter through eliminating related costs and re-directing investment across our strategic growth opportunities
Read that as mass layoffs as well as other disinvestment. I described in my March post that POLS also lost Ohio University, its third-largest client. In this week’s earnings call (remembering that POLS was still part of Pearson through June 30th), Pearson described the revenue impacts [emphasis added].
Virtual Learning sales decreased 15%, primarily due to an expected 69% decrease in the OPM business given the previously announced ASU contract loss.
Note that the ASU loss is only part of the problem. For its part, Pearson had a layoff in March associated with the announcement of the sale to Regent, but nowhere near the level needed to deal with this loss of revenue. Furthermore, the company initiated a strategic review in Summer 2022 leading up the March 2023 sale announcement, and they had analyzed the numbers. Pearson understood additional massive cuts were required and that such cuts would likely cost tens of millions of dollars in severance costs if done under the Pearson banner.
From the comments of Friday’s post we get further detail of the justifications used [emphasis added].
Back on July 10th, 2023 [ed. another source puts this meeting on July 12th], there was a Townhall meeting hosted by the Boundless Learning (formerly POLS) leadership team, which informed us that there would be changes to our contract with Maryville University who is one of our biggest OPM accounts.
The biggest change was the fact that moving forward, we would only be supporting Maryville Nursing Programs and all other programs including numerous Undergraduate and Certificate programs would no longer be supported by Boundless Learning (POLS). Throughout the meeting they shared data derived from months’ worth of research and analytics which outlined how the undergraduate programs were just not as profitable as the Grad Nursing programs which is why they decided to stop supporting all programs with Maryville outside of Nursing.
This change is what led to the mass layoffs that took place early this week. During the meeting, one of the executive leaders acknowledged that this change was something they had been expecting and embracing for, for months. They even mentioned the fact that this is why there was a hiring freeze in effect and why they stopped back-filling positions that became available months ago.
Pearson Knew
I’ve been involved in and seen private equity acquisitions in the education space, and these deals are not done with the parent company blind to strategy. Bidders work to convince the selling company of their plans, pitching the strategic thesis. Companies like Pearson that are selling parts of their organization understand what is going to happen in general.
Take a distressed organization sale like POLS, however, and consider the terms of the deal that make this an even bigger issue. Regent offered nothing for the group – only a promise to share a portion of the profits and a portion of the re-sell, as I described in March.
There appears to be no upfront payment for the POLS business; instead, Pearson will receive 27.5% of adjusted EBITDA (profit, more or less) for the next six years, and if Regent cleans up and (re)sells the POLS unit, Pearson will get 27.5% of the proceeds.
The only upside to Pearson financially in this deal is:
Getting the known layoff liabilities of layoffs and unprofitable contracts off its books;
Getting a cut of the profits; and
Getting a cut of the re-sell.
Therefore Pearson leadership must have asked for Regent plans in these areas.
And the details were already playing out before the close of the deal. Based on multiple sources, POLS / Boundless held a town hall in mid June notifying staff of new employment contracts that included the no severance clauses and no 401(k) sharing. This should not have been a surprise, as multiple commenters at TheLayoff predicted the no severance situation back in March, after the Pearson cuts.
“When the job massacre comes, we will not be offered a severance!”
“I envy everyone who was let go hoping my name will be called next. I’m sure there will be more layoffs before the final ownership transition and more when clueless Regent takes hold. Notice in the last meeting [POLS execs] wouldn’t confirm future benefits or potential severance for employees who are let go.”
Pearson Supported
The third reason comes from information that I’ve discovered since the Friday post. Pearson is actively supporting POLS / Boundless to this day. This is no we sold the company and have nothing to do with it any longer situation.
Pearson was and still is providing the internal IT systems to help with the operations of POLS / Boundless, with the exception of the HR system. MS Teams, Outlook, Salesforce, etc. POLS / Boundless employees still use pearson.com emails. And every POLS / Boundless employee was given the role of “Contingent Employee” as of July 5th on the internal Pearson systems. To make matters worse, the staff let go had their camera and microphone usage cut off before the announcement (presumably through MS Teams) and all system access cut off half an hour after the announcement. Pearson IT systems.
As I pointed our Friday, Pearson still maintains the web presence for the POLS / Boundless unit on the Pearson website.
The ongoing branding and IT system support is not insignificant. Pearson is actively supporting POLS / Boundless to this day.
Primary and Secondary Responsibility
Clearly Regent and POLS / Boundless leadership bear primary responsibility for how they are treating staff and customers. Brutal, heartless, and short-sighted. But Pearson (unlike Regent) knows education and should care about its corporate reputation. Pearson is complicit in this mess.
Deep thinking question. Can you think or name one SaaS or non-SaaS solution where the market for that solution overwhelmingly believes it is only Legacy and thus an antiquated offering best found in the diggings of Troy; or outdated, non-useful because it is called “X” and thus the entire industry of said “X” is therefore not the “latest”; or that the end-user that accesses it, needs this or that, to enhance their experience, and the “Xs” can’t do it; or that “Y” which pitches ABC, but in reality, doesn’t entirely do all that, is still better than “X”?
I thought about it, and my answer was no. I can’t even think of a type of solution that still has a large audience base; a lot of folks love it and support it; it offers, as a whole, a lot of the latest capabilities and technology around it, and can offer a strong UI/UX for the end-user. Not one. I can’t think of a solution that does all that, and yet, continues to get pounded that it is none of that; or that just the name of the type is enough to make people think that it is none of those things. Not one.
Yet here we are. Naysayers continue to pontificate on information that is just wrong. The premise that an LXP, for example, does so much more than an LMS is 100% untrue. LXPs offer assigned learning. They, thus are not learner-centric, the moment the client decides to go assigned learning. If you want to provide only compliance content to your learners, you can. If you want to limit the scope of what they can take and experience, you can. The skills are, overall, not as strong nor advanced, compared to many LMSs in the market. The vast majority of LXPs today are within a learning platform, or an LMS or as an add-on option to either of those offerings. I should add the Learning Platforms is the second biggest segment because a Personal Dev Platform, or Employee Experience Platform, where learning is the core component, is in essence, a learning platform.
Regardless, the number of 100% true LXPs, as they were defined, is minimal in the entire industry of learning systems. The others? Some have a content marketplace; others don’t – but all claim they are an LXP. The core for them is the UI – which is either GRID or Playlist – and thus, they pitch they are an LXP. The funny thing here is that there are all types of learning systems, including LMSs that have that same UI.
Content curation? LMSs (as a whole) have a lot of capabilities around this, and ditto for many learning platforms. LXPs? Minute. Do you want to tap into a bookmark extension and pull down content tied around whatever content you have, OR 100% free? Besides the two original LXPs, I haven’t seen any others in the “we call ourselves an LXP” do it. I have seen a PDP do it and some LMSs. Do you want to find content on the internet and pull it right into your learning system (sans an extension)? I see this more with LMSs and Learning Platforms, than an LXP (again, aside from Degreed, and EdCast – and the latter does a so-so job on it).
Do you want the top level of skills and capabilities in the industry? The most that are out there (I base this on my template, which has more than 100 skill capabilities) is Cornerstone. An LMS. Higher than Degreed, the original LXP. Higher than EdCast. Higher than even Pluralsight, which is, uh, a learning platform. Docebo, an LMS, scores around 62%. Cornerstone, BTW is around 90%. Both are LMSs. Those other LXPs? Not even in the 35% range. Thrive, a former legit LXP, is now an LMS.
The best Learning Platform with a legit LXP in it is probably Juno Journey. They had it early on, and it continues to improve – i.e. the entire system that is, not just the LXP, which is there, does a good job, but the system is designed to go way beyond that. I would even argue that this Learning Platform could easily slide into an LMS. The second who does a solid job and is within an LMS, is Bealink.
Fuse does an outstanding job with bringing in content, free, and using a very cool search capability, has always had this unique UI/UX around communities, which, if they streamlined them down, could go cohorts, and their data visualization rocks. They are a Learning Platform, but again, can easily slide into an LMS (which I think they are, but that’s another story).
LMSs are coming into the market all the time. More in, then less out. If they were dated wonky systems that can’t do as much as an LXP, they wouldn’t be rolling in. I didn’t see one new 100% LXP at LTUK. Nor at DevLearn. The dominant player in each of those shows was an LMS, followed by a learning platform.
In my upcoming NexGen Leader Pack (coming in late July), the majority are LMS vendors.
The Comments
I posted on my LinkedIn Learning thread about this upcoming blog post and one about whether you really needed an LXP, and the comments rolled in. The ones that caught my eye more were the ones that presented such perspectives as (I am paraphrasing these)
Learner wants access to content in a moment of need; tailored to them
LXPs forced LMSs to upgrade their UI/UX
LXP focuses on learners and their continuous development (The LMS doesn’t do this)
These were both fair statements and not the first time, I’ve read such points of view.
Let me address each one
The learner wants access to content in a moment of need, tailored to them
100% agree, but any learning system can do this. Seriously, this comes to whomever bought the system, and how they want to use it. It’s not the system’s fault here. If I want to do assigned learning with specific due dates, I can do in an LXP, Learning Platform, EXP, Talent Dev Platform, PDP, and yes, LMS. If I want to offer my learners to pick the content they are interested in, I can, without assigned learning. My LMS did, back in 2000. And actually, even LMSs, I were aware of, could do the same thing. 100% of online courses were doable and available. As in time of need, this is precisely why an LMS was created. Because, ILT, on-site, was the only way, the main way to learn (excluding the guides nobody read) didn’t and still doesn’t offer that. Web-Based Training – the term coined back then to refer to e-learning which folks now misuse, was all about “just in time learning, ” “learn when I want to learn, 24/7,” “self-paced, not driven by someone else,” “you can re-learn or go back and learn over and over again, as much and as often you want.”
The systems back then, and especially the courses, were effective in instructional design and e-learning developed; thus, you could get really interactive and engaging content if you so choose. Or you could do total static (which I still see a lot of today). As the client, you could choose whether you wanted 100% let folks pick, some assigned, some picked, or all assigned. With any learning system on the market, you can still do this today. And FWIW, the majority of people in white-collar/office professions, access their learning system, outside of the workplace. This isn’t new. It was that way even back in 2000. I think as systems evolve with Gen-AI and the LLM, tailored can get even better – but we are still so very early; any vendor that says “100% we do it,” there are always, “What about this?” LLMs are not perfect. So never assume they are.
LXPs forced LMSs to upgrade their UI/UX
A common conjecture, I often hear. I often see this, whereas with new means better from a UI/UX. A fresh look is always needed, but while it might have a slick UI, the UX may be oversimplified, or ineffective. I saw a system, recently, that had a nice UI, really slick, and they used widgets (not new, BTW, but I digress). They noted that one of the widgets (which can be changed), is in this area. It turns out that they didn’t want to have any blank space, so something had to go there. Now is that effective? To me, no.
LMSs that went to a whole new level
GeoLearning – had a version that, on the user side, enabled you to have, say, not just your logo behind a desk – it was 3D the UI version, but also the carpet (colors all matched yours – now that is white-label). I had 3D desks (for ILT), a gallery (which offered a lot of options), a bookstore (with e-commerce), and two additional levels. If you want to move from one level to another, go via an elevator. So, very cool. I think that is pretty amazing. I owned it in the early 2000s.
Docebo was the first vendor to have the content marketplace visually seen, a very modern UI/UX, one-click buy and go into your system for content/courses – regardless of whether you bought them. This was several years ago.
Litmos, before being acquired by Callidus Cloud, which was acquired by SAP – UI/UX was highly modern. The system took off out of nowhere. With less than five people working there. Feature-rich. Oh, we were talking again several years ago.
The first vendor to have geolocation in their mobile app? ExpertusOne, in 2015. LMS vendor.
And if you want to get more specific about the whole playlist, YouTube or NetFlix-like appearance, and search for video – it was established by a Learning platform – Video Learning Platform – known as MediaCore. Eventually acquired by Workday, and was the core for Workday Learning. Surprise!
As for the LXPs, overall, pushing, LMSs forcing them to have a better UI/UX, I never saw that. Did they push the LMS market as a whole to go with more content curation aspects? Yes, but not every LXP offered that – to the level that a Degreed or EdCast did. The Playlist angle? LinkedIn Learning offered that once they rolled out, they are not an LXP, let alone a good learning platform. HA Plus, their content playlists are not fully aligned to say what the person seeks. I’ve written about that too.
LXPs failed in two areas that a lot of LMSs offered, metrics. They are overwhelmingly poor (out of the box, i.e. comes with the system). You want better with Degreed? Degreed Intelligence is it – and it rocks. EdCast? Domo. Both are additional costs. There were other problems they had, that a large chunk of LMSs didn’t have – one being multiple levels of approval (a nice capability to offer).
LXP focuses on learners and their continuous development (The LMS doesn’t do this)
This ties into another perspective around learner-centric methods and informal. LXPs offer assigned learning, which a chunk of their clients uses. Why you may ask did LXPs add this feature when they pushed the whole learner-centric and informal angle – to offset why you should have an LMS? (ignoring that any LMS can offer and does offer informal learning, and can go learner-centric – if the client wants to do that)
Their clients. And whom did the LXPs target and still do? L&D. Not training. Only L&D, folks who (as a whole), use assigned learning. You see, they had L&D folks who were using an LMS, and using assigned in that, and while they bought into the whole learner-centric, they invariably started to ask for more and more features they had in their learning platform or LMS. Rather than push back against this, LXP vendors capitulated in various ways and capabilities, which is why they became ubiquitous to a learning platform or LMS.
Any learning platform offers continuous development. I do not like the whole “continuous” angle around a job role. Because that job may not be around in a couple of years, OR that person leaves your company and starts a French Fries Sketch Doodle pad. Continuous should be personal and professional development and not just skills tied to job roles, or the “potential opportunity” they may get. The folks who are big fans of personal and professional development are folks with a training background. Again, not all, but it is one of the modality differences compared to L&D. LXPs, as you can quickly see, are all about employees. Yes, you can use it for B2B/customer training, or an association, but how many people do you know, whose background in L&D and thus OD, oversee customer training or association education programs? I never met one. The feature sets heavily zero in on employees, which is fine, but plenty of audience segments are not employees. When I say employees, it’s a white-collar/office workforce. Not blue-collar.
Bottom Line
For those who provided their perspectives, I truly thank you and appreciate them. I understand the reasons behind it, for you only see what you can see, or are aware of, whereas as an analyst, I see just way more, and have been around in the e-learning space, since the early days. Which totally dates me. No, I am not 221, though.
Enlightenment is a multi-way street, and I always love those who say, “What about this or that?”
To me, we can’t learn, unless, well, we learn.
In the end, though, your learning experience can be matched in an LMS, as it can be with a learning platform or any learning system that is out there.
LMSs, though, are not outdated relics. Do some have a UI/UX that rumbles out of the stone ages – sure, but plenty of SaaS offerings do not match up with the coolest new UI/UX out there, and people still use them. Ditto with Learning Platforms. And Ditto with LXPs – regardless if they are standalone, or a part of an LP or LMS.
They often say beauty is in the eye of the beholder.
When it comes to your learning, that beauty is being driven, not by the end-user or system type, but by the person overseeing the entire system and/or department.
So, let’s put the blame, where it really belongs.
Because it isn’t the LMS.
It’s to those, who see the beauty of assigned learning, lack of learner-centricity, lack of tailored course/content experiences, and everything else you see as evidence of what is wrong or bad with the LMS.
And who are they?
The same folks who oversee the department/and learning system.
To support an agile workforce in the face of constant change, learning and development (L&D) professionals are applying agile and lean business approaches to employee training. Often described as “adaptive learning,” the goal is to provide the precise information workers need when they need it. Adaptive learning integrates AI-driven features for customized learning paths based on the performance...
In a conversation, we were discussing how L&D fares. Badly, of course, but we were looking at why. One of the problems is that L&D folks don’t have credibility. Another was that they don’t measure. I didn’t raise it in the conversation, but it’s come up before that they’re also not being strategic. That came up in another conversation. Overall, there are two steps for L&D to really make an impact on.
Now, I joke that L&D isn’t doing well what it’s supposed to be doing, and isn’t doing enough. My first complaint is that we’re not doing a good job. In the second conversation, up-skilling came up as an important trend. My take is that it’s all well and good to want to do it, but if you really want persistent new skill development, you have to do it right! That is, shooting for retention and transfer. Which will be, by the way, the topic of my presentation at DevLearn this year, I’ve just found out. Also the topic of the Missing LXD workshop (coming in Asia Pacific times this July/Aug), in linking that learning science grounding to engagement as well.
I’ve argued that the most important thing L&D can do is start measuring, because it will point out what works (and doesn’t). That’s a barrier that came up in the first conversation; how do we move people forward in their measurements. We were talking about little steps; if they’re doing learner surveys (c.f. Thalheimer), let’s encourage them to move to survey some time after. If they’re doing that, let’s also have them ask supervisors. Etc.
So, this is a necessary step. It’s not enough, of course. You might throw courses at things where they don’t make sense, e.g. where performance support would work better. Measurement should tell you that, in that a course isn’t working, but it won’t necessarily point you directly to performance support. Still, measurement is a step along the way. There’s another step, however.
The second thing I argue we should do is start looking at going beyond courses. Not just performance support, but here I’m talking about informal and social learning, e.g. innovation. There are both principled and practical reasons for this. The principled reason is that innovation is learning; you don’t know the answer when you start. Thus, knowing how learning works provides a good basis for assisting here. The practical reason is it gives a way for L&D to contribute to the most important part of organizational success. Instead of being an appendage that can be cut when times are tough, L&D can be facilitating the survival and thrival strategies that will keep the organization agile.
Of course, we’re running a workshop on this as well. I’m not touting it because it’s on offer, I’m behind it because it’s something I’ve organized specifically because it’s so important! We’ll cover the gamut, from individual learning skills, to team, and organizational success. We’ll also cover strategy. Importantly, we have some of the best people in the world to assist! I’ve managed to convince Harold Jarche, Emma Weber, Kat Koppett, and Mark Britz (each of which alone would be worth the price of entry!), on top of myself and Matt Richter. Because it’s the Learning Development Accelerator, it will be evidence-based. It’ll also be interactive, and practically focused.
Look, there are lots of things you can do. There are some things you should do. There are two steps for L&D to do, and you have the opportunity to get on top of each. You can do it any way you want, of course, but please, please start making these moves!
Coming off the seismic shift to virtual learning during the pandemic, education is facing another wave of transformation, this time from technology. Although the rise of generative AI has many educators worried about plagiarism, it actually holds the potential to revolutionize the way students learn and academic institutions operate.
According to Tony Sheehan, a Gartner VP Analyst focused on education, GenAI tools can make large swaths of information accessible in ways we never imagined. Educators and institutions first need to build trust in the technology and determine how to handle short-term anxieties. Then students can learn how to interact with generative AI tools, and use them to emphasize creativity and improve their time management to learn more efficiently.
This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.
This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.
The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.
This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.
https://www.elearningmarketplace.co.uk/core-learning-acquires-elearning-marketplace/ Core Learning Services Ltd, the UK-based eLearning...24th May 2023
When reviewing organisational Diversity & Inclusion (D&I) and/or Environmental Social Governance (ESG) approaches, cognitive diversity – also known as neurodiversity – deserves considerable attention, says Johnny Timpson OBE, former Cabinet Officer D
This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.
This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.
By facilitating further education and training, employers ensure a longer employee work lifespan and the adaption of skills to transform into specialties or using new technologies and software.
The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Have you ever tried reading a services guide about over 150 ways a professional services can help clients with audit, tax and advisory services? All I have to say is, good luck!
It’s dry content, but nevertheless critical for client-facing staff in a professional services firm — such as KPMG — to know. Previous attempts with static content were not engaging but cost effective, while road shows by product leaders were engaging but not cost effective.
KPMG has over 230,000 staff in 150 countries, helping clients with their opportunities and challenges with audit, tax and advisory services. Needless to say, the scale is large. We were faced with an engagement and cost challenge, and a global scale of consequence.
The solution
We needed an engaging, cost-effective, scalable solution to help KPMG’s client-facing staff better serve their clients. We piloted a gamified learning solution in Australia which proved the concept was a success before being funded for a global program. KPMG Globerunner was subsequently activated in over 80 countries and has been used by more than 100,000 people.
Top 5 takeaways
Design is the fun part, execution is the hard part. I’ve seen many people over the years get excited about the concept of gamification. While that energy is a great start, the reality is after the vision boards from the design workshops are packed away, the hard part begins. Business cases, financials, branding compliance, IT security, privacy, marketing, sales and other “less exciting” elements all need close attention. Make sure you understand as much of the journey as possible, no matter how difficult.
The balance between “fun’ and ‘lame” is a fine one. You will probably lose sleep over whether the digital experience you are creating is engaging or lame. Expect this! Risk and cultural requirements of your organization may chip away at your creative vision, but the reality is that you need a licence to operate.
Make global assumptions about local conditions at your peril. Accept the high degree of difficulty creating digital experiences that need to be engaging across cultures. For example, users in the U.S. may be very comfortable sharing stories about their experiences and successes, but this may not be the case for Japanese users. Additionally, the way the experience is activated across regions and countries may differ significantly and need to be fully investigated to ensure success.
Sell business results to the business, user benefits to the users. I stopped using the term“gamification” when speaking with the business side, as it distracted our meetings away from the business outcomes I needed to convince them of. Likewise, we focused communications to users about the benefits to making their daily lives easier, rather than how the business would benefit. Tailor communications to your audience.
Fan the enjoyment flames, snuff out unproductive play. It’s incredibly rewarding and energizing hearing from users about how the experience has helped them have more relevant conversations with their clients. However, it can be equally disappointing to learn that some users enjoy the experience so much, they have moved into an unproductive play mode. For example, a user may be playing for an excessive time to climb leaderboards. Identify and limit this activity.
I will be continuing my conversation about gamification in an upcoming episode of “Voices of CLO,” in which I will share more from my experience with this program, and some of the things I learned along the way.
There has been a lot of discussion this week about the University of Arkansas System board voting against the proposed acquisition and affiliation agreement with the University of Phoenix, as described by the Arkansas Gazette. [full-page audio link]
The University of Arkansas System board of trustees declined to give their blessing to a potential affiliation with the University of Phoenix during a special Zoom meeting Monday, albeit with a narrow majority with five members voting against the proposal, four voting in favor and trustee Kelly Eichler abstaining. [snip]
Under the proposal, Arkansas nonprofit Transformative Education Services would purchase Phoenix — estimates are that price could be around $500 million — and the UA System would enter into an affiliation and licensing agreement with Phoenix that would’ve provided revenue to the system.
The system also would have had the opportunity to access technology, content and course management and data analytics used by Phoenix. In return, Phoenix would’ have gained an affiliation with the system and transitioned from for-profit to nonprofit status.
The vote was non-binding, but most observers believe it would be career-ending if the system president moves forward with the deal without board support.
Confusing Statements
What stood out to me in the description of Monday’s board meeting were two quotes by proponents of the deal. One was covered by Inside Higher Ed:
At a board meeting last week, one trustee who favored the affiliation (and voted for it on Monday), Ted Dickey, said that passing on the arrangement would be like Blockbuster failing to buy Netflix. “If we’re not willing to disrupt our own business, someone else will,” he said, as reported by KNWA.
During the brief discussion prior to Monday’s vote, [trustee] Fryar attempted to equate the Phoenix deal with the move 50 years ago that saw Fred Smith move FedEx to Memphis because Little Rock would not extend its runway. He said the University of Arkansas System has the chance to not repeat that mistake.
“To me, this is a FedEx moment,” Fryar said.
On its surface, this level of analysis by the Arkansas System board of trustees is stunning. There is no rational way to compare the University of Phoenix in 2023 with Netflix in 2000 or FedEx in 1973. Both Netflix and FedEx were in rapid growth mode and had yet to hit their prime, while the University of Phoenix has been in a decade+ decline with a toxic brand.
The most charitable interpretation of the trustee comments is that they believe the University of Phoenix acquisition / affiliation agreement is the only way to disrupt current operations, but $500+ million acquisition of a declining university with little institutional control is a very expensive and risky bet to make.
Data Issues
Perhaps part of the problem is that it is nearly impossible to find an enrollment chart for the University of Phoenix over most of its lifetime to see the big picture. One challenge is that the US Department of Education’s IPEDS database is not set up for longitudinal data analysis at the institution level, at least when institutions or systems change names and identifiers over time.
In the case at hand, the University of Phoenix has opened and closed more than 100 campus locations, each with their own IPEDS identifier, and they changed how they reported online enrollments in 2014. I found one way to get around this limitation by using the Delta Cost Project data from 1994 – 2012 and combining with our own IPEDS data set starting in 2012.
Enrollment Chart
The following chart is based on Fall Enrollment data (not a perfect measure, but the most consistent one to show historical trends). I have broken it out by campus enrollments and online enrollments, but labeled with total enrollments for each academic year (AY2010 = Jul 2009 thru Jun 2010).
The University of Phoenix began its remarkable growth in the late 1990s, when online was a small part of the mix, and it peaked in AY2010 with nearly half a million students, mostly online. But then the university system declined to the point where it had fewer than 90,000 enrollments in AY2022, and according to media reports roughly 79,000 enrollments today, essentially all online. That is an 83% drop from its peak.
It is true that the current ownership and management have been working on a turnaround for 4 – 5 years, and you can see the enrollment declines moderating in that period. But there is no rationale way to see that chart and think Netflix or FedEx.
Analysis and Descriptions
Purdue University started this conversion trend with its acquisition of the for-profit Kaplan University, turning it into Purdue Global in 2017. Say what you will about that deal (and I have), but president Mitch Daniels knew what Purdue was buying, and they invested heavily into turning around Kaplan / PG’s enrollment decline.
There may be valid reasons for deals of this nature (a public university or system acquiring a mostly-online for-profit university), but the public deserves real analysis from trustees and honest descriptions of proposed deals. Based on news reports, I don’t think that was happening in Arkansas.
If there is a version of this same chart already available and I missed it, please be kind when letting me know that I might not have needed to pull this data together today.
Interactive Designers will have to adapt or die. AI and Generative AI (not the same thing) has started to play a major part of the online learning landscape, right across the learning journey. it is now being used for learner engagement, syllabus planning, core skills identification, learner support, content creation, assessment and so on. It will eat relentlessly into the traditional skills that have been in play for nearly 35 years. The old, core skillset was writing, media production, interactions and assessment, all delivered through an authoring language. It remained unchanged for nearly 30 years. In many ways it got worse as the tools began to determine the content, so we got lots of cartoony content, with click on speech bubbles, clumsy, drag and drop, stock photos and MCQs. It was expensive and took months.
Every online learning company on the planet is now having strategy meetings to face uo to the challenge.
This is not easy as many of those involved in traditional online content creation will find it difficult to adapt. Others, however, will embrace the change. Many will have to identify individuals with the skills and attitudes to deal with this new demand. This means understanding the new technology (not trivial), learning how to write for new dialogic tools, and dealing more with AI-aided design and curation, rather than doing this for themselves. It’s a radical shift.
In a Keynote over five years ago, I summarised this shift as follows...
This is the new version…
In another context, using a tool like ChatGPT meant not using traditional interactive designers, as the software largely does this job. It identifies the learning points, automatically creates the interactions, finds the curated links and assesses, formatively and summatively. It creates content in minutes not months. This is the way online learning is going. I’m involved in several projects where Generative AI is appearing in real product. One was launched at BETT this week (Glean), others are on their way. This stuff is here, now.
The gear-shift in skills is interesting and, although still uncertain, here’s some suggestions based on my concrete experience of making and observing this shift in a number of different companies.
1. Technical understanding
Designers, IDs, LXDs, Learning Engineers or whatever they’re called now or in the future, will need to know far more about what the software does, its functionality, strengths and weaknesses. In some large projects we have found that a knowledge of how the NLP (Natural language Processing) works has been an invaluable skill, along with an ability to troubleshoot by diagnosing what the software can, or cannot do. Those with some technical understanding fare better here as they understand both the potential and limitations.
This is not to say that you need to be able to code or have pure AI or data science skills. It does mean that you will have to know, in detail, how the software works. If it uses semantic techniques, make the effort to understand the approach, along with its weaknesses and strengths. With ChatGPT, for example, you really do need to keep up with the speed of releases, ChatGPT came out in late Nov 2022, ChatGPT, an order of magnitude better came out in March 2023. I see far too many people still using and basing their opinions on ChatGPT3. Keep up or become irrelevant.
In a series of category errors, any of the silly clickbait 'look at what I've just done' screen shots don't really understand the underlying technology. most are from ChatGPT3, not 4, like using a version of Wikipedia from 2003. The fine tuning, RLHF and guardrailing is ignored, yet these are the things learning professionals need to know about the technology. This will take time. You always have to go through the clickbait phase to get to the serious comment and use cases. The good news is the amazing things people are doing with the tool, especially in learning.
Similarly with data analysis. With traditional online learning, the software largely delivers static pages with no real semantic understanding, adaptability or intelligence, hence the stickiness of SCORM. AI created content is very different and has a sort of ‘life of its own’, especially when it uses machine learning. At the very least get to know what the major areas of AI are, how they work and feel comfortable with the vocabulary.
2. Writing
Text remains the core medium in online learning. It remains the core medium in online activity generally. We have seen the pendulum swing towards video, graphics and audio but text will remain a strong medium, as we read faster than we listen, it is editable and searchable. That is why much social media and messaging is still text at heart. When I ran a large traditional online learning company we regarded writing as the key skill for IDs. We put people through literacy tests before they started, no matter what qualifications they had. It proved to be a good predictor, as writing is not just about turn of phrase and style, it is really about communications, purpose, order, logic and structure. I was never a fan of ‘storytelling’ or ‘creativity’ as identifiable skills.
However, the sort of writing one has to do in the new world of AI has more to do with being sensitive to what generative AI does, and dialogue. Prompt writing is important and this is where experienced IDs and graphic artists can excel. Prompting is best done by domain experts. A good graphic artists will know how to ask for the right image in terms of style, fonts, look and feel, with all the right parameters. Similarly with good IDs, who will know how to prompt for great questions, not just fact checking. It is a matter of taking your skills and applying them to using these new tools and technologies.
3. Interaction
Hopefully we will see the reduction in the formulaic Multiple-Choice Question production. MCQs are difficult to write and often flawed. Then there is the often vicariously used ‘drag and drop’ and hideously patronising ‘Let’s see what Philip, Alisha and Sue think of this… ‘ you click on a face and get a speech bubble of text. I find that this is the area where most online learning really sucks. This, I think, will be an area of huge change as the limited forms of MCQ start to be replaced by open input; of words, numbers and short text answers. NLP allows us to interpret this text. There is also voice interaction to consider, which many will implement, so that the entire learning experience, all navigation and interaction, is voice-driven. This needs some extra skills in terms of managing expectations and dealing with the vagaries of speech recognition software. If you don’t know about Whisper, you should. Personalisation may also have to be considered. These tools are basically AI on tap. This software is far too complex to build on your own. Yet it makes smart implementation in scale possible.
4. Media production
As online learning became trapped in ‘media production’ most of the effort and budget went into the production of graphics (often illustrative and not meaningfully instructive), animation (often overworked) and video (not enough in itself). Media rich is not necessarily mind rich and the research from Nass, Reeves, Mayer and many others, shows that the excessive use of media can inhibit learning. Unfortunately, much of this research is ignored. We will see this change as the balance shifts towards effortful and more efficient learning. There will still be the need for good media production but it will lessen as AI becomes multimodal, creating text, images, audio and video, even 3D worlds.
Video is never enough in learning and needs to be supplemented by other forms of active learning. AI can do this, making video an even stronger medium. Curation strategies are also important. We often produce content that is already there but AI helps automatically link to content or provides tools for curating content. Lastly, a word on design thinking. The danger is in seeing every learning experience as a uniquely designed thing, to be subjected to an expensive design thinking process, when design can be embodied in good interface design. We are now in the world of rapid design by smart software that has done a lot of the A/B testing on a gargantuan scale. These methods look more and more out of date.
5. Assessment
So many online learning courses have a fairly arbitrary 70-80% pass threshold. The assessments are rarely the result of any serious thought about the actual level of competence needed, and if you don’t assess the other 20-30% it may, in healthcare, for example, kill someone. There are many ways in which assessment will be aided by AI in terms of the push towards 100% competence, adaptive assessment, digital identification, open input, transfer, good generated rubrics and so on. This will be a feature of more adaptive and dialogue-driven AI driven content. Generative AI produces assessments at speed and with relevance to the competences. That was never the case in traditional online learning.
6. Data skills
SCORM is looking like an increasingly stupid limit on online learning. To be honest it was from its inception – I was there. Completion is useful but rarely enough. It is important to supplement SCORM with far more detailed data on user behaviours. But even when data is plentiful, it needs to be turned into information, visualised to make it useful. That is one set of skills that is useful, knowing how to visualise data. Information then has to be turned into knowledge and insights. This is where skills are often lacking. First you have to know the many different types of data in learning, how data sets are cleaned, then the techniques used to extract useful insights, often machine learning. You need to distinguish between data as the new oil and data as the new snake oil.
We take data, clean it, process it, then look for insights – clusters and other statistically significant techniques to find patterns and correlations. For example, do course completions correlate with an increase in sales in those retail outlets that complete the training? Training can then be seen as part of a business process where AI not only creates the learning but does the analysis and that is all in a virtual and virtuous loop that informs and improves the business. It is not that you require deep data scientist skills, but you need to become aware of the possibilities of data production, the danger of GIGO, garbage-in/garbage out and the techniques used in this area. AI is now a feature of data-centric learning solutions. Acquire some basic knowledge of data science, nothing fancy, just get to know the lie of the land.
7. User testing
In one major project , we produced so much content, so quickly, that the client had trouble keeping up on quality control at their end. We were making it faster than it could be tested! You will find that the QA process is very different, with quick access to the actual content, allowing for immediate testing. In fact, AI tends to produce less mistakes in my experience as there is less human input, always a source of spelling, punctuation and other errors. I used to ask graphic artists to always cut and paste text as it was a source of endless QA problems. The advantage of using AI generated content is that all sides can screen share to solve residual problems on the actual content seen by the learner. We completed one large project without a single face-to-face meeting. This quick production also opens up the possibility of quick testing with real learners.
8. Learning theory - pedAIgogy
In my experience, few interactive designers can name many researchers or identify key pieces of research on, let's say the optimal number of options in a MCQ (answer at foot of this article), retrieval practice, length of video, effects of redundancy, spaced-practice theory, even the rudiments of how memory works (episodic v semantic). This is elementary stuff but it is rarely taken seriously. With AI you can build pedagogy, or what I call pedAIgogy, into the prompting and therefore learning experiences. We are doing precisely this on one product.
With the implementation of AI, the AI HAS to embody good pedagogic practice. Bill Gates recently published an excellent piece on Generative AI saying that learning will be it b] greatest benefit, but te piece was marred by him pushing ‘learning styles’. Greg Brokman, of Open AI, did te same retweeting a tool based on learning styles. We know better than this and can build good, well-researched, learning practice into the software. Hopefully, this will drive online learning away from long-winded projects that take months to complete, towards production that takes minutes not months, and learning experiences that focus on learning not appearance.
Communications with AI developers and data scientists is a challenge. They know a lot about the software but often little about learning and the goals. On the other hand designers know a lot about communications, learning and goals. Agile techniques, with a shared whiteboard, scrums and superfast production are useful. I love these. There are formal agile techniques around identifying the user story, extracting features then coming to agreed tasks. Comms are tougher in this world so learn to be forgiving. There will inevitable be friction between the old and the new. Treat that as a normal.
Then there’s communications with the client and SMEs. This can be particularly difficult, as some of the output is AI generated, and as AI is not remotely human (not conscious or cognitive) it can produce mistakes. The good news is that these are now rare. You learn to deal with this when you work in this field. To be honest, A=all of those learning folk telling me that AI shouldn't be used in learning, as it sometimes has an error or two, will happily use content with learning styles, Myers-Briggs, Bloom's pyramid, Maslow and no end of bogus theory and content in courses
This new approach is often not easy for clients to understand, as they will be used to design document, scripts and traditional QA techniques. I had AI once automatically produce a link for the word ‘blow’, a technique nurses ask of young patients when they’re using sharps or needles. The AI linked to the Wikipedia page for ‘blow’ – which was cocaine – easily remedied but odd. You have to be careful but that has always been te case. I can barely think of a single training project where the SME content was spot on.
The great news is that this all means we can reduce iterations with SMEs, even cut them out altogether, as the software often has more knowledge and can write it to any level or style. The cause of much of the high cost of online learning is expensive SMEs and endless iterations. If the AI is identifying learning points and curated content, using already approved documents, PPTs and videos, the need for SME input is lessened. This saves a ton of time and money.
10. Make the leap
AI is here. We are, at last, emerging from a 30 year paradigm of media production and multiple choice questions, in largely flat and unintelligent learning experiences, towards smart, intelligent online learning, that behaves more like a good teacher, where you are taught as an individual with a personalised experience, challenged and, rather than endlessly choosing from lists, engage in effortful learning, using dialogue, even voice. As a Learning designer, Interactive designer, Project Manager, Producer, whatever, this is the most exciting thing to have happened in the last 30 years of learning. Most of the Interactive Designers I have known, worked with and hired over the last 30 plus years have been skilled people, sensitive to the needs of learners but we must always be willing to 'learn', for that is our vocation. To stop learning is to do learning a disservice. So make the leap!
Conclusion In addition, those in HR and L and D will have to get to grips with AI. It will change the very nature of the workforce, which is our business. This means it will change WHY we learn WHAT we learn and HOW we learn. Almost all online experiences are now mediated by AI - Facebook, Twitter, Instagram, Amazon, Netflix.... except in learning! But that has just dramatically change. Generative AI heralds a new era, a renaissance oin learning, where we can learn anything, at anytime, anywhere using sophisticated AI tutors. What is needed is a change in mindset, as well as tools and skills. It may be difficult to adapt to this new world, where many aspects of design will be automated. I suspect that it will lead to a swing away from souped up graphic design back towards learning. That would will be a good thing.
Artificial intelligence is reshaping the Internet (and our life) in unprecedented ways. For us in the education, AI ripples are felt throughout the field and while some educators are still viewing it with skepticism, I believe this is the AI era par excellence!
Our students are already leveraging the power of generative AI in their learning (e.g, ChatGPT) and whether you like it or not, we have no choice but to embrace the educational potential of this technology and learn about how to effectively it integrate in our instructional practice.
As an educator and EdTech researcher, I have been closely following AI over the last few months with a special focus on exploring the immense possibilities it offers in terms of creating meaningful learning experiences.
In this regard, I reviewed a wide variety of AI tools that I believe can revolutionize the way we create and deliver educational content. These include:
In this article, I want to touch on the use of AI in creating effective and engaging presentations. More specifically, I will introduce you to some of the best AI presentation makers that will help you create beautiful and professional presentations in just a few clicks.
PowerPoint is among the best presentation makers out there. It has been around for many years and for several of us, their first presentation was created through it. Since the introduction of AI into the digital scene, PowerPoint has its own share of this technology and there are now several AI features that you can use to power up your presentation skills on PowerPoint.
I recently watched this video from Kevin Statvert in which he discussed the different ways to enhance your PowerPoint skills using AI and thought it is worth adding to this list. Kevin talked about a number of integrated AI features to use on your PowerPoint including an AI powered designer, Cameo and live video, presentation coach, subtitles, and more. Check out the entire video tutorial to learn more about how to leverage the power of artificial intelligence in your PowerPoint presentations.
Beautiful.ai is an AI-assisted presentation platform that enables you to create stunning, professional presentations in minutes. It features a large gallery of ready-made, smart slides with drag and drop capabilities for easy customization and an edit control function so you can change any value or size of elements on your slide. Beautiful.ai also allows users to add their own brand colors, fonts and logos for a consistent look across all their presentations.
In addition to traditional text-based slides, Beautiful.ai offers an array of rich media content such as images, videos and audio tracks from sites such as Unsplash, Pexels and Pixabay. With its powerful collaboration tools, you can easily add other team members, share your presentations and work with anyone, anywhere.
Designs.ai is an AI-powered design platform that enables users to create stunning presentations with ease and efficiency. It offers a variety of presentation templates, graphic elements, customizable layouts, and more.
To start using Design AI, open the platform on your device, sign in or sign up then search for presentation to start designing. Select a pre-made presentation template of your choice, customize it the way you want: you can add images, logos, branding elements, various fonts, colors, layouts, and more. Once your presentation is ready, you can download it in PDF, JPG, or PNG file.
Presentation AI is another interesting AI presentation maker to try out. Presentation AI creates stunning decks from a simple text description. The tool also offers a wide variety of editable professionally designed templates that you can easily customize to your needs.
Other features provided by Presentation AI include form to deck allowing you to generate a deck with your inputs based on your own prompts, deliver decks on Zoom or in-person, control access, track effectiveness, track engagement and impact of your content, manage feedback by sharing links, and more. You can also "write your story, or paste content from a doc or webpage - go from draft to deck in no time."
Synthesia AI is one of the promising AI text to video generators that has been making rounds online. Synthesia offers this handy AI video presentation maker that enables you to create beautiful video presentation using text prompts. Synthesia is simple and easy to use, no video or graphic design knowledge required. The tool also supports over 120+ languages.
Start with choosing a video template from Synthesia' s template library, create a brief presentation script and include individual video scenes and narration. Next, copy and paste your presentation script into Synthesia and watch as it automagically generates voice-overs for your text.
You can then personalize your video presentation by adding images, AI avatars, music, animations, transitions, screen recordings, and more. When you are done, you can share your video presentation through a generated link, upload it to YouTube, or embed it in your website.
Tome is an all-in-one presentation and storytelling tool that enables users to create entire narratives from scratch or add additional content pages in seconds. Equipped with features such as DALL·E 2 tile for creating images, drag-and-drop creation tools, responsive pages, one-click themes, live web embeds, video recording and sharing, and more, Tome makes it easy to create captivating stories using the generative power of AI.
With native video recording, users can easily talk through a nuanced point or add a personal touch to their story or presentation even if they are not in the same place at the same time. Tomes are designed to fit any device so that viewers can enjoy the content with ease.
Slides AI is an AI-powered tool that automates the process of creating slides for presentations, saving you time and energy. It offers a variety of presentation types, including educational, sales, and conference styles.
You can simply enter text and select the slide type you want then you can customize it the way you want. When done, click to export it. SlidesAI.io is also integrated with Google Workspace and can be downloaded from its marketplace.
Magic Presentation is a recent addition to Canva's suite of powerful AI tools recently announced during Canva Creative Live event that took place a few day ago in Sydney Australia.
Canva Magic Presentation is an innovative tool for creating effective and professional presentations. It allows you to quickly generate a first draft of your presentation with an outline, slides, and content. This can be done in just a few words by entering your idea into the tool. The result is a visually appealing presentation with easy-to-understand slides and content.
Magic Presentation also offers some powerful features to help users customize their presentations. You can easily tweak slides with fonts, colors, images, graphics, and more. Live previewing allows you to see the changes you make in real-time.
This post originally appeared in Educational Technology and Mobile Learning ( www.educatorstechnology.com ).
This week I’ve been fortunate enough to get access to both Google Bard and the New Bing, so a day later, I’m here to share my first impressions.
What is the new Bing?
“The new Bing” is the product of Microsoft’s unholy alliance with OpenAI, makers of the now infamous ChatGPT. This development promises to have seismic effects on the search ecosystem, with Microsoft’s CEO saying they’re happy to accept “demonetization” of search in their pursuit of market share, and Google extremely concerned about the threat ChatGPT technology poses as an alternative to their core search business.
Of course, by now we’ve all also seen various viral posts and tweets showing just how dangerous it can be to use chat AI as a search engine, but that’s a topic for another day. For now, the point is that Bing is making moves.
When I perform a search on “old bing” now, I can see this box inviting me to try the new one. You’ll notice a key detail here: it’s only available in Microsoft Edge. Yikes. Big Microsoft Energy. Fortunately for you, the reader, I have dusted off everyone’s fourth-favorite browser so you don’t have to.
Performing the same search in the new Bing, I can see identical organic results, but rather different features:
The “mustelid masters” box above the organic results is new, and contains AI-generated text with a voice-to-speech capability. It’s a six-part story, with sometimes surprising accompanying imagery:
You can see here that a picture of wrestling has been sourced to accompany the text about badgers wrestling. These AI-generated boxes don’t appear for most queries — only clear and uncontroversial informational intents.
The phrase “Mustelid masters” itself seems to be original to this box.
Lastly, one of the tabs in the story cites the Wisconsin Badgers, and a page which is entirely unrelated to the content at hand, so perhaps Bing is also citing its sources for disambiguation here?
You’ll notice the addition of an “Open Website” button next to the top result on the SERP — perhaps a way of compensating a little for loss of organic click through rate?
The “chat” tab is also present on old Bing, but just shows you a message telling you to go to Microsoft Edge.
The phrase “conversational search” here is interesting, given this was a phrase Google introduced in 2013.
If we do use Microsoft Edge, we see a chatbot interface in this tab, but with some nice additions. Switching over to this from a regular search result pre-loads my previous query from organic search:
There’s a bunch of different modes available at the top, and also citations in the search results — both welcome improvements over the likes of ChatGPT.
Now, how about Bard?
What is Google Bard?
Well, not very self-confident, for one. But that’s probably a good thing.
Bard is also, right now, not anywhere near as integrated with search. In their announcement on February 6th, Google teased Bard in a way that made it look very much like a SERP feature, similar to Bing:
However, the version we have to play with now is more of a dedicated chatbot interface.
It was probably already the case that Google was pushed to move far sooner than they hoped with this technology, and of course they have much more to lose from messing with organic search than Bing does. So, it shouldn’t be a surprise to see the slower and more cautious approach.
Now, it probably should be noted that chatbots are not really designed for me to just enter a one word keyword like “badgers”, like I might do in organic search. But, like Bing’s chat tab, I get something resembling an informational result. So, let’s compare side by side.
The most obvious difference, at least to an SEO’s eye, is the presence of citations in the Bing result. Not knowing where source information comes from is one of the biggest challenges for users when dealing with this kind of technology, so that’s a huge differentiator.
Bard does claim to include citations. My colleague Mike was able to trigger them, and captured it in this clip. It’s definitely far, far less ubiquitous than on Bing.
That said, I like that the framing of Google’s solution — with multiple draft answers presented and “enter a prompt here” — which makes it clearer I’m dealing with something that is not a source of unassailable truth.
I was also intrigued by the localization of Bing’s result. It mentions the UK in its response, which is where I’m searching from, and shows UK websites in the citations. So I asked them both a follow-up question about my location:
Bing repeats itself, but Bard just seems to assume I’m in the US. Unfortunate.
Slightly commercial query
Many SEOs will be more interested in how technology like this might fit into their marketing funnel. Let’s try a classic top-of-funnel query:
This may be a contentious point when SEOs start optimizing for these answers, and want to measure their results. Of course, regular organic rankings can vary massively between locales and even days of the week, but generally speaking, if you search twice from the same computer (in private browsing windows etc) you’ll get the same results. Not so here.
Neither solution fell for some obvious conspiracy theory bait, which is encouraging to see. I actually don’t mind at all Google’s more cautious “I can’t assist with that” here. I wasn’t able to provoke a similar reaction out of Bing for any query, but I also wasn’t able to provoke it to say anything abhorrent - I’m sure others will, though.
What next for SEOs?
For both platforms there are major questions before SEOs can really engage and consider them an important part of their work.
For Bing, will this have adoption? Most SEOs have not made the habit of optimizing for Bing in recent years, but there is already talk of increased Bing market share.
For Bard, how, if at all, will this be integrated in search? The current platform is clearly marked as an experiment, and is more like ChatGPT than it is like the mock ups Google showed us in February. Or will users be encouraged to use it as its own thing?
For both platforms, there are big questions about how SEOs might go about optimizing to get their clients mentioned, and indeed favorably mentioned in results - there are lots of nefarious possibilities here, and Wikipedia is probably the most obvious. Once mentioned, how does one measure this? When I clicked through to my own site from Bing’s chat tab, it just appeared like any other Bing organic traffic. Rank tracking is an interesting problem too, and you can be sure that Moz and STAT will be posting in future about how we’re handling these features — watch this space!
XML, that’s pretty techie stuff, isn’t it?! Structured content already sounds friendlier, but it’s still miles away from the daily practice of ‘writing a document’. That’s something we are all familiar with and anyone can contribute to. What are the advantages of XML, and how can both the ‘techies’ and the subject-matter-experts collaborate on the same content?
What is a technical writer?
A technical writer is responsible for making complex information understandable. Not as a subject-matter-expert, but as a communications professional. Putting that information in a clear and concise way, preferably structured and classified with metadata. See for more information also our post: “What is structured content authoring?”
Need for technical writers
Technical writers are hard to find nowadays. Most of the content in organizations is created by subject-matter experts. Contributing a document via email, copying some text from a previous Word-document, that’s the traditional approach to ‘collaborative editing of documents’. And in the end, these documents are converted into XML format by the technical writing team.
Organizations are looking into ways to get subject-matter experts involved in the process of drafting and editing of their core documents. By contributing directly to the source, although making sure that formatting and the schema are respected.
Collaboration with technical writers
Technical writers have faith in their native XML editors, like Oxygen XML Editor or XMetal. That’s absolutely fine for us. With Fonto Editor, the subject-matter-experts can work from exactly the same repository. The content that is edited within Fonto will be checked out from the CCMS when a subject-matter-expert is working on the content and will be stored once changes are made. When the same content is opened in a ‘traditional XML editor’, the content will be locked for the subject-matter-expert.
And to make sure that the content created in Fonto is valid according to the schema, the content is validated real-time. This means that an element that isn’t allowed on a certain position, according to the schema, cannot be inserted at that position. Think about a list in a header, for example. The buttons in the menus do only appear if they make sense at that position in the document. Otherwise, they are greyed out or even completely hidden.
Assisted writing
To make sure that your subject-matter-experts not only write content as they are used to doing in a Word document, but also add the relevant XML structure, we’ve introduced assisted writing. Custom dictionaries, but also preferred terms, can be suggested to the author, as well as recognition of product names and suggestions to tag them.
Combining this with regular expressions, gives the ability to recognize patterns. This could contribute to tag dosage forms, for example, in pharmaceutical content.
This altogether helps the subject-matter-expert to produce content that’s tagged correctly, to speed up the document creation process.
Visualizing changes
Having said that, our track-changes on steroids (Fonto Document History) can display the changes in files that have been made outside Fonto Editor next to changes made in Fonto Editor. It’s just the ‘changes’ that are reflected over time, no matter what tool has been used.
This makes it attractive for technical writers to collaborate with subject-matter experts in the same documents, although both with their own XML authoring tools. It’s a setup we see in multiple organizations where Fonto is implemented.
The field of learning and development is undergoing a major transformation in the company of artificial intelligence. For organizations to remain competitive in today’s rapidly evolving work environment, they must keep up with these advancements.
The rise of AI in L&D
L&D is now widely accepted as a value-add across the most successful organizations today, helping to develop, engage and retain their most valued employees. According to LinkedIn’s 2022 Workplace Learning Report, “opportunities to learn and grow” are the most highly rated culture-drivers worldwide.
However, L&D practitioners, who usually help others in their organizations adapt to change, are now faced with adapting to change themselves, as AI tools become more accessible in everyday life. The most popular of these tools, the generative AI ChatGPT developed by OpenAI which recently received a multi-year $10 billion investment by Microsoft, has expanded the possibilities of what is possible in L&D today. This article explores the benefits of using AI in talent development, as well as exploring the possible challenges and risks of doing so.
The benefits of AI in L&D
Increased efficiency and scalability. AI can help increase the efficiency of L&D practitioners at all levels, allowing them to better scale their offerings and reach more employees with less upfront effort. As AI tools expand and become integrated into the systems we use every day, they will drive efficiency at an even faster rate. For instance, Microsoft has recently integrated ChatGPT into its Bing search engine and announced plans to further integrate it into other Microsoft products, like Word. This level of accessibility will allow L&D professionals to quickly draft initial workshop outlines, implementation plans, communications and detailed content on a variety of subject matters.
Personalized learning experiences. AI can help L&D practitioners quickly develop personalized learning experiences, tailored to the individual needs of each team or individual. With the correct amount of input information entered in a prompt, an extremely individualized learning journey can be outlined in seconds, with robust learning material developed with a few more follow-up prompts.
Increased ROI. The potential for increased ROI is not just highly likely, but already being achieved in some organizations that are using AI effectively. By quickly creating more customized content, employees can receive the learning they need when they need it as opposed to traditional learning approaches that are often event-based and developed in a one-size-fits-all method.
Virtual coaching. AI will soon be able to provide virtual coaching on-demand, making these services more accessible and convenient for employees at all levels to reap the benefits of an often-costly service. While AI in its current form is nowhere near on-par with highly experienced certified coaches, it is not inconceivable that in the near future they will be able to exhibit more conversational skills, empathy and critical thinking.
Interactive simulations and gamification. AI can create interactive simulations and help gamify learning to make it more engaging and more likely to stick. Role-playing scenarios with peers are a common tool used to practice new skills before using them in real-time. With AI, these scenarios can be customized, in real-time, to an employee’s needs, when and where they need it, not just during group workshops.
Possible use cases and example prompts
Training outline:
You are a trainer, facilitator and L&D manager at a successful tech company. You are professional, smart and well-spoken. Write a training workshop outline on best practices for one-on-one meetings with employees for managers. Make it engaging, interactive and useful.
Engagement activities:
What are some examples of interactive exercises that I could facilitate virtually to drive engagement and learning on meetings best practices? Provide me with five examples and a description of the activity.
Description and learning objectives:
Can you provide a high-level description of this outline? Provide me with a brief overview and learning objectives.
Communications:
Can you create a relaxed and helpful Slack message to a channel for managers only? The Slack message should aim to inform and advertise the new offering to managers to get them and their teams to participate in it once launched.
Interview prep and gamification:
Let’s gamify the interview process. You are the interviewer. I am the interviewee. For each answer that you judge me to have answered in a satisfactory and correct manner, you will give me one point. For each answer that you judge me to have answered incorrectly, you will subtract one point. After five questions, give me a tally.
You are a hiring and L&D manager at a large tech company working on their people team. You are looking to hire a director of learning and employee engagement. Ask a relevant interview question related to the subject matter expertise someone in that role would be expected to have every time I ask you to ask me a question.
Please start by asking me one question. I will submit my response. I would like you to give me feedback based on my response with areas for improvement.
Personalized learning plans:
You are an expert trainer and certified coach in the ___ industry. You are developing learning plans based on your team’s professional development goals. You have a new manager that needs to learn basic skills management skills across five one-hour virtual sessions. Create a lesson plan and outline for each session with a focus on one core skill per session. This new manager is an excellent communicator and develops her team well, but they need help delegating work and leaning into productive conflict and debate more.
Suggest a couple of books that focus on these topics in a business environment to supplement the five sessions.
Naming:
Give me name suggestions for an onboarding program at a company named _____ in the ____ industry. The new hire orientation is focused on culture transfer, department overviews, and our brand story. I want a catchy name instead of just calling it “onboarding”. Our brand is seen as smart, progressive, at the cutting edge and is in the hyper-growth stage. Give me 10 suggestions.
Challenges and risks
Ensuring data privacy and security. Organizations are rightfully concerned with employees feeding potentially confidential information into these public tools like ChatGPT. But, as AI tools begin to become monetized, we can expect them to begin to offer Enterprise License Agreements where an instance of the tool can be privately accessed by all employees within an organization’s existing IT-infrastructure, negating the risk of data leaks.
Copyright infringement and liability. Recently, Getty Images sued a generative AI image creation tool, Stable Diffusion by Stability AI, for using its expansive, copyrighted image library to train their software. This case, and others like it that will follow, will set the precedence for how AI companies will be allowed to operate in our market. How will this case affect the company of an employee who used the tool to generate images that look similar to a Getty image that they then used in a marketing campaign or internal presentation? Questions like these will have to be answered and in the meantime, it’s best that L&D professionals err on the side of caution.
Addressing skill gaps. Organizations need to address the development of new AI-related skills as the roles of many professionals will change once AI is more widely adopted. Training on how to input the proper prompts to get useful outputs will be critical. L&D professionals will shift from being creators, as many instructional designers are now for instance, to primarily serving as editors. Their work will be focused more on customizing content to their company’s culture, fact-checking information and facilitating or program managing.
Creating a culture that supports AI-driven learning. The importance of creating a culture that supports and embraces AI-driven L&D cannot be understated. Culture plays a critical role in the successful adoption and implementation of any change initiative, and AI’s adoption is no different. Its introduction into everyday work-life has to be a well-planned and implemented endeavor with buy-in garnered from across the organization.
Inaccurate information. The current state of AI is such that it cannot be trusted to give completely accurate information all the time. ChatGPT itself has a “limitations” warning claiming it “may occasionally generate incorrect information.” As such, any AI output should be thoroughly reviewed and fact-checked by a knowledgeable professional that is equipped to make judgements on the fidelity of the content.
Ensuring ethical use of AI. Organizations should consider writing AI-related policies that directly outline which areas of work are acceptable for AI-usage and which are not. There are also ethical issues that must be addressed, such as:
1. Bias: AI algorithms can perpetuate and amplify existing biases based on the data it has learned from. Organizations should actively work to reduce bias in AI algorithms by conducting regular reviews, testing for bias and implementing a rigorous fact-checking process.
2. Job loss: AI-powered L&D and other emerging tools can nearly automate certain tasks and processes, leading to the fear, or reality, of job loss for some employees. Organizations should invest in upskilling and reskilling programs to support employees whose jobs may be impacted by AI.
Conclusion
While the emergence of everyday AI use will be a huge boon to L&D professionals in terms of efficiency and productivity, the ethical concerns and risks counterbalance the benefits at first. As generative AI becomes more knowledgeable, effective and nuanced though, the benefits will far outweigh the risks.
Ultimately, L&D departments and businesses that do not begin to at least explore these new tools may be left behind as this technology empowers their competitors. The future of L&D lies in AI use, with possible use-cases yet to have been discovered, let alone perfected. All L&D teams should begin to invest their time in exploring generative AI tools and the possible use cases that may make their jobs easier while also making them more effective and productive.
This study aims to investigate a mediated-effects model of organizational support and citizenship behaviour. The model proposes organizational support as an antecedent of citizenship behaviour and commitment to change (CTC) as a mediator in the organizational support–citizenship behaviour relationship.
Cross-sectional survey data were collected from university faculty (n = 221) and analyzed using structural equation modelling.
The findings showed that organizational support significantly contributes to increased citizenship behaviour and commitment of university faculty to program accreditation as an enterprise change process. The authors conclude that university-level organizational support shapes faculty’s CTC both directly and indirectly. The findings have significant practical implications for higher education institutions (HEIs) where new practices that aim at improving institutional effectiveness are embraced.
The study is cross-sectional (i.e. one-time data collection), which restricts the ability to make generable inferences about cause-and-effect relationships. Although the authors tested a model, longitudinal research is needed to unpack the processes of organizational support, commitment and citizenship behaviour. During enterprise change management, organizations work tirelessly to build and maintain citizenship behaviour. Therefore, considering citizenship behaviour in relation to other processes over time is important. However, relying on one source of data may represent another limitation, which increases concerns about common method bias in the current investigation.
The study findings offer a number of implications to HEIs in contexts where accreditation is perceived as an enterprise change process. Universities, similar to any other organizations, rely consistently on methods and mechanism through which employees’ professional performance, engagement and involvement can be enhanced. Accreditation has always been examined by exploring externally focused variables such as global reputation, organizational prestige and international prominence. The present study, however, draws attention to how perceived organizational support (POS) may be an equally important lever that needs to be considered before accreditation is introduced in HEIs. University chancellors, deans and other university leaders can directly influence organizational support by creating a system that weighs the extra work needed, the human resources and the incentives, and developing a plausible action plan.
It is unlikely that all faculty members will maintain quality relationship with the university leadership and immediate leaders such as department chairpersons or the college dean. This unlikelihood increases during crisis and change time. The study findings showed that POS contributes significantly to organizational citizenship behaviour. Therefore, it could be argued that the resistance to change that tends to be associated with accreditation can be mitigated by showing employees that support is accessible and attainable from up-line and immediate leaders. The findings suggest that commitment serves as an integral mediating mechanism between organizational support and citizenship behaviour. Indeed, commitment can be fully examined in practice from the perspective of its three-pronged structure (i.e. affective, continuance and normative). The findings provide credence to the notion that accreditation as an enterprise change process cannot be achieved without employee commitment and organizational support.
As a result of adopting globalized techniques, HEIs in Arab nations have undergone significant changes. In the Arab context, the adoption of academic program accreditation in HEIs has been seen as an enterprise change process with both supporters and detractors. In other words, implementing new systems or procedures results in changes that might upend personnel at any given organization. Therefore, it is contended that how well an organization responds to resistance to change will likely depend on the interaction of organizational, contextual and individual-related characteristics.
In a conversation, we were discussing how L&D fares. Badly, of course, but we were looking at why. One of the problems is that L&D folks don’t have credibility. Another was that they don’t measure. I didn’t raise it in the conversation, but it’s come up before that they’re also not being strategic. That came up in another conversation. Overall, there are two steps for L&D to really make an impact on.
