It seems that these days I can’t open my email without at least one or two invitations to join yet another AI-focused webinar for L&D professionals. I’ve even seen a few new certificates that are centered around the use of AI in Learning and Development roles.
Personally, I’m excited about the potential of AI to shape the way that we teach and learn.
For context, I currently work as Senior Learning Partner for Cencora, a pharmaceutical distributor based out of Pennsylvania. My Master’s is in Education, and I taught literature and rhetoric for five years for a high-performing charter school. As much as I loved teaching, I found that a disproportionate amount of my time was spent doing the “drudgery” of teaching: paperwork and data entry. And the software we were using didn’t help. Instead of making my role easier, the tools I used created additional work, redundancy in a system that was already maxed out.
In 2018, I left the classroom to study UX (software) design. My hope was (and is) to someday create better software for teachers so that they can stay in the classroom long enough to become experts in their field–before burning out.
My current role involves both ed tech and tech ed, as I teach our sales and service associates to use and sell technology more effectively. I project manage, design and measure learning experiences across a variety of business units.
As generative AI becomes the buzzword of 2024, it’s impossible not to wonder how Artificial Intelligence will impact those of us in L&D. Whereas a majority of the content available now is centered around how to utilize AI for content generation and project management, I’m more curious about how AI will change the landscape of commercial learning over the next decade–and beyond.
I’m not a fortune teller, but after much thought, here are a few of my predictions, as well as the action steps you might consider taking to future proof your team.
Prediction 1: In the next two years, we’ll see a shift in the way we allocate resources for instructional design and content generation.
This change is already in flight. Tools like Synthesia allow L&D teams to create video content that once took weeks in a matter of hours. The same is true of graphic design and even technical writing.
As content becomes increasingly easier to create, it will be important not to adopt a “more is more” mentality. A quality over quantity mindset will be imperative as we become more selective about what’s worth creating, pushing and assigning to our workforce.
Recommendation: Reallocate Resources Away from Content Generation
In the past, the content our teams have generated has been one of the ways we demonstrate value to our organization. This will be the first wave of change, and it’s crucial to accurately assess how we’re currently allocating resources, keeping instructional and graphic design teams lean while upskilling our designers in generative AI products like ChatGPT.
Prediction 2: In the next five years, we’ll see a shift away from asynchronous eLearning.
To be honest, I’m kind of over asynchronous learning anyway. And that says a lot coming from someone whose job it is to create eLearning.
Over the past decade, the trend has been to economize by minimizing live learning experiences whenever possible. My biggest take-away, as both a student and a teacher, has been that people learn in community.
Simply dumping content into a queue for consumption is never going to create meaningful mindset shifts. A library of lengthy eLearning assignments assumes that humans are strictly rational beings, ones that when presented with new information will automatically internalize it and act accordingly.
This is not the case. Organizational change requires that we hold space for people to process new information and offer scaffolding and accountability that supports new habit formation.
Recommendation: Start investing in facilitation training.
Develop a facilitation competency model (or use mine). Help your L&D professionals make live training more meaningful by teaching them how to facilitate sessions that are engaging rather than passive.
Prediction 3: In the next ten years, we’ll see a shift in the way we measure learning and provide feedback.
While we’re not quite there yet, we’re already starting to see AI’s ability to summarize and make recommendations based on the data it’s fed. AI is currently able to draw out themes from meetings, sales calls, and even emails. It can deduce the tone of a text message and offer suggestions on how to make it more professional. It can even organize your to-do list into a more logical sequence.
Within the next decade, I’d like to see learning professionals utilize AI to provide meaningful feedback and coaching. We all know that the most effective way to provide feedback is through 1:1 coaching. That said, we often default to multiple-choice style assessments due to time and resource constraints. Put simply, multiple choice is cheap. But forcing a learner to choose between four canned responses is a much less rigorous task than asking them to respond to a nuanced situation in their own words.
Recommendation: Start gathering data and researching ways to use AI for feedback.
Look for integrations and tools like RingDNA that help to accurately gauge the quality of work being done.
Prediction 4: In the next fifteen years, we’ll see a shift towards experiential learning in a virtual environment.
This is probably the shift I’m most excited about. People are learners by default; it’s part of what makes us human. We can’t help but learn. The environment itself will always be the most effective teacher, and one of the difficulties or limitations of the classroom is that it pulls people out of the environment in which they’ll ultimately apply their knowledge.
Of course, we can’t just throw our learners into the field and let them try and fail on the job. Depending on the line of work, the consequences could be catastrophic (not to mention unethical). That said, if we were able to use a virtual environment to mirror the actual challenges one might face in their role, the impact on learning would be wild.
Flight simulators are an example of this style of learning. The simulations we use to train associates in other fields are far less sophisticated and effective. I’m excited to see VR become more ubiquitous in the next fifteen years.
Recommendation: Continue to experience and engage in learning.
VR training is something that’s probably not possible for the majority of L&D teams right now. Continue, whenever possible, to be a student. We pull from what we know, so if our experience as a learner is to sit down and slog through nine eLearning modules, it’s what we inevitably create for our learners. If our experience is being summoned and talked at for 90 minutes on Zoom, we’re likely to design learning experiences that do the same.
Reflect on all the formal and informal learning experiences you have. Be curious about what works and why. Build your L&D network. Ask, “How did you learn that?”
Conclusion
Like I said, I’m no Matt Groening. More than anything else, I’m curious about what the future has to hold. Change is inevitable, and while we can’t know for sure how the world of learning and design will transform, the worst thing we can do is assume or hope that it won’t. I’m excited for what the future holds.
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