jamie billingham
A graphic novel-style illustration depicting a diverse group of people engaged in self-directed learning. Some figures are using tools like tablets, books, and VR headsets, while others collaborate, exchanging ideas and supporting one another. In the background, a sleek, futuristic AI figure acts as a guide, overseeing the learning process. The scene features flowing lines and vibrant colors to emphasize the dynamic, creative nature of heutagogy, with subtle tech elements like circuit boards and mathematical symbols in the background, representing the role of technology in the learning process

Generative learning, heutagogy, and AI: The future of learning

Posted on by Jamie
Reading Time: 5 minutes

 

A while ago, I wrote about a generative learning framework proposed by Logan Fiorella in Making Sense of Generative Learning. Logan’s framework focuses on how learners actively construct knowledge by integrating new information with what they already know. Remember schema theory? That’s the cognitive framework where our brains organize knowledge into networks called schemas, which help us interpret the world.

In Fiorella’s model, schema theory is foundational to generative learning activities (GLAs). These activities rely on the learner’s active role in constructing knowledge. Fiorella breaks down learning into three core sense-making modes: explaining, visualizing, and enacting. Each serves a unique cognitive function to help learners understand and retain new information.

The three modes of learning

Fiorella’s framework hinges on these modes—let’s break them down:

Explaining

Explaining or elaborating helps learners organize and integrate new information. Two key theories support this:

  • Cognitive load theory: Explaining reduces cognitive load by organizing information into schemas, which promotes deeper understanding.
  • Self-explanation effect: When learners generate their own explanations or elaborations, they make connections between new and prior knowledge, boosting comprehension.

Visualizing

Visualizing taps into our visual and verbal systems to enhance memory.

  • Dual coding theory: Proposes that pairing visuals with verbal information improves memory and understanding by activating both systems.
  • Embodied cognition: Suggests that mental simulations from visualizing help us grasp abstract concepts more concretely.

Enacting

Physical activity during learning also plays a big role.

  • Embodied learning: Engaging physically with learning material strengthens memory through sensorimotor experiences.
  • Situated learning theory: Highlights the importance of context and interaction, where learners engage in real-world tasks to build knowledge.

Other generative learning models

There are of course many other generative learning models that overlap with Fiorella’s approach:

  • ICAP framework: Chi and Wylie’s framework categorizes activities by cognitive engagement—Interactive, Constructive, Active, and Passive. Constructive activities, where learners generate beyond what’s provided, lead to deeper understanding.
  • SOI model: This model (by Mayer) emphasizes three processes—Selecting, Organizing, and Integrating—helping learners actively structure and integrate new information, much like generative learning.
  • Cognitive load theory (link to video): While not strictly a generative model, it explains how instructional design can reduce mental effort. Techniques like drawing or mapping help externalize and simplify complex information.
  • Problem-based learning (PBL): A hands-on approach where learners solve real-world problems by applying prior knowledge. It’s inherently generative since it encourages learners to create solutions through active engagement.

These models all stress that learning happens when we’re actively involved, not just passively receiving information.

How generative learning connects to heutagogy

I’ve been thinking a lot about heutagogy lately (the fancy word for self-determined learning). By lately I mean for the last decade. It has some real synergies with generative learning. Both frameworks emphasize learner autonomy and active engagement. I like that. Let’s take a look at where they connect.

Key Intersections

  • Learner agency: Heutagogy encourages learners to take control of their learning path, just like generative learning asks them to actively construct knowledge.
  • Self-reflection and metacognition: Both frameworks value reflection, encouraging learners to think critically about their learning processes. Metacognition is my favorite M word. I have loved it for decades.
  • Non-linear learning paths: Heutagogy’s flexible approach aligns well with generative learning’s diverse methods, like drawing or mapping, to explore topics.
  • Capability development: Heutagogy and generative learning both go beyond knowledge acquisition, aiming to equip learners with the skills to apply knowledge in new situations.

AI, generative learning, and heutagogy: A perfect match for the workplace

Now, let’s get to the good stuff. How can technology—especially AI— bring heutagogy and generative learning to life in workplace learning.

Personalization and adaptivity

  • Customized learning paths: AI can analyze performance and preferences to create personalized learning experiences, aligned with heutagogical principles. Yes, I know there are issues with this relating to always-on surveillance in the workplace. We can figure it out. I have faith in us!
  • Real-time feedback: AI can also provide immediate feedback, helping learners adjust their strategies in real time. I mean seriously. Just think about that for a minute.

Content creation and management

  • Automated content generation: AI can generate and update training materials, making sure content stays relevant while reducing the workload on Learning & Development (L&D) teams. Yeah! This is perhaps the most explored use of generative AI.. right now.
  • Diverse content delivery: AI can tailor content for different learners, ensuring it’s relevant and accessible to everyone. Because we all know differentiation is critical but we don’t do it because of time and technological limitations. Go UDL 🙂

Supporting self-determined learning

  • Empowering learners: AI tools like chatbots and virtual assistants can act as guides, encouraging learners to explore topics on their own. I had an argument with one the other day though.. So you do have to be careful or disempowering also. I felt so defeated…
  • Access to resources: AI can curate resources based on the learner’s progress and interests, supporting a self-directed learning journey.

Efficiency and engagement

  • Learning in the flow of work: AI can enable learning within the work environment by providing just-in-time support. Again, I know, surveillance. We will figure it out, Geez.
  • Motivation through personalization: Tailoring learning experiences increases motivation and engagement—core elements in heutagogy and generative learning.

Shaping the future of learning

Generative learning and heutagogy both highlight the importance of active engagement and learner autonomy. With the power of AI, we have the opportunity to create more dynamic, learner-centered environments in workplace learning. This facilitates adaptability and continuous development. But achieving this requires more than just technology. It demands proactive leadership, well-informed planning, and cross-functional collaboration.

In the late 70s, the Club of Rome published a report called No Limits to Learning, which emphasized that there are many ways to learn—one of them being through shock. While shock can be an effective teacher, it’s often an unpleasant and reactive way to learn. Think of movies like The Day the Earth Stood Still, or really any disaster movie you can think of, where crisis forces change. But there’s a better way. 

True innovation in learning happens through anticipation and participation. Anticipation is the ability to look ahead, imagine different futures, and understand the broader implications of our decisions. It’s about asking, “What if?” However, anticipation alone isn’t enough. It must be coupled with participation—engaging people at all levels to ensure a broad base of support and collaborative action.

This principle is crucial in business and societal change. And we are at a point in our evolution where we desperately need positive change in so many areas. So, how do we get there? It starts with you—whether you’re a leader in workplace learning, an innovator in technology, or someone passionate about shaping the future of learning and development. Let’s work together to anticipate the needs of tomorrow and participate in building learning systems and processes that are inclusive, adaptive, and ready for anything.

AI is disrupting learning design and development in every way possible. And that may be a good thing. In fact it’s probably a necessary thing if we’re to survive and thrive into the next decade or two. Remember, we can’t solve our problems with the same thinking [and systems] we used when we created them. 

How are you and your organization preparing for the future of learning? How can we better innovate, through anticipation and participation, to create more effective, learner-centered environments?

 

Cross posted to LinkedIn. Share your thoughts there, and let’s start shaping the future today.

NotebookLM did an amazing job generating the summary, FAQ, and of course the podcast. Except I could not convince it I was female…

Listen on Soundcloud