From Data to Dialogue: Reclaiming Learning in the Age of AI

Part 2 — The Generative Turn: From Prediction to Participation

In the first part of this series, we explored why today’s AI often gets learning wrong. Not because it lacks intelligence, but because it overlooks what makes learning human: motivation, emotion, and metacognition.

So where do we go from here?

If Part 1 was about what AI misunderstands, Part 2 is about what we can teach it by designing and using AI that encourages learners to generate knowledge, not just reproduce it. This is the heart of Generativism: the idea that learning happens through creation, explanation, and transformation, not passive consumption.

The Generative Mindset

Generativism is not a new concept. In fact, it traces back to Wittrock’s (1974) insight that learners actively build meaning by linking new ideas to prior knowledge. But its significance has sharpened in the age of generative AI.

As Mairéad Pratschke (2024) argues in Generative AI and Education, generativism has evolved into a digital pedagogy which presents a framework for designing, delivering, and assessing learning in collaboration with AI tools. In this view, generative learning is no longer just about students producing knowledge; it’s about co-creating understanding with machines, using AI to extend exploration and imagination.

When AI is treated as a partner in inquiry, not a producer of answers, learning becomes dialogic again. The process shifts from using AI to thinking with AI.

Example 1: Generative Writing Feedback

Traditional writing assistants focus on surface-level polish including grammar, clarity, tone. A generative approach looks deeper.

Imagine an AI feedback tool that doesn’t just correct a sentence but prompts the student to explain their reasoning:

“You used the phrase ‘this shows that’. What connection are you trying to make here?”

It could then offer scaffolded questions:

“How does this example strengthen your argument?”
“What counterpoint might challenge this idea?”

Each prompt draws the student back into meaning-making. Instead of being a shortcut to better prose, AI becomes a mirror for thought. The student writes, reflects, and revises. This is learning through articulation, not automation.

Example 2: Student-Generated Quizzes

In a generative classroom, learners don’t just answer questions, they write them.

Picture a platform where students use an AI assistant to create quiz questions for their peers. The system analyses each item, identifies whether it tests recall, inference, or evaluation, and suggests how to deepen it:

“This question focuses on factual recall. Could you reframe it to ask why or how?”

Students learn to think like assessors, anticipating misconceptions and clarifying concepts. This not only reinforces their own understanding but fosters metacognitive awareness, the realisation that how we question is as important as what we know (Chi & Wylie, 2014).

Example 3: Creative Co-Design with AI

In a design or media course, students might use AI tools to explore variations on a theme by generating images, scripts, or prototypes.

A generative approach doesn’t end with the AI output; it begins there. Students critique the results: Which version best reflects my intent? What assumptions did the AI make? How could I refine my prompt to express a clearer vision?

This recursive cycle of prompt → response → reflection → revision, turns creative production into a metacognitive exercise. It’s not just about making artefacts; it’s about understanding the relationship between ideas, tools, and judgment.

From Prediction to Participation

These examples share a common thread: AI becomes a participant in learning, not a predictor of outcomes.

Predictive AI asks, “What will the learner do next?”
Participatory AI asks, “How can we help the learner think next?”

Generativism (as presented by reframes AI as a conversational partner in which it prompts, challenges, and supports the learner’s own cognitive effort. The emphasis shifts from efficiency to exploration, from correctness to curiosity.

Why Generativism Matters Now

As schools rush to adopt generative AI, the temptation is to use it for convenience: faster marking, instant lesson plans, automatic summaries. Those efficiencies matter, but they don’t transform learning.

The real transformation happens when we use AI to open cognitive space, not close it. When it helps students explain, question, and make connections, it supports the very processes of motivation, emotion, and metacognition, that Part 1 showed AI has historically neglected.

That’s the promise of the Generative Turn: to make learning active again.

Steps for Educators

1. Recast AI tools as thinking partners.
   Ask how a tool can provoke reasoning, not just polish products.

2. Build generative tasks.
   Replace “write a summary” with “use AI to generate three interpretations and critique them.”

3. Teach prompt refinement as reflective practice.
   Show students that changing a prompt isn’t gaming the system. Instead it’s a form of inquiry.

4. Reward the process, not the product.
   Assess how learners use AI to explore ideas, not just the final output they submit.

Coming Next: The Conversational Classroom, Building Dialogue with AI

In the final part of this series, we’ll look at Laurillard’s Conversational Framework; a model for turning generative principles into structured dialogue. We’ll explore how AI can take part in that conversation: prompting reflection, supporting feedback loops, and helping teachers scale meaningful interactions without losing the human voice at the centre.

Because if generative learning teaches students to create knowledge, conversational design shows us how to share it.

References

Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational psychologist, 49(4), 219-243.

Hooshyar, D., Šír, G., Yang, Y., Kikas, E., Hämäläinen, R., Kärkkäinen, T., ... & Azevedo, R. (2025). Towards responsible AI for education: Hybrid human-AI to confront the Elephant in the room. arXiv preprint arXiv:2504.16148.

Laurillard, D. (2013). Teaching as a design science: Building pedagogical patterns for learning and technology. Routledge.

Pratschke (2024). “Generativism.” In Generative AI and Education: Digital Pedagogies, Teaching Innovation and Learning Design (SpringerBriefs in Education), Chapter 4, pp. 57–72.

Wittrock, M. C. (1974). Learning as a generative process. Educational psychologist, 11(2), 87-95.