AI is a means of transforming information

There’s been considerable discussion about using AI to summarise information. Summaries certainly have their place e.g. when you need a quick overview or refresher. However, they inevitably reduce information density, which can be problematic when you’re trying to learn.

Recently, a colleague expressed strong opposition to students recording lectures and creating AI-generated summaries. I certainly understand this perspective (no-one wants to lecture to a room of recording devices), but I think it misses a broader opportunity.

If we think of “summarisation” as just one form of information transformation, it’s easy to see how this general principle supports a personal approach to learning.

Information as raw material for learning

Imagine recorded lectures as the “raw material” that supports learning. We can perform various transformations on this material, creating an entirely new relationship with the content, where summarisation is only one option in a rich palette of possible transformations.

When students have access to recorded lectures, they can transform that information in numerous ways:

• Translation: Convert the content into different languages or professional jargon appropriate for specific contexts.
• Simplification: Explain complex terms simply—distinct from summarisation, which condenses information rather than clarifying it.
• Connection: Relate concepts from one lecture to another, building an interconnected knowledge network.
• Personalisation: Explore the lecture from personally meaningful perspectives. For instance, a ballet dancer might reframe examples of sports injuries originally presented using rugby scenarios.
• Expansion: Elaborate on specific slides or concepts to include more detail, creating deeper understanding.
• Critique: Analyse the strengths, limitations, or potential biases in the presented information.
• Application: Transform theoretical concepts into practical scenarios relevant to one’s future practice.
• Visual mapping: Convert verbal information into concept maps, diagrams, or other visual representations.
• Questioning: Generate thoughtful questions that probe deeper into the material or identify gaps.
• Contextualisation: Place the lecture content within historical, cultural, or scientific developments in the field.
• Alternative explanations: Develop different ways of explaining the same concept for various learning preferences.
• Synthesis: Integrate information from multiple sources, including the lecture, readings, and other materials.

Embracing information transformation in education

Instead of resisting the recording and transformation of lectures, we should embrace this approach. Making audio recordings available to students, and then teaching them how to transform that information in meaningful ways could significantly enhance their learning experience.

This approach shifts the focus from passive consumption of summaries, to active engagement that opens up possibilities. It recognises that different students learn differently and may benefit from various forms of information transformation. Most importantly, it prepares students for the real-world skill of adapting and transforming information for different contexts and purposes—a capability increasingly valuable in our complex information landscape.

What other transformations might be valuable in your own learning or teaching context? How might we better prepare students to become adept at transforming information rather than merely consuming it?