As generative AI becomes increasingly embedded in academic life, we face a critical choice in how we use AI to support learning. The default use case (and the one that most people think is the only use-case) has us outsourcing our thinking to AI. We focus on their use as content generators—machines that produce essays, solve problems, or provide the answers that we passively consume.
But this approach misses the true potential of AI as an input for deeper thinking and learning.
AI in the experiential learning cycle
The experiential learning cycle provides a useful framework for thinking about how AI can enhance intellectual work. By thoughtfully integrating AI into each phase of the cycle, we can create more space for meaningful learning while preserving critical thinking and judgement.
1. Concrete experience: AI as scenario generator
Instead of bypassing experiences, AI can create rich starting points for exploration:
- Generate complex disciplinary scenarios that require different levels of expertise to analyse
- Simulate conversations or discussions representing diverse viewpoints
- Create novel research questions or design challenges with specific constraints
When AI generates these experiences, its output becomes your input—a starting point for deeper engagement rather than a finished product.
2. Reflective observation: AI as thinking partner
During reflection, AI can help process experiences and provide alternative perspectives:
- After writing a draft, ask AI to identify patterns in your thinking or potential blind spots
- Use AI to explore how others might approach the problem you’re working on
- Have AI suggest different theoretical lenses through which to view your observations
The goal is engaging with AI reflectively to extend your thinking rather than replacing it.
3. Abstract conceptualisation: AI as knowledge integrator
AI can help organise insights into coherent frameworks without doing the conceptual work for you:
- Ask AI to visualise connections between ideas you’ve been studying, then refine and expand this map yourself
- Use AI to suggest how your observations might relate to existing theoretical frameworks in your field
- Have AI identify recurring themes in your notes or research data as a starting point for your own analysis
When we use AI to organise knowledge rather than simply produce conclusions, we strengthen our own conceptual frameworks.
4. Active experimentation: AI as feedback generator
Finally, AI can support applying new concepts in practical situations:
- Use AI to simulate different outcomes based on your approach to a problem
- Ask AI to generate varying scenarios to test the robustness of your ideas
- Have AI suggest potential obstacles when planning to apply new knowledge
Using AI to support learning
The real value of AI for learning isn’t in producing assignments faster—it’s in creating space for the meaningful learning that builds professional competence.
Consider Emma, a physiotherapy student preparing for clinical placements. Note that for each item in the learning cycle below, there are many other directions that Emma could move with the AI.
Rather than having AI write her case notes or literature reviews, she:
- Starts with experience: Asks AI to generate a detailed case study of a patient with complex lower back pain symptoms, including medical history, lifestyle factors, and previous treatments. This gives her a rich scenario to analyse before encountering similar cases in her placement. Emma can have the AI generate a case that’s personalised to her placement and her current level of understanding, something that no lecturer would be able to do at scale.
- Enhances reflection: After forming her initial assessment, Emma prompts AI to provide alternative clinical perspectives—what might a specialist in geriatric physiotherapy notice? How would a sports medicine practitioner approach this case? She compares these viewpoints with her own thinking, identifying assumptions she hadn’t considered.
- Builds concepts: Emma then uses AI to help integrate her observations into a more comprehensive framework. She asks AI to create a concept map showing relationships between different pain mechanisms, treatment approaches, and recovery factors. Emma modifies this map based on her own understanding, creating a more nuanced conceptual model of chronic pain management.
- Tests applications: Before her next clinical session, Emma asks AI to simulate how the patient might respond to different treatment approaches. She develops a treatment plan incorporating her enhanced understanding, using AI feedback to refine her approach. When working with actual patients, she notices improvements in her clinical reasoning.
Throughout this process, Emma remains the primary thinker and decision-maker. The AI creates space for her to focus on what truly matters: developing the clinical reasoning skills that will make her an effective practitioner, rather than simply completing assignments.
Moving from passive consumption to active collaboration
When we view AI outputs as inputs to our own thinking process, we transform a potentially passive technology relationship into an active, growth-oriented partnership. This approach:
- Preserves your disciplinary expertise and critical judgement
- Reduces time spent on routine learning activities
- Creates more space for deep, meaningful work
- Models thoughtful technology use in a professional learning context
Learning to work with AI is becoming a crucial academic skill. By developing intentional routines for AI collaboration, you can harness its capabilities while preserving the essential elements that support meaningful learning.