AI, the Flipped Classroom and a Possible Future of the Lecture

A proof of concept argument for student-centred module leaders

A tweet recently caught my attention https://x.com/ihtesham2005/status/2041576806810370553?s=20. It described an MIT student who had developed what he called “context stacking” — uploading lecture materials, readings and related papers into an AI tool before each class, then using carefully constructed prompts to build a mental model of the content before setting foot in the lecture hall. By the time he arrived, the professor wasn’t teaching him anything new. They were confirming, refining and occasionally surprising him. That surprise, he said, was the only thing he wrote down.

This is not simply pre-reading with extra steps. Using generative AI as an external thinking partner, this student was identifying gaps in his own understanding before the lecture began — doing what good tutors have always done, asking not “what do you know?” but “where does your understanding break down?” This maps directly onto the higher-order thinking that Bloom et al. (1956) place at the top of the learning hierarchy: analysis, evaluation, synthesis. Traditionally, students only reached those levels during revision or assessment. This approach gets them there before the lecture starts.

This is, in effect, an AI-enhanced flipped classroom. Bergmann and Sams (2012) argued that class time should be reserved for learning activities that most benefit from human interaction. If AI can now handle the initial construction of conceptual understanding, the classroom becomes available for exactly that. Biggs and Tang (2011), whose constructive alignment framework connects learning outcomes, activities and assessment into a coherent whole, would recognise the logic immediately: align the preparation to the outcome, and the lecture becomes the place where understanding is tested, not transmitted.

The Risk Inside the Opportunity

There is a genuine risk embedded in this approach. A student who arrives with a confidently pre-constructed mental model may filter everything the lecturer says through that model, accepting what confirms it and discarding what challenges it. In STEM subjects this can be particularly consequential: a subtly wrong understanding held with confidence may be harder to correct than no understanding at all.

Kahneman’s (2011) distinction between System 1 and System 2 thinking is instructive here. AI-assisted pre-learning risks producing fluent, fast System 1 confidence — the feeling of understanding — where slow, deliberate System 2 scrutiny is required. Bjork and Bjork (2011) identified “desirable difficulties” as central to deep learning: the productive struggle that counterintuitively strengthens long-term retention. A further risk, less discussed, is coordinated misconception at scale: if a whole cohort context-stacks using similar prompts, they may arrive sharing the same confident errors.

The methodological answer is not to abandon peer discussion but to structure it differently. Rather than asking groups to arrive at a shared model, the task should be to stress-test their models against challenging, preferably unseen questions: does the model still hold? A lecturer who deliberately unsettles a pre-formed mental model is not undermining a student’s preparation — they are completing it. The goal is not to arrive at the lecture with answers. It is to arrive with better questions, and with enough structure in one’s thinking to recognise, rather than deflect, the moment when an expert says something that doesn’t fit.

What This Means for the Academic

If the student’s job is to arrive with better questions, the academic’s job becomes something far more interesting than content delivery. The academic most at risk is not the expert practitioner who contextualises theory through their own research and professional experience. The academic most at risk is the one whose primary function is delivering established content — and students can already find that content elsewhere. Boyer (1990) argued that teaching and research are not in tension but expressions of the same scholarly identity. AI-assisted preparation, used well, restores academics to that identity: real-time sense-making, the ability to respond to where students actually are, and the capacity to generate connections no uploaded document anticipated.

Critically, lived experience remains beyond what AI can authentically replicate. When an academic says “in my own research I found…” or “working on project X, this is what happened…” they are doing something an AI cannot: demonstrating embodied disciplinary judgement, visibly comfortable with complexity and genuinely responsive in the moment. Students recognise this. It is not merely what the academic knows but the evident confidence with which they inhabit that knowledge that establishes authority. Research on active versus traditional lecturing consistently finds that student-centred instruction produces superior learning outcomes (Lasnier et al., 2022; Wieman, 2014). Chickering and Gamson’s (1987) pillars of quality higher education — contact with faculty, active learning and prompt feedback — describe exactly what this approach makes possible.

Who This Is For — and What That Demands

It is important to be honest about the scope of this argument. Context stacking as a structured pedagogical approach is most immediately applicable to module leaders who already have genuine autonomy over their formative activities, and who already lean toward student-centred practice. For those academics, the workload shift required is not radical: reflecting carefully on what formative questions best expose shallow understanding is good practice anyway. It is, as Biggs and Tang (2011) would frame it, a matter of aligning activities more deliberately to intended outcomes.

This is not, however, an argument for leaving the approach to individual discovery. That path privileges the already privileged: students with the digital literacy and self-regulation to experiment independently, and academics with the confidence and autonomy to innovate alone. Wingate (2006) argued that study skills should be embedded in the curriculum rather than treated as bolt-on extras. AI-assisted study methodology should now be considered foundational academic literacy, no different from library skills or academic writing. Without structured development, generative AI risks producing convergent homogenisation of thinking rather than deeper understanding (Kirkpatrick et al., 2025).

The Responsibility of Early Adopters

This is a proof of concept argument, not a universal prescription — and that distinction carries a specific obligation. Champions of new pedagogical approaches are structurally fragile: they leave, get promoted, or burn out, and the approach collapses with them unless it has been evaluated and embedded beyond individual enthusiasm. The responsibility on early adopters is therefore clear: document rigorously, evaluate honestly, and disseminate actively — through internal teaching and learning conferences, through peer networks, and through publication in practitioner journals.

The workload question in particular remains underresearched and deserves honest investigation rather than reassurance. Institutional buy-in is more likely to follow demonstrated outcomes than theoretical argument. Teaching-focused institutions, often more dependent on student income than research income, have strong structural incentives to get pedagogy right — and the evidence base for active, student-centred approaches is already compelling (Freeman et al., 2014). What is needed now is evidence that this specific approach works, for which students, under which conditions, and at what cost.

Conclusion

The MIT student did not use AI to avoid learning. He used it to arrive ready to learn at a level most students never reach until revision, if at all. The question for educators is not whether students will use these tools — they will — but whether those of us with the autonomy and inclination to respond will do so rigorously enough to build an evidence base that eventually moves the approach beyond the already-converted.

The irreplaceability of the human academic does not lie in knowing more than an AI. It lies in being present in the room, reading the moment, and asking the question that unsettles comfortable certainty and replaces it with something harder and more durable. That is not a role under threat from generative AI. It is a role that generative AI, used well by students, finally makes possible at scale — for those willing to meet them there.

References

Bergmann, J., & Sams, A. (2012). Flip your classroom: Reach every student in every class every day. International Society for Technology in Education.

Biggs, J., & Tang, C. (2011). Teaching for quality learning at university (4th ed.). Open University Press.

Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher et al. (Eds.), Psychology and the real world (pp. 56-64). Worth Publishers.

Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: Handbook 1: Cognitive domain. David McKay.

Boyer, E. L. (1990). Scholarship reconsidered: Priorities of the professoriate. Carnegie Foundation for the Advancement of Teaching.

Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE Bulletin, 39(7), 3-7.

Freeman, S., et al. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. https://doi.org/10.1073/pnas.1319030111 PMid:24821756 PMCid:PMC4060654

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Kirkpatrick, J., et al. (2025). University students' perceptions of generative AI for critical thinking and creativity. Innovations in Education and Teaching International.  https://doi.org/10.1080/14703297.2025.2600476

Lasnier, A., et al. (2022). Effect of active learning versus traditional lecturing on the learning achievement of college students in humanities and social sciences: A meta-analysis. Higher Education.  https://doi.org/10.1007/s10734-022-00977-8

Wieman, C. (2014). Large-scale comparison of science teaching methods sends clear message. Proceedings of the National Academy of Sciences, 111(23), 8319-8320. https://doi.org/10.1073/pnas.1407304111 PMid:24853505 PMCid:PMC4060683

Wingate, U. (2006). Doing away with 'study skills'. Teaching in Higher Education, 11(4), 457-469. https://doi.org/10.1080/13562510600874268

Appendix:

CONTEXT STACKING

BUILD → TEST → FIX → STRESS → VALIDATE

1. Load Inputs (2 days before): Readings, slides, extra sources, problem sets, include notes from last week.

2. Map Ideas: Identify 5 core concepts and connections. Possible Prompt after stage 1: What are the 5 core concepts this week's content is built on, and how do they connect to what I studied last week?

3. Find Gaps: What must you understand to teach it?

Possible Prompt after stage 2: What would I need to understand to teach this to someone with zero background? What would I need to genuinely understand about this material to teach it to someone with zero background?

4. Fix Gaps (90 min): Study only weak areas.

5. Think Like Examiner: What questions expose shallow understanding? Possible prompt: What question would expose someone who understands this topic only at a surface level? What question could a professor ask that exposes shallow understanding?

6. Lecture: Confirm understanding, note surprises only.

7. Lock It In: Reflect and rate confidence

Note: This blog is to be read alongside the companion blog. In this post Clauda.AI was used as a 'co-author' helping phrase things but most importantly (the focus of the campion blog), being a harsh critic- Devils Advocate' - the prompts used were specific on this, not just give me the answer, but stress-test; it in some respects the reverse what Generative AI have a tendency to do of giving things that confirm your thinking. See https://llmapplied.blogspot.com/2026/04/genai-as-co-author-and-more-importantly.html for more details on the process.

All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon