From Expert Lecturer to Guide in the Classroom

Faculty concerns, practical examples, and the case for thoughtful AI use in higher education

The traditional way of teaching, where the professor delivers all the knowledge from the front of the room, worked when information was harder to access. Students can now get explanations and drafts almost instantly. AI has sped that change up.

Our real value comes from designing work that builds judgment and skills AI cannot replace on its own. We still bring deep knowledge of our subjects, but we apply it by guiding students through problems and helping them evaluate information.

Magana’s T3 framework gives a clear structure for this. The first level focuses on efficiency, such as using AI for routine tasks like quiz questions. The second level treats AI as a partner for thinking, where students critique outputs and revise using course material. The third level involves projects that reach beyond the classroom (Magana, 2019).

One faculty member described the practical change: “Recently, I’ve been using generative AI to help me write like an open educational resource textbook. That’s definitely something I would not have been able to do, I mean, not able to do as quickly or as efficiently without AI” (from project interviews).

Another reflected on the overall approach: “I think this is going to really… free me up” (from project interviews).

Try this with one assignment you already give. If a student gave the prompt straight to AI, what important part of the learning would they miss? Add one requirement that asks them to show their own thinking or connect it to their experience. That makes the work harder for AI to complete and more valuable for students.

References

Magana, S. (2019). Disruptive classroom technologies: A framework for innovation in education. Corwin Press. https://maganaeducation.com/what-is-the-t3-framework-for-innovation/