Why can GenAI be useful?
Further discussion.
For a broader reflection on authorship, plagiarism, and generative AI, see the preprint usingGenAI.pdf.
For practical advice on writing effective prompts, see Tips for good prompting and the beginner-friendly website Learn Prompting (external).

A short motivation

Generative AI can be useful in teaching because it is fast at producing drafts, variations, and reformulations. For teachers, this can help when preparing examples, worksheets, short explanations at different levels, or feedback templates. For students, this can support practice, Socratic dialogues, self-testing, reformulation of questions, or guided review of a topic.

At the same time, Generative AI is not a source of mathematical truth. It can be helpful as a drafting partner, a simulator of questions, or a generator of alternative explanations, but all mathematical claims still need human checking. In a workshop like this, the goal is therefore not to replace the teacher or the student: it is to identify tasks where GenAI usefully saves time, broadens access, or creates good practice opportunities.

These activities are intended mainly for teachers and departments discussing classroom practice. They can also be adapted for student-facing guidance, but the main purpose of the site is to support reflective discussion about teaching.

Group activities

Each small group may choose one of the activities below. The pages include a goal, a suggested workflow, and concrete prompts that can be tested with a free GenAI interface.

At the end, each group should give a brief 2-minute report on what they tried, what worked well, and what limitations or risks they found.

Activity 1: Build a worksheet faster

Focus: teacher preparation

Use GenAI to create a first draft of a short worksheet, then evaluate what still requires mathematical and pedagogical revision.

Activity 2: Design better student practice

Focus: student learning process

Explore whether GenAI can help students practice actively instead of passively copying answers.

Activity 3: Critique a GenAI answer

Focus: limits, risks, and verification

Treat the GenAI as a fallible assistant and discuss how to detect hallucinations, hidden assumptions, or weak explanations.

Activity 4: Generate a Python visualization of Riemann sums

Focus: teaching visualization and code generation

Use GenAI to draft a small Python script that visualizes left, right, or midpoint Riemann sums for a one-variable function.

Activity 5: When does GenAI use help, and when does it make students passive?

Focus: learning benefits, dependency, and good boundaries

Discuss concrete situations in which GenAI use supports understanding, and contrast them with situations in which it mainly encourages shortcut-taking or avoidance of thinking.