AI-Generated Slides: Are They Good? Can Students Tell?
Juho Leinonen, Lisa Zhang, Arto Hellas

TL;DR
This study evaluates the quality and perception of AI-generated slides in education, finding that students view AI and human slides similarly and that coding tools produce the most accurate and pedagogically sound slides.
Contribution
It provides an empirical assessment of AI-generated slides' quality and student perception, highlighting their potential in instructional design.
Findings
Coding assistants produce the most accurate and pedagogically sound slides.
Students rate AI-generated slides similarly to instructor-created slides.
Students cannot reliably distinguish between AI-generated and human-created slides.
Abstract
As generative AI (GenAI) tools become easily accessible, there is promise in using such tools to support instructors. To that end, this paper examines using GenAI to help generate slides from instructor authored course notes, emphasizing instructor and student perceptions. We examine an end-to-end education tool (NotebookLM), two general-purpose LLMs (Claude, M365 Copilot), and two coding assistants (Cursor, Claude Code). We first analyze whether GenAI generated slides are ``good'' via narrative assessment by educators. We choose the best slides to use (with some modification) in a real course setting, and compare the student perception of human vs. AI generated slides. We find that coding assistant tools produce slides that were most accurate, complete, and pedagogically sound. Additionally, students rate GenAI slides to be of similar quality as instructor-created slides, and cannot…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
