Hint-Writing with Deferred AI Assistance: Fostering Critical Engagement in Data Science Education
Anjali Singh, Christopher Brooks, Warren Li, Juho Kim, Xu Wang

TL;DR
This study explores how deferred AI assistance in hint-writing activities enhances students' debugging skills and critical engagement in data science education.
Contribution
It introduces and evaluates a deferred AI support design that improves hint quality and student learning outcomes compared to other methods.
Findings
Deferred AI assistance yields the highest-quality hints.
Students better identify mistakes with deferred AI support.
Activities promote critical engagement and debugging skills.
Abstract
Generating hints for incorrect code is a cognitively demanding task that fosters learning and metacognitive development. This study investigates three designs for personalized, scalable, and reflective hint-writing activities within a data science course: (i) writing a hint independently, (ii) writing a hint with on-demand AI assistance, and (iii) deferred AI assistance, in which students first write a hint independently and then revise it with the help of an AI-generated one. We examine how AI support can scaffold the learning process without diminishing students' productive cognitive effort. Through a randomized controlled experiment with graduate-level students (N=97), we found that deferring AI assistance leads to the highest-quality hints. Further, this design helps students identify a wide range of mistakes they otherwise struggle to identify without any AI assistance. Students…
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.
