By the final survey wave, using LLMs for debugging and understanding concepts was nearly universal among surveyed students¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Final-wave usage for debugging and understanding concepts approached 100% and 93% of respondents, with code writing least common at 57%. → Yuan An 2026

Evidence¶

Yuan An 2026¶

Yuan An, Lei Wang. (2026). Student Use of LLMs and the Limits of AI-Generated Question Difficulty in Data Science Courses. https://arxiv.org/abs/2609.27063 doi:10.48550/arxiv.2609.27063

q2 · i? · associational · r1

Descriptive final-survey analysis (n=14) of usage modes found debugging selected by 100%, understanding concepts 93%, checking work 79%, and writing code 57%, per Figure 3.

"By the final survey wave, using LLMs for debugging and understanding concepts was nearly universal (Figure 3)."

Discussion¶

Related Claims¶

• Students shifted AI usage toward debugging and concept explanation over the semester, with 31 of 89 reporting more debugging use — related
• Students in an OOP course report using GenAI significantly more often for explanation seeking and debugging than for code generation — related
• LLM use for debugging is significantly negatively correlated with student final grades
• LLM use for seeking additional explanations does not significantly impact final grades
• Students' AI workflows shifted toward guided, step-by-step use and more sophisticated prompting and critical evaluation over the semester — related
• Random assignment to AI access produces a strong first stage: about 70 percent of treated students use AI, most often to explain concepts — related