Quantifying Shortfalls in Students? AI-Supported Programming Practices
Why this work is in the frame
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Bibliographic record
Abstract
Generative AI assistants are permeating programming classrooms, yet little is known about whether students adopt sound usage habits.This study surveyed 50 undergraduates in Computer Science / Computer Information Systems courses to examine the frequency of 11 recommended AI-support behaviors, such as planning queries, verifying AI-generated output, and integrating AI with personal code.Substantial shortfalls were observed in critical forethought and verification steps, especially among students using AI for simpler tasks.In contrast, students applying AI to more complex assignments demonstrated stronger behaviors, although certain foundational habits remained weak across the board.The paper proposes five low-overhead scaffolds to help instructors close these usage gaps and improve AI literacy in programming education.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it