Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
Bibliographic record
Abstract
The integration of large language models (LLMs) into educational settings represents a significant technological breakthrough, offering substantial opportunities alongside profound ethical challenges. Higher education institutions face the widespread use of these tools by students, requiring them to navigate complex decisions regarding their adoption. This includes determining whether to allow the use of LLMs, defining their appropriate scope, and establishing guidelines for their responsible and ethical application. In the context of computer science education, these challenges are particularly acute. On the one hand, the capabilities of LLMs significantly enhance the tools available to developers and software engineers. On the other hand, students’ over-reliance on LLMs risks hindering their development of foundational skills. This study examines these challenges and proposes strategies to regulate the use of LLMs while upholding academic integrity. It focuses on the specific impact of LLMs in programming education, where dependence on AI-generated solutions may erode active learning and essential skill acquisition. Through a comprehensive literature review and drawing on teaching experience and guidelines from global institutions, this study contributes to the broader discourse on the integration of these advanced technologies into educational environments. The goal is to enhance learning outcomes while ensuring the development of competent, ethical software professionals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".