Beyond the Prompt: Student Strategies, Ethical Reflections, and Learning with ChatGPT in Computer Science
Bibliographic record
Abstract
Abstract This study explores how undergraduate computer science students critically evaluate, strategically engage with, and ethically reflect on their use of ChatGPT during programming tasks. Drawing on data from 21 students who completed five Java-based activities, maintained weekly reflective journals over four weeks, and participated in semi-structured interviews, the research offers a short-term longitudinal qualitative study perspective on student–AI interaction. Findings reveal that students evolved from passive users to active co-creators, developing increasingly refined prompting strategies and critically assessing AI-generated outputs. While most students viewed ChatGPT as a valuable learning companion, particularly for code structuring, debugging, and explanation, they also identified limitations, such as generic responses, overreliance, and concerns around authorship and data privacy. Students with disabilities highlighted ChatGPT’s accessibility benefits, raising important questions about equitable AI policy in higher education. The study proposes a framework for the pedagogical and institutional integration of GenAI tools that balances personalised support with ethical and critical engagement. Implications are offered for computing educators, curriculum designers, and policymakers seeking to embed AI responsibly in computer science education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".