Beyond the Prompt: Student Strategies, Ethical Reflections, and Learning with ChatGPT in Computer Science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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