Can ChatGPT as a generative AI tool enhance university student academic achievement?
Bibliographic record
Abstract
The Chat Generative Pre-trained Transformer (ChatGPT) is one of the most representative generative AI (GenAI) that has been used widely in higher education. However, ChatGPT’s usage has raised some concerns about its effectiveness in improving academic performance. This study uses a meta-analysis to examine the impact of ChatGPT on university student academic achievement. The results suggest that ChatGPT has a large positive effect on university students’ academic achievement (SMD = 0.961, 95% CI=[0.706, 1.216], p < 0.001). Moreover, class size, learning length, ChatGPT role, and region significantly moderate ChatGPT’s effects. Overall, ChatGPT shows better effect (a) with a class size of <50; (b) over 1–3 months; (c) for non-STEM subjects; (d) when acting as a learning assistant or virtual tutor; and (e) when targeting Asian university students.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".