Understanding the Impact of Using Generative AI Tools in a Database Course
Bibliographic record
Abstract
Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) have led to changes in educational practices by creating opportunities for personalized learning and immediate support. Computer science student perceptions and behaviors towards GenAI tools have been studied, but the effects of such tools on student learning have yet to be determined conclusively. We investigate the impact of GenAI tools on computing students' performance in a database course and aim to understand why students use GenAI tools in assignments. Our mixed-methods study (N=226) asked students to self-report whether they used a GenAI tool to complete a part of an assignment and why. Our results reveal that students utilizing GenAI tools performed better on the assignment part in which LLMs were permitted but did worse in other parts of the assignment and in the course overall. Also, those who did not use GenAI tools viewed more discussion board posts and participated more than those who used ChatGPT. This suggests that using GenAI tools may not lead to better skill development or mental models, at least not if the use of such tools is unsupervised, and that engagement with official course help supports may be affected. Further, our thematic analysis of reasons for using or not using GenAI tools, helps understand why students are drawn to these tools. Shedding light into such aspects empowers instructors to be proactive in how to encourage, supervise, and handle the use or integration of GenAI into courses, fostering good learning habits.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".