An Exploration of How Generative AI Affects Workflow and Collaboration in a Software Engineering Course
Bibliographic record
Abstract
How does Generative AI (GenAI) impact how students work and collaborate in a software engineering course? To explore this question, we conducted an exploratory study in a project-based course where students developed three versions of a system across agile sprints, with unrestricted access to GenAI tools. From survey responses of 349 students, we found that the technology was used extensively with 84% of students reporting use and 90% of them finding the technology useful. Through semi-structured interviews with 24 of the students, we delved deeper, learning that students used GenAI pervasively, not only to generate code but also to validate work retrospectively, such as checking alignment with requirements and design after implementation had begun. Students often turned to GenAI as their first point of contact, even before consulting teammates, which reduced direct interpersonal collaboration. These results suggest the need for new pedagogical strategies that address not just individual tool use, but also design reasoning and collaborative practices in GenAI-augmented teams.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".