A Precarious Partnership: Student Perceptions of Generative AI in Post-Secondary Learning
Bibliographic record
Abstract
Generative AI (GenAI) technologies offer both potent possibilities and significant risks for learning. As such, post-secondary learners require critical competencies for strategically regulating learning with AI. While student beliefs about GenAI impact their interactions and decisions, research of students’ perspectives is emergent. This study aimed to examine post-secondary students’ perceptions about the effective use of GenAI for learning. Participants were 125 undergraduate students at a university in Canada. Results indicated students were confident in their ability to use GenAI for learning, perceived GenAI to be effective for learning, and reported using GenAI in a wide range of academic tasks, particularly when faced with cognition and time management challenges. Finally, access to information, personalized learning support, new learning approaches, and time were considered benefits of GenAI. However, students expressed concerns about academic integrity, information accuracy, and the impact on personal learning. Implications for scaffolding learners’ development of skills for self-regulating interactions with AI critical for post-secondary and beyond are discussed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".