Exploring the relationship between undergraduate students’ goal orientations and their use of generative AI
Bibliographic record
Abstract
The widespread availability of generative-AI (genAI) tools has disrupted higher education. Instructors’ attitudes vary; some treat genAI use as misconduct, while others integrate it into courses. However, creating informed guidelines is difficult without understanding how students use genAI; some may engage in misconduct, while others use genAI to support learning. Our interest lies in better understanding this variability. We hypothesize that students’ genAI use is associated with their achievement goal orientation (AGO) (Elliot, 1999). Achievement-goal orientation is a context-dependent measure of students’ motivations for achievement: mastery-oriented students focus on developing competence, while performance-oriented students focus on demonstrating competence (Elliot, 1999). Achievement-goal orientation has been linked to academic dishonesty, with mastery-oriented students engaging in it less (Fritz et al., 2023). We hypothesize that performance-oriented students are more likely to use genAI to complete assignments without supporting learning, while mastery-oriented students may use it to advance knowledge. To test this, students in a first-year biology course created a concept map for an upcoming assessment and were encouraged to use ChatGPT. They submitted their concept map and ChatGPT log. We measured achievement-goal orientation using the Achievement Goal Questionnaire-Revised (Elliot & Muryama, 2008) and collected data on genAI use, prior AI experience, demographics, and course performance. This talk will present these findings and discuss their implications for AI-use policies. Ethics approval for this study was obtained from the University of Guelph's Research Ethics Board (REB #23-08-014).
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".