Exploring the relationship between undergraduate students’ goal orientations and their use of generative AI
Session Type
Presentation
Room
Physics and Astronomy, room 106
Start Date
17-7-2025 10:30 AM
End Date
17-7-2025 11:00 AM
Keywords
Generative AI, Achievement Goal Orientation
Primary Threads
Education Technologies and Innovative Resources
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).
Elements of Engagement
This talk will provide participants with opportunities to share their thoughts with other WCSE participants in-person and online about (1) GenAI use among undergraduate and (2) potential individual and classroom factors that may influence students' use of GenAI.
Exploring the relationship between undergraduate students’ goal orientations and their use of generative AI
Physics and Astronomy, room 106
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).