ENHANCING STUDENT ENGAGEMENT AND PERFORMANCE THROUGH COLLABORATIVE, AI-INTEGRATED ASSESSMENTS IN HIGHER EDUCATION
DOI:
https://doi.org/10.35631/IJMOE.830011Keywords:
AI In Education, Assessment Design, Collaborative Learning, Higher Education, Student EngagementAbstract
This study investigates how redesigned assessments that integrate collaboration, artificial intelligence (AI) tools, and critical thinking activities enhance student engagement and academic performance in higher education. Grounded in Self-Determination Theory, Social Interdependence Theory, and Assessment for Learning principles, the research positions assessment design as a key driver of motivation and learning. A mixed-methods approach was employed, collecting data from 88 undergraduate students enrolled in a first-year commerce unit at a private offshore university. Quantitative data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), while qualitative responses were thematically examined. Results indicate that collaboration quality (β = 0.510, p < .001), AI integration (β = 0.168, p < .001), and critical thinking skills (β = 0.250, p < .001) significantly improve student engagement, which strongly predicts academic performance (β = 0.810, p < .001). Qualitative findings corroborate these results, highlighting gains in teamwork, communication, analytical thinking, and responsible AI use. The study demonstrates that collaborative, AI-integrated assessment design can significantly contribute to engagement and performance, offering actionable insights for educators seeking to align assessment practices with 21st-century learning outcomes. However, the findings are based on a single institutional context. Future research could expand across disciplines and multiple contexts to validate the model’s applicability.
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