Navigating AI integration in higher education: assessing the impact of assessment formats and motivation types on students’ AI usage intentions

Abstract

Recent advances in artificial intelligence (AI) are reshaping teaching and learning, prompting renewed policy attention within higher education. While prior research has explored the antecedents of students’ AI usage intentions, limited attention has been given to how these intentions are shaped by assessment formats and motivational orientations. This study investigates how group-based versus individual-based assessments affect students’ intentions to use AI—specifically ChatGPT—for learning and exams, considering autonomous and controlled motivations. Survey data from 193 undergraduate and postgraduate business students were analyzed using co-variance-based structural equation modelling techniques. The findings reveal that group-based assessments increase students’ likelihood of using AI for learning. While neither autonomous nor controlled motivation directly encourages AI use for learning, controlled motivation reduces AI usage. Notably, autonomous motivation positively moderates the relationship between individual-based assessment and AI use for learning. Finally, students’ intention to use AI for learning translates into its use for exams. Implications of these findings for academic institutions, educators and students are discussed.

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