Étude de l’adoption de l’intelligence artificielle par des personnes enseignantes du postsecondaire au Québec en fonction de cinq types d’usages

Abstract

This study aims to explore the adoption of five AI use cases among postsecondary teachers: success prediction, feedback, plagiarism detection, course material creation, and assessment. Teachers (n = 127) scored their opinions on factors of attitude, performance expectancy, perceived ease of use and anxiety, along with AI literacy factors (technical knowledge, pedagogical use, and ethics). Structural equations models were estimated to analyze the intention to use. Results show that a higher level of technical knowledge is associated with lower performance expectations.