Integrating Generative AI into Business Classrooms: Effects on Student Motivation and Learning Engagement

Abstract

The rapid advancement of artificial intelligence is reshaping industries, workplaces, and educational systems worldwide. In higher education, generative artificial intelligence (GenAI) tools are increasingly integrated into classroom instruction, assessment, and student learning processes. Despite widespread adoption, there remains limited empirical evidence examining how these tools influence core psychological drivers of academic success, particularly student motivation. Understanding this relationship is critical because motivation mediates engagement, persistence, and achievement across disciplines.

Since late 2022, generative artificial intelligence (GenAI)—exemplified by large language models (LLMs) such as ChatGPT—has diffused rapidly into education, prompting questions about how these tools shape students’ motivation to learn. Motivation matters because it mediates engagement, persistence, and achievement across disciplines. In higher education especially, GenAI can alter the learning ecology by changing task demands, perceived competence, and the feedback landscape. Recent guidance from international agencies (e.g., UNESCO; OECD) highlights both opportunity and risk, calling for human-centered designs that protect learner agency and academic integrity.

Keywords: generative AI, student motivation, higher education, instructional design, self-determination theory

DOI: 10.7176/CEIS/17-1-06

Publication date: March 28^th, 2026