Generative AI and Learning Dynamics in Higher Education: A Longitudinal Empirical Study

ABSTRACT Background Generative AI tools such as ChatGPT are increasingly integrated into higher education. Empirical evidence on how they shape learning dynamics over time remains limited. Previous studies have often examined single learning outcomes, neglecting the interplay between cognitive, motivational, and metacognitive processes that evolve throughout the semester. Objectives This longitudinal study investigated how generative AI influences students' knowledge gain, motivation, cognitive load, critical thinking, and reflective use across an entire semester. It further explored whether prior knowledge and AI experience moderated these learning effects, addressing concerns about potential Matthew effects. Methods A quasi‐experimental design was implemented with three parallel classes of business informatics students ( N = 87) assigned to different instructional conditions: tutor‐AI use, unguided AI, and a control group without AI. Data were collected at three measurement points (T1–T3). Results and Conclusion Students showed significant knowledge gains across all groups, but no additional benefits of AI integration. Motivation remained largely stable; germane cognitive load increased and was positively associated with knowledge gain. Critical thinking remained stable, while reflective use was higher in the AI condition and positively associated with critical thinking. No evidence emerged for a Matthew effect, indicating that AI did not intensify performance inequalities. Overall, the findings did not support the view of generative AI as a consistent learning outcome. Its impact depended on pedagogical framing and learners' metacognitive regulation, underscoring the need for structured guidance and reflective engagement in AI‐supported education.