The shifting landscape of student engagement: A pre-post semester analysis in AI-enhanced classrooms

The rapid integration of AI-based tools in education calls for a critical assessment of how these technologies impact student engagement. This study explores the perceived effects of AI chat tools by analyzing pre- and post-semester survey data from 724 to 642 students, respectively, across diverse disciplines and demographic groups. Initially, students reported high levels of engagement in key areas such as academic self-efficacy, autonomy, interest, and self-regulation. However, by the semester's end, all these areas showed a noticeable decline. This suggests that while AI tools offer initial benefits, their long-term effectiveness in maintaining engagement may be limited due to challenges in integrating the tools consistently or the novelty effect fading. To capture and quantify these trends effectively we used four latent engagement factors—Academic Self-Efficacy and Preparedness, Autonomy and Resource Utilization, Interest and Engagement, and Self-Regulation and Goal Setting—identified in our previous comprehensive factor analysis. Despite the overall decline, certain groups of students and specific conditions led to not only maintaining but even improving engagement levels. This study highlights the importance of tailored strategies that consider aspects such as age, discipline, usage frequency, duration of use, quality of teacher support, and the type of AI used to maximize the benefits of AI in education. These strategies can sometimes counterbalance the decline, ensuring the long-term effectiveness of AI-enhanced learning environments.