A Case Study of the Impact of Artificial Intelligence – Assisted Learning on Student Engagement at Quanzhou College of Technology

Abstract

This study examined the impact of artificial intelligence-assisted learning on student engagement at Quanzhou College of Technology, focusing on three key components: personalized learning pathways, AI-powered feedback, and interactive learning tools. The research was motivated by the increasing integration of AI technologies in higher education and the need to understand their effectiveness in enhancing student engagement. Guided by Constructivist Learning Theory, the objectives of this study were: 1). to examine the impact of personalized learning
pathways on student engagement at Quanzhou College of Technology, 2). To examine the role of AI-powered feedback in influencing student engagement, 3). To examine the effect of interactive learning tools on enhancing student engagement.
To achieve these objectives, a quantitative research design was employed, using a structured questionnaire to collect data from 410 undergraduate students who had experienced AI-assisted learning. Stratified random sampling was used to ensure proportional representation across different fields of study and year levels. The questionnaire included a five-point Likert scale to measure students’ perceptions of personalized learning pathways, AI-powered feedback, interactive learning tools, and student engagement. Data were analyzed using descriptive statistics to summarize respondents’ demographic characteristics and inferential statistics, including correlation and regression analyses, to test the hypotheses.
The findings revealed that all three AI-assisted learning components significantly and positively influenced student engagement. Interactive learning tools emerged as the most influential factor, explaining 46.8% of the variance in student engagement, followed by AI-powered feedback at 43.3% and personalized learning pathways at 40%. The results suggested that interactive learning tools, such as simulations and gamified platforms, were particularly effective in transforming passive learning into active participation. AI-powered feedback was also found to play a crucial role by providing timely and actionable insights that helped sustain motivation and focus. Personalized learning pathways, while beneficial, showed a relatively lower impact, indicating a need for further enhancement of adaptive learning systems.
The study demonstrates that AI-assisted learning has significant potential to improve student engagement by making learning more personalized, timely, and interactive. It is recommended that Quanzhou College of Technology invest in the development of advanced AI systems that prioritize interactive and feedback-driven learning experiences. Ensuring equitable access to these technologies and addressing potential challenges related to data privacy and algorithmic bias are essential for maximizing the benefits of AI-assisted learning. The findings offer valuable insights for educators and administrators seeking to leverage AI to create more engaging and effective learning environments.

Keywords: personalized learning pathways, AI-powered feedback, interactive learning tools, student engagement

6317195458 Wang Weizhi, 2568 (2025). Advisor: Dr. Zhang Li, สารนิพนธ์ (Independent Study), A Case Study of the Impact of Artificial Intelligence – Assisted Learning on Student Engagement at Quanzhou College of Technology, นำเสนอในที่ประชุมวิชาการ (Conference), The 7th STIU International Conference: Leadership & Social Sustainability in the Tech-Driven Era, ปริญญาโท (Master’s Degree), บัณฑิตวิทยาลัย (Graduate School), Master of Business Administration, International Business Management, Siam University, Bangkok, Thailand – มหาวิทยาลัยสยาม กรุงเทพมหานคร ประเทศไทย