Promoting student engagement with GPTutor: An intelligent tutoring system powered by generative AI

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Udgivet i:International Journal of Educational Technology in Higher Education vol. 22, no. 1 (Dec 2025), p. 77
Hovedforfatter: Bai, Haoran
Andre forfattere: Lui, Wing Cheung, Khiatani, Paul Vinod
Udgivet:
Springer Nature B.V.
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100 1 |a Bai, Haoran  |u The Hong Kong Polytechnic University, Department of Computing, Hung Hom, Hong Kong SAR, China (GRID:grid.16890.36) (ISNI:0000 0004 1764 6123) 
245 1 |a Promoting student engagement with GPTutor: An intelligent tutoring system powered by generative AI 
260 |b Springer Nature B.V.  |c Dec 2025 
513 |a Journal Article 
520 3 |a Intelligent Tutoring Systems (ITS) are computer systems that mimic human tutoring behavior while providing immediate feedback. With the rise of Generative Artificial Intelligence (GenAI), numerous ITS integrated with GenAI have been developed. Student engagement is critical for improving learning processes and outcomes. Therefore, it is important to examine the effectiveness of ITS integrated with GenAI in promoting student engagement in educational practice. This paper presents an explanatory mixed-method case study involving 880 undergraduate students who used GPTutor, an ITS powered by GenAI. First, a survey research was conducted to investigate the relationship between students’ actual interaction with GPTutor and their self-reported student engagement from three dimensions: Behavioral Engagement, Cognitive Engagement, and Emotional Engagement. Next, focus groups were conducted with a subsample of survey participants to better understand how and under what circumstances GPTutor improved student engagement. The focus groups also explored potential design improvements for GPTutor and other ITS powered by GenAI. The results of the survey research revealed a complex relationship between feature usage and student engagement. Specifically, engagement with the chatbot is significantly and positively associated with behavioral and emotional engagement, but not cognitive engagement. The exercise generator feature had no significant associations with any of the three dimensions of student engagement. The results of the focus groups shed some light on these relationships, revealing how GPTutor was used only when it was perceived as useful, and this perceived usefulness was shaped by the students’ perception of the difficulty of the course and whether their support system could adequately address questions they may have. Its usefulness was found to increase as the course progressed, particularly as examinations approached. As the examinations approached, it was increasingly clear that the exercise generator was preferred over the chatbot. The participants also made this clear by expressing how GPTutor could be improved, notably by increasing the capabilities of the chatbot to include multimodal media, like video recordings of lectures. In general, leveraging survey data, interview data, and back-end trace data from GenAI, this research makes an original contribution to AI-supported effective learning environments and design strategies to optimize the educational experiences of higher education students. 
653 |a Teaching 
653 |a Support systems 
653 |a Higher education 
653 |a Perceptions 
653 |a College students 
653 |a Research 
653 |a Student participation 
653 |a Learning processes 
653 |a Emotions 
653 |a Learning environment 
653 |a Chatbots 
653 |a Usefulness 
653 |a Artificial intelligence 
653 |a Support networks 
653 |a Tutoring 
653 |a Human-computer interaction 
653 |a Science education 
653 |a Case studies 
653 |a Design optimization 
653 |a Learning 
653 |a Independent study 
653 |a Feedback 
653 |a Polls & surveys 
653 |a Undergraduate students 
653 |a Natural language 
653 |a User interface 
653 |a Lectures 
653 |a Undergraduate study 
653 |a Cognitive-behavioral factors 
653 |a Digital technology 
653 |a Cognition 
653 |a Learning management systems 
653 |a Learning activities 
653 |a Distance learning 
653 |a Teachers 
653 |a Design improvements 
653 |a Data 
653 |a Education 
653 |a Knowledge 
653 |a Educational materials 
653 |a Effectiveness 
653 |a Behavior 
653 |a Focus groups 
653 |a Generative artificial intelligence 
653 |a Educational activities 
653 |a Video recordings 
653 |a Students 
653 |a Large language models 
653 |a Research Design 
653 |a Distance Education 
653 |a Cultural Background 
653 |a Outcomes of Education 
653 |a Programming 
653 |a Educational Practices 
653 |a Geometry 
653 |a Academic Achievement 
653 |a Learner Engagement 
653 |a Participant Satisfaction 
653 |a Educational Resources 
653 |a Intelligent Tutoring Systems 
653 |a Influence of Technology 
653 |a Learning Experience 
653 |a Educational Technology 
653 |a Instructional Materials 
653 |a Emotional Response 
700 1 |a Lui, Wing Cheung  |u The Hong Kong Polytechnic University, Department of Computing, Hung Hom, Hong Kong SAR, China (GRID:grid.16890.36) (ISNI:0000 0004 1764 6123) 
700 1 |a Khiatani, Paul Vinod  |u The Hong Kong Polytechnic University, Department of Applied Social Sciences, Hung Hom, Hong Kong SAR, China (GRID:grid.16890.36) (ISNI:0000 0004 1764 6123) 
773 0 |t International Journal of Educational Technology in Higher Education  |g vol. 22, no. 1 (Dec 2025), p. 77 
786 0 |d ProQuest  |t Political Science Database 
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