Integrating Intelligent Tutoring Systems into Digital Learning Environments: Effects on Adaptive Learning and Learner Engagement

Abstract

This study explores the effects of bringing generative artificial intelligence into Intelligent Tutoring Systems for adaptive learning and learner engagement in higher education. Using quantitative Cross-sectional design, data were gathered from 200 undergraduate students from Prince Sattam Bin Abdulaziz University using a structured questionnaire with generative AI integration features, adaptive learning, and the multidimensional engagement of learners using 5-point Likert scale. Structural equation modeling using the partial least square analysis was used to analyze relationships between variables. The study results showed that the integration of generative AI was significantly associated with increasing adaptive learning (b = 0.69, p < .001) and engagement on the part of the learner (b = 0.63, p < .001). Adaptive learning was a partial mediator of the association between generative AI integration and engagement suggesting that better instructional personalization was at the heart of how generative AI integration can encourage student engagement. Furthermore, dynamic content generation, conversational feedback and personalised adaptation were found to have a cumulative collective impact of 64% on learning effectiveness, with dynamic content generation being the most significant predictor. The results show that the use of generative AI enhanced tutoring systems significantly enhances the strength of instructional adaptivity and supports consistent cognitive, behavioral, and emotional engagement. The research concludes that generative AI integrated in Intelligent Tutoring Systems holds a transformative potential for the personalization of digital education with important consequences for scalable and effective higher education instruction.