Constructing personalised adaptive learning paths through intelligent learning assistants: Insights from users

With the pervasive adoption of intelligent learning assistants, the demand for genuinely personalised adaptive learning paths is accelerating. However, current path-construction approaches are largely shaped by teacher perspectives and static learner profiles, often overlooking the genuine needs of learners. To address this gap, this study adopts a user-centred approach. Using grounded theory, we systematically analysed Zhihu discussions related to ‘AI and personalised learning’, supplemented by in-depth interviews. Our findings reveal four key modules that drive the dynamic evolution of learning paths: initial path matching, dynamic path adaptation, human-AI collaborative support, and outcome-driven optimisation. These modules interact to form an integrated system for constructing and sustaining personalised adaptive learning paths. This study proposes a novel mechanism model linking technological design with authentic learning experiences and offers practical insights for educators and institutions to embed AI into instruction, ultimately enhancing learning personalisation and adaptation.