Generative AI-Supported Personalized Learning Resources: Efficacy, Reliability, and Mechanisms of Student Preference

This study examines a context engineering workflow for the generation, evaluation, and integration of large language model-generated personalized preview learning materials. Under an LLM-based rubric evaluation, the workflow achieved significantly higher scores than single-prompt, dual-agent, and deep-research approaches across the evaluated preview materials. The stability analysis showed relatively stable repeated LLM ratings, while paired-samples t-tests indicated no statistically significant mean differences between LLM and expert ratings for either the basic or advanced version. Mediation analyses further identified distinct associations between motivation, engagement, and students’ preview preferences: extrinsic motivation showed a direct positive association with preference for advanced materials, whereas intrinsic motivation showed significant indirect associations with preferences for both basic and advanced materials through emotional and cognitive engagement. No significant engagement-mediated pathways were observed for extrinsic motivation. These findings suggest that generative AI workflows based on context engineering may provide a promising approach for developing differentiated preview materials and supporting learner diversity in voluntary pre-class learning.