Blended learning has emerged as a popular trend in education; however, as students from higher vocational colleges who are often in the lower 50% of national standardized entrance exam achievers, their teachers face unique challenges in implementing blended learning. This study summarized effective blended learning strategies in higher vocational education and training (VET) classrooms by comparing 53 award-winning and 40 daily course videos in China. Firstly, video analysis and Lag sequential analysis were employed to identify effective strategies for implementing blended learning in diverse VET course types. A set of general and specific blended learning strategies was developed to help VET teachers adapt instructional approaches accordingly. Secondly, a questionnaire survey among 215 VET teachers revealed positive perceptions of the strategies in terms of usability, ease of use, perceived behavioral control, and intention to use them. The present research provides valuable guidance for VET teachers to effectively implement blended learning strategies in diverse course types, contributing to the understanding of effective blended learning strategies in VET and addressing the gap in research for this unique teaching stage.

Understanding how students cognitively engage with generative artificial intelligence (GenAI) has become a pressing concern in educational technology research. This study investigated how motivational orientations and GenAI role perceptions are associated with the quality and structure of metacognitive engagement in authentic student-GenAI dialogues. Using purposive sampling, 24 Chinese undergraduates drawn from 20 academic disciplines participated in individual retrospective interviews. They narrated the context and reasoning behind 120 authentic GenAI interaction logs comprising 688 messages in total, of which 344 were student-initiated. Our analyses reveal that intrinsically motivated students predominantly positioned GenAI as an instructor or collaborator, whereas extrinsically motivated students disproportionately treated it as a replacement tool. Intrinsic motivation was associated with significantly higher engagement in higher-order metacognitive dimensions (evaluation and elaboration), forming reflective-iterative co-occurrence networks, while extrinsic motivation produced task-completion-oriented patterns concentrated in lower-order processing. Our analyses further suggest that students who saw GenAI as a collaborator had a complete metacognitive chain (i.e., high engagement) and those who saw GenAI as a replacement tool exhibited no higher-order metacognitive connections. Overall, these findings suggest that the metacognitive consequences of GenAI use are neither uniform nor inherent to the technology. They are systematically conditioned by motivational orientations and perceived GenAI roles during interactions.