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Cunling Bian

Publications and source records attributed to Cunling Bian.

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Scaffolding Critical Engagement with GenAI: Transforming Ethnic Minority Preparatory Students' Collaborative Discourse in Prompt Engineering Tasks

Generative AI (GenAI) holds significant promise for advancing educational equity among ethnic minority students by broadening access to learning resources and mitigating linguistic barriers. However, these benefits are counterbalanced by the risk of cognitive laziness, whereby students may treat GenAI as an answer engine or shortcut rather than as a partner in thinking. This design-based research investigated how pedagogical scaffolding can shift students from passive consumption to critical co-creation with GenAI. The study involved 78 ethnic minority preparatory students in China participating in a three-week GenAI course that integrated a human-in-the-loop workflow and teacher modeling with contrasting cases to disrupt uncritical reliance on GenAI. We employed epistemic network analysis to examine collaborative discourse, thematic analysis to analyze student reflections, and paired-samples t-tests to assess changes in prompt self-efficacy. Results revealed a phenomenon of strategic repurposing: initially, students instrumentalized strategy talk to coordinate efficient copying; however, after the intervention, they realigned strategic planning to scaffold critical evaluation and peer co-construction. Qualitative findings further indicated that the teacher's scaffolding helped students overcome their initial authority bias and prompt paralysis, repositioning themselves as active gatekeepers of AI-generated content; these shifts were corroborated by a significant increase in students' prompt self-efficacy. The study suggests that, particularly for ethnic minority students, technical training alone is insufficient; educators should design targeted pedagogical interventions around human-AI collaboration to prevent cognitive complacency and cultivate epistemic agency.

cs.CY

View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning

Skeleton-based human action recognition has been drawing more interest recently due to its low sensitivity to appearance changes and the accessibility of more skeleton data. However, even the 3D skeletons captured in practice are still sensitive to the viewpoint and direction gave the occlusion of different human-body joints and the errors in human joint localization. Such view variance of skeleton data may significantly affect the performance of action recognition. To address this issue, we propose in this paper a new view-invariant representation learning approach, without any manual action labeling, for skeleton-based human action recognition. Specifically, we leverage the multi-view skeleton data simultaneously taken for the same person in the network training, by maximizing the mutual information between the representations extracted from different views, and then propose a global-local contrastive loss to model the multi-scale co-occurrence relationships in both spatial and temporal domains. Extensive experimental results show that the proposed method is robust to the view difference of the input skeleton data and significantly boosts the performance of unsupervised skeleton-based human action methods, resulting in new state-of-the-art accuracies on two challenging multi-view benchmarks of PKUMMD and NTU RGB+D.

cs.CV