VLM-fusion: an intelligent diagnostic system for rural teacher professional development integrating vision-language models and adaptive learning path optimization.

Rural teachers face persistent professional development challenges stemming from geographic isolation, limited training resources, and scarce peer collaboration opportunities. This paper presents an intelligent diagnostic system that integrates vision-language model (VLM) capabilities with adaptive learning path optimization to deliver personalized, context-aware development support for rural educators. We first construct a four-dimensional competency model encompassing pedagogical content knowledge, practical teaching skills, information and digital literacy, and rural contextual adaptability, with weights determined via the Analytic Hierarchy Process. A multimodal feature fusion diagnostic model employing cross-modal attention mechanisms jointly reasons over classroom video, teaching design texts, artifacts, and supplementary data streams, while a gated fusion strategy ensures robustness under missing-modality conditions common in rural settings. An Actor-Critic reinforcement learning algorithm generates adaptive learning paths by optimizing a composite reward function that balances competency improvement, resource accessibility, and temporal feasibility constraints. Experiments on a dataset of 386 rural teachers from 47 schools across five provinces, with competency ground truth established through structured expert panel ratings, suggest that the proposed VLM-Fusion model attains an F1-score of 84.9% (mean ± 0.6% across five runs) and a Spearman rank correlation of 0.918 in competency diagnosis, comparing favorably against unimodal and conventional fusion baselines. A 12-week quasi-experimental field study (rather than a long-term randomized controlled design) with 96 teachers further indicates that the adaptive path recommendation is associated with a mean competency improvement of 23.7%, exceeding expert-curated (14.2%) and self-directed (8.9%) conditions, while narrowing within-group competency variance over time. We wish to flag upfront that these improvement figures were derived using the same multimodal diagnostic pipeline that generated the personalized recommendations, not an entirely independent assessment procedure; readers should therefore treat the numerical magnitude with appropriate caution, as we discuss at length in the Limitations.