Addressing the shortcomings of current English teaching courseware in terms of static and personalized adaptation, and the fact that computer vision technology in education primarily focuses on classroom monitoring and lacks direct linkage with courseware design, this paper proposes a multimodal interactive perception-based dynamic adaptation algorithm for English courseware content. This algorithm collects text and image features from the courseware using an EAST-L network and a lightweight Mask R-CNN, and combines optimized Media Pipe Hands and an emotion classification model to obtain learner gesture and emotion data. Through dynamic interval normalization and an attention mechanism, multimodal features are fused to construct a capability-state association model, and real-time adaptation of courseware content is achieved based on an adjustment intensity formula. The experiment used 300 English courseware materials from primary and secondary schools as the resource set and conducted an 8-week test on 200 learners aged 10-15. The results showed that the accuracy rate of courseware content adaptation reached 92.3%, an improvement of 37.6% compared to traditional static courseware; the average interactive response latency was 72ms (≤85ms); the average score of the experimental group was 89.2 points, an improvement of 18.7% compared to the control group, and the attention maintenance rate increased by 29.4%. The research indicates that this algorithm can effectively improve the personalization and interactivity of courseware, providing a new path for visual technology-assisted educational resource design.

Huizhuo Zhang, Haibin Qiu, Jie Wang. Research on Algorithm Research for English Teaching Courseware Design Assisted by Computer Vision Technology. International Journal of New Developments in Education (2026), Vol. 8, Issue 8: 44-52. https://doi.org/10.25236/IJNDE.2026.080806.

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