Intelligence-Driven Multi-Dimensional Collaborative Model for Blended University English Teaching

Against the transformation of higher education via digital technologies, traditional university English teaching faces challenges such as limited teacher–student interaction and single evaluation methods. This study integrates web-based technologies into blended teaching, proposing an intelligence-driven multidimensional collaborative model that builds a three-way interaction system of teachers, artificial intelligence platforms, and resource repositories to realize real-time learning behavior collection, personalized recommendations, and closed-loop feedback. Through a 16-week quasi-experiment, indicators including online duration, resource adoption rate, exam scores, and satisfaction were analyzed. Results showed the experimental group had 18% higher online engagement, an average final score of 82.3 ± 5.6, and high satisfaction with modules such as resource push. The model addresses online–offline learning disconnect and enhances precise teaching, enriching knowledge on web-based technologies in language teaching and providing references for web-enabled blended learning system design.