Teacher behavioral optimization mechanisms in Chinese university blended English teaching under ai empowerment: a study on intelligent technology-driven pedagogical efficacy enhancement pathways

Abstract

Artificial intelligence AI has migrated from speculative prospect to operational reality in higher education language classrooms, yet the teacher-level behavioural transformations that turn algorithmic affordances into measurable learning gains remain under-specified. An eighteen-month sequential-explanatory mixed-methods investigation therefore surveyed 428 university English lecturers from seventeen Chinese institutions, conducted forty-seven semi-structured interviews, and compiled seventy-two hours of classroom observations. The study specifically examined the implementation of ChatGPT, Grammarly AI, automated speech recognition systems ASR, and adaptive learning platforms such as DynamicLearning and SmartSparrow. Multilevel models indicated that AI-mediated practices increased student engagement p 0.001 and boosted examination performance by 0.78 standard deviations when compared with traditional instruction. Four optimisation pathways emerged: adaptive content delivery, personalised feedback architectures, real-time learning analytics, and collaborative teacher-AI decision-making. The most potent mechanism error-pattern-driven feedback yielded an 0.83-SD achievement gain, while analytics-based trend monitoring raised classroom-management efficiency by 56 %. A quadratic growth-curve revealed a behavioural quality tipping point at 3.2/5, below which abandonment odds quadrupled. Professional-identity tensions and role ambiguity surfaced as the most severe barriers; comprehensive professional development exerted the single strongest enabling effect. The findings underscore that capital expenditure on smart infrastructure must be matched by strategically scaffolded behavioural recalibration if AI investments are to mature into sustainable pedagogical value.