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A Preliminary Exploration of Constructing a Human-in-the-Loop Teaching Model in English Language Testing Courses Empowered by AI

Author(s)

Yajie Shen

Affiliation(s)

Xi’an Fanyi University, Xi’an, Shaanxi, China

Abstract

With the rapid advancement of Artificial Intelligence and Large Language Models in education, English language testing courses face a pressing need to transition from exam-oriented to competency-based approaches. This paper proposes and constructs the “AI-Prep-Activity-AI-Assess” teaching model, elucidating its theoretical foundation, three-stage operational framework, and role division in the classes: Pre-class (AI-Prep) focuses on intelligent diagnosis and personalized guided learning; In-class (Activity) centers on task-driven human-machine collaborative interaction; Post-class (AI-Assess) employs adaptive assessment as its primary means. Through literature review, model design, and case analysis, the study finds that the 3A model effectively enhances students’ practical abilities and learning motivation, supports teachers’ transition from knowledge disseminators to instructional organizers and emotional guides, and demonstrates significant advantages in personalized feedback, formative assessment, and closed-loop teaching. Concurrently, this paper identifies limitations in large-scale empirical validation, tool adaptability, data privacy, and academic integrity, which warrant further research.

Keywords

Human-in-the-Loop; Artificial Intelligence; Teaching Model

References

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