An Integrated Fuzzy AHP-TOPSIS Framework for Evaluating AI-Enhanced Competencies of College English Teachers in Blended Learning Environments

Abstract

In response to the accelerating adoption of generative artificial intelligence, university English education is increasingly being reshaped into an intelligent blended learning environment, consequently raising the competency requirements associated with teachers’ use of AI. Drawing upon the Teacher Artificial Intelligence Capability Framework issued by relevant international authorities in 2024, while also accounting for the intercultural communication orientation of university English and the specific instructional conditions of blended learning, this study establishes an assessment framework for teachers’ AI capabilities comprising five primary dimensions and fifteen secondary indicators. To address the ambiguity and uncertainty inherent in conventional subjective assessment, a multi-criteria decision analysis approach is incorporated, resulting in a quantitative evaluation framework that combines Fuzzy AHP and Fuzzy TOPSIS. The resulting measurements identify AI teaching methods and AI ethics and safety as the principal dimensions shaping teachers’ overall AI competence. In addition, five representative digital profiles of foreign language teachers are evaluated through simulated Fuzzy TOPSIS ranking and proximity coefficient analysis. The results demonstrate that collaborative and integrated teachers achieve the strongest overall performance, particularly when advanced instructional approaches are combined with robust ethical and safety controls. The study therefore offers human resource professionals and academic administrators in higher education a systematic, transparent, and quantitative basis for teacher evaluation and decision-making. At the same time, it highlights a central principle for foreign language teacher development in the intelligent era: technological adoption should remain subordinate to, and guided by, sound pedagogical principles.