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AI-Empowered Teaching Quality Evaluation in Higher Education: A Three-Path Practical Framework
JIANG Jingjing, HU Shurui
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DOI:10.17265/2161-623X/2026.05.001
Shanghai Jiao Tong University, Shanghai, China
This paper explores how artificial intelligence can be embedded into classroom teaching quality evaluation in higher education and proposes a practical framework organized around three layers: perception, analysis, and service. In response to the limited coverage of expert observation, fragmented evaluative evidence, and the weak translation of feedback into improvement, three implementation paths are developed: AI-based video analysis, AI-supported data analysis, and AI-enabled intelligent assistants. Video analysis generates traceable process evidence from classroom activities, teacher-student behaviors, and interaction patterns. Data analysis integrates AI-generated indicators, expert observation, student feedback, and teacher self-evaluation to support comparison, diagnosis, and trend tracking. Intelligent assistants provide policy interpretation, result explanation, task reminders, and improvement support for teachers, supervisors, and administrators. The paper further proposes a five-stage implementation process from data preparation to follow-up review. It argues that AI should not replace human evaluators in making final judgments, but should provide explainable and reviewable evidence for a teaching quality evaluation system moving from one-time scoring toward continuous improvement.
artificial intelligence, teaching quality evaluation, higher education, video analysis, data analysis, intelligent assistant
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