INTEGRATING ARTIFICIAL INTELLIGENCE INTO TEACHER PERFORMANCE EVALUATION: EVIDENCE FROM UNDERGRADUATE INSTITUTIONS IN ANHUI PROVINCE

Abstract

Purpose: China's educational evaluation reforms and rapid developments in AI have posed challenges to traditional teacher performance evaluation systems at the undergraduate level. These challenges include evaluation criteria being too one-dimensional, over-emphasis on quantitative research, inadequate monitoring and feedback. This paper develops the author’s previous research on teacher performance evaluation reforms at the undergraduate level in Anhui Province. This paper attempts to identify ways AI can be used in teacher performance evaluation to support and facilitate the improvement of strategic, evaluative, and developmental alignment and performance evaluation reforms. Methodology/approach: The study relies on a mixed-methods approach rooted in the theories of strategic human resource management. First, a systematic review of the literature, both domestic and global, on teacher performance evaluations, AI in educational management, and strategic human resource management. Second, an AI performance evaluation model is created by adapting an existing strategy-oriented performance evaluation framework for undergraduate educators in Anhui Province and incorporating data analytics, intelligent performance evaluation, and multi-source performance evaluation systems. The evaluation of the proposed model relies on empirical data collected using teacher questionnaires from some undergraduate institutions in Anhui Province, utilizing various statistical techniques such as reliability and validity analyses and structural equation modeling. Originality/Relevance: Use of smart data analysis and multi-faceted assessment tools mitigate the overemphasis on research output metrics and encourage a more holistic and equitable recognition of teaching, research, and community service contributions. Employees experience greater feelings of equity and motivation to perform under the AI evaluative structure. This research examines the evaluation of university faculty using artificial intelligence as a strategic resource management tool in the higher education system. The study responds to a national policy requirement to eliminate the ‘five-only’ framework of evaluation and the current evaluations in undergraduate colleges in Anhui Province, which are described as evaluative, disappointing to teachers, and lacking feedback. Teacher evaluation with AI shows promise in improving objectivity, accuracy, and a developmental focus in evaluation. Key findings: The findings indicate that AI can close the gap between the university’s strategic goals and the individual aims of the faculty, enhance the accuracy and justifiability of evaluation criteria, and strengthen evaluation criteria and systems for feedback and sustained improvement. Theoretical/methodological contributions: The integration of artificial intelligence and the theories of strategic human resource management and performance evaluation gives rise to a new theory in the management of higher education. The new theory is the foundation of the construction of a systematic AI evaluative structure, which is a valuable and unique contribution of this work. The systematic AI evaluative structure, which is based on the principles of goal management, competence-driven evaluative criteria, and intelligent feedback systems, is an empirically proven model for transformative reform of the evaluative frameworks of teacher performance in higher education, especially in institutions in Anhui Province.