高校教师人工智能胜任力及其定量评价

Artificial Intelligence Competency of Higher Education Faculty and Its Quantitative Evaluation

摘要: 以推动高校教师发展为目的，提出高校教师AI胜任力定量评价指标体系，体系涵盖教学、科研、教研等教育教学场景和掌握力、扩展力、卓越力递增能力结构，采用熵权TOPSIS法对参与问卷调查的受访者评估胜任力，并运用t-test与ANOVA比较不同人口统计学变量间胜任力的差异性。研究结果表明：1) 高校教师人工智能胜任力整体上处于中度略微偏上水平，结构上呈现出低厚顶尖的金字塔结构。2) 相较于掌握力和拓展力，卓越力是高校教师人工智能胜任力的关键影响因素。3) 高校教师人工智能胜任力在教龄、职称上表现出显著差异性，低职称、小教龄群体表现出更高的胜任力。在量化分析的基础上，从促进技术熟练到教学以及教科研深入融合等方面提出提升建议。

Abstract: To advance the professional development of higher education faculty, this study proposes a quantitative evaluation framework for AI competency. The framework encompasses educational scenarios including teaching, research, and pedagogical activities, structured around progressively enhanced competencies: Mastery Ability, Expansion Capability, and Excellence Power. Entropy-weighted TOPSIS analysis was employed to assess competency among survey respondents, while t-tests and ANOVA were used to compare competency differences across demographic variables. The findings reveal: 1) Higher education faculty AI competency generally resides at a moderately high level, exhibiting a pyramid structure characterized by a low base and narrow top. 2) Compared to mastery ability and expansion capability, excellence power is the key determinant of university faculty AI competency. 3) Significant differences in AI competency were observed across teaching experience and professional title, with lower-title and less-experienced groups demonstrating higher competency. Based on quantitative analysis, improvement recommendations were proposed, ranging from enhancing technical proficiency to deepening the integration of teaching, research, and educational practice.