文章摘要

吴丹,孙昕玦.面向高校师生的人工智能素养一体化测评工具包构建研究[J].中国图书馆学报,2026,52(4):104~129
面向高校师生的人工智能素养一体化测评工具包构建研究
Building an Integrated AI Literacy Scale based Assessment Toolkit for University Faculty and Students
投稿时间：2026-01-21  修订日期：2026-05-31
DOI：
中文关键词: 人工智能素养  量表开发  高校学生  高校教师  人智交互
英文关键词: Artificial intelligence literacy  Scale development  University students  University faculty  Human AI interaction
基金项目:
作者单位
吴丹 华中师范大学信息管理学院 湖北 武汉430079 
孙昕玦 武汉大学信息管理学院 湖北 武汉430072 
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中文摘要:
      随着人工智能技术与高等教育的深度融合,提高高校师生的人工智能素养已成为推动教育变革的关键议题。本研究立足KSAVE模型、UNESCO人工智能能力框架与TPACK模型等理论基础,构建面向高校师生的人工智能素养一体化测评工具包（AILS工具包）。该工具包包括态度、知识、能力、伦理四个共同核心维度,保持师生结构同构,为教师增设人工智能培育维度,同时在核心量表的基础上整合水平分级、差异化评估路径与结果解读等功能模块；水平分级框架进一步参照能力发展阶段理论,建立由基础感知、发展应用、熟练整合到创新引领的四级递进体系。在应用层面,工具包提供了覆盖院校评估、教师发展、个体诊断与课程追踪的差异化评估路径,并提出师生数据联动分析框架。AILS工具包可实现对师生人工智能素养的区分诊断与整合评估,为高校系统性规划人工智能教育及评估培养成效提供了兼具理论深度与实践价值的评测工具。图4。表10。参考文献43。
英文摘要:
With the deepening integration of artificial intelligence （AI） technology and higher education,the scientific assessment of AI literacy among faculty and students has become a critical issue for educational transformation. However,existing instruments predominantly target a single population and lack integrated cross role assessment solutions grounded in a unified theoretical framework. Drawing upon the KSAVE model,UNESCO AI competency frameworks,and the TPACK model,this study constructs an integrated AI Literacy Scale based Assessment Toolkit （AILS toolkit） for university faculty and students.
The AILS toolkit maintains structural isomorphism between the faculty scale （AILS-CT） and the student scale （AILS-CS） across four shared core dimensions—attitude,knowledge,capability,and ethics—while adding an AI pedagogy dimension exclusively for faculty to capture their second order capability of translating personal AI competence into educational practice. Beyond the core scales,the toolkit integrates three functional modules:a proficiency level diagnostic framework,differentiated assessment pathways,and result interpretation guidelines. Drawing on skill acquisition stage theory,the proficiency framework further defines four progressive levels—basic awareness,developmental application,proficient integration,and innovative leadership—along three unified criteria of cognitive depth,application autonomy,and scope of influence,thereby providing a more objective basis for level classificationIn prior research,AILS-CS was developed and validated for university students. Building on this work,the present study developed and validated AILS-CT for faculty,confirming the stability of its five factor structure and satisfactory reliability and validity； cross group structural comparison further demonstrated structural consistency between the two scales across the four shared core dimensions. At the application level,the toolkit provides four differentiated assessment pathways—institutional evaluation,faculty development,curriculum tracking,and individual diagnosis—and proposes a faculty-student data linkage analysis framework that supports horizontal comparison across shared dimensions and examines the influence of the AI pedagogy dimension on student AI literacy.
Overall,the AILS toolkit enables both differentiated diagnosis and integrated assessment across faculty and student populations,advancing AI literacy measurement from single population scale development to system level toolkit design. It offers an assessment instrument with both theoretical depth and practical value for systematically planning AI education and evaluating educational outcomes in higher education. 4 figs. 10 tabs. 43 refs.
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