| 1,177 | 0 | 126 |
| 下载次数 | 被引频次 | 阅读次数 |
在区域经济发展不平衡、院校资源分配不均、高水平师资分布失衡等问题严重制约高职教育公平进程的背景下，如何依托生成式人工智能赋能高职教育公平成为破局关键。以社会认知理论为分析框架，基于2 622份问卷调查数据，综合采用结构方程模型（SEM）和模糊集定性比较分析（fsQCA）方法，探究了生成式人工智能对高职学生教育公平感的影响机制及其赋能高职教育公平的组态路径。研究发现，高职院校GenAI生态、高职学生GenAI支持和GenAI素养均对教育公平感有显著正向影响，而GenAI使用无显著影响。GenAI生态可以通过促进GenAI使用、加大GenAI支持、提高GenAI素养间接产生作用。fsQCA进一步识别出四条可以提升高职学生教育公平感的路径，其中高职学生GenAI素养作为核心条件发挥作用。
Abstract：In the context where regional economic development is uneven, the allocation of educational resources among institutions is imbalanced, and the distribution of high-level teaching staff is also unbalanced, which seriously hinders the progress of higher vocational education fairness, how to leverage generative artificial intelligence to empower higher vocational education fairness has become the key to breaking the deadlock. Based on the social cognition theory as the analytical framework, and using 2,622 sets of questionnaire survey data, the structural equation model(SEM) and fuzzy set qualitative comparative analysis(fsQCA) methods were comprehensively employed to explore the influence mechanism of generative artificial intelligence on the sense of educational fairness of higher vocational students and the configuration path that empower the fairness of higher vocational education. The research found that the GenAI ecosystem in higher vocational colleges, the support for GenAI and the GenAI literacy provided by higher vocational college students all have a significant positive impact on the sense of educational equity, while the use of GenAI has no significant effect. The GenAI ecosystem can exert an indirect influence by promoting the use of GenAI, enhancing the support for GenAI, and improving the literacy in GenAI. The fsQCA further identified four paths that can enhance the sense of educational fairness among higher vocational college students,among which the GenAI literacy of higher vocational college students plays a core role.
[1]胡姝，张浩，蔡彤彤.我国高等职业教育质量对产业结构变迁的支撑分析——基于《职业教育质量年报》的研究[J].宏观质量研究，2024(2):116-128.
[2]陈子季.学习宣传贯彻全国职业教育大会精神加快构建现代职业教育体系——专访教育部职业教育与成人教育司司长陈子季[J].国家教育行政学院学报，2021(5):3-10.
[3][28]唐玉溪，何伟光.后发跨越式赶超：智能时代中国高职教育变革路向研究[J].中国远程教育，2023(12):68-75.
[4][57][69]王羽菲，和震.人工智能赋能职业教育：现实样态、内在机理与实践向度[J].中国远程教育，2022(5):1-8+76.
[5]黄利文，王健.论人工智能时代职业教育的转型与创新[J].南京社会科学，2020(10):142-148.
[6][58][60]刘邦奇，聂小林，王亚飞，等.生成式AI赋能教育：技术框架、应用场域及价值——2024智能教育发展研究报告[J].中国电化教育，2025(3):61-70.
[7]李玉顺，安欣，代帅，等.数字教育促进教育公平实践的反思[J].开放教育研究，2023(3):69-78.
[8]廖宏建，张倩苇.“互联网+”教育精准帮扶的转移逻辑与价值选择——基于教育公平的视角[J].电化教育研究，2018(5):5-11.
[9]HONG H,KIM Y.Applying artificial intelligence in career education for students with intellectual disabilities:The effects on career self-efficacy and learning flow[J].Educ Inf Technol,2024(29):25237-25256.
[10]韩世梅.我国教育信息化促进教育公平的政策演进、问题分析和发展建议[J].中国远程教育，2021(12):10-20.
[11]许恒，黄超凡，王雅琪，等.数字化发展对教育公平的影响研究：理论机制与运行路径[J].中国电化教育，2023(10):57-65.
[12]KASNECI E,SESSLER K,K??CHEMANN S,et al.ChatGPT for good?On opportunities and challenges of large language models for education[J].Learning and Individual Differences,2023(103):102274.
[13][24][32][42]吴青，刘毓文.ChatGPT时代的高等教育应对:禁止还是变革[J].高校教育管理，2023,17(3):32-41.
[14][25][33][59]祝智庭，戴岭，胡姣.高意识生成式学习：AIGC技术赋能的学习范式创新[J].电化教育研究，2023(6):5-14.
[15][34][43][73]RUDOLPH J,ISMAIL M F B M,POPENICI S.Higher education's generative artificial intelligence paradox:The meaning of chatbot mania[J].Journal of University Teaching and Learning Practice,2024,21(6):1-35.
[16]JIANG X,LI J,CHEN C H.Enhancing Critical Thinking Skills with ChatGPT-Powered Activities in Chinese Language Classrooms[J].International Journal of Chinese Language Teaching,2024,5(1):47-73.
[17]LIN C J,LEE H Y,WANG W S,et al.Enhancing reflective thinking in STEM education through experiential learning:The role of generative AI as a learning aid[J].Educ Inf Technol,2025(30):6315-6337.
[18][26]COTTON D R,COTTON P A,SHIPWAY J R.Chatting and cheating:Ensuring academic integrity in the era of ChatGPT[J].Innovations in Education&Teaching International,2024,61(2):228-239.
[19]GUNASEKARA S,SAARELA M.Trade-offs between fairness and performance in educational AI:Analyzing post-processing bias mitigation on the OULAD[J].Information and Software Technology,2026(189):107933.
[20][35]BANDURA A.Social cognitive theory of self-regulation[J].Organizational Behavior and Human Decision Processes,1991,50(2):248-287.
[21][47]张缨斌，姜丽滢，周晶晶，等.师范生生成式人工智能教育应用认知与理念：现状、问题及对策[J].中国教育信息化，2024(10):32-43.
[22][40][50]李艳，许洁，贾程媛，等.大学生生成式人工智能应用现状与思考——基于浙江大学的调查[J].开放教育研究，2024(1):89-98.
[23][31][41]KASNECI E,SESSLER K,K??CHEMANN S,et al.ChatGPT for good?On opportunities and challenges of large language models for education[J].Learning and Individual Differences,2023(103):102274.
[27][56][67]徐晔.高等职业教育智能生态系统：内涵、结构与实践路径[J].中国远程教育，2021(7):18-24.
[29]CHAN C K Y,ZHOU W.An Expectancy Value Theory(EVT)Based Instrument for Measuring Student Perceptions of Generative AI[J].Smart Learning Environment,2023,10(64):1-22.
[30][61]朱莎，李嘉源，况秀林，等.生成式人工智能何以赋能学生数字素养培育——基于信息科技课程的实证研究[J].中国电化教育，2025(2):75-83.
[36]ZHANG L,XU J.The paradox of self-efficacy and technological dependence:Unraveling generative AI's impact on university students'task completion[J].Internet and Higher Education,2024(65):100978.
[37]CHIU T K F,CHEN Y,YAU K W,et al.Developing and validating measures for AI literacy tests:From self-reported to objective measures[J].Computers and Education:Artificial Intelligence,2024(7):100282.
[38][70]孙立会，周亮.生成式人工智能素养：概念演变、框架构建与提升路径[J].现代远距离教育，2025(1):1-18.
[39]ACOSTA-ENRIQUEZ B G,FARRO??AN E V R,ZAPATA L I V,et al.Acceptance of Artificial Intelligence in University Contexts:A Conceptual Analysis Based on UTAUT2 Theory[J].Heliyon,2024,10(9):e11750.
[44][49]杨宗凯，王俊，吴砥，等.ChatGPT/生成式人工智能对教育的影响探析及应对策略[J].华东师范大学学报（教育科学版），2023(7):26-35.
[45]吕晓俊，刘帮成.高校大学生公平心理与行为的研究——基于教育公平感的视角[J].上海交通大学学报（哲学社会科学版），2009(6):72-78.
[46]卢强，左明章，原渊.基于技术接受模型的农村教师同步课堂采纳与使用影响因素研究[J].中国远程教育，2018(7):61-69+80.
[48]MARENGO A,KARAOGLAN-YILMAZ FG,YILMAZ R,CEYLAN M.Development and validation of generative artificial intelligence attitude scale for students[J].Front.Comput.Sci.,2025(7):1528455.
[51][68][71]VENKATESH V,THONG J Y L,XU X.Unified theory of acceptance and use of technology:a synthesis and the road ahead[J].Journal of the association for Information Systems,2016,17(5):328-376.
[52]王思遥，黄亚婷.促进或抑制：生成式人工智能对大学生创造力的影响[J].中国高教研究，2024(11):29-36.
[53]LEE S,PARK G.Development and validation of ChatGPT literacy scale[J].Current Psychology,2024(43):18992-19004.
[54]PODSAKOFF P M,ORGAN D W.Self-reports in organizational research:Problems and prospects[J].Journal of Management,1986,12(4):69-82.
[55]汤丹丹，温忠麟.共同方法偏差检验：问题与建议[J].心理科学，2020(1):215-223.
[62]蔡建东，杨小锋.组态视域下学前教师数字技术采纳行为意向诱发机制研究——基于模糊集的定性比较分析（fsQCA）[J].电化教育研究，2022(4):116-124.
[63]GRECKHAMER T,MISANGYI V F,FISS P C.Studying configurations with qualitative comparative analysis:best practices in strategy and organization research[J].Strategic Organization,2018,16(4):482-495.
[64]FISS P C.Building better causal theories:A fuzzy set approach to typologies in organization research[J].Academy of Management Journal,2011,54(2):393-420.
[65]贾旻，陈晓煜.我国优质高等职业教育资源空间布局：形态、机制与优化[J].现代远程教育研究，2025(2):102-112.
[66][72]吴庆华，郭丽君.生成式人工智能时代高职院校的教学变革：挑战、框架与路径[J].大学教育科学，2023(6):112-120.
(1)生成式人工智能是指一种基于深度学习模型生成类似人类思维特征的内容（如图像、文字）以响应复杂多样的提示（如语言、说明、问题）的技术。
基本信息:
中图分类号:G719.2;G434
引用信息:
[1]杜盼盼,李锐,梁瑜倩.生成式人工智能何以赋能高职教育公平？——基于SEM和fsQCA的研究[J].中国职业技术教育,2026,No.932(04):16-29+41.
基金信息:
国家社会科学基金教育学一般项目“人工智能赋能视角下‘拔尖计划’本科生的深度学习研究：内涵再构、影响机制与提升策略”（项目编号：BIA240122,主持人：吕林海）
2026-02-20
2026-02-20