A multi-level factors model affecting teachers' behavioral intention in AI-enabled education ecosystem

被引:4
作者
Wu, Di [1 ]
Zhang, Xinyan [2 ]
Wang, Kaili [3 ]
Wu, Longkai [4 ]
Yang, Wei [5 ]
机构
[1] Cent China Normal Univ, Educ Informatizat Strategy Res Base, Minist Educ, Wuhan, Hubei, Peoples R China
[2] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan, Hubei, Peoples R China
[3] Cent China Normal Univ, Res Ctr Sci & Technol Promoting Educ Innovat & Dev, Strateg Res Base, Minist Educ, Wuhan, Peoples R China
[4] Cent China Normal Univ, Natl Expt Base Intelligent Social Governance Major, Wuhan, Peoples R China
[5] Cent China Normal Univ, Natl Engn Res Ctr ELearning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China
来源
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT | 2025年 / 73卷 / 01期
关键词
Artificial intelligence (AI); Multi-level factors model; Behavioral intention; Teachers; AI-enabled education ecosystem (AI-e3); ARTIFICIAL-INTELLIGENCE; PEDAGOGICAL PRACTICES; TECHNOLOGY; PERSPECTIVES; SATISFACTION; MATHEMATICS; INTEGRATION; ACCEPTANCE; ADOPTION; ICT;
D O I
10.1007/s11423-024-10419-0
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Artificial Intelligence (AI) is driving ecological shifts and systemic reforms in education. As practitioners of educational reform, teachers' behavioral intention to experience and accept the effectiveness of AI technologies will affect the quality of educational change. From an educational ecology perspective, this study explores the impact of core elements within three dimensions-technologies, pedagogies, and cultures-on teachers' behavioral intention to use AI in an AI-enabled educational ecosystem (AI-e3) environment. The study uses a multi-level mediation model to analyze data of 4349 teachers from 189 primary and secondary schools from a western province of China. The results indicated that school-level dimensional elements, directly or indirectly, influenced teachers' behavioral intention to use AI, mediated by teacher-level dimensional elements. The findings are relevant to school administrators and policy makers, who should consider the key influences on teachers' behavioral intentions to use AI and promote the effective application of AI science for educational change.
引用
收藏
页码:135 / 167
页数:33
相关论文
共 60 条
[51]   Special Session: AI for K-12 Guidelines Initiative [J].
Touretzky, David ;
Martin, Fred ;
Seehorn, Deborah ;
Breazeal, Cynthia ;
Posner, Tess .
SIGCSE '19: PROCEEDINGS OF THE 50TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, 2019, :492-493
[52]   Blockchain Technology Adoption in Smart Learning Environments [J].
Ullah, Nazir ;
Mugahed Al-Rahmi, Waleed ;
Alzahrani, Ahmed Ibrahim ;
Alfarraj, Osama ;
Alblehai, Fahad Mohammed .
SUSTAINABILITY, 2021, 13 (04) :1-18
[53]   Technology Acceptance Model 3 and a Research Agenda on Interventions [J].
Venkatesh, Viswanath ;
Bala, Hillol .
DECISION SCIENCES, 2008, 39 (02) :273-315
[54]   Cognitive engagement with technology scale: a validation study [J].
Vongkulluksn, Vanessa W. ;
Lu, Lin ;
Nelson, Michael J. ;
Xie, Kui .
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT, 2022, 70 (02) :419-445
[55]   Participant or spectator? Comprehending the willingness of faculty to use intelligent tutoring systems in the artificial intelligence era [J].
Wang, Shanyong ;
Yu, Haotian ;
Hu, Xianfeng ;
Li, Jun .
BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, 2020, 51 (05) :1657-1673
[56]  
Wang T., 2021, Comput. Educ.: Artif. Intell., V2, P100031, DOI DOI 10.1016/J.CAEAI.2021.100031
[57]  
Wang YM, 2021, EDUC TECHNOL SOC, V24, P116
[58]   Towards a new paradigm: the development and validation of a scale to explore technology-enhanced feedback literacy among primary and secondary school teachers [J].
Yang, Yunying ;
Luo, Zelan ;
Dong, Yan ;
Kurup, Premnadh M. ;
Wang, Yu .
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT, 2023, 71 (02) :391-413
[59]   A research framework of smart education [J].
Zhu Z.-T. ;
Yu M.-H. ;
Riezebos P. .
Smart Learning Environments, 3 (1)
[60]   The role of student engagement in promoting teachers' continuous learning of TPACK: based on a stimulus-organism-response framework and an integrative model of behavior prediction [J].
Zhou, Chi ;
Wu, Di ;
Li, Yating ;
Yang, Harrison Hao ;
Man, Shuo ;
Chen, Min .
EDUCATION AND INFORMATION TECHNOLOGIES, 2023, 28 (02) :2207-2227