Learner Perceptions of Artificial Intelligence-Generated Pedagogical Agents in Language Learning Videos: Embodiment Effects on Technology Acceptance

Abstract

Artificial intelligence generates vibrant characters, encompassing teachers, peer students, and advisors within diverse educational media. However, the impact of the perceived embodiment of such characters in language learning videos on students’ technology acceptance and adoption is unclear. Integrating structural equation modeling into thematic analysis, this study analyzes 1042 valid responses from higher education students to bridge this research gap. Our study reveals that four subdimensions of embodiment (human-likeness, credibility, learning facilitation, and engagement) significantly and positively predict higher-education students’ perceived ease of use and usefulness of artificial intelligence-generated virtual teachers in language learning videos. Notably, an exception arises, as human-likeness does not significantly predict students’ perceived ease of use in our research context. Students’ perceived systemic interactivity and impact on the learning process emerge as pivotal mediators. The qualitative thematic analysis identifies students’ concerns about classroom administration, developmental support, technical issues, deprived interpersonal collaboration, and liberal attainment cultivation with the virtual teacher presence. This study can illuminate artificial intelligence technology designs and applications in education.

Keywords:

• Embodiment
• technology acceptance
• structural equation modeling
• artificial intelligence
• language learning videos
• pedagogical agents

Acknowledgements

The authors would like to extend sincere gratitude to their funders, journal editors, anonymous reviewers, and all other participants who offered help with reviewing and publishing this paper. The first author would like to express his special gratitude to his supervisors for the current program in Beijing Language and Culture University.

Ethical approval

The study was approved by the institutional review board of Beijing Language and Culture University. All researchers can provide written informed consents.

Authors contribution

Yupeng Lin: Conceptualization; Data curation; Formal analysis; Investigation; Validation; Visualization; Writing – original draft; Methodology; Project administration; Writing – review and editing. Zhonggen Yu: Conceptualization; Funding acquisition

Data availability statement

We make sure that all data and materials support our published claims and comply with field standards.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by Key Research and Application Project of the Key Laboratory of Key Technologies for Localization Language Services of the State Administration of Press and Publication, “Research on Localization and Intelligent Language Education Technology for the ‘Belt and Road Initiative’” (Project Number: CSLS 20230012), and Special fund of Beijing Co-construction Project-Research and reform of the “Undergraduate Teaching Reform and Innovation Project” of Beijing higher education in 2020-innovative “multilingual +” excellent talent training system (202010032003).

Notes on contributors

Yupeng Lin

Yupeng Lin, is a postgraduate in applied foreign linguistics at Faculty of Foreign Studies, Beijing Language and Culture University. He has written over 30 manuscripts, first-authored seven articles in reputable journals, and reviewed for multiple highly-ranked journals. He researches into technology-enhanced education and EFL learning.

Zhonggen Yu

Zhonggen Yu, Professor (distinguished) and Ph.D. Supervisor in Faculty of Foreign Studies, Research Fellow of Academy of International Language Services, National Base for Language Service Export, Beijing Language and Culture University, has published over 200 papers in distinguished journals. His research interest includes educational technologies and language acquisition.