Roe, Jasper, Perkins, Mike, Somoray, Klaire, Miller, Dan, and Furze, Leon (2025) Can synthetic avatars replace lecturers? An exploratory international study of higher education stakeholder perceptions. International Journal of Educational Technology in Higher Education, 22. 71.

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Abstract

Advances in technologies which use Generative Artificial Intelligence (GenAI) to mimic a person’s likeness or voice have led to growing interest in their use in educational contexts. However, little is known about how key stakeholders (teaching faculty and professional staff) perceive and intend to use these tools. This study investigates higher education employees’ perceptions and intentions regarding the use of synthetic avatars (alternatively known as deepfakes) through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Using a mixed-methods approach that combined quantitative survey data (n = 173) with qualitative text response, we found that academic stakeholders demonstrated a relatively low intention to adopt these technologies (M = 41.55, SD = 34.14) and held complex, often contradictory views about their implementation. Stakeholders identified potential benefits, including enhanced student engagement through interactions with historical figures, improved accessibility through voice synthesis, and reduced workload in content creation. However, they expressed significant concerns about the exploitation of academic labour, institutional cost-cutting leading to automation, degradation of human relationships in education, and broader societal impacts, such as environmental costs and information validity. Quantitative analysis revealed that adoption intentions were most strongly associated with hedonic motivation, with a gender-specific interaction in the evaluation of price value. Qualitative findings highlighted significant concerns regarding ethical implications, resource inequities, and the impact on professional identity. These results suggest that traditional technology acceptance models should be expanded to consider broader ethical and structural factors. Based on these findings, we propose a three-pillar framework for implementing synthetic avatar technologies in higher education that emphasises establishing robust institutional policies and governance structures, developing comprehensive professional development and support systems, and ensuring equitable resource allocation guided by evidence-based implementation strategies. This study enhances our understanding of how emerging AI technologies can be thoughtfully integrated into higher education while maintaining academic integrity and professional autonomy of educators.

Item ID:	89792
Item Type:	Article (Research - C1)
ISSN:	2365-9440
Keywords:	Deepfakes; Synthetic media; GenAI; Artificial intelligence; Higher education; Perceptions
Copyright Information:	© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0
Date Deposited:	20 Jan 2026 23:26
FoR Codes:	52 PSYCHOLOGY > 5205 Social and personality psychology > 520505 Social psychology @ 25% 39 EDUCATION > 3904 Specialist studies in education > 390405 Educational technology and computing @ 50% 39 EDUCATION > 3903 Education systems > 390303 Higher education @ 25%
SEO Codes:	16 EDUCATION AND TRAINING > 1603 Teaching and curriculum > 160304 Teaching and instruction technologies @ 100%
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