University of Kwazulu-Natal

Exploring Trust In Generative Ai For Higher Education Nature

Bonisiwe Shabane
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exploring trust in generative ai for higher education nature

Humanities and Social Sciences Communications , Article number: (2025) Cite this article We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. Although Generative Artificial Intelligence (GenAI) offers transformative opportunities for higher education, its adoption by educators remains limited, primarily due to trust concerns. This systematic literature review aims to synthesise peer-reviewed research conducted between 2019 and August 2024 on the factors influencing educators’ trust in GenAI within higher education institutions.

Using PRISMA 2020 guidelines, this study identified 37 articles at the intersection of trust factors, technology adoption, and GenAI impact in higher education from educators’ perspectives. Our analysis reveals that existing AI trust frameworks fail to capture the pedagogical and institutional dimensions specific to higher education contexts. We propose a new conceptual model focused on three dimensions affecting educators’ trust: (1) individual factors (demographics, pedagogical beliefs, sense of control, and emotional experience), (2) institutional strategies (leadership support, policies, and training support),... Our findings reveal a significant gap in institutional leadership support, whereas professional development and training were the most frequently mentioned strategies. Pedagogical and socio-ethical considerations remain largely underexplored. The practical implications of this study emphasise the need for institutions to strengthen leadership engagement, align GenAI adoption strategies with educators’ values, and develop comprehensive training frameworks that address ethical and pedagogical concerns.

This work contributes a multidimensional view of educators’ trust in GenAI and provides a foundation for future research. The datasets used in the current study are available from the authors upon reasonable request. Aler Tubella A, Mora-Cantallops M, Nieves JC (2024) How to teach responsible AI in higher education: challenges and opportunities. Ethics Inf Technol 26(1):3. https://doi.org/10.1007/s10676-023-09733-7 Đerić, E.; Frank, D.; Milković, M.

Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers. Information 2025, 16, 622. https://doi.org/10.3390/info16070622 Đerić E, Frank D, Milković M. Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers. Information.

2025; 16(7):622. https://doi.org/10.3390/info16070622 Đerić, Elena, Domagoj Frank, and Marin Milković. 2025. "Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers" Information 16, no. 7: 622.

https://doi.org/10.3390/info16070622 Đerić, E., Frank, D., & Milković, M. (2025). Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers. Information, 16(7), 622. https://doi.org/10.3390/info16070622

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. *Correspondence: Nigel J. Francis, francisn10@cardiff.ac.uk; Sue Jones, suejones@ibms.org ORCID: Nigel J. Francis, orcid.org/0000-0002-4706-4795; Sue Jones, orcid.org/0009-0002-4931-6332; David P.

Smith, orcid.org/0000-0001-5177-8574 Received 2024 Nov 10; Accepted 2024 Dec 24; Collection date 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted... No use, distribution or reproduction is permitted which does not comply with these terms. Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods.

This paper explores the dual-edged nature of GenAI’s integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and... Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing... Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in...

By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education. arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

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Humanities And Social Sciences Communications , Article Number: (2025) Cite

Humanities and Social Sciences Communications , Article number: (2025) Cite this article We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. Although Generative Artificial Intelligence (Ge...

Using PRISMA 2020 Guidelines, This Study Identified 37 Articles At

Using PRISMA 2020 guidelines, this study identified 37 articles at the intersection of trust factors, technology adoption, and GenAI impact in higher education from educators’ perspectives. Our analysis reveals that existing AI trust frameworks fail to capture the pedagogical and institutional dimensions specific to higher education contexts. We propose a new conceptual model focused on three dime...

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This work contributes a multidimensional view of educators’ trust in GenAI and provides a foundation for future research. The datasets used in the current study are available from the authors upon reasonable request. Aler Tubella A, Mora-Cantallops M, Nieves JC (2024) How to teach responsible AI in higher education: challenges and opportunities. Ethics Inf Technol 26(1):3. https://doi.org/10.1007/...

Trust In Generative AI Tools: A Comparative Study Of Higher

Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers. Information 2025, 16, 622. https://doi.org/10.3390/info16070622 Đerić E, Frank D, Milković M. Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers. Information.

2025; 16(7):622. Https://doi.org/10.3390/info16070622 Đerić, Elena, Domagoj Frank, And Marin Milković.

2025; 16(7):622. https://doi.org/10.3390/info16070622 Đerić, Elena, Domagoj Frank, and Marin Milković. 2025. "Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers" Information 16, no. 7: 622.