University of Kwazulu-Natal

Transforming Education Exploring The Influence Of Generative Ai On

Bonisiwe Shabane
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transforming education exploring the influence of generative ai on

The emergence of Generative Artificial Intelligence (AI) marks a revolutionary advancement in education. This study explores the profound impact of implementing Generative AI on teachers’ teaching performance, with a focus on enhancing teaching effectiveness and pedagogical practices. This research uses a survey methodology, employing a proportionated stratified random sampling technique. A total of 466 participants, consisting of teachers, were involved in this study, with questionnaires serving as the primary tool for data collection. The primary data analysis method used in this study was the Structural Equation Model (SEM). Research indicates that Generative AI significantly enhances teaching performance by improving ease of use, usefulness, and learning.

Teacher perceptions of AI’s usability influence its integration into student-focused learning, learning material development, and teaching practice enhancement. Additionally, the ease of learning is crucial for its adoption. Alongside these promising opportunities, the study also highlights challenges that need to be addressed for successful AI integration in education, such as technical limitations and the necessity for teacher training. By exploring the application of Generative AI in depth, this research offers valuable insights into leveraging technology to foster more inclusive, personalized, and practical education in the digital age. 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.

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

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