University of Kwazulu-Natal

Open Access Generative Ai In Education A Systematic Review

Bonisiwe Shabane
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open access generative ai in education a systematic review

The integration of Generative Artificial Intelligence (AI) in education presents a transformative frontier, shaping teaching, learning, and research practices in higher education institutions. This systematic review explores recent studies on the application of Generative AI in education, examining its benefits, challenges, and implications. Through an analysis of literature from prominent databases, key themes emerge surrounding Generative AI's multifaceted applications. These include personalized learning, academic integrity policy development, multimodal writing enhancement, and research innovation. While Generative AI offers promising benefits such as enhanced teaching methodologies, accessibility, and efficiency, challenges persist regarding academic integrity, algorithmic bias, and equitable distribution of resources. Additionally, the review identifies areas lacking sufficient exploration within education research, emphasizing the need for interdisciplinary collaboration, ethical guidelines, and innovative pedagogical approaches to harness Generative AI's transformative potential responsibly.

Generative Artificial Intelligence (AI) has emerged as a transformative force in various fields, including education. Its ability to autonomously produce content, simulate human-like behaviours, and facilitate personalized learning experiences has garnered significant attention within the educational landscape. In this systematic review, we delve into the application of Generative AI in Education, with a specific focus on its implications for Education. Generative AI refers to a subset of artificial intelligence techniques that involve the generation of new content, such as images, text, audio, and even videos, mimicking human creativity and problem-solving capabilities 1. It encompasses various methodologies, including but not limited to generative adversarial networks (GANs), recurrent neural networks (RNNs), and transformers. Generative AI operates on the principle of learning from vast amounts of data to generate new outputs that are indistinguishable from those produced by humans.

This technology has found applications in diverse domains, ranging from art and entertainment to healthcare and finance. In recent years, significant advancements in artificial intelligence (AI), particularly in generative AI, have propelled it to the forefront of discussions within the tech industry. Like numerous other sectors, education stands poised for transformation through the utilization of generative AI technologies like ChatGPT, Bard, DALL-E, Mid journey, and DeepMind 2. As generative Artificial Intelligence (AI) continues to evolve rapidly, in the next few years, it will drive innovation and improvements in education, but it will also create a myriad of new challenges 3, 4. 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 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. You have full access to this open access article

The release of ChatGPT in late 2022 marked the beginning of a rapid transformation in higher education, soon followed by the development of multimodal generative AI programs. As this technology becomes increasingly integrated into teaching and learning, it is crucial to evaluate its current use and impact. This systematic literature review captures the initial academic response to generative AI, providing insights into how higher education has adopted this transformative technology in its first two years. The findings indicate that while some themes from the pre-ChatGPT era persist, new and emerging trends—particularly in fostering creativity, critical thinking, learning autonomy, and prompt literacy—are now taking shape. This shift underscores a growing emphasis on the pedagogical integration of generative AI. However, the review also highlights a key tension: while generative AI enhances efficiency, it raises concerns about overreliance, potentially leading to the outsourcing of critical cognitive and metacognitive skills.

To address these challenges and fully harness the potential of generative AI, future research should focus on exploring multimodal generative AI tools and fostering student–teacher-AI collaboration. Avoid common mistakes on your manuscript. The release of OpenAI’s ChatGPT-3.5 in November 2022 marked a transformative moment for education, sparking the development of generative AI (GenAI) tools that are reshaping teaching, learning, and assessment practices (EDUCAUSE, 2023). GenAI, powered by Large Language Models (LLMs), can summarize and generate content across various modalities, including text, image, audio, and video (MIT News, 2023). Initially, single-modal GenAI tools like DALL-E for image, Suno for music, and Google’s Imagen for video gained traction (McKinsey & Company, 2024). By late 2023, the advent of multimodal programs, such as GPT-4, Google’s Gemini, and Meta’s ImageBind, marked a new phase, enabling simultaneous integration and generation across media types — a development that expands the...

GenAI has immense potential to advance pedagogical approaches and the experiences of teaching, learning, and assessment in higher education. Yet, as a technology that is both “transformative” and “disruptive,” its future development demands a nuanced understanding of its current applications and the untapped potential it holds (McCormack, 2023; Robert, 2024). Since late 2022, researchers and practitioners have increasingly integrated GenAI into their teaching and learning, leading to a rapid increase in studies on its applications. Yet, despite this growing body of research, significant gaps remain in understanding which GenAI tools are used, how they are applied, and for what learning tasks. A comprehensive exploration is therefore essential to map the current landscape, uncover GenAI’s unrealized potential, and address its associated challenges.

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