University of Kwazulu-Natal

A Systematic Review Of Generative Artificial Intelligence In Education

Bonisiwe Shabane
-
a systematic review of generative artificial intelligence in education

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. 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.

You have full access to this open access article This systematic review aims to provide a detailed and comprehensive overview of the use of Generative Artificial Intelligence (GAI) in K-12 education. Using the PRISMA method, this research examined articles published between 2016 and 2024, selecting 197 relevant studies from 5 databases and 2 journals. The methodology included the use of the Mentefacto Map to identify keywords and define inclusion and exclusion criteria, ensuring a systematic and replicable approach. The findings of the review indicate that GAI offers significant opportunities to personalize learning experiences, motivate students, improve assessment methods, and introduce innovative and immersive teaching practices. The use of ChatGPT in educational environments is a clear example of this.

There are several critical issues that need attention, such as the need for continuous teacher training on ICT and the development of ministerial guidelines that also address ethical and privacy concerns. There is a notable lack of concrete studies and specific experiments, not only focused on STEM disciplines but also open to the arts/humanities, as well as practical examples of GAI use in daily teaching... An original contribution of this review lies in identifying specific gaps in the existing literature, along with the proposal of research perspectives and insights that have so far been underexplored. Avoid common mistakes on your manuscript. Educational Technologies (ET) have long supported teaching through innovation and access to resource. Despite these advantages, teacher training on ICT has posed a constant challenge, due to the need to continuously adapt to evolving tools and methodologies.

From the rise of personal computers in the mid-1990s to Augmented, Virtual and Mixed Reality, technologies have continuously transformed and challenged teaching. Artificial Intelligence (AI) is set to disrupt current teaching more than any other technology, especially following the introduction of Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP). With the launch of ChatGPT, the first user-friendly Large Language Model (LLM), AI has transformed human–machine interaction, marking a significant, irreversible shift in education. The evolution of Generative Artificial Intelligence (GAI) promises to transform many sectors, including education, but it raises complex ethical and knowledge-related issues for students and teachers, as well as challenges regarding its correct applicability... This aspect becomes even more relevant when considering a particularly sensitive age level, such as K-12 students, who have more specific and individualized needs. Although empirical evidence attests to the effectiveness of emerging Educational Technologies in teaching practice (Crompton, 2022; Dawson, 2023; Zafari, 2022), current literature lacks comprehensive guidance on access to resources, content creation and methodologies combining...

Many studies also point to the lack of ministerial guidelines or frameworks for training on AI, with AI or by AI (Casal-Otero et al., 2023; Sanusi et al., 2023; Wang et al., 2024a, 2024b;... This research investigates the current use of GAI in the K-12 Education, based on limited recent literature. A period from 2016 to the present was chosen because that year marked the beginning of growing academic interest in the use of AI in education. The PICOC framework (Population—Intervention—Comparison—Outcome—Context), a widely used framework in systematic reviews in education, health sciences, public policy and other fields to structure research questions and ensure rigorous analysis of evidence, was used in this... Our research integrity and auditing teams lead the rigorous process that protects the quality of the scientific record 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

People Also Search

A Not-for-profit Organization, IEEE Is The World's Largest Technical Professional

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. The integration of Generative Artificial Intelligence (AI) in education presents a transformative frontier, shaping teachin...

While Generative AI Offers Promising Benefits Such As Enhanced Teaching

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 guidelin...

It Encompasses Various Methodologies, Including But Not Limited To Generative

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 e...

You Have Full Access To This Open Access Article This

You have full access to this open access article This systematic review aims to provide a detailed and comprehensive overview of the use of Generative Artificial Intelligence (GAI) in K-12 education. Using the PRISMA method, this research examined articles published between 2016 and 2024, selecting 197 relevant studies from 5 databases and 2 journals. The methodology included the use of the Mentef...

There Are Several Critical Issues That Need Attention, Such As

There are several critical issues that need attention, such as the need for continuous teacher training on ICT and the development of ministerial guidelines that also address ethical and privacy concerns. There is a notable lack of concrete studies and specific experiments, not only focused on STEM disciplines but also open to the arts/humanities, as well as practical examples of GAI use in daily ...