University of Kwazulu-Natal

Exploring The Impact Of Generative Artificial Intelligence In Educatio

Bonisiwe Shabane
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exploring the impact of generative artificial intelligence in educatio

Generative artificial intelligence (GAI) has brought new ideas for optimizing students’ learning. Despite increasing attention on the effects of GAI on learning outcomes (LO), research results are inconsistent. While GAI’s educational benefits are qualitatively described, there is substantial debate about its actual impact on students’ LO. The study sought to quantify GAI’s impact on students’ LO, evaluating its overall and average effects, and examining four key moderating factors: functional types of GAI, educational levels, intervention duration, and knowledge domains. Based on the screening criteria, 26 empirical studies were selected from 5,887 peer-reviewed papers. Two researchers collaboratively completed the literature screening and coding process.

The research employed a meta-analytic method to calculate the impact of GAI on learners’ LO, and examined four moderating factors. GAI exerts a significant but small overall effect on students’ LO (g = 0.392), with varying impacts on physical (g = 0.701), social-emotional (g = 0.347), and intellectual (g = 0.372) outcomes. The changes of GAI’s functional types have no significant effect on LO, but three other moderating factors do, showing significant statistical differences. GAI more significantly impacts primary school students, especially in supporting their intellectual and social-emotional outcomes. Longer interventions have a greater effect on LO than short ones, particularly intellectual and physical outcomes. GAI’s effects vary across knowledge domains, possibly due to its adaptability in different subjects.

Long-term GAI in higher education boosts intellectual and physical outcomes, especially in education and humanities and arts, while short-term use in primary education enhances social-emotional outcomes. Integrating diverse learning components and adjusting GAI implementation parameters can optimize its effectiveness in terms of enhancing LO across different levels of education. This is a preview of subscription content, log in via an institution to check access. Price excludes VAT (USA) Tax calculation will be finalised during checkout. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Ait Baha, T., El Hajji, M., Es-Saady, Y., & Fadili, H.

(2024). The impact of educational chatbot on student learning experience. Education and Information Technologies, 29(8), 10153–10176. https://doi.org/10.1007/s10639-023-12166-w 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. In the field of higher education, generative artificial intelligence (GenAI) has become a revolutionary influence, shaping how students access and use library resources. This study explores the intricate balance of both positive and negative effects that GenAI might have on the academic library experience for higher education (HE) students. The key aspects of enhanced discovery and retrieval, personalization and engagement, streamlined research processes, and digital literacy and information evaluation potentially offered through using generative AI will be considered.

These prospective advantages to HE students offered by using GenAI will be examined through will be examined through the theoretical framework of the Technological Acceptance Model (TAM) introduced by Davis et al. in 1986, which suggests that perceived usefulness and perceived ease of use are key factors in determining user acceptance and utilization of technology. The adoption of GenAI by higher education students will be analyzed from this viewpoint before assessing its impact on their use of library resources. A. Subaveerapandiyan, “Application of Artificial Intelligence (AI) In Libraries and Its Impact on Library Operations Review,” Library Philosophy and Practice (e-journal) 7828 (August 2023): 14, https://digitalcommons.unl.edu/libphilprac/7828. Abhijit Sinha and Sudin Bag, “Intention of Postgraduate Students towards the Online Education System: Application of Extended Technology Acceptance Model,” Journal of Applied Research in Higher Education 15, no.

2 (May 2022): 1–20, https://doi.org/10.1108/JARHE-06-2021-0233. Abid Hussain, “Use of Artificial Intelligence in the Library Services: Prospects and Challenges,” Library Hi Tech News 2 (2023): 15–17, https://doi.org/10.1108/LHTN-11-2022-0125. Adetoun A. Oyelude, “AI and Libraries: Trends and Projections,” Library Hi Tech News 38, no. 10 (December 2021): 1–4, https://doi.org/10.1108/LHTN-10-2021-0079. Published online by Cambridge University Press: 25 June 2025

This article examines the impact of generative artificial intelligence (GAI) on higher education, emphasizing its effects in the broader educational contexts. As AI continues to reshape the landscape of teaching and learning, it is imperative for higher education institutions to adapt rapidly to equip graduates for the challenges of a progressively automated global workforce. However, a critical question emerges: will GAI lead to a more inclusive future of learning, or will it deepen existing divides and create a future where educational access and success are increasingly unequal? This study employs both theoretical and empirical approaches to explore the transformative potential of GAI. Drawing upon the literature on AI and education, we establish a framework that categorizes the essential knowledge and skills needed by graduates in the GAI era. This framework includes four key capability sets: AI ethics, AI literacy (focusing on human-replacement technologies), human–AI collaboration (emphasizing human augmentation), and human-distinctive capacities (highlighting unique human intelligence).

Our empirical analysis involves scrutinizing GAI policy documents and the core curricula mandated for all graduates across leading Asian universities. Contrary to expectations of a uniform AI-driven educational transformation, our findings expose significant disparities in AI readiness and implementation among these institutions. These disparities, shaped by national and institutional specifics, are likely to exacerbate existing inequalities in educational outcomes, leading to divergent futures for individuals and universities alike in the age of GAI. Thus, this article not only maps the current landscape but also forecasts the widening educational gaps that GAI might engender. This study underscores the critical need for policy and education leaders to adopt and implement comprehensive and inclusive policies in higher education to effectively leverage the capabilities of generative artificial intelligence (GAI). Our analysis shows sharp disparities in GAI readiness across top universities, which implies an impending widening of educational inequalities in the absence of effective policy measures.

Policymakers must prioritize the development of robust GAI integration strategies that not only enhance curricula with essential AI skills and ethics but also ensure equitable access for all individuals and institutions. By systematically aligning educational frameworks with the evolving demands of the AI era, we can equip graduates with the necessary tools to thrive in a digitally driven future under transformative technological advancement. Generative artificial intelligence (GAI), including transformative technologies such as ChatGPT, is rapidly changing the contours of various sectors of human life (Zawacki-Richter et al., Reference Zawacki-Richter, Marín, Bond and Gouverneur2019; Galindo et al., Reference... One domain standing at the center of this monumental transformation is higher education (Hannan and Liu, Reference Hannan and Liu2023). As policymakers and leaders navigate the threshold of an era where AI technologies possess the power to redefine traditional learning and teaching methodologies (Novak and Gowin, Reference Novak and Gowin1984; Jung, Reference Jung2018; Li,... How prepared are our higher education institutions to embrace this transformation?

More crucially, will the future of GAI-enhanced education be a future of expanded opportunity or a future of deepening divides, where only the privileged few benefit while the majority are left behind? The advent of GAI presents a dual challenge for higher education worldwide (OECD, 2023). The first challenge is awareness and comprehension: educational institutions must comprehend the meanings and implications of the rise of GAI for the future of work and the teaching and learning of higher education. This understanding will help them identify the essential knowledge and skills in the AI era. The second, and conceivably more significant challenge, is reconfiguration and transformation. Major changes, including curriculum reforms and institutional restructuring, are often necessary to incorporate or strengthen the capacities essential for the AI era in university education, preparing them for a future increasingly intertwined with AI...

Addressing these challenges requires an analysis from both theoretical and empirical perspectives, which constitutes the essence of this study. Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing Affiliation School of Special Education, Handan University, Handan, China Roles Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Software, Writing – original draft, Writing – review & editing Affiliation Faculty of Arts, University of Auckland, Auckland, New Zealand As generative artificial intelligence (AI) rapidly transforms educational landscapes, understanding its impact on students’ core competencies has become increasingly critical for educators and policymakers.

Despite growing integration of AI technologies in classrooms, there remains a significant knowledge gap regarding how these tools influence the development of essential 21st-century skills in secondary education contexts. This study addresses this gap by investigating the relationships between generative AI applications and two critical student outcomes: innovation capability and digital literacy. Through structural equation modeling analysis of data collected from 500 students across grades 7–12, the research reveals three key findings: Firstly, generative AI applications demonstrate a substantial positive effect on students’ innovation capability (β... Secondly, AI integration significantly improves digital literacy (β = 0.835, p < .001) by facilitating sophisticated information processing and active technological engagement. Thirdly, a strong bidirectional relationship exists between innovation capability and digital literacy (β = 0.791, p < .001), suggesting these competencies mutually reinforce each other in AI-enhanced learning environments. The model demonstrates robust explanatory power with excellent fit indices.

By integrating the Technology Acceptance Model with Diffusion of Innovations theory, this study advances theoretical understanding of AI’s educational impact while providing practical guidelines for educators. The findings underscore the importance of strategic AI integration in educational curricula and suggest specific pathways for developing critical student competencies in the digital age.

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The research employed a meta-analytic method to calculate the impact of GAI on learners’ LO, and examined four moderating factors. GAI exerts a significant but small overall effect on students’ LO (g = 0.392), with varying impacts on physical (g = 0.701), social-emotional (g = 0.347), and intellectual (g = 0.372) outcomes. The changes of GAI’s functional types have no significant effect on LO, but...

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