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International Research Journal of Advanced Engineering and Technology

Open Access Peer Review International
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GENERATIVE ARTIFICIAL INTELLIGENCE IN EDUCATIONAL CONTEXTS: A SYSTEMATIC REVIEW OF OPPORTUNITIES, CHALLENGES, AND ETHICAL IMPLICATIONS

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Dr. Elara V. Quinn 1 , Prof. Jian W. Lin 1 ,
4 Department of Educational Policy, The Minerva Institute of Technology, London, United Kingdom
4 Department of Educational Policy, The Minerva Institute of Technology, London, United Kingdom

Abstract

Background: The rapid emergence of Generative Artificial Intelligence (GAI), exemplified by tools like large language models (LLMs) and conversational agents, presents a pivotal moment for global education systems. Understanding the comprehensive impact of these technologies is critical for guiding pedagogical practice and institutional policy in higher education and beyond.

Objective: This systematic review synthesizes the current academic literature to delineate the primary opportunities and critical challenges associated with integrating GAI technologies in educational contexts, while concurrently addressing the urgent need for clear ethical and policy frameworks.

Method: A systematic review methodology was employed, analyzing published literature focused on GAI and education between 2023 and 2024. The review focused on identifying core themes related to technological implementation, student experience, educator roles, and institutional integrity.

Findings: Key opportunities identified include the potential to personalize learning experiences, automate routine administrative and grading tasks for educators, and boost creativity and digital multimodal composing skills among students. Conversely, significant challenges revolve around academic integrity concerns and the risk of plagiarism and cheating the potential for GAI models to harbor and amplify unfair biases and a widespread deficit in clear ethical rules and guidelines for responsible implementation. A deep analysis of integrity management indicates that the technological pursuit of GAI detection is largely futile and carries significant ethical and systemic costs, necessitating an imperative shift toward assessment redesign.

Conclusion: GAI is not merely a disruptive technology but a transformative partner in education, requiring stakeholders to adopt a proactive, balanced approach. Institutions must rapidly develop clear, equitable, and enforceable policies to manage integrity risks and mitigate bias, ensuring GAI is used to enhance, rather than compromise, educational quality and fairness.

 

Keywords

Generative AI, Large Language Models, Academic Integrity, Higher Education Policy

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