ETHICAL IMPLICATIONS OF AI IN EDUCATION: HARMONISING PRIVACY, EQUITY, AND ACADEMIC INTEGRITY IN THE AGE OF AI
ETHICAL IMPLICATIONS OF AI IN EDUCATION: HARMONISING PRIVACY, EQUITY, AND ACADEMIC INTEGRITY IN THE AGE OF AI
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Date
2026
Authors
OLADAPO, Yemisi Oluremi
SALAMI, Kudirat Olawumi
ADEKEYE, Olubola
Journal Title
Journal ISSN
Volume Title
Publisher
Library and Information Management Forum Vol. 28. No. 1 2026
Abstract
Artificial Intelligence (AI) is increasingly transforming education through personalised learning,
automated assessment, learning analytics, and administrative support. However, its integration
has created ethical concerns relating to student data privacy, equitable access, algorithmic bias,
and academic integrity. The problem that motivated this study is that many educational institutions
adopt AI tools faster than they develop clear ethical safeguards, leaving learners vulnerable to
surveillance, exclusion, biased decision-making, and misuse of AI-assisted academic work. The
study examined the ethical challenges posed by AI integration in educational settings, synthesised
global perspectives and best practices on AI ethics in education, and proposed actionable
recommendations and strategic guidelines for responsible use. The study adopted a narrative
review research design. The population of the study comprised relevant academic literature, policy
documents, and empirical studies on AI ethics in education. Google Scholar, African Journals
Online (AJOL), and Education Resources Information Center (ERIC) served as the main sources
of data, while a literature review matrix was used as the instrument for data extraction and
organisation. Data were analysed through thematic narrative synthesis, with attention to privacy,
equity, academic integrity, and policy guidance. The findings revealed that AI in education can
erode student privacy through extensive data collection, amplify educational inequality through
unequal access and biased algorithms, and weaken academic integrity through AI-generated
academic work. The review also found that many institutions lack comprehensive and contextspecific
ethical frameworks for AI adoption. The study concluded that AI can improve education
only when innovation is balanced with privacy protection, fairness, transparency, and academic
honesty. It recommended that educators, administrators, policymakers, and technology developers
should strengthen data governance, promote equitable access, redesign assessment practices, and
implement clear ethical policies for AI use in education.
Description
Journal of Library and Information Management Forum