Library and Information Management Forum

Permanent URI for this collection

https://kubanni.abu.edu.ng/handle/123456789/13127

Browse

Browsing Library and Information Management Forum by Author "ADEKEYE, Olubola"

Now showing 1 - 1 of 1
  • Item
    ETHICAL IMPLICATIONS OF AI IN EDUCATION: HARMONISING PRIVACY, EQUITY, AND ACADEMIC INTEGRITY IN THE AGE OF AI
    (Library and Information Management Forum Vol. 28. No. 1 2026, 2026) OLADAPO, Yemisi Oluremi; SALAMI, Kudirat Olawumi; ADEKEYE, Olubola
    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.