The Use of Artificial Intelligence in Higher Education: A Systematic Literature Review of Learning Effectiveness and Ethical Issues
DOI:
https://doi.org/10.53905/edu.v1i02.10Keywords:
artificial intelligence, higher education, learning effectiveness, ethical issues, academic integrity, systematic literature reviewAbstract
Purpose of the study: This study systematically reviews the use of artificial intelligence (AI) in higher education, focusing on learning effectiveness and ethical issues associated with AI integration. The review aims to examine how AI technologies influence student learning outcomes, identify ethical challenges emerging from AI adoption, and determine the factors moderating the effectiveness of AI in educational settings.
Materials and methods: A systematic literature review following PRISMA guidelines was conducted using peer-reviewed studies indexed in Scopus and Web of Science between 2018 and 2024. The search process identified 5,419 records, of which 47 studies met the inclusion criteria for final analysis. Data were analysed using narrative synthesis and thematic analysis supported by NVivo software.
Results: The findings reveal that AI contributes positively to personalised learning, academic performance, student engagement, and feedback efficiency when supported by appropriate pedagogical design and institutional readiness. AI applications such as intelligent tutoring systems, learning analytics, chatbots, and generative AI tools demonstrated significant potential to enhance teaching and learning processes. However, the review also identified substantial ethical concerns, including algorithmic bias, academic integrity violations, data privacy risks, lack of transparency, and unequal access to AI technologies. Three major moderating factors influencing AI effectiveness were institutional digital readiness, faculty AI literacy, and the maturity of ethical governance frameworks.
Conclusions: AI offers transformative opportunities for higher education, but its implementation must be accompanied by strong ethical governance, transparent institutional policies, and adequate educator preparation. Universities should prioritise responsible AI integration to maximise educational benefits while minimising ethical risks. Future research should investigate long-term educational impacts and expand evidence from underrepresented regions and educational contexts.
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