An application of Artificial Intelligence in Teaching and Learning: A Systematic Literature Review of Its Implications for University Student and Academic Achievement
DOI:
https://doi.org/10.53797/anp.jssh.v7i1.9.2026Keywords:
Artificial Intelligence, Higher Education, Student Motivation, Academic Achievement, Systematic Literature ReviewAbstract
Artificial intelligence (AI) is increasingly embedded in higher education through adaptive learning systems, intelligent tutoring systems, generative AI assistants and learning analytics, yet evidence on what this integration does to students' motivation and academic achievement remains fragmented. This systematic literature review synthesises twenty studies published between 2022 and 2025 and retrieved from Scopus, following the PRISMA 2020 statement and the Synthesis without Meta-analysis reporting guideline. Records were screened in four stages, appraised against four quality criteria and analysed by thematic synthesis. The corpus is dominated by favourable findings: eighteen of the twenty studies report positive motivational effects, attributed mainly to personal pacing, immediate feedback and increased learner autonomy. The evidentiary basis for those claims, however, is thinner than the pattern suggests. Only half of the included studies report primary empirical data, only four use an experimental design and only one reports a control group comparison, so the apparent consensus rests substantially on conceptual, bibliometric and review papers. Evidence on academic achievement is weaker still, with several studies inferring achievement gains from motivational proxies rather than measuring them. Recurrent risks include over-reliance on generated output, algorithmic bias, data privacy, uneven digital readiness and reduced social presence. The review concludes that AI integration is associated with improved motivation and shows promise for achievement, but that current evidence cannot establish effectiveness. Longitudinal, controlled and discipline-specific studies, together with Malaysian evidence aligned to national generative-AI guidance, are the clear priorities.
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Copyright (c) 2026 Muhammad Adib Muhammad, Justin Inggang Benang, Nazran Hajar, Muhammad Safwan Noor Azan, Morgan Setia, Erickson Engga Umpang, Mohamad Amiruddin Ismail
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