AI integration in higher education: a systematic literature review of tools, perceptions, and pedagogical impacts

Artificial Intelligence (AI) is rapidly transforming higher education, offering both opportunities and challenges in teaching, learning, and assessment. This systematic literature review (SLR) examines the integration of AI into higher education settings, focusing on its applications, stakeholder perceptions, learning outcomes, and emerging pedagogical practices. A total of 100 peer-reviewed journal articles, published between January 2020 and December 2024, were analysed using the PRISMA framework. The review also employed Voyant Tools, a text mining software, to extract frequently used terms and uncover thematic patterns across studies. Five core themes emerged: (1) AI applications in teaching and learning, (2) personalised learning and skill development, (3) teacher and student perceptions of AI, (4) motivational and engagement strategies, and (5) innovative AI-driven teaching methods such as virtual tutors, AR, and simulations. Findings indicate that AI fosters personalised learning environments, enhances teacher effectiveness, and improves student motivation through adaptive feedback and gamification. However, challenges persist, including ethical concerns, limited AI literacy, potential overreliance, and uneven access across educational contexts. Notably, perceptions differ between generally optimistic students and educators, who express caution regarding AI’s long-term pedagogical implications. This review recommends future longitudinal studies, broader exploration across disciplines, and inclusive policy development. These steps are essential to ensure that AI is adopted not only efficiently but also ethically and equitably in higher education.