Artificial intelligence in higher education: a systematic review of its impact on student engagement and the mediating role of teaching methods

Synthesis: Long, Wang, Md Rashid, and Lu (2026) systematically review 73 peer-reviewed studies (2015–early 2025, Scopus and Web of Science) on AI in higher education and its impact on student engagement, with a focus on the mediating role of teaching methods. They find that AI tools — chatbots, adaptive systems, predictive analytics — enhance engagement most effectively when embedded within interactive pedagogies (flipped classrooms, project-based learning, scaffolded feedback loops). They introduce the PMAISE model (Pedagogical Mediation of AI for Student Engagement), mapping the alignment between AI technologies, pedagogical strategies, and the affective, behavioral, and cognitive dimensions of engagement, and examine ethics, data privacy, and structural barriers to equitable adoption.

Key Findings

1. 73 studies (2015–early 2025), PRISMA-guided. A systematic review from Scopus and Web of Science, coded by AI type, engagement outcomes, and instructional strategies.
2. Pedagogy mediates AI's effect on engagement. AI enhances engagement most effectively within interactive pedagogies — flipped classrooms, project-based learning, and scaffolded feedback loops — rather than through tool deployment alone.
3. PMAISE model introduced. The review proposes a conceptual framework (Pedagogical Mediation of AI for Student Engagement) aligning AI technologies, pedagogical strategies, and the affective, behavioral, and cognitive dimensions of engagement.
4. Teaching methods amplify or inhibit AI effects. Concrete examples demonstrate that the same AI tool can boost or fail to boost engagement depending on the surrounding instructional design.
5. Equity and ethics concerns. Structural barriers to equitable AI adoption (e.g., socioeconomically disadvantaged students more vulnerable to unstable internet or limited device access), alongside data privacy and ethical concerns, are examined.

Implications

This review connects Student Engagement and Student Engagement to the central AIED finding that instructional design mediates tool effectiveness — an AI tool is only as engaging as the pedagogy it is embedded in. It supports the Instructional Design and Active Learning emphasis of the wiki and extends Feedback Loop and Scaffolding research to engagement outcomes. The PMAISE model's three engagement dimensions (affective, behavioral, cognitive) align with Student Engagement, while its attention to equity and infrastructure barriers connects to Digital Divide and Equity In AI Education. As a systematic review, it also exemplifies synthesis methods in AIED research.

Connected Concepts

• Student Engagement
• Higher Ed
• AI Education
• Active Learning
• Project Based Learning
• Scaffolding
• Feedback
• Instructional Design
• Digital Divide
• Equity In AI Education
• Meta Analysis Systematic Review

Connected Articles

• GenAI Motivation Engagement 2026 — GenAI motivation and engagement
• Icap Cognitive Engagement LLM Agents — ICAP cognitive engagement with LLM agents
• Effects Of AI Chatbot Supported Cooperative Flipped Classroom On Student Collabo — AI chatbot cooperative flipped classroom
• Scaffolding Critical Engagement GenAI Minority Students — Scaffolding critical engagement with GenAI
• Students Engagement With Generative AI In Academic Learning A Self Determination — Student engagement with GenAI and self-determination
• GenAI Tutor Engagement Patterns — GenAI tutor engagement patterns

Citation

Long, D. Y., Wang, S., Md Rashid, S., & Lu, X. T. (2026). Artificial intelligence in higher education: a systematic review of its impact on student engagement and the mediating role of teaching methods. Frontiers in Education, 10, 1648661. https://doi.org/10.3389/feduc.2025.1648661