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AI Summary of Peer-Reviewed Research

This page presents an AI-generated summary of a published research paper. The original authors did not write or review this article. [See full disclosure ↓]

Publishing process signals: STRONG — reflects the venue and review process. — venue and review process.

Teaching methods shape AI’s engagement effects in higher education

Multiple university students seated at desks in a modern classroom, focused on desktop computer monitors, with one student in the foreground wearing a dark shirt and a disposable coffee cup visible on the desk.
Photo by Flipsnack on Unsplash · Unsplash License
Research area:Computer ScienceArtificial IntelligenceStudent engagement

What the study found

AI tools in higher education were reported to support student engagement most effectively when they were combined with interactive teaching methods. The review also introduces the PMAISE model, which links AI technologies, pedagogical strategies, and engagement dimensions.

Why the authors say this matters

The authors conclude that thoughtful pedagogical mediation is crucial for using AI in a context-sensitive, evidence-based, and pedagogically meaningful way in higher education. The study suggests this approach may help maximize AI’s educational benefits.

What the researchers tested

This systematic review examined 73 peer-reviewed articles published between 2015 and early 2025. The studies were retrieved from Scopus and Web of Science, screened using predefined inclusion criteria, and coded with a structured framework covering AI types, engagement outcomes, and instructional strategies.

What worked and what didn't

AI tools such as chatbots, adaptive systems, and predictive analytics were described as working best when paired with flipped classrooms, project-based learning, and scaffolded feedback loops. The review says teaching methods could amplify or inhibit AI’s effects on affective, behavioral, and cognitive engagement, and it also discusses concerns about ethics, data privacy, and structural barriers to equitable AI adoption.

What to keep in mind

This summary is based only on the abstract, so detailed study-by-study limitations are not available. The review covers articles up to early 2025 and focuses on higher education.

Key points

  • The review analyzed 73 peer-reviewed articles from 2015 to early 2025.
  • AI tools were reported to enhance student engagement most effectively when paired with interactive pedagogy.
  • Examples named in the abstract include chatbots, adaptive systems, and predictive analytics.
  • The review introduces the PMAISE model for aligning AI, teaching methods, and engagement dimensions.
  • The abstract notes concerns about ethics, data privacy, and structural barriers to equitable AI adoption.

Disclosure

Research title:
Teaching methods shape AI’s engagement effects in higher education
Publication date:
2026-02-02
DOI:
10.3389/feduc.2025.1648661
OpenAlex record:
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Image credit:
Photo by Flipsnack on Unsplash · Unsplash License
AI provenance: AI provenance information is not available for this post.

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