A Systematic Review of Educators' Engagement with AI in Problem-Based Learning

Synthesis: This PRISMA 2020 systematic review (50 peer-reviewed articles, 2015–2024) examines how educators engage with AI-driven Problem-Based Learning (PBL) tools through a Human–Computer Interaction (HCI) lens, aligned with SDG 4 (Quality Education). Three dominant themes emerge: AI-enhanced PBL environments strengthen educator–student collaboration via real-time feedback, intelligent scaffolding, and data-informed decisions; AI supports adaptive and personalized learning that improves engagement and problem-solving; yet persistent ethical concerns (data privacy, algorithmic bias, educator autonomy) and underdeveloped AI-based Assessment practices limit adoption. The authors propose an AI-Enhanced Academic Interaction Model (AEAIM) integrating the Technology Acceptance Model, Constructivist Learning Theory, and Krashen's Input Hypothesis, and stress that AI must empower rather than replace educator Agency.

Key Findings

AI-driven PBL environments enhance educator and student collaboration through real-time feedback, intelligent scaffolding, and data-informed instructional decisions.
AI tools support adaptive and personalized learning experiences that improve learner engagement and problem-solving skills.
Persistent ethical concerns — data privacy, algorithmic bias, and erosion of educator autonomy — accompany AI integration and require human oversight.
AI-based assessment within PBL remains underdeveloped: only ~10% of reviewed studies examined AI-driven assessments, indicating weak empirical evidence on automated feedback, formative assessment, and learning analytics.
Only 45% of educators report confidence using AI-driven PBL platforms without additional training, highlighting a gap in AI usability support and literacy.
Opaque AI models raise educators' cognitive load and reduce Trust and adoption, underscoring the need for Explainable AI (XAI) and user-centered interfaces.

Study Design & Method

Systematic literature review following PRISMA 2020 (Identification → Screening → Eligibility → Inclusion), starting from 3,500 records and yielding 50 peer-reviewed articles (2015–2024).
Databases searched: Web of Science, Scopus, IEEE Xplore, SpringerLink, Taylor & Francis Online, Elsevier (ScienceDirect), Consensus, and Google Scholar, using Boolean (AND/OR) and truncation operators.
Thematic analysis of the 50 studies using NVivo 14 with two independent coders (inter-rater reliability κ = 0.82).
Results framed around an HCI lens (usability, explainability, cognitive load, decision autonomy) and three thematic codes: educators' perceptions/challenges in AI adoption, AI's effectiveness in supporting PBL, and AI's impact on learning outcomes and SDG 4 equity.

Implications for AI in Education

Educators need structured AI usability and literacy training to interpret AI-generated feedback, personalize learning pathways, and retain instructional control.
Institutional barriers — funding constraints, inconsistent AI policies, and resistance — must be addressed for sustainable AI-PBL adoption.
Policymakers should establish regulatory frameworks governing AI transparency, bias mitigation, and accountability in AI-generated assessments.
AI developers should pursue human-centered design so AI functions as an assistive tool supporting — not replacing — pedagogical expertise, while reducing cognitive load via explainable interfaces.
The AEAIM offers an integrative adoption model (TAM + constructivism + Krashen's Input Hypothesis) that could guide future research and tool design.

Connected Concepts

Connected Articles

Citation

Amdan, M.A., Asim, A., Rosman, N.F., Janius, N., Dasuki, F.H., & Johnny, N.K. (2026). A systematic review of educators' engagement with AI in problem-based learning. Quantum Journal of Social Sciences and Humanities, 7(1).