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 Learner 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.

What this means for practice

• Instructors. Keep AI in an assistive role over problem-based learning: the review's model has AI supporting educator–student collaboration through real-time Feedback, intelligent Scaffolding, and data-informed decisions, with human oversight retained so pedagogical control stays with the educator.
• Instructors. Ask for usability and AI literacy training before the platform arrives, and read low confidence as a systems gap rather than a personal one: the review reports that only 45% of educators feel confident using AI-driven PBL platforms without additional training, and that opaque models raise cognitive load while depressing Trust and adoption.
• Instructors. Press for explainable AI and transparent rules when your institution adopts a PBL platform, because data privacy, algorithmic bias, and the erosion of educator autonomy recur across the 50 reviewed studies.
• Instructors. Treat AI-driven assessment as unproven rather than settled: only about 10% of the reviewed studies examined AI-driven assessments, so automated feedback, formative assessment, and learning analytics in PBL have thin empirical support behind them.
• Instructors. Use the review's integrative adoption model (TAM plus constructivism and Krashen's Input Hypothesis) as a lens when evaluating a PBL tool, and raise the structural blockers — funding constraints, inconsistent AI policies, and staff resistance — with institutional leadership, since the review identifies these as what stalls sustainable adoption.

Limitations

• The synthesis rests on 50 articles distilled from 3,500 records through a PRISMA 2020 flow (2,500 screened, 900 full texts, 250 eligible, 50 included) and coded thematically by two independent coders (κ = 0.82); it is a narrative synthesis with no meta-analytic pooling, so no pooled effect sizes are reported.
• Only about 10% of the included studies examined AI-driven assessments, while AI in Education (30%) and Digital Learning Environments (20%) dominated the corpus, so the review's conclusions about automated feedback, formative assessment, and learning analytics rest on a small slice of the evidence base.
• The widely cited 45% educator-confidence figure comes from a single external study cited within the review rather than from the review's own coding of its 50 articles, so that claim is second-hand evidence.
• Studies focused solely on student engagement without educator perspectives were excluded, as were non-peer-reviewed sources, so the review reports nothing on learner-side outcomes and omits gray literature; full texts are said to have been assessed for "methodological rigor," but no validated quality-appraisal instrument is reported.

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).