A SYSTEMATIC REVIEW OF EDUCATORS’ ENGAGEMENT WITH AI IN PROBLEM-BASED LEARNING

This systematic review examines the role of Artificial Intelligence (AI) in supporting Problem-Based Learning (PBL) within the Human–Computer Interaction (HCI) framework in education, with explicit alignment to Sustainable Development Goal 4 (Quality Education). The review aims to: (i) examine how educators interact with AI-driven PBL tools, (ii) evaluate the pedagogical impact of AI-enhanced PBL on teaching and learning outcomes, and (iii) identify key research gaps related to AI, HCI, and PBL adoption in educational contexts. Guided by the PRISMA 2020 framework, 50 peer-reviewed journal articles published between 2015 and 2024 were systematically selected from major academic databases. The selected studies were analyzed using thematic analysis supported by NVivo 14 to ensure transparency. The findings reveal three dominant themes. First, AI-driven PBL environments enhance educator and student collaboration by facilitating real-time feedback, intelligent scaffolding, and data-informed instructional decisions. Second, AI tools support adaptive and personalized learning experiences that improve learner engagement and problem-solving skills. However, these benefits are accompanied by persistent ethical concerns related to data privacy, algorithmic bias, and educator autonomy. Third, AI-based assessment practices within PBL contexts remain underdeveloped, indicating a lack of robust empirical evidence on automated feedback, formative assessment, and learning analytics integration. Based on the synthesis, this review proposes the AI-Enhanced Academic Interaction Model (AEAIM), integrating the Technology Acceptance Model, Constructivist Learning Theory, and Krashen’s Input Hypothesis to explain educators’ adoption and use of AI-enhanced PBL. The review contributes theoretically by advancing an integrative adoption framework and practically by highlighting design, ethical, and policy implications for sustainable AI integration in education.