A Critical Pedagogy-Based Andragogical Self-Learning Framework for AI/IoT-Enabled Hybrid Adult Education: A Systematic Review and Conceptual Model Development - Amarulloh; Herpratiwi; Rangga Firdaus; Viyanti (Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Negeri Jakarta, 2026)
This study aims to develop a validated conceptual model—the Andragogical Self-Learning Framework for AI/IoT-Enabled Hybrid Education (ASFAIHE) that integrates Knowles’ andragogy and heutagogy with AI/IoT-enabled hybrid learning environments, mediated by critical pedagogy principles of learner agency and digital equity. Method: A systematic mixed-methods review was conducted following PRISMA 2020 guidelines. Electronic searches were performed across five major databases (PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar) for peer-reviewed studies published between 2005 and 2025. Inclusion criteria required studies to address adult learning theories (specifically andragogy or related models), AI and/or IoT in educational contexts, critical pedagogy principles, and hybrid or digital learning environments. Of 3,456 initially identified records, 85 studies met all inclusion criteria after two-stage independent screening. Qualitative data were analyzed through thematic synthesis following Thomas and Harden’s approach using NVivo; quantitative findings were synthesized via random-effects meta-analysis using R’s ‘metafor’ package; bibliometric mapping was conducted using VOSviewer. Results: Thematic synthesis yielded four interrelated themes: (1) Learner Autonomy and Scaffolding, (2) Adaptive Feedback Loops, (3) Contextual Sensing via IoT, and (4) Data Privacy and Ethical Concerns. Meta-analysis revealed that AI-driven adaptive systems significantly enhance learner engagement (pooled g = 0.65; 95% CI [0.52, 0.78], p < 0.001) and self-efficacy (g = 0.58; 95% CI [0.45, 0.71], p < 0.001). These findings were integrated into the ASFAIHE model, which conceptualizes adult learner engagement as a function of AI personalization, IoT contextual feedback, and critical consciousness. Contribution: This study produces a theoretically grounded and empirically supported conceptual model that constitutes a novel design architecture for hybrid AI learning ecosystems in adult education. The model advances existing frameworks by systematically embedding critical pedagogy as an ethical and transformative mediator. Longitudinal and cross-cultural empirical validation is recommended to strengthen the model’s generalizability.