Harmonizing technology and pedagogy: A systematic review of Aidriven Competency learning models in education
TL;DR
Findings reveal that AIdriven interventions particularly those employing humancentered design, multimodal analytics, and outcomebased knowledge graph mapping—significantly improved competency gains, engagement, and instructional alignment.
Abstract
The rapid integration of artificial intelligence (AI) into education has redefined the nature of competency development, yet empirical evidence remains fragmented and inconsistent across contexts. While AI interventions are often promoted for enhancing personalization, assessment, and learner engagement, uncertainty persists regarding their pedagogical authenticity, equity implications, and measurable impact on learning outcomes. This systematic literature review aimed to evaluate the effect of AIenabled instructional interventions (Intervention) on learners’ competency outcomes (Outcome) among students and educators in formal and nonformal education (Population) compared with conventional or nonAIassisted pedagogies (Comparison).Following the PRISMA 2020 reporting standard and CASP appraisal procedures, five databases (Scopus, Web of Science, ERIC, ACM Digital Library, and IEEE Xplore) were systematically searched for studies published between 2019 and 2025. Inclusion criteria encompassed empirical research where AI formed a core pedagogical, analytical, or delivery component, with sufficient data for quantitative or qualitative synthesis. Twenty studies met initial screening criteria, and ten satisfied full eligibility for inclusion in the final synthesis. Data were analyzed through a mixedmethod approach combining narrative synthesis and qualityweighted comparison. Findings reveal that AIdriven interventions particularly those employing humancentered design, multimodal analytics, and outcomebased knowledge graph mapping—significantly improved competency gains, engagement, and instructional alignment. However, disparities in institutional readiness, data integration, and ethical governance persist, constraining scalability. The review concludes that sustainable AI competency development requires harmonizing technological innovation with pedagogical integrity and equity frameworks, emphasizing the human role in guiding AImediated learning ecosystems.