Intelligent Chatbot for Open Learning Resource Navigation and Support: A Review and Meta-Analysis
Keywords:
Intelligent chatbots, Open learning resources, Educational technology, Meta-analysis, Learning supportAbstract
This meta-analysis examines intelligent chatbots' effectiveness in open learning resource navigation and educational support. We systematically reviewed 28 studies from 2018-2024 using PRISMA guidelines. Our methodology employed comprehensive database searches across PubMed, IEEE Xplore, and ACM Digital Library, with random-effects meta-analysis calculating effect sizes. We hypothesized that intelligent chatbots would significantly enhance learning outcomes, resource accessibility, and student engagement in open learning environments. Results demonstrate moderate positive effects (g = 0.527, p < 0.001) on learning performance, with superior effectiveness in STEM fields (g = 0.673) and text-based interactions (g = 0.548). Chatbots showed particular strength in course navigation efficiency (g = 0.612) and sustained implementation periods of 1-6 months. Quality assessment revealed 39% low-risk studies with minimal publication bias. Discussion emphasizes chatbots' potential to democratize educational access while addressing ethical concerns and technological limitations. Challenges include data privacy, algorithmic bias, and over-reliance on artificial intelligence.
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