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Artificial Intelligence-Based Learning Analytics for Educational Improvement

Author(s) Patrice Moundounga Mouity
Country Gabon
Abstract Artificial intelligence-based learning analytics has emerged as an important approach for transforming educational data into actionable insights that can support teaching, learning, assessment, and institutional decision-making. Conventional educational analysis often depends on periodic assessments and manually interpreted information, whereas artificial intelligence can process large and diverse datasets to identify patterns in learner behavior, engagement, performance, and progression. This study examines AI-based learning analytics for educational improvement through a conceptual and literature-based research design.
The analysis focuses on predictive analytics, learner profiling, automated feedback, recommendation systems, performance prediction, early identification of learning difficulties, engagement analysis, and intelligent educational decision support. AI-based analytics can help educators identify patterns that may require intervention and can support more personalized learning experiences. Institutions can also use analytics to evaluate educational processes, monitor learner progression, and support evidence-informed planning. However, the implementation of AI-based learning analytics raises concerns regarding data quality, privacy, algorithmic bias, explainability, cybersecurity, teacher readiness, and responsible data governance.
Automated predictions should not be treated as definitive judgments because educational behavior is influenced by contextual factors that may not be fully captured by digital data. The study concludes that AI-based learning analytics can contribute to educational improvement when computational analysis is integrated with teacher expertise, transparent governance, and pedagogically meaningful interventions. Future research should investigate explainable AI, privacy-preserving analytics, multimodal learner modeling, responsible predictive systems, and longitudinal evaluation of educational outcomes.
Keywords Artificial Intelligence, Learning Analytics, Educational Improvement, Predictive Analytics, Learner Modeling, Educational Data Mining, Personalized Learning, AI in Education, Digital Education, Intelligent Decision Support.
Field Engineering
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-27
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