Explainable Artificial Intelligence In Online Learning: A Review Of Current Applications And Future Research Direction
Keywords:
Explainable Artificial Intelligence, Online Learning, Learning Analytics, Human-Centered AI, Systematic Literature ReviewAbstract
Artificial Intelligence (AI) has become increasingly embedded in online learning environments, supporting personalized learning, learning analytics, automated feedback, and early identification of at-risk students. Many AI models are like a "black box," which means it's hard to see how they make decisions. This can make it difficult to understand them and trust them. Because of this, there is an increasing interest in Explainable Artificial Intelligence (XAI). This study reviews existing research about explainable artificial intelligence (XAI) in online learning using the PRISMA 2020 method. A total of 2,136 records were retrieved from six major academic databases, of which 114 studies published between 2018 and 2026 met the inclusion criteria and were analyzed thematically across three domains: current applications, persistent challenges, and future research directions. Findings show that post-hoc explanation techniques, particularly SHAP and LIME, alongside transformer-based architectures and interactive explainable dashboards, are the dominant approaches for supporting student performance prediction, engagement analysis, and adaptive learning. Despite this progress, the review identifies recurring challenges: pedagogical usability, the lack of human-centered evaluation standards, limited model generalizability across educational contexts, and unresolved ethical concerns regarding fairness and accountability. The review argues that future XAI research must move beyond technical explainability toward human-centered design that integrates pedagogical theory, personalized learning analytics, and standardized evaluation frameworks. This synthesis provides a timely, evidence-based foundation for researchers and practitioners seeking to design more transparent, trustworthy, and pedagogically meaningful AI systems for online education.
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