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Intelligent Tutoring Systems for Advanced Educational Applications

Author(s) Narciso Martí
Country Spain
Abstract Intelligent Tutoring Systems (ITS) represent an advanced category of educational technologies designed to provide adaptive, personalized, and interactive learning support through artificial intelligence and computational techniques. Unlike conventional digital learning systems that primarily deliver standardized instructional materials, intelligent tutoring systems can analyze learner responses, identify knowledge gaps, adjust instructional content, provide feedback, and recommend learning activities according to individual requirements. This study examines intelligent tutoring systems for advanced educational applications through a conceptual and literature-based research design. The study focuses on learner modeling, domain knowledge representation, pedagogical decision-making, natural language interaction, adaptive feedback, intelligent assessment, personalized learning pathways, and learning analytics. ITS can support learners by providing immediate feedback, individualized practice, guided problem-solving, and continuous learning assistance.
Teachers can also use tutoring-system data to identify areas requiring instructional attention and monitor learner progress. However, effective implementation is influenced by system accuracy, instructional quality, teacher readiness, accessibility, infrastructure, learner acceptance, privacy, cybersecurity, and explainability. Excessive dependence on automated tutoring may also reduce opportunities for human interaction if systems are not appropriately integrated with teacher-led instruction. The study concludes that intelligent tutoring systems can contribute to advanced educational applications when artificial intelligence is aligned with established pedagogical principles and human instructional expertise. Future research should focus on explainable tutoring systems, multimodal learner modeling, conversational tutoring, affect-aware systems, immersive tutoring environments, responsible AI, and longitudinal evaluation of learning outcomes.
Keywords Intelligent Tutoring Systems, Artificial Intelligence, Adaptive Learning, Personalized Education, Learner Modeling, Educational Technology, Intelligent Assessment, Automated Feedback, Digital Learning, AI in Education
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-02
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