Artificial Intelligence Models for Automated Educational Support

Carlos Delgado Kloos

Artificial Intelligence Models for Automated Educational Support

Author(s)	Dr. Carlos Delgado Kloos
Country	Spain
Abstract	Artificial Intelligence (AI) has become an increasingly important component of contemporary educational technology, offering computational models capable of supporting teaching, learning, assessment, communication, and academic administration. Automated educational support refers to the use of intelligent systems to provide learners and educators with timely information, personalized recommendations, automated feedback, instructional assistance, and administrative services. This study examines artificial intelligence models for automated educational support through a conceptual and literature-based research design. The analysis focuses on intelligent tutoring systems, natural language processing, machine learning, recommendation systems, automated feedback, learning analytics, conversational agents, predictive models, and generative AI applications. AI-based educational support can assist learners by responding to questions, identifying learning difficulties, recommending educational resources, providing formative feedback, and facilitating personalized learning pathways. For educators, AI can reduce repetitive tasks, support performance monitoring, and provide analytical information for instructional decision-making. However, effective implementation requires careful consideration of accuracy, transparency, algorithmic bias, privacy, cybersecurity, accessibility, teacher oversight, and technological infrastructure. Automated support should complement human teaching rather than replace the professional and social functions of educators. The study proposes an integrated framework connecting learner data, AI processing, automated support functions, feedback, teacher validation, and continuous learning improvement. The analysis indicates that AI models can strengthen educational support when they are pedagogically aligned, human-centered, transparent, and responsibly governed. Future research should investigate adaptive AI tutors, explainable educational AI, multimodal learner support, privacy-preserving analytics, human-AI collaboration, and long-term effects of automated educational assistance.
Keywords	Artificial Intelligence, Automated Educational Support, Intelligent Tutoring Systems, Educational Technology, Machine Learning, Personalized Learning, Learning Analytics, AI Feedback, Digital Education, Intelligent Learning.
Field	Engineering
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-05-01

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American International Journal of Advanced Research and Development

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Artificial Intelligence Models for Automated Educational Support

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