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AI in Education: Early Detection of Mental Health Challenges for Inclusive and Supportive Learning

AI in Education: Early Detection of Mental Health Challenges for Inclusive and Supportive Learning
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Author(s): Efthymia Efthymiou (Zayed University, UAE), Dimitra V. Katsarou (University of the Aegean, Greece), Maria Sofologi (University of Ioannina, Greece), Kalliopi Megari (City College, University of York, Europe Campus, Greece & University of Western Macedonia, Greece), Soultana Papadopoulou (University of Ioannina, Greece), Evangelos Mantsos (University of Thessaly, Greece), Alexandros Argyriadis (Hellenic Mediterranean University, Greece)and Agathi Argyriadi (UAE University, UAE)
Copyright: 2026
Pages: 44
Source title: AI in Learning, Educational Leadership, and Special Education: Innovations and Ethical Dilemmas
Source Author(s)/Editor(s): Maria Efstratopoulou (United Arab Emirates University, UAE), Agathi Argyriadi (Frederick University, Cyprus)and Alexandros Argyriadis (Hellenic Mediterranean University, Greece)
DOI: 10.4018/979-8-3373-0573-8.ch009

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Abstract

As schools confront an escalating mental health crisis among students, artificial intelligence (AI) emerges as both a solution and a complex ethical challenge. The ability of AI to analyze vast amounts of data through natural language processing, sentiment analysis, and behavioral pattern recognition provides a proactive approach to identifying early signs of emotional distress. By monitoring shifts in academic engagement, social interactions, and behavioral trends, AI moves beyond traditional, reactive mental health interventions, enabling earlier and more targeted support. However, while AI-driven detection is compelling, its implications raise urgent questions about its role in education and student well-being. Beyond technical feasibility, the long-term psychological, academic, and social impact of AI-driven mental health detection remains unexplored. While AI nurtures more inclusive and supportive learning environments, it becomes a tool of surveillance, reinforcing biases, or enabling dependency on automated decision-making.

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