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

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.