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In response to the growing mental health challenges among college students, researchers from the University of Alabama at Birmingham (UAB) have developed a groundbreaking AI tool designed to assist counselors in identifying students at risk of anxiety and depression—before their conditions worsen.
Mental Health America estimates that nearly 60 million Americans suffered from mental illness in 2024, and studies from UAB reveal a sharp increase in diagnoses of post-traumatic stress disorder (PTSD) and acute stress disorder among college students since 2017. As mental health struggles continue to rise in the U.S., timely intervention has become critical.
Yusen Zhai, Ph.D., Director of the UAB Community Counseling Clinic, spearheaded the development of the AI tool, which leverages machine learning to analyze data that schools already collect, such as age, gender, race, ethnicity, academic year, and major. By identifying patterns within these variables, the tool can predict which students may be at a heightened risk for developing anxiety or depressive disorders.
“Current predictive models rely on data from students who have already sought help from health providers,” Dr. Zhai explained. “This often misses students who might not yet realize they need help or those who face barriers to accessing care. Our AI model identifies students who may require assistance, even among those who haven’t used health services.”
Most existing models have been developed using clinical samples, which often overlook the broader student population. To address this, Dr. Zhai and his team focused on creating a predictive tool that uses non-clinical data—like socioeconomic factors, sense of belonging, financial stress, and disability status—that research has linked to higher rates of anxiety and depression.
The AI models developed by Zhai and his team show strong predictive accuracy, making them valuable tools for counselors to identify at-risk students across the general student population, not just those who have sought help. The aim is to catch issues early, as untreated mental health problems can lead to more severe outcomes.
“Timely treatment is crucial,” said Zhai. “Our tool can help mental health professionals make better-informed decisions and provide the right interventions before conditions escalate.”
Although the AI models are designed to assist counselors, they are not intended to replace human expertise. Rather, they serve as an additional resource that enhances the work of mental health professionals while maintaining a compassionate, person-centered approach.
Looking ahead, Zhai and his team plan to refine their tool to detect a wider range of mental health issues, including substance use and suicide risk. They also hope to expand their research to support mental health interventions in K-12 schools and the broader population.
“Mental health disparities can have serious consequences. With technology rapidly advancing, we are committed to continuing our research to create tools that help close gaps in mental health care and improve outcomes,” Dr. Zhai concluded.
This innovative AI tool has the potential to revolutionize mental health care on college campuses, ensuring that students get the support they need before their conditions worsen.
For more information, refer to Zhai’s research article, Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students, published in the Journal of Counseling & Development (2024).
