Short abstract
Some schools have begun employing artificial intelligence (AI)–based tools to help identify students at risk for suicide and self-harm. The authors provide a preliminary examination of how these programs are implemented, how stakeholders perceive the effects of the programs, and their potential benefits and risks. The authors then offer recommendations for school leaders, policymakers, and technology developers to consider.
Keywords: Artificial Intelligence, Children, Mental Health and Illness, Students, Suicide
Abstract
In response to the widespread youth mental health crisis, some kindergarten-through-12th-grade (K–12) schools have begun employing artificial intelligence (AI)–based tools to help identify students at risk for suicide and self-harm. The adoption of AI and other types of educational technology to partially address student mental health needs has been a natural forward step for many schools during the transition to remote education. However, there is limited understanding about how such programs work, how they are implemented by schools, and how they may benefit or harm students and their families.
To assist policymakers, school districts, school leaders, and others in making decisions regarding the use of these tools, the authors address these knowledge gaps by providing a preliminary examination of how AI-based suicide risk monitoring programs are implemented in K–12 schools, how stakeholders perceive the effects that the programs are having on students, and the potential benefits and risks of such tools. Using this analysis, the authors also offer recommendations for school and district leaders; state, federal, and local policymakers; and technology developers to consider as they move forward in maximizing the intended benefits and mitigating the possible risks of AI-based suicide risk monitoring programs.
Suicide is the second leading cause of death among youth age ten to 19. Youth suicide is a rapidly growing phenomenon; rates increased by 70 percent between 2009 and 2019 (Agency for Healthcare Research and Quality, 2022; Centers for Disease Control and Prevention, 2021). Youth hospital visits for mental health reasons, including suicide, significantly increased during the first two years of the coronavirus disease 2019 (COVID-19) pandemic (Overhage et al., 2023).
The increase in youth suicide risk since 2009 coincides with a shortage of youth mental health professionals. One study found that mental health workforce shortages, which occur most often in rural areas and low-income communities, were significantly correlated with increased suicide rates in five- to 19-year-olds (Hoffmann et al., 2023).
Research suggests that evidence-based suicide prevention programming in schools has the potential to help fill gaps in youth mental health care (Singer, Erbacher, and Rosen, 2019). Schools are on the front line of addressing youth mental health and suicide concerns (Ayer and Colpe, 2022). However, many U.S. schools are facing other challenges, including ongoing pandemic recovery efforts, teacher burnout, and teacher shortages (Doan et al., 2023; Nguyen, Lam, and Bruno, 2022; Steiner et al., 2022).
In response to the widespread youth mental health crisis, some kindergarten through 12th grade (K–12) schools have begun employing artificial intelligence (AI)-based tools to help identify students at risk for suicide and self-harm. The adoption of AI and other types of educational technology (EdTech) to partially address student mental health needs has been a natural forward step for many schools during the transition to remote education during the COVID-19 pandemic. However, there is limited understanding about how such programs work, how they are implemented by schools, and how they may benefit or harm students and their families (Bason, 2021; Laird et al., 2022; Madhusudan, 2021; Office of Senator Elizabeth Warren, 2022; Patterson, 2021; Shinde, 2021).
To assist policymakers, school districts, school leaders, and others in making decisions regarding the use of these tools, this study addresses these knowledge gaps by providing a preliminary examination of how AI-based suicide risk monitoring programs are implemented in K–12 schools, how stakeholders perceive the effects that the programs are having on students, and the potential benefits and risks of such tools. Using this analysis, we also offer recommendations for school and district leaders; state, federal, and local policymakers; and technology developers to consider as they move forward in maximizing the intended benefits and mitigating the possible risks of AI-based suicide risk monitoring programs.
Key Findings
Our analysis produced the following key findings:
Interviews with school staff, EdTech company representatives, health care professionals, and advocacy group staff and members suggest that AI-based suicide risk monitoring tools can help identify K–12 students who are at risk for suicide and provide reassurance for school staff and caregivers.
Prior research shows that AI-based suicide risk prediction algorithms—and, by extension, student activity monitoring in schools—can compromise student privacy and perpetuate existing inequalities.
There is a need for data that show how accurately AI-based algorithms can detect a student's risk of suicide and whether the use of these tools improves student mental health.
K–12 schools and their broader communities are often not sufficiently resourced to respond to youth mental health challenges, even with the use of AI-based suicide risk monitoring.
Key community members—including pediatric providers, mental health counselors, and caregivers—play important roles in the implementation of these tools, but community members might be unaware of how the tools are used by K–12 schools to detect student suicide risk.
Notes
Funding for this research was provided by gifts from RAND supporters and income from operations and was undertaken by RAND Education and Labor.
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