How AI is Changing Mental Health: A Guide for School Psychologists
Are we ready to trust AI with student mental health? AI is already being explored for tasks like identifying at-risk students, enhancing therapy, and streamlining clinician workloads. This blog breaks down how school psychologists can approach these tools with both curiosity and caution. On the go? Download the podcast and give a listen.
I've been digging into the article, "The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality." The piece explores a range of emerging technologies—including smartphone apps, VR, and digital phenotyping—but since this blog focuses on AI, we'll zero in on the article's content related to generative artificial intelligence (AI) and what it means for school psychology.
Generative AI in Mental Health
AI chatbots are evolving from rigid, rule-based tools to more flexible systems powered by large language models (LLMs). These models are better at handling complex interactions and can respond in ways that feel more natural and empathetic. This opens up possibilities for personalized mental health support in new ways.
Where LLMs Are Being Used
Prevention: LLMs can offer low-risk, personalized psychoeducation using vetted resources. Some early tools have engaged teens on mental health topics.
Risk Prediction: These models can help identify suicidal ideation and other risks, sometimes nearing clinical accuracy—though safety and bias concerns remain.
Diagnosis Assistance: They can aid in predicting conditions like depression and complement clinical decision-making.
Treatment Optimization: LLMs can support treatment planning using individual data to guide therapy and medication choices. Tools like ChatGPT have even been tested in ADHD and mood disorder care.
Crisis Support: AI might help in high-stakes scenarios, but the risks are higher, and safeguards are essential.
Ongoing Therapy: Some are using LLMs for ongoing support, though the boundary between chatbot and therapist remains fuzzy.
Behind-the-Scenes Uses
LLMs aren't just for direct interaction. They're also being explored for:
Drafting clinical notes
Training staff
Supporting clinical decisions
Key Risks and Challenges
Transparency: It's often unclear how AI is trained or evaluated.
Bias: Models trained on biased data can reinforce stereotypes or inequities.
Ethics: Past examples show potential for harm—from unsafe advice to lack of informed consent.
Lack of Standards: Until clear guidelines exist, use may be limited to backend support.
Why This Matters for School Psychologists
Psychoeducation: AI tools could help students access accurate, safe mental health info—if used under staff guidance.
Workflow Support: Tools might draft documentation or analyze trends, giving you more time with students.
Ethical Scrutiny: Student-facing AI must be used cautiously, with an evidence-based lens.
Privacy and Safety First: Any tools must meet clear ethical and safety standards.
Keep the Human Connection: AI should support—not replace—the relationships at the core of our work.
Equity Matters: We must be mindful of how AI might harm marginalized students and advocate for inclusive tools.
Build Digital Literacy: Students and staff need to understand how AI works and how to use it responsibly.
Implementation Isn’t Easy: Adoption depends on training, policy clarity, and system fit.
Conclusion
As AI tools grow more common in education and mental health, school psychologists are in a unique position to lead with wisdom and care. Our job isn’t just to explore what’s possible—it’s to safeguard what matters most: equity, ethics, and the student-centered relationships that define our field. Let’s shape the future of digital mental health with intention.
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