AI and Telepsychology Workforce Models to Address School Psychologist Shortages in High-Need Districts

Abstract

School psychologist shortages remain a critical challenge in high-need districts, where ratios often exceed 1:1,200, limiting timely mental health support for students. This desk-based study developed hybrid AI and telepsychology workforce models to address these shortages. A systematic literature review synthesized best practices for telepsychology delivery and artificial intelligence tools in school mental health, identifying effective triage, consultation, and service expansion strategies. Agent-based modeling and Monte Carlo simulations, parameterised from NASP and NCES data, projected a 52.6% improvement in caseload ratios, reducing the average from 1:1,211 to 1:574 while enhancing equity and maintaining strong accuracy (AUC = 0.96) and cost-effectiveness. The models integrate AI triage with telepsychology to augment limited personnel and improve access in rural districts. Findings support scalable, technology-driven frameworks aligned with the NASP Practice Model. Policy recommendations emphasise licensure portability, infrastructure investment, and phased implementation for sustainable adoption. This interdisciplinary approach offers practical, replicable solutions for mitigating workforce shortages and advancing equitable mental health services in under-resourced schools.