Mitigating AI Bias in School Psychology: Toward Equitable and Ethical Implementation

The integration of Artificial Intelligence (AI) into school psychology is evolving rapidly, presenting both opportunities and challenges. AI has the potential to enhance educational and mental health services by facilitating data-driven decision-making, streamlining administrative tasks, and offering personalized interventions for students. However, biases inherent in AI systems—reflecting the prejudices of developers and historical data—pose significant risks of exacerbating systemic inequalities. This paper examines AI’s historical context, current applications in education, and specific uses within school psychology. It also discusses the socio-political factors contributing to algorithmic biases, data privacy concerns, and language/cultural inequities in AI systems. Recommendations are offered to mitigate AI biases, emphasizing the importance of diverse representation in AI development, comprehensive policy formation, transparency, and community involvement. Addressing these concerns is crucial for ensuring that AI contributes equitably to student success.

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Published
2025-09-15
DOI
https://doi.org/10.31234/osf.io/mh4rj_v2
Primary Topic
Resilience and Mental Health
Type
article
Field-Weighted Citation Impact
0.00
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article

Mitigating AI Bias in School Psychology: Toward Equitable and Ethical Implementation

Jeffrey Brown, Adam Lockwood
Resilience and Mental Health
article

Mitigating AI Bias in School Psychology: Toward Equitable and Ethical Implementation

Jeffrey Brown, Adam Lockwood
article en

Abstract

The integration of Artificial Intelligence (AI) into school psychology is evolving rapidly, presenting both opportunities and challenges. AI has the potential to enhance educational and mental health services by facilitating data-driven decision-making, streamlining administrative tasks, and offering personalized interventions for students. However, biases inherent in AI systems—reflecting the prejudices of developers and historical data—pose significant risks of exacerbating systemic inequalities. This paper examines AI’s historical context, current applications in education, and specific uses within school psychology. It also discusses the socio-political factors contributing to algorithmic biases, data privacy concerns, and language/cultural inequities in AI systems. Recommendations are offered to mitigate AI biases, emphasizing the importance of diverse representation in AI development, comprehensive policy formation, transparency, and community involvement. Addressing these concerns is crucial for ensuring that AI contributes equitably to student success.

Kent State University (US)
Openalex Percentile: Top 65%
Resilience and Mental Health
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