Artificial Intelligence (AI) is becoming an increasingly important tool in education and school psychology. It has the potential to enhance how we serve students by making data-driven decisions, streamlining paperwork, and offering personalized support. But it’s also crucial to recognize that AI isn’t just a neutral tool—it reflects the biases and limitations of its creators. In this blog post, we'll explore how AI impacts school psychology, the risks of bias and inequity, and how we can move toward ethical, equitable use. This post is based on a preprint of a paper that my colleague Jeffrey Brown and I recently wrote, focusing on mitigating AI bias in school psychology.

AI in School Psychology: The Promise and The Pitfalls

AI offers promising applications in school psychology, such as personalized learning, report writing, and even assisting with psychological assessments. Imagine an AI tool that can quickly generate tailored recommendations for an intervention or provide instant administrative support, freeing school psychologists to spend more time directly with students. These tools can make our work more efficient and responsive.

However, with great power comes great responsibility—and significant risk. AI systems are only as good as the data used to train them and the people who design them. Historically, these technologies have been developed by teams that often lack diversity. This lack of representation has led to AI tools that, knowingly or unknowingly, perpetuate systemic biases—issues that we as school psychologists are acutely aware of and work against every day.

How Bias in AI Can Affect Our Students

AI bias can take many forms, and it can negatively affect the students who are already most vulnerable. When AI models are trained on biased historical data, they can end up making unfair decisions. For instance, algorithms might disproportionately flag students of color as being at risk for disciplinary action simply because they’ve been trained on data reflecting biased school discipline practices. Such issues can reinforce the very inequities we aim to dismantle.

Moreover, AI tools can fail to understand students from diverse backgrounds. For example, AI systems that aren’t designed to recognize non-standard forms of English might misinterpret communication from students who speak in different dialects. This can lead to incorrect evaluations and reinforce barriers for students who need more support, not less.

Practical Steps Toward Ethical AI Use in Schools

It’s not all bad news—there are ways to mitigate these biases and use AI ethically in our schools. Here are some key steps we can take:

• Develop Comprehensive Policies: Schools and developers must collaborate to create policies that address bias and transparency. Diverse representation in AI development is key, as it helps build tools that better reflect the communities they serve.

• Involve Diverse Stakeholders: Engaging educators, parents, students, and community members in the development and implementation of AI tools ensures that these technologies meet real-world needs and prevent blind spots that could lead to inequitable outcomes.

• Transparency and Accountability: Companies developing AI tools for schools should make it clear how their tools work—from the data they use to the safeguards they have in place. Without transparency, it's impossible to know if these systems are fair or effective.

• Protect Student Privacy: AI needs data, but that doesn’t mean privacy should be compromised. Schools should adopt robust data protection measures and minimize the collection of sensitive information.

• Push for Investment in Equitable AI: Advocating for funding to support equitable AI development can help ensure these technologies benefit all students. Encouraging diversity in tech fields and fostering community-driven approaches to AI design are critical steps forward.

Key Takeaways for School Psychologists

• AI has the potential to revolutionize school psychology by providing data-driven insights and automating routine tasks, but it also carries the risk of reinforcing systemic inequities.

• AI bias can affect students in many ways, from misidentifying students of color for disciplinary action to misunderstanding culturally diverse language use.

• To ensure equitable outcomes, it’s crucial that we develop policies, involve diverse voices, maintain transparency, protect privacy, and push for investment in equitable AI.

For more details, you can check out our full preprint linked here or listen to our podcast above. Let's work together to ensure AI serves all our students, equitably and ethically.