The Future of AI in Educational Assessment: Insights for School Psychologists

I've been diving into AI-Assisted Assessment in Education: Transforming Assessment and Measuring Learning by Goran Trajkovski and Heather Hayes. The book is packed with forward-thinking insights about how AI may transform educational assessment—and it’s especially relevant for school psychologists and educators committed to equitable, student-centered support.

This post highlights key trends and takeaways. I’ll be covering additional chapters in the future, so consider bookmarking this or subscribing for updates.

Why This Matters for School Psychologists

Assessment is the main role for the majority of school psychologists. As AI becomes more integrated into how we measure learning, we face both major opportunities and serious ethical and practical challenges. For those focused on student development, well-being, and fair access, these shifts are not just technical—they’re deeply personal and professional.

Emerging Technologies Reshaping Assessment

Several technologies are poised to redefine what assessment looks like in schools:

Multimodal Data Integration

AI systems may soon evaluate more than just written or numerical responses. For example, a virtual simulation could analyze a student’s tone of voice, facial expressions, or gestures to provide richer, real-time feedback on communication or social skills. These approaches aim to reflect the complexity of real-world learning.

Augmented and Virtual Reality (AR/VR)

AR and VR can immerse students in realistic, context-rich scenarios. Assessments embedded in these experiences could measure problem-solving, collaboration, or adaptability in ways traditional formats can’t. These tools may shift how we understand skills that matter beyond the classroom.

Advanced Natural Language Processing (NLP)

NLP is making it easier to evaluate student writing, oral language, and even higher-order thinking. Emerging systems will better handle multiple languages and cultural nuance, which could improve fairness for multilingual learners.

Dynamic Conversational Assessments

Future AI tools may hold adaptive conversations with students—asking follow-up questions based on their answers, probing deeper into reasoning and understanding. This has the potential to replicate the intellectual back-and-forth of a great teacher-student interaction.

Predictive Analytics and Early Warning Systems

AI systems can use real-time and historical data to flag students at risk of disengagement or academic difficulty. These tools are already being piloted to support earlier, more personalized interventions. A student flagged for low participation, for example, might be referred for counseling or receive tailored instructional support.

The goal is not to label, but to respond—proactively and with care.

Personalized Learning Recommendations

AI’s ability to recommend content based on individual learning patterns holds promise for differentiated instruction. In the future, platforms may dynamically adjust assignments or suggest alternative materials that better match a student’s skill level and learning profile.

School psychologists can play an important role in helping educators understand and apply these recommendations meaningfully.

The Future Role of School Psychologists in AI-Assisted Assessment

While The Future of AI in Educational Assessment doesn’t name school psychologists directly, it outlines several functions where our expertise is essential—and increasingly so as AI becomes embedded in assessment systems.

Here are the key areas where school psychologists will play a growing role:

Identifying and Supporting At-Risk Learners

AI-powered Early Warning Systems (EWS) use predictive analytics to detect risk factors before they escalate. These systems suggest interventions like referrals to counseling or targeted instructional support—interventions that school psychologists often help design and implement. Our ability to evaluate root causes and develop holistic support plans will be critical in ensuring these systems are used ethically and effectively.

Interpreting Holistic, Multimodal Assessments

AI is shifting assessment toward complex constructs like creativity, social-emotional skills, and communication strategies, often using nontraditional data (e.g., gestures, tone of voice). School psychologists are uniquely qualified to interpret these data within the context of developmental, cultural, and environmental factors—ensuring that assessments reflect real student needs, not just algorithmic output.

Human Oversight and Contextualization

The book emphasizes that AI insights must be interpreted by humans. School psychologists understand how trauma, executive functioning, home environment, and identity can affect performance. Our role will increasingly involve translating AI data into actionable insights that account for the whole child.

Promoting Equity and Guarding Against Bias

As AI systems become more influential in decision-making, concerns about algorithmic bias and equity grow. School psychologists are already trained to spot disparities and advocate for fair assessment practices. Our participation is vital in reviewing AI-assisted assessments for cultural responsiveness, accessibility, and unintended consequences.

Collaborating with Interdisciplinary Teams

AI implementation requires collaboration among educators, technologists, administrators, and families. School psychologists bring a systems-level perspective, helping teams anticipate unintended outcomes, facilitate communication, and promote ethical use of assessment tools across contexts.

Expanding Professional Development

As AI tools proliferate, educators will need guidance on interpreting data and integrating insights into practice. School psychologists can serve as professional learning leaders, helping colleagues make sense of new tools while staying grounded in evidence-based practices and student-centered decision-making.

Looking Ahead in This Series

My hope is that this post is just the beginning. In future entries, I plan to explore chapters from AI-Assisted Assessment in Education that dive deeper into design frameworks, emerging formats, diverse learners, and real-world implementation. Topics may include:

• Foundations of AI in Educational Assessment

• The AI-Assisted Assessment Creation Framework

• Innovative Question Types and Formats

• AI in Assessment Analysis and Improvement

• Implementing AI-Assisted Assessment in Educational Institutions

• Formative Assessment and Feedback

• AI in High-Stakes and Standardized Testing

Want to stay informed? Subscribe to the blog or follow me on LinkedIn or Facebook for updates, upcoming chapter breakdowns, and practical applications of AI in education and school psychology.

And if you’re a school psychologist wondering, “Where do I fit into all this?” — the answer is: everywhere data meets action, insight meets empathy, and systems need leadership. The thing is, I believe that your role is about to grow.

Have questions or insights of your own? Reach out—I’d love to hear how you’re navigating these emerging trends in your school or district.