How Artificial Intelligence Could Strengthen School Consultation—Without Replacing the Human Consultant
Shengtian Wu, Cara Dillon, and I recently published a new article examining how artificial intelligence could support school consultation across the full problem-solving process.
The article, “Artificial Intelligence as an Augmented Partner in School Consultation: Applications Across the Problem-Solving Process,” appears in the Journal of Educational and Psychological Consultation.
Our central argument is straightforward: AI should not replace the school psychologist, consultant, teacher, or other professional involved in consultation. Instead, it may serve as an augmented partner—a tool that helps professionals organize information, reflect on their practice, prepare materials, analyze data, and provide more sustained support to consultees.
Key Takeaways
AI may help school psychologists spend less time on administrative tasks and more time on collaborative, relationship-centered consultation.
Across problem identification, analysis, implementation, and evaluation, AI can support organization, skill development, data visualization, and follow-up assistance.
AI-generated information should be treated as a hypothesis for professional review rather than as an independent recommendation or decision.
Human consultants remain responsible for accuracy, privacy, cultural responsiveness, ethical judgment, and all final decisions.
Why School Consultation Is an Important Area for AI
Consultation is one of the most valuable functions performed by school psychologists. Through consultation, school psychologists work collaboratively with teachers, families, and other professionals to address students’ academic, behavioral, and social-emotional needs.
However, consultation is also frequently constrained by time.
School psychologists often spend substantial portions of their workweeks completing evaluations, writing reports, managing special education requirements, and handling other administrative responsibilities. These demands can reduce the time available for preventive, collaborative, and indirect services.
AI may help address part of this problem.
By reducing the time required for tasks such as summarizing information, preparing documents, creating data-collection tools, graphing progress-monitoring data, and drafting follow-up materials, AI could free school psychologists to spend more time on the relational and interpretive aspects of consultation.
That distinction is critical. The goal is not to automate consultation. The goal is to automate or streamline selected tasks surrounding consultation so that professionals can focus more fully on the people involved.
The Augmented Triad
Traditional school consultation is often described as a triadic relationship involving:
A consultant, such as a school psychologist
A consultee, such as a teacher
A client, typically a student
In our article, we propose thinking about AI as an additional support within this system.
AI does not become an independent decision-maker or an equal professional member of the team. Rather, it functions as a cognitive, organizational, and procedural aid. It can support the work of the consultant and consultee while responsibility remains with the human professionals.
This framework preserves an essential principle: the consultant remains accountable for professional judgment, ethical decision-making, interpretation, and the quality of the consultation relationship.
AI Across the Four Phases of Problem-Solving Consultation
We organized the article around four commonly recognized phases of problem-solving consultation:
Problem identification
Problem analysis
Intervention implementation
Intervention evaluation
AI may provide different forms of assistance during each phase.
1. Problem Identification
During problem identification, the consultant and consultee clarify the concern, define it in observable terms, prioritize needs, and determine what additional information should be collected.
AI could help the consultant prepare customized meeting templates, structure interview questions, generate note-taking forms, or create data-collection tools tailored to a specific concern.
For example, a consultant might use AI to create:
A direct-observation form
A Goal Attainment Scale
A Check-In/Check-Out card
A structured consultation note template
AI may also support consultation training. Students and practicing professionals could rehearse consultation skills, practice asking clarifying questions, or receive feedback on skills such as paraphrasing, summarizing, and reflective listening.
These applications may be particularly useful when access to live supervision or consultation coaching is limited.
2. Problem Analysis
During problem analysis, the consultant and consultee review the data, establish a goal, select an intervention, anticipate barriers, and develop an implementation plan.
AI could assist by turning a complex plan into a clearer implementation guide that answers practical questions such as:
Who will implement the intervention?
What exactly will they do?
When and where will it occur?
How often will it occur?
How will implementation fidelity be measured?
What barriers are likely to emerge?
AI may also help consultants produce brief instructional materials, examples, practice activities, visual supports, or plain-language explanations of an intervention.
For a teacher who understands an intervention conceptually but is uncertain about how to use it in the classroom, AI-generated scenarios or rehearsal materials may provide an additional layer of support.
AI may also create graphs or visual summaries of baseline data, allowing the consultant and consultee to spend less time preparing the data and more time interpreting it together.
3. Intervention Implementation
Once an intervention begins, consultees often need support between formal meetings.
Questions arise. Procedures are forgotten. Unexpected barriers emerge. A strategy that seemed clear during planning may feel much less clear in the middle of a busy classroom day.
A carefully designed AI assistant could provide on-demand clarification, examples, reminders, practice prompts, or reflective questions between consultation sessions.
For instance, an AI tool might help a teacher:
Review the steps of an intervention
Practice behavior-specific praise
Think through a difficult implementation scenario
Identify possible barriers
Reflect on which parts of the intervention are going well
Prepare questions for the next meeting with the consultant
This could extend the consultant’s instructional support beyond scheduled meetings.
However, this is also an area where caution is especially important. AI systems can generate inaccurate or fabricated information. Even when a consultant has reviewed one response, the system may produce a different response when the prompt or context changes.
Therefore, AI should not become an unsupervised source of intervention guidance. Human oversight remains essential.
4. Intervention Evaluation
During evaluation, the consultant and consultee determine whether the intervention was implemented as intended, whether the student made progress, and what should happen next.
AI could help organize and summarize multiple forms of information, including:
Academic performance data
Behavioral observations
Intervention-fidelity data
Teacher reflections
Student response patterns
Baseline and intervention comparisons
It may also help generate graphs, summarize trends, identify areas where more data are needed, and prepare questions for a structured debriefing conversation.
The value of AI here is not that it makes the final decision. Rather, it may make the available information easier to review and discuss.
The consultant must still determine whether the data are accurate, whether the intervention was implemented with fidelity, whether observed changes are meaningful, and whether contextual or cultural factors affect interpretation.
The Human Consultant Remains Central
Throughout the article, we emphasize that AI cannot replace the relational, contextual, and ethical work of consultation.
Effective consultation depends on trust. It requires empathy, responsiveness, cultural humility, professional judgment, and an understanding of the school and classroom context.
AI cannot fully understand the dynamics between a teacher and student. It cannot independently determine whether a recommendation is realistic in a particular classroom. It does not bear professional or legal responsibility for the consequences of a decision.
The school psychologist or other consultant remains responsible for:
Verifying the accuracy of AI-generated material
Evaluating recommendations
Protecting confidentiality
Interpreting data
Considering culture and context
Ensuring that interventions are appropriate
Making and communicating professional decisions
AI may support this work, but it does not assume accountability for it.
Privacy, Consent, and Data Protection
Many of the most promising AI applications involve sensitive information.
Consultation records may include student educational records, behavioral data, teacher information, meeting transcripts, intervention plans, and progress-monitoring results. These data may be protected by FERPA, professional ethics codes, district policies, and other privacy requirements.
Professionals should not place identifiable student or consultee information into a public consumer AI system merely because the system is easy to access.
Before using AI with protected information, schools and practitioners must examine:
Whether the platform is institutionally approved
Whether an appropriate data-sharing agreement is in place
How information is stored and retained
Whether submitted information is used for model training
Who can access the data
Whether users have provided appropriate informed consent
Whether recording or transcription is permissible
A Business Associate Agreement or comparable agreement may provide an important legal safeguard in some settings, but such an agreement does not eliminate every privacy, security, or ethical concern. Never enter student information into a AI tool that you are not 100% certain will protect student data even if the student data that you provide is redacted/anonymized.
AI Outputs Should Be Treated as Hypotheses
One practical recommendation from the article is to treat AI-generated conclusions as hypotheses rather than directives.
An AI-generated summary, interpretation, intervention suggestion, or pattern should serve as a starting point for professional review.
The consultant should ask:
Is this accurate?
Is it supported by the available data?
Does it fit the student and classroom context?
Is it culturally responsive?
Is it feasible?
What information may be missing?
Could bias have shaped the output?
This stance preserves the value of AI while reducing the risk of uncritical reliance.
A Tool for Extending Human Capacity
The most productive way to think about AI in consultation is not as a replacement for expertise, but as a tool for extending human capacity.
AI may help consultants prepare more efficiently, organize information more clearly, provide more consistent follow-up support, and make data easier to understand.
But the central mechanism of effective consultation remains human.
It is the consultant who builds trust, recognizes nuance, understands context, exercises ethical judgment, and helps another person translate a plan into meaningful action.
The future of consultation will likely involve closer collaboration between human professionals and AI systems. The challenge is to develop that collaboration in a way that strengthens professional practice without diminishing accountability, relationships, or care.
Final Takeaways
AI is most useful in consultation when it extends human capacity without displacing professional judgment or relationships.
Consultants can use AI to prepare materials, organize information, support implementation, and make progress-monitoring data easier to interpret.
Protected student or consultee information should be used only with approved systems, appropriate safeguards, and informed consent when required.
The field now needs empirical research examining whether AI-assisted consultation improves efficiency, implementation fidelity, professional learning, and student outcomes.
Read the Article
If you are interested in reading the articles, the first 50 people can access the article here for free. After that, a free pre-print is always available here.
Wu, S., Dillon, C., & Lockwood, A. B. (2026). Artificial intelligence as an augmented partner in school consultation: Applications across the problem-solving process. Journal of Educational and Psychological Consultation. Advance online publication. https://doi.org/10.1080/10474412.2026.2704776
AI Disclosure: Generative AI was used to assist with drafting and editing this post and to create the accompanying image. I reviewed and revised the content and take responsibility for its accuracy and final form.