AI and Skill Development in School Psychology: Are We Sacrificing Mastery for Speed?
Key Points (For Readers on the Go)
A January 2026 study found a 17% reduction in conceptual mastery when AI was used to learn new skills.
AI did not significantly improve speed when learning unfamiliar material.
The largest decline was in “debugging” skills—catching and fixing errors.
Passive AI use can weaken professional judgment.
AI should be a thought partner, not a substitute.
Introduction: Why This Matters for School Psychologists
A January 2026 study titled How AI Impacts Skill Formation by folks at the Anthropic Fellows Program for AI Safety Research examined how artificial intelligence affects the development of new professional skills. The study focused on software developers learning a new programming library, but the implications extend far beyond coding.
For school psychologists, the question is immediate and practical:
If AI helps us complete tasks faster, are we still developing the skills we need to supervise, defend, and refine that work?
As AI becomes more common in report writing, data interpretation, consultation planning, and graduate training, we must ask whether efficiency is coming at a cost.
What the Study Found
The researchers conducted a randomized experiment. One group learned a new tool with AI assistance. The other group learned without it.
The findings were clear:
Participants using AI scored 17% lower on measures of conceptual understanding.
AI did not significantly improve completion time in the main study.
The largest skill gap appeared in debugging—the ability to identify and fix errors.
In short: AI helped complete the task. It did not reliably build mastery.
What “Debugging” Means in School Psychology
In software engineering, debugging means identifying and correcting errors.
In school psychology, debugging looks different but is equally critical.
It might include:
Detecting inconsistencies in report content.
Identifying flawed logic.
Catching when intervention plans drift from the functional hypothesis.
Noticing when counseling goals don’t match the presenting concern.
Noticing when recommendations do not match functional data.
These are not clerical tasks. They are judgment tasks.
If AI generates an interpretation or recommendation, the psychologist must be capable of identifying subtle problems. That requires deep understanding—not surface familiarity.
The study suggests that when AI removes friction during learning, it may also reduce the opportunity to build those skills.
Why Friction Builds Competence
One of the most important findings in the study was this:
The group without AI encountered more errors.
Those errors forced deeper engagement.
They learned more.
Struggle is not inefficiency. It is often the mechanism of mastery.
In school psychology, this is familiar. Writing your own interpretation forces you to:
Reconcile conflicting scores.
Revisit theoretical models.
Consider base rates.
Check assumptions about disability classification.
Reflect on ecological context.
If AI resolves those tensions too quickly, the learning process may be shortened, but so is the development of professional reasoning.
Implications for Graduate Training
This has serious implications for training programs.
Graduate students today can generate:
Psychoeducational reports.
Behavioral hypotheses.
Intervention plans.
Case conceptualizations.
But if they rely on AI before forming their own hypotheses, they may never fully develop the skill of constructing those ideas independently.
The risk is not that students use AI.
The risk is that they use it before they think.
If students learn to outsource early reasoning, their ability to supervise AI later may be weaker.
Implications for Practicing School Psychologists
This is not just a graduate training issue.
Experienced practitioners also learn new skills:
New assessment tools.
Emerging frameworks.
Novel intervention approaches.
New district data systems.
If AI summarizes, drafts, and structures everything during that learning phase, the practitioner may complete the task without truly integrating the underlying concepts.
The study’s 17% drop in conceptual mastery is a warning sign.
When we use AI while learning something new, we must remain actively engaged.
Not All AI Use Is Equal
The study identified six patterns of AI use.
The highest-performing group used AI for conceptual inquiry. They asked “why” and “how” questions. They still resolved errors independently.
The lowest-performing groups relied on AI to generate answers or repeatedly fix problems.
The difference was not AI versus no AI.
The difference was cognitive engagement versus delegation (offloading).
This is an important distinction for school psychology practice.
AI can:
Clarify theoretical models.
Explain scoring logic.
Help brainstorm interventions.
Offer alternative interpretations.
But it should not:
Replace hypothesis generation.
Write conclusions without review.
Determine eligibility reasoning.
Serve as the sole author of conceptual analysis.
AI works best as a thought partner—not as a substitute for thinking.
Ethical Considerations: Competence, Supervision, and Equity
The ethical implications are significant.
School psychologists are bound by standards of professional competence. Competence requires:
Ongoing skill development.
Accurate interpretation.
Awareness of limitations.
If AI use weakens the development of foundational skills, practitioners may unintentionally fall below their own competence standards.
There are also equity implications. If AI smooths over errors in biased or incomplete reasoning, the practitioner must be skilled enough to catch it. Otherwise, errors may disproportionately affect vulnerable students.
AI does not remove professional responsibility. It increases it.
Practical Guidance: How to Stay Engaged
Here are structured ways to use AI while preserving skill development:
Generate your hypothesis before prompting AI.
Write a brief interpretation draft before asking for revision.
Use AI to challenge your reasoning, not replace it.
Ask AI to explain “why,” not just provide output.
Periodically complete tasks without AI to maintain fluency.
When learning something new, consider delaying AI use until after you have attempted the task independently.
This preserves friction—the engine of mastery.
Final Takeaways
AI may reduce skill development when used passively.
Friction during learning builds professional judgment.
AI should function as a thought partner, not a substitute.
Internal Links
If you found this helpful, you may also be interested in:
How School Psychologists Are Using AI
https://www.apa.org/pubs/highlights/spotlight/school-psychologists-aiMitigating AI Bias in School Psychology
https://lockwoodconsulting.net/blog/mitigating-ai-bias-in-school-psychology-why-it-matters-and-what-we-can-do
AI Use Disclosure - Portions of this post were drafted with the assistance of an AI writing tool and revised by the author for accuracy, clarity, and professional judgment.