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Integrating Large Language Model Applications in Graduate Applied Behavior Analysis Education.

Jillian B Wilson¹, Allen Karsina¹

¹Department of Applied Behavior Analysis, Regis College, 235 Wellesley Street, Weston, MA 02493 USA.

Behavior Analysis in Practice

|June 22, 2026

Summary

This summary is machine-generated.

Applied behavior analysis (ABA) graduate programs should integrate artificial intelligence (AI), specifically large language models (LLMs), into their curriculum. This ensures students develop essential AI skills while maintaining academic integrity and ethical standards in ABA education.

Keywords:

Artificial intelligence Behavior analytic education Large language model

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Area of Science:

Behavioral Science
Educational Technology
Artificial Intelligence

Background:

Rapid advancements in artificial intelligence (AI) necessitate curriculum updates in graduate programs.
Applied Behavior Analysis (ABA) graduate programs face challenges in integrating AI while upholding academic integrity.

Purpose of the Study:

To examine the integration of large language models (LLMs) in ABA education.
To provide strategies for faculty to support student learning and preserve academic integrity.
To explore practical applications of AI in ABA coursework and assessment.

Main Methods:

Review of current AI capabilities and their relevance to ABA education.
Analysis of pedagogical models for incorporating AI into conceptual and performance-based assessments.
Development of recommendations for ethical AI use, prompt engineering, and grading rubrics.

Main Results:

Identified practical strategies for integrating AI into ABA assessments to maintain instructional standards.
Emphasized the importance of ethical AI use and authentic demonstration of competence.
Proposed policy development, including behavioral contracts and adaptive learning platforms.

Conclusions:

ABA graduate programs must proactively adapt to AI integration to prepare students for the future.
Thoughtful incorporation of LLMs can enhance ABA education, provided ethical guidelines and clear expectations are established.
Future directions include AI-powered adaptive learning and competency tracking for ABA students.