These answers draw in part from “Workshop: Integrating AI and Design Thinking Within Behavior Analysis: Enhancing Practice Through Technology” by Beth Garrison, PhD, BCBA, LBS, LBA (BehaviorLive), and extend it with peer-reviewed research from our library of 27,900+ ABA research articles. Clinical framing, BACB ethics code references, and cross-links below are synthesized by Behaviorist Book Club.
View the original presentation →Design thinking is a user-centered problem-solving methodology with five phases: empathize, define, ideate, prototype, and test. It is relevant to behavior analysts because it provides a structured approach to innovation and creative problem-solving that complements the analytical rigor of behavior analysis. While behavioral methodology excels at understanding and modifying existing behavior, design thinking excels at imagining new solutions, creating user-friendly materials, and developing novel service delivery models. The empathy and prototyping phases align naturally with behavioral observation and data-driven evaluation.
Machine learning is a broad category of AI that involves algorithms learning from data to make predictions or identify patterns. Chatbots like large language models are one specific application of machine learning, trained on text data to generate human-like responses. Other machine learning applications relevant to behavior analysis include pattern recognition in treatment data, predictive analytics for client outcomes, and computer vision for behavioral observation coding. Understanding this distinction helps behavior analysts recognize the full range of AI applications available, beyond just text generation tools.
A wireframe is a simplified visual representation of a digital product, showing the basic layout, structure, and functionality without detailed design elements. Behavior analysts can use wireframes to prototype ideas for data collection apps, parent training platforms, visual schedule tools, or any other technology solution they envision. Modern AI tools can generate wireframes from text descriptions, making this process accessible to non-designers. The wireframe serves as a conversation starter with stakeholders, a testing tool with potential users, and a specification document for developers if the idea moves to full development.
Both involve systematic observation and information gathering, but they serve different purposes and use somewhat different methods. Behavioral assessment focuses on identifying the functions of specific behaviors and the environmental variables that influence them. The design thinking empathize phase focuses on understanding the user's overall experience, including their emotions, frustrations, unmet needs, and goals. Behavioral assessment typically uses structured tools like functional analysis and preference assessment. Empathy research typically uses interviews, observation, and journey mapping. For behavior analysts, combining both approaches provides a richer understanding of stakeholder needs.
Design thinking is particularly well-suited to organizational problems because it centers the perspectives of the people affected by those problems. Staff retention, training effectiveness, scheduling efficiency, and family engagement are all organizational challenges that benefit from the empathy-driven, iterative approach of design thinking. Combined with behavioral analysis of the organizational contingencies contributing to these problems, design thinking provides a methodology for generating and testing innovative solutions that purely analytical approaches might not produce.
Before using AI in a design thinking process, ensure that any client or stakeholder data used to inform the process is handled confidentially and with appropriate consent. Establish that AI platforms used for content generation or prototyping are compliant with relevant privacy regulations. Verify all AI-generated content for accuracy before including it in prototypes or sharing it with stakeholders. Be transparent with participants about the use of AI tools in the development process. Ensure that innovations developed through this process will be evaluated using evidence-based methods before clinical implementation.
Design thinking does not require design expertise. Start by selecting a small, well-defined problem in your practice, such as a parent training handout that families consistently struggle with. Follow the five phases sequentially: observe how families currently use the handout and interview them about their experience, define the specific problems to solve, brainstorm multiple possible solutions without judgment, build a quick prototype of the most promising idea, and test it with a few families. Many free online resources provide templates and guides for each phase. The methodology is intentionally accessible to non-designers.
Neural networks are a type of machine learning architecture loosely inspired by biological neural systems. They process information through layers of interconnected nodes, learning patterns from training data to make predictions or generate output. Behavior analysts should understand neural networks at a conceptual level because they power many of the AI tools increasingly used in healthcare and education, including language models, image recognition systems, and predictive analytics. This understanding helps practitioners evaluate AI tools critically, communicate effectively about technology with interdisciplinary colleagues, and make informed decisions about AI adoption in their practice.
AI tools can significantly accelerate prototyping by generating content, creating visual layouts, and simulating user interactions. A behavior analyst prototyping a social skills training app could use AI to generate example content for different skill levels, create visual mockups of screen layouts, and simulate how a user might interact with different features. AI can also analyze user feedback on prototypes to identify patterns and suggest improvements. This reduces the technical barrier to prototyping, allowing behavior analysts to test ideas quickly without needing graphic design or software development skills.
The key is viewing design thinking as a methodology for generating hypotheses and evidence-based evaluation as the methodology for testing them. Design thinking produces innovative solutions through empathy, creativity, and rapid prototyping. These solutions are then evaluated using behavioral research methods, including data collection, single-subject designs, and comparison to established approaches. An innovation that survives this evaluation process becomes part of your evidence-based toolkit. An innovation that does not meet the evidence threshold is modified or abandoned. This cycle of creative generation and empirical evaluation produces both innovation and accountability.
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Workshop: Integrating AI and Design Thinking Within Behavior Analysis: Enhancing Practice Through Technology — Beth Garrison · 5 BACB Ethics CEUs · $110
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All behavior-analytic intervention is individualized. The information on this page is for educational purposes and does not constitute clinical advice. Treatment decisions should be informed by the best available published research, individualized assessment, and obtained with the informed consent of the client or their legal guardian. Behavior analysts are responsible for practicing within the boundaries of their competence and adhering to the BACB Ethics Code for Behavior Analysts.