This guide draws 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 extends it with peer-reviewed research from our library of 27,900+ ABA research articles. Citations, clinical framing, and cross-links below are synthesized by Behaviorist Book Club.
View the original presentation →The intersection of artificial intelligence, design thinking, and behavior analysis represents an emerging frontier for practitioners seeking to enhance their practice through technology and structured innovation methods. This workshop-oriented topic introduces behavior analysts to two powerful frameworks, AI and design thinking, that can complement behavioral expertise in solving complex clinical and organizational problems.
The clinical significance of this integration lies in the growing complexity of the challenges behavior analysts face. Modern practice demands not only technical behavioral expertise but also the ability to develop creative solutions to novel problems, prototype new service delivery models, and leverage technology to scale effective interventions. Behavior analysts who can combine their scientific training with design thinking methodology and AI literacy are better positioned to address these demands.
Artificial intelligence encompasses a range of technologies including machine learning, neural networks, and natural language processing. For behavior analysts, understanding these fundamental concepts is not about becoming computer scientists but about developing sufficient literacy to evaluate AI tools, understand their capabilities and limitations, and make informed decisions about their use in practice. The healthcare and education sectors are being transformed by AI applications, and behavior analysts who remain uninformed about these developments risk falling behind in their ability to serve clients effectively.
Design thinking is a user-centric problem-solving methodology that originated in product design and has been adapted for healthcare, education, and social innovation. Its five phases, empathize, define, ideate, prototype, and test, provide a structured approach to innovation that complements the systematic, data-driven methods behavior analysts already use. Where behavior analysis excels at understanding and modifying existing behavior, design thinking excels at imagining and creating new solutions.
The combination of these three disciplines creates a powerful toolkit. Behavior analysis provides the scientific foundation for understanding behavior-environment relationships. Design thinking provides a methodology for creative problem-solving and user-centered innovation. AI provides tools for processing information, identifying patterns, and prototyping solutions at scale. Together, they enable practitioners to develop more innovative, effective, and scalable approaches to the challenges they encounter.
Beth Garrison's workshop focuses on practical application, including the development of wireframe prototypes using AI and design thinking principles. This hands-on approach ensures that practitioners leave with tangible skills they can apply immediately, rather than abstract knowledge about technologies and methodologies they have never used. The emphasis on prototyping is particularly valuable, as it provides a low-cost way to test ideas before investing significant resources in implementation.
The application of design thinking to healthcare and human services has gained substantial momentum over the past decade. Originally developed at Stanford University and popularized by design consultancy IDEO, design thinking has been adopted by hospitals, healthcare systems, educational institutions, and social service organizations worldwide. Its emphasis on understanding the end user's experience, generating multiple possible solutions before committing to one, and testing ideas through rapid prototyping aligns well with the values of evidence-based practice.
For behavior analysts, design thinking offers a structured approach to a type of problem-solving that behavioral training sometimes underemphasizes. While behavior analysis provides excellent tools for understanding why behavior occurs and how to change it, the field has less structured methodology for imagining entirely new service delivery models, creating user-friendly materials, or developing technology solutions from scratch. Design thinking fills this gap by providing a repeatable process for innovation.
The five phases of design thinking each have behavioral parallels that make the methodology accessible to behavior analysts. The empathize phase, which involves understanding the user's experience through observation and interview, parallels the behavioral emphasis on direct observation and environmental assessment. The define phase, which clarifies the problem to be solved, mirrors the importance of operationally defining target behaviors and identifying the questions that assessment will answer. The ideate phase encourages generating multiple possible solutions without premature evaluation, which can complement the behavioral tendency toward established, evidence-based approaches by opening space for creative alternatives.
The prototype and test phases are where design thinking and behavior analysis converge most naturally. Building a quick, inexpensive prototype of a solution and testing it with real users parallels the behavioral practice of implementing a brief trial of an intervention and evaluating its effects through data. Both approaches emphasize iterative refinement based on empirical feedback rather than commitment to a predetermined plan.
AI technology adds a new dimension to both design thinking and behavioral practice. Machine learning algorithms can process large datasets to identify patterns that inform the empathize and define phases of design thinking. Natural language processing can generate and refine content during the ideate phase. AI-powered prototyping tools can create functional wireframes and user interfaces during the prototype phase, dramatically reducing the technical barrier to building and testing solutions.
The ethical dimensions of integrating AI and design thinking into behavior analysis practice require careful consideration. The BACB Ethics Code (2022) establishes obligations around competence, client welfare, and evidence-based practice that must guide the adoption of new methodologies and technologies. Code 1.05 (Practicing Within Scope of Competence) requires behavior analysts to ensure they have adequate training before applying new approaches. Code 2.01 (Providing Effective Treatment) requires that new methods be grounded in evidence and serve client welfare.
The workshop format of this training is significant because both AI literacy and design thinking are best learned through practice rather than lecture. Understanding machine learning conceptually is different from actually using AI tools to generate solutions. Understanding design thinking phases is different from actually empathizing with users, defining problems, and building prototypes. Hands-on experience bridges the gap between knowledge and application.
The practical applications of AI and design thinking in behavior-analytic practice extend across multiple domains, from direct service delivery to program development, organizational improvement, and professional training. Understanding these applications helps practitioners identify opportunities to enhance their work using these complementary methodologies.
In direct clinical practice, design thinking can improve the development of intervention materials, data collection systems, and caregiver training resources. Rather than creating these materials based solely on clinical expertise, the design thinking approach begins with empathy for the end user. A data collection sheet designed using design thinking would start with observing how RBTs actually use current data sheets in session, identifying pain points and sources of error, and iteratively testing improved designs. The result is more user-friendly tools that improve data quality and reduce the burden on direct service providers.
AI can enhance clinical practice through data analysis and pattern identification. Machine learning algorithms applied to treatment data across multiple clients can identify variables associated with faster skill acquisition, greater generalization, or reduced problem behavior. While these patterns do not replace individualized clinical judgment, they can inform hypothesis generation and suggest variables worth investigating for specific clients.
Program development benefits substantially from the design thinking approach. When creating a new group social skills program, for example, design thinking would involve empathizing with all stakeholders including clients, families, and staff, defining the specific problems the program needs to solve, generating multiple possible program models, building a prototype of the most promising model, and testing it with a small group before full implementation. This systematic approach to program development reduces the risk of investing resources in programs that do not meet stakeholder needs.
Organizational improvement is another high-value application. Behavior analysts in leadership positions can apply design thinking to challenges like staff retention, training effectiveness, and service delivery efficiency. Combined with behavioral analysis of the organizational performance problems, design thinking provides a methodology for developing innovative solutions that traditional approaches might not generate.
Wireframe prototyping, a key component of this workshop, has specific relevance for behavior analysts developing technology-based solutions. A BCBA who identifies a need for a better parent training app, a more efficient scheduling system, or an improved data visualization tool can use wireframing to create a visual representation of their idea. This prototype can then be shared with stakeholders for feedback, refined based on input, and potentially developed into a functional tool. AI-powered design tools can assist with wireframe creation, making this process accessible even to practitioners without technical design skills.
The integration of design thinking with behavioral principles can also improve the consumer experience of ABA services. Families interacting with ABA organizations encounter intake processes, treatment planning meetings, progress reports, and communication systems that may or may not be designed with the user experience in mind. Applying design thinking to these touchpoints, with empathy for the family's perspective as the starting point, can identify improvements that increase engagement, satisfaction, and treatment adherence.
Professional training and continuing education can be enhanced through both AI and design thinking. Training programs designed using design thinking principles would begin with understanding the learner's actual needs, challenges, and preferences rather than assuming what they need to know. AI can personalize training content, adapt difficulty levels, and provide immediate feedback, creating more effective learning experiences than traditional didactic instruction.
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Integrating AI and design thinking into behavior analysis practice raises ethical considerations that practitioners must address proactively. The BACB Ethics Code (2022) provides a framework for navigating these concerns, though its application to emerging technologies requires thoughtful interpretation.
Competence obligations under Code 1.05 require behavior analysts to practice within the boundaries of their training and competence. Behavior analysts who begin using AI tools or design thinking methodology should ensure they have adequate understanding of these approaches before applying them in clinical contexts. This does not mean becoming an AI expert or a certified design thinker, but it does mean understanding the fundamental principles, capabilities, limitations, and potential risks of each approach.
The ethical use of AI in behavior analysis involves all the confidentiality and accuracy concerns discussed in AI-focused trainings. When using AI as part of a design thinking process, such as generating content for a prototype or analyzing user data to inform the empathize phase, behavior analysts must ensure that client information is protected, that AI-generated content is verified for accuracy, and that the limitations of AI output are understood and communicated.
Design thinking raises its own ethical considerations. The empathize phase involves gathering information about users' experiences, which may include sensitive information about their challenges, frustrations, and unmet needs. This information must be handled with the same confidentiality standards that apply to all clinical data. When conducting empathy interviews with clients or families, behavior analysts should clarify the purpose of the conversation, obtain appropriate consent, and ensure that shared information is used only for its intended purpose.
The ideation phase of design thinking encourages generating creative solutions without premature judgment. While this open-ended exploration can generate innovative approaches, behavior analysts must eventually evaluate these ideas against evidence-based standards. A creative solution that emerges from design thinking still needs to be grounded in behavioral principles and supported by evidence before it is implemented clinically. The design thinking process should complement, not replace, the evidence-based decision-making that defines behavior-analytic practice.
Prototyping and testing raise questions about informed consent and risk. When testing a prototype intervention, material, or system with clients or families, behavior analysts must ensure that participants understand they are trying something new, that their feedback will be used to improve the product, and that participation is voluntary. Prototypes should be evaluated for potential risks before testing, and testing should be monitored closely to identify and address any unintended negative effects.
The relationship between innovation and evidence-based practice requires careful navigation. The BACB Ethics Code requires behavior analysts to recommend and implement treatments supported by evidence. Design thinking encourages creating novel solutions that, by definition, do not yet have an established evidence base. Reconciling these obligations involves using design thinking to develop innovations that are then evaluated using behavioral research methods. The innovation process generates hypotheses, and behavioral methodology tests them.
Transparency with clients and stakeholders about the use of AI tools and design thinking processes is an ethical best practice. When developing new materials, programs, or systems using these approaches, informing stakeholders about the methodology, seeking their input, and sharing the iterative development process builds trust and ensures that the end product serves the intended users.
Intellectual property considerations may arise when AI tools are used to generate content or designs. The legal landscape around AI-generated content ownership is evolving, and behavior analysts should be aware of the terms of service of the AI tools they use, particularly regarding ownership of generated content. If prototypes or materials developed using AI are intended for commercial distribution, legal consultation may be advisable.
Deciding when and how to integrate AI and design thinking into your behavioral practice requires a systematic evaluation of your needs, resources, and the potential benefits and risks of each application. The following framework can guide these decisions.
Assessing your practice for design thinking opportunities begins with identifying pain points and unmet needs. Where are clients, families, or staff struggling with current systems, materials, or processes? Where have traditional approaches failed to solve recurring problems? Where do stakeholders express frustration or dissatisfaction? These pain points represent opportunities for the empathy-driven, user-centered approach that design thinking provides.
Evaluating AI opportunities requires an honest assessment of where time is currently spent and where technology could add value. Map your weekly activities and identify tasks that are time-intensive but relatively routine, such as data entry, report formatting, or literature searching. These are potential candidates for AI assistance. Also identify areas where pattern detection in large datasets could inform clinical decisions, such as analyzing treatment data across clients to identify factors associated with better outcomes.
The decision to prototype a solution should be based on a clear problem definition and evidence that current approaches are insufficient. Before investing time in building a prototype, ensure that you have adequately understood the user's perspective through the empathize phase and that you have clearly defined the problem to be solved. A prototype without a clear problem definition is an exercise in technology for its own sake rather than a purposeful innovation.
Evaluating prototype effectiveness should use behavioral measurement principles. Define observable, measurable outcomes that the prototype should achieve, collect baseline data on current performance, implement the prototype, and measure the same outcomes. This data-driven approach to innovation evaluation leverages the behavior analyst's existing methodological strengths.
Team-based decision-making about AI and design thinking adoption is generally preferable to individual adoption. Involving colleagues, supervisors, and stakeholders in decisions about new tools and methodologies ensures broader buy-in, identifies potential concerns early, and distributes the learning curve. When proposing AI or design thinking initiatives, present a clear rationale, a plan for evaluation, and a realistic assessment of both benefits and risks.
Resource assessment is a practical consideration. Design thinking processes require time for empathy research, ideation sessions, and prototype development. AI tools may require financial investment, training time, and infrastructure changes. Assessing whether the expected benefits justify these resources prevents overcommitment to initiatives that may not deliver sufficient return.
Finally, consider the sustainability of any innovation. A brilliant prototype that requires ongoing technical expertise you do not have, or a design thinking process that demands time your organization cannot consistently allocate, will not produce lasting improvements. Evaluate not just whether an idea can be implemented but whether it can be maintained over time.
The integration of AI and design thinking into your behavior analysis practice represents an opportunity to expand your professional toolkit without abandoning the scientific foundation that defines the discipline. These are complementary approaches that can enhance what you already do well.
Start with design thinking by selecting a single, well-defined problem in your practice and working through the five phases. Choose something manageable, like redesigning your parent training handouts or improving your session scheduling process. Experience the methodology firsthand before attempting to apply it to larger, more complex challenges. The hands-on learning is far more valuable than theoretical understanding.
Develop AI literacy gradually. Experiment with AI tools in low-stakes contexts, such as generating ideas for social stories, summarizing articles for your own learning, or creating draft templates for non-clinical documents. Build your understanding of what AI does well, what it does poorly, and where the boundaries of safe use lie. This experiential knowledge will serve you better than any single training event.
Combine the approaches by using design thinking to identify problems and ideate solutions, and AI tools to assist with prototyping and testing. If you identify that RBTs need better visual supports for complex skill acquisition programs, use design thinking to understand their specific needs, AI to generate and refine visual support options, and behavioral data to evaluate which prototypes are most effective.
Share what you learn with colleagues. The behavior analysis community benefits from practitioners who explore new methodologies and report back on their experiences. Whether through formal presentations, informal discussions, or written case descriptions, sharing your integration experiences contributes to the field's collective understanding of how these approaches can enhance practice.
Maintain your behavioral identity throughout this process. AI and design thinking are tools to be used by behavior analysts, not replacements for behavioral thinking. Your training in functional assessment, evidence-based intervention, and data-driven decision-making remains your core professional strength. New tools and methodologies are valuable only to the extent that they enhance this foundation.
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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.