AI is rapidly entering mental-health education, yet our field still knows little about how future therapists, current clinicians, and faculty actually feel about learning with AI.
Our research project at CBTLAB aims to answer this question.
This post offers an overview of why we launched the study, what we are investigating, and how the findings will guide the future of therapist training.
Why This Research Matters
Training in CBT is facing a global challenge: demand for mental-health services continues to grow, but access to high-quality, skill-based education remains limited.
AI-supported training tools — such as virtual clients and interactive practice modules — could help close this gap.
But before we build and scale these tools responsibly, we need to understand:
• How do trainees perceive AI in their clinical education?
• What excites them?
• What concerns them — ethically, practically, or emotionally?
• What conditions make them willing (or unwilling) to use AI for learning therapy?
Educational innovation must begin with listening.
What the Study Examines:
The project focuses on students, clinicians, and educators — three groups whose attitudes shape the future of training.
We use the UTAUT2 model as a framework to explore the predictors of behavioral intention to use an AI-driven CBT training platform.
This includes constructs such as:
• performance expectancy (does AI actually help me learn?)
• effort expectancy (how easy will it be to use?)
• social influence (what do peers, faculty, and supervisors think?)
• facilitating conditions (do I imagine having the support I need?)
• hedonic motivation (is learning with AI enjoyable?)
• habit (or, in our adaptation, “I can imagine using this tool regularly”)
We also examine how demographic factors — age, gender, and prior experience with AI — influence willingness to use these tools.
Quantitative data show patterns.
Qualitative feedback shows the human story behind those patterns.
What We Expect to Learn
Through this study, we hope to map the landscape of attitudes toward AI-supported training.
Key questions include:
• What barriers prevent trainees from engaging with AI tools?
• What features or formats increase trust, confidence, and curiosity?
• How do clinicians imagine using AI in supervision, skill practice, or ongoing professional development?
• How do educators view AI as a teaching partner — or as a risk?
The goal is not to promote AI, but to understand how to design it in ways that honor the values and realities of the profession.
How the Findings Will Shape CBTLAB
The results will directly inform how we build the platform:
• which competencies require the clearest scaffolding,
• which practice formats feel most intuitive to learners,
• what types of feedback are seen as helpful or trustworthy,
• how to design tools that reduce anxiety rather than amplify it,
• how to address ethical questions transparently.
This research is not an add-on to product development — it is the foundation of responsible product development.
Our intention is to create training tools that align with the needs, expectations, and values of the people who will use them.
Why We Are Sharing This Work
Mental-health education is strongest when it evolves collaboratively.
By documenting our research process, we aim to open a transparent conversation about the opportunities and risks of AI in preparing future CBT clinicians.
If you are a student, a practicing therapist, or an educator, your voice matters in shaping this future - express your interest by writint the email to Denis Ivanov divanov@alliant.edu
Our research project at CBTLAB aims to answer this question.
This post offers an overview of why we launched the study, what we are investigating, and how the findings will guide the future of therapist training.
Why This Research Matters
Training in CBT is facing a global challenge: demand for mental-health services continues to grow, but access to high-quality, skill-based education remains limited.
AI-supported training tools — such as virtual clients and interactive practice modules — could help close this gap.
But before we build and scale these tools responsibly, we need to understand:
• How do trainees perceive AI in their clinical education?
• What excites them?
• What concerns them — ethically, practically, or emotionally?
• What conditions make them willing (or unwilling) to use AI for learning therapy?
Educational innovation must begin with listening.
What the Study Examines:
The project focuses on students, clinicians, and educators — three groups whose attitudes shape the future of training.
We use the UTAUT2 model as a framework to explore the predictors of behavioral intention to use an AI-driven CBT training platform.
This includes constructs such as:
• performance expectancy (does AI actually help me learn?)
• effort expectancy (how easy will it be to use?)
• social influence (what do peers, faculty, and supervisors think?)
• facilitating conditions (do I imagine having the support I need?)
• hedonic motivation (is learning with AI enjoyable?)
• habit (or, in our adaptation, “I can imagine using this tool regularly”)
We also examine how demographic factors — age, gender, and prior experience with AI — influence willingness to use these tools.
Quantitative data show patterns.
Qualitative feedback shows the human story behind those patterns.
What We Expect to Learn
Through this study, we hope to map the landscape of attitudes toward AI-supported training.
Key questions include:
• What barriers prevent trainees from engaging with AI tools?
• What features or formats increase trust, confidence, and curiosity?
• How do clinicians imagine using AI in supervision, skill practice, or ongoing professional development?
• How do educators view AI as a teaching partner — or as a risk?
The goal is not to promote AI, but to understand how to design it in ways that honor the values and realities of the profession.
How the Findings Will Shape CBTLAB
The results will directly inform how we build the platform:
• which competencies require the clearest scaffolding,
• which practice formats feel most intuitive to learners,
• what types of feedback are seen as helpful or trustworthy,
• how to design tools that reduce anxiety rather than amplify it,
• how to address ethical questions transparently.
This research is not an add-on to product development — it is the foundation of responsible product development.
Our intention is to create training tools that align with the needs, expectations, and values of the people who will use them.
Why We Are Sharing This Work
Mental-health education is strongest when it evolves collaboratively.
By documenting our research process, we aim to open a transparent conversation about the opportunities and risks of AI in preparing future CBT clinicians.
If you are a student, a practicing therapist, or an educator, your voice matters in shaping this future - express your interest by writint the email to Denis Ivanov divanov@alliant.edu
