AI-Powered Critical Reflection: Measuring Transformative Learning Through Simulated Decision-Making

Music City Center 

Critical reflection is essential for leadership development, career adaptability, and effective decision-making, yet measuring its depth and impact remains a challenge. This study introduces a generative AI-driven simulation framework to model and analyze reflective learning in real-world decision-making scenarios.
AI agents dynamically generate context-specific dilemmas faced by professionals in healthcare, education, and business leadership, guiding learners through AI-facilitated reflective dialogues. Responses are analyzed using Natural Language Processing (NLP) and topic modeling to track reasoning patterns and cognitive shifts.
To quantify transformative learning, we apply Latent Semantic Analysis (LSA) and BERT embeddings to measure changes in cognitive complexity, while longitudinal mixed-effects models assess behavioral adaptation over time.
Preliminary results show that AI-driven reflective coaching enhances critical thinking, adaptability, and problem-solving efficiency, particularly for individuals in career transitions. This study advances quantitative methods for assessing reflection, offering a scalable, AI-powered framework for professional education and coaching

Generative AI

Transformative Learning

NLP

Reflective Coaching

Professional Development

Cognitive Complexity 

Section on Statistics and Data Science Education