Institute for Research in Social Communication SAS
Topic
Development of Metacognition in Complex Problem-Solving Using Generative Artificial Intelligence in Medical Students
PhD. program
Health psychology
Year of admission
2025
Name of the supervisor
doc. Mgr. Kamila Urban, PhD.
Contact:
Receiving school
Faculty of Social and Economic Sciences
Annotation
Consultant: PhDr. Marek Urban, PhD.
This PhD project explores how metacognitive strategy instruction, supported by large language models (LLMs) as adaptive tutors, can enhance complex problem-solving (CPS) among medical students. By helping future physicians become aware of and regulate their thinking processes, these AI-based tutors guide learners to reflect on their progress, adjust strategies, and strengthen essential metacognitive skills (e.g., planning, monitoring, regulation, and self-evaluation). Their personalized, real-time feedback aims to improve the use of metacognitive competencies across the diverse and often unpredictable contexts encountered in medical training and clinical practice.
Within the scope of health psychology, the capacity to self-regulate, manage complex information, and make informed decisions is foundational to effective patient care. By improving metacognitive abilities in students, the project seeks to enhance not just academic performance but also long-term clinical outcomes. The anticipated outputs include the development and evaluation of a metacognitive strategy intervention with LLM support, providing empirical insights into the impact of AI-driven tutoring on CPS. Additionally, this work will offer practical recommendations for educators, program directors, and trainers aiming to integrate AI-based feedback into curricula for the benefit of future healthcare professionals.
This PhD project explores how metacognitive strategy instruction, supported by large language models (LLMs) as adaptive tutors, can enhance complex problem-solving (CPS) among medical students. By helping future physicians become aware of and regulate their thinking processes, these AI-based tutors guide learners to reflect on their progress, adjust strategies, and strengthen essential metacognitive skills (e.g., planning, monitoring, regulation, and self-evaluation). Their personalized, real-time feedback aims to improve the use of metacognitive competencies across the diverse and often unpredictable contexts encountered in medical training and clinical practice.
Within the scope of health psychology, the capacity to self-regulate, manage complex information, and make informed decisions is foundational to effective patient care. By improving metacognitive abilities in students, the project seeks to enhance not just academic performance but also long-term clinical outcomes. The anticipated outputs include the development and evaluation of a metacognitive strategy intervention with LLM support, providing empirical insights into the impact of AI-driven tutoring on CPS. Additionally, this work will offer practical recommendations for educators, program directors, and trainers aiming to integrate AI-based feedback into curricula for the benefit of future healthcare professionals.