Name
Shaping Growth through Artificial Intelligence: A Qualitative Exploration
of Learning and Clinical Reasoning Development among Medical Students
Date & Time
Sunday, June 7, 2026, 4:38 PM - 4:53 PM
Location Name
Oglethorpe G
Speakers
Authors
Sindhu Vasantha A, Karpaga Vinayaga Institute of Medical Sciences & Research Center, Maduranthagam, 603 308, Tamil Nadu, India
Russell D'Souza, Chair, Department of Education, UNESCO Chair in Bioethics, Melbourne, Australia
Krishna Mohan Surapaneni, Panimalar Medical College Hospital & Research Institute, Chennai, India
Presentation Topic(s)
Technology and Innovation
Description
PURPOSE
Artificial intelligence (AI) has become increasingly integrated into
medical education, offering adaptable learning pathways that may influence
growth-oriented behaviours and clinical reasoning skills. This study explored
MBBS students’ perceptions of AI-assisted learning and examined how
engagement with AI-enabled modules shaped their academic development,
reflective thinking and approach to complex clinical scenarios. The objective
was to understand the role of AI as a supportive tool in fostering deeper
learning and professional confidence.
METHODS
A qualitative exploratory design was adopted. MBBS students from Years 1–4
who had access to AI-based learning resources voluntarily participated. Data
were collected using reflective journals and semi-structured focus group
interviews that focused on learning adaptability, motivation, reasoning
approach and mindset transformation. Inductive thematic analysis was
performed. Coding was independently conducted by two researchers, with
triangulation used to maintain credibility and ensure analytical consistency.
RESULTS
Three major themes were identified. Enhanced conceptual clarity: students
reported that AI-supported resource suggestions helped reinforce difficult
areas and improved knowledge integration. Strengthening of clinical
reasoning: participants described increased readiness to interpret unfamiliar
cases and felt AI-generated problem scenarios improved constructive thinking.
Shift towards self-directed learning: several students reflected improved
resilience, motivation and willingness to pursue continuous improvement,
attributing this to AI-based feedback that encouraged critical evaluation of
their learning strategies.
CONCLUSIONS
AI-assisted learning demonstrated potential to facilitate academic growth,
improve preparedness for clinical reasoning and promote reflective learning
behaviours among medical students. Participants perceived AI as a valuable
educational support that encouraged autonomy and adaptability. These findings
highlight the promise of AI-driven strategies in medical curricula. Further
longitudinal research is recommended to evaluate sustained impact and
potential for wider integration.
Artificial intelligence (AI) has become increasingly integrated into
medical education, offering adaptable learning pathways that may influence
growth-oriented behaviours and clinical reasoning skills. This study explored
MBBS students’ perceptions of AI-assisted learning and examined how
engagement with AI-enabled modules shaped their academic development,
reflective thinking and approach to complex clinical scenarios. The objective
was to understand the role of AI as a supportive tool in fostering deeper
learning and professional confidence.
METHODS
A qualitative exploratory design was adopted. MBBS students from Years 1–4
who had access to AI-based learning resources voluntarily participated. Data
were collected using reflective journals and semi-structured focus group
interviews that focused on learning adaptability, motivation, reasoning
approach and mindset transformation. Inductive thematic analysis was
performed. Coding was independently conducted by two researchers, with
triangulation used to maintain credibility and ensure analytical consistency.
RESULTS
Three major themes were identified. Enhanced conceptual clarity: students
reported that AI-supported resource suggestions helped reinforce difficult
areas and improved knowledge integration. Strengthening of clinical
reasoning: participants described increased readiness to interpret unfamiliar
cases and felt AI-generated problem scenarios improved constructive thinking.
Shift towards self-directed learning: several students reflected improved
resilience, motivation and willingness to pursue continuous improvement,
attributing this to AI-based feedback that encouraged critical evaluation of
their learning strategies.
CONCLUSIONS
AI-assisted learning demonstrated potential to facilitate academic growth,
improve preparedness for clinical reasoning and promote reflective learning
behaviours among medical students. Participants perceived AI as a valuable
educational support that encouraged autonomy and adaptability. These findings
highlight the promise of AI-driven strategies in medical curricula. Further
longitudinal research is recommended to evaluate sustained impact and
potential for wider integration.
Presentation Tag(s)
International Presenter, Student Presentation