Medical educators often collect rich student feedback through open-ended surveys and focus groups, but manual coding can make it difficult to identify patterns quickly and consistently. Qualitative analysis in medical education becomes especially valuable when researchers need to connect student perceptions with measurable learning outcomes. A mixed-methods study published in PLOS One on November 5, 2026, examined whether undergraduate medical students developed higher-order cognitive skills by building physical physiology exhibits. The study combined pre-test and post-test scores with open-ended feedback and a 96-minute focus group discussion. This article explains what the researchers found and shows how AI-enabled qualitative analysis can help medical education researchers organize transcripts, identify themes, compare segments, and preserve the evidence behind conclusions.
Key Takeaways
According to the PLOS One study, "Crafting of physical exhibits by undergraduate medical students", collaborative physical model-building was associated with improved higher-order cognitive test scores and positive student perceptions in a 2023 medical education intervention.
The study shows why qualitative analysis in medical education matters: student comments explained how teamwork, faculty support, practical application, and planning constraints shaped the learning experience, while quantitative scores measured knowledge change.
- The PLOS One researchers included 50 first-year MBBS students in a study conducted between February 17, 2023, and October 31, 2023.
- The median higher-order cognitive post-test score increased from 12 before the intervention to 16 afterward, with statistical significance reported at p < 0.01.
- The qualitative component included open-ended feedback from all 50 students and a 96-minute focus group discussion with 10 purposively selected students.
- The researchers identified enabling factors, hindering factors, and learning gains in deductive analysis of feedback, then used inductive thematic analysis for the focus group transcript.
- One student described the learning method as "way better and easier than anything else because this is an application-based learning method, " according to the PLOS One study.
What the Medical Education Study Measured
The PLOS One researchers measured both learning outcomes and student perceptions, creating an explanatory sequential mixed-methods design in which qualitative findings helped explain quantitative results.
The intervention involved four teams of 12 or 13 students who created physical exhibits about sensory pathways, light and conjunctival reflexes, the micturition reflex, and the automatic bladder. The students had three weeks to develop the exhibits with faculty guidance.
The researchers assessed higher-order cognitive skills with a validated 30-item multiple-choice test administered before and after the intervention. The researchers collected qualitative evidence through open-ended questionnaire responses and a focus group discussion that was audio recorded, transcribed, anonymized, and reviewed by multiple researchers.
The qualitative analysis used a predefined framework of enabling factors, hindering factors, and learning gains for the open-ended responses. The researchers reported that no new codes emerged after approximately 25 of the 50 feedback forms, but they analyzed all 50 forms to confirm consistency and complete representation.
The study reported that students valued collaboration and faculty support, while students also identified limited technical expertise, material availability, logistics, and insufficient planning as barriers. One participant summarized a key weakness by saying, "We did not have a blueprint of the logistics for the materials."
Findings Snapshot: Qualitative Analysis in Medical Education
| Date or study stage | Measure | Value | Research implication |
|---|---|---|---|
| February 17, 2023 | Student participants | 50 first-year MBBS students | The intervention captured feedback from an intact undergraduate cohort. |
| February 17 to October 31, 2023 | Study setting | Department of Physiology, Rajkot, Gujarat, India | The findings reflect one tertiary-care medical education setting. |
| Three weeks | Model-building period | Four student teams | The qualitative data covered a defined, time-limited active-learning project. |
| Before intervention | Median higher-order cognitive test score | 12, IQR 10 to 14 | The baseline established the starting point for application and analysis skills. |
| After intervention | Median higher-order cognitive test score | 16, IQR 15 to 20 | The post-test score was significantly higher than the pre-test score, with p < 0.01. |
| After intervention | Focus group discussion | 10 students, 96 minutes | The discussion provided detailed evidence about student experiences and mechanisms. |
| During qualitative coding | Open-ended feedback analyzed | All 50 feedback forms | The researchers used complete-case qualitative analysis after observing code saturation at approximately 25 forms. |
Implications for Medical Education Researchers
Why should researchers combine quantitative and qualitative evidence?
Medical education researchers should combine quantitative and qualitative evidence when they need to measure whether an intervention worked and understand why students experienced it in a particular way.
The PLOS One study used test scores to identify improved performance and student narratives to identify mechanisms such as applying theory to practice, learning from peers, and receiving faculty feedback. The combined design also exposed implementation problems that a score alone could not show, including material procurement and project planning.
What themes should medical educators look for in student feedback?
Medical educators should examine themes related to learning gains, collaboration, faculty support, workload, logistics, assessment, and recommendations for future curriculum design.
The PLOS One researchers found that students connected physical model-building with deeper conceptual understanding and teamwork, while students also recommended better planning, smaller deadlines, broader topic choice, and curriculum integration. These themes can guide changes to instructional design rather than merely summarize satisfaction.
How can AI support qualitative analysis without replacing researcher judgment?
AI can support qualitative analysis by accelerating transcript review, suggesting codes, grouping related responses, comparing participant segments, and retrieving evidence for researcher review.
AI does not independently establish educational validity or replace contextual interpretation. The PLOS One researchers used repeated reading, multiple reviewers, consensus discussions, and investigator triangulation, so an AI-assisted workflow should preserve human review, transparent coding decisions, and direct links to participant evidence.
How Evidano Helps With AI Qualitative Analysis
Evidano helps researchers analyze medical education interviews, focus groups, and open-ended surveys
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, focus groups, open-ended surveys, and research documents.
Researchers can upload transcripts, papers, reports, and spreadsheet-based survey responses to Evidano's qualitative research features, then use AI-assisted thematic, content, frequency, and cross-segment analyses to organize evidence.
Problem: Manual coding slows analysis of student feedback
Solution: Evidano can help researchers identify recurring themes across open-ended responses and focus group transcripts, while researchers review and refine the proposed interpretation.
For a study like the PLOS One intervention, researchers could organize comments under themes such as learning gains, enabling factors, hindering factors, teamwork, faculty support, and planning.
Problem: Researchers need to compare feedback across participant groups
Solution: Evidano's cross-segment analysis can help compare responses by team, intervention group, demographic category, course, or other researcher-defined segments.
Segment comparisons can help medical education teams examine whether perceptions differ between student groups while keeping the interpretation grounded in the original responses.
Problem: Researchers need an auditable way to interrogate their dataset
Solution: Evidano's AI chat over uploaded documents and analyses can help researchers ask focused questions, locate supporting passages, and test emerging interpretations against the dataset.
Evidano encrypts data and does not use customer data to train third-party models, which is relevant when research datasets contain sensitive student or participant information. Researchers should still follow institutional ethics, consent, anonymization, and data-governance requirements.
FAQ: Qualitative Analysis in Medical Education
What is qualitative analysis in medical education?
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Qualitative analysis in medical education is the systematic interpretation of non-numeric data, such as interviews, focus groups, open-ended survey responses, and reflective comments.
The PLOS One study used deductive thematic analysis for open-ended feedback and inductive thematic analysis for a focus group transcript to understand student experiences of physical exhibit creation.
What did the 2026 PLOS One study find about physical exhibits?
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The 2026 PLOS One study found that 50 medical students had higher median post-test scores after creating physical physiology exhibits, with scores increasing from 12 to 16 and p < 0.01.
The qualitative findings indicated that students perceived gains in conceptual understanding, teamwork, communication, and practical application, while students also reported challenges involving planning, technical knowledge, and access to materials.
How was qualitative data analyzed in the study?
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The researchers analyzed open-ended responses deductively using three predefined themes and analyzed the focus group transcript inductively to develop codes, categories, and broader themes.
The researchers reviewed coding across multiple team members, resolved discrepancies through consensus, and used investigator triangulation to strengthen credibility.
Can AI analyze medical student feedback?
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AI can analyze medical student feedback by helping researchers transcribe, code, summarize, compare, and retrieve evidence from qualitative datasets.
AI-assisted analysis should support, rather than replace, researcher judgment, contextual interpretation, ethical review, and validation of themes against verbatim participant data.
What are the limitations of the PLOS One study?
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The PLOS One study did not assess long-term knowledge retention, long-term changes in problem-solving or critical thinking, or individual outcomes within the team-based intervention.
The study also used a convenience sample from one setting, so the findings should not be treated as proof that the intervention will produce identical results in every medical education context.
Conclusion and Next Steps
The November 5, 2026, PLOS One study demonstrates how mixed-methods research can connect measurable learning gains with the student experiences behind those gains. Qualitative analysis revealed that collaboration, faculty guidance, practical application, and planning shaped the value of the physical exhibit intervention. AI-enabled qualitative analysis can help medical education researchers process comparable feedback datasets faster while preserving researcher oversight and evidence-based interpretation. Try Evidano for free to analyze transcripts, open-ended surveys, and education research documents in one workspace.
Topics
- qualitative analysis in medical education
- AI qualitative analysis
- medical student feedback analysis
- thematic analysis in education
- mixed-methods medical education research
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