Nursing education researchers often need to connect survey scores with what students actually experienced during clinical practicum. The challenge is that open-ended responses contain valuable context about communication, nursing skills, theory-to-practice gaps, and stress, but manual coding can make synthesis slow and difficult to compare across groups. This article examines how AI-enabled qualitative research can help analyze nursing student feedback, using a November 5, 2026 PLOS One study by Hsiu-Wen Tsai and Shu-Ching Chen as a practical example. The study combined quantitative measures with written responses from 699 nursing students. The analysis shows how thematic coding can clarify survey findings while also demonstrating why researchers must distinguish perceptions from observed competence, associations from causes, and coding assignments from respondent prevalence.
Key Takeaways
According to the PLOS One study by Tsai and Chen, AI qualitative analysis of nursing student feedback can help researchers organize practicum experiences into themes, connect open-ended comments with survey results, and identify where interpretation requires caution. The November 5, 2026 study analyzed 699 complete records and produced 217 non-mutually-exclusive segment-to-theme assignments from two open-ended questions.
- According to Tsai and Chen’s November 5, 2026 study, all 699 analyzed nursing students answered both open-ended practicum questions, although not every response contained a substantively interpretable idea.
- According to the November 5, 2026 PLOS One report, the qualitative analysis produced 128 coding assignments for perceived practicum gains and 89 assignments for practicum difficulties.
- According to Tsai and Chen, initial intercoder percentage agreement was 92% before consensus discussions, but the researchers did not calculate Cohen’s kappa.
- According to the study authors, nursing-process feedback aligned with a quantitative importance-performance gap of 0.229, with a 95% confidence interval of 0.179 to 0.280.
- According to the November 5, 2026 study, qualitative assignments represented coded segments rather than unique participants, so the 217 assignments were not prevalence estimates.
What the Nursing Student Feedback Study Found
According to Tsai and Chen’s November 5, 2026 cross-sectional study, researchers invited all 730 eligible third-year nursing students at Hsin-Sheng Junior College of Medical Care and Management in northern Taiwan to participate. The data were collected from October to November 2023 after students completed their fundamental nursing practicum.
According to the study, 28 invited students did not participate, 702 returned consent forms and questionnaires, and 3 incomplete questionnaires were excluded, leaving 699 participants. The researchers paired quantitative questionnaire measures with two open-ended questions: “What was your greatest gain during the practicum? ” and “What was your greatest difficulty during the practicum? ”
According to Tsai and Chen, two researchers independently reviewed Chinese-language responses, developed codes inductively, compared their coding, examined divergent cases, and reached final decisions through consensus. Selected quotations were translated into English, back-translated, and checked against the original responses.
According to Tsai and Chen, the most relevant qualitative material concerned perceived gains in communication and nursing skills, alongside difficulties involving nursing-process assignments, theory-to-practice integration, and stress. The authors cautioned that the written responses provided context for the quantitative results but did not establish causal explanations or the prevalence of particular experiences.
The authors wrote that the findings “describe students’ perceptions and exploratory associations rather than observed competence, validated stress constructs, or causal effects.” That sentence is an important boundary for AI qualitative research because automated theme detection should organize evidence without turning self-reported perceptions into clinical-performance claims.
Nursing Student Feedback Findings Snapshot
| Date | Metric | Value | Research implication |
|---|---|---|---|
| October to November 2023 | Eligible students invited | 730 | The study attempted a cohort-wide census of third-year nursing students. |
| November 5, 2026 | Final analyzed participants | 699 | The qualitative and quantitative findings were based on complete records. |
| November 5, 2026 | Initial intercoder agreement | 92% | The researchers reported strong preliminary agreement before consensus review. |
| November 5, 2026 | Meaningful assignments for practicum gains | 128 | The assignments captured coded ideas, not necessarily 128 students. |
| November 5, 2026 | Meaningful assignments for practicum difficulties | 89 | The assignments were non-mutually-exclusive and could include multiple ideas. |
| November 5, 2026 | Total segment-to-theme assignments | 217 | The total should not be interpreted as a participant count or prevalence rate. |
| November 5, 2026 | Importance-performance gap for nursing process | 0.229, 95% CI 0.179 to 0.280 | Written comments about care plans and theory-practice integration provided context for a relative survey gap. |
Implications for Nursing Education Researchers
How can qualitative analysis connect survey scores with student experience?
Qualitative analysis can explain what a survey pattern may mean, but it cannot automatically validate the pattern. According to Tsai and Chen’s November 5, 2026 study, written comments about care-plan preparation and applying theory in practice helped contextualize the nursing-process domain, which appeared below the sample grand means in the importance-performance analysis.
Researchers can use thematic analysis to compare quantitative results with student language, identify convergence and divergence, and create a joint display. The study authors retained both convergent and divergent findings rather than forcing the qualitative and quantitative evidence to agree.
Why should researchers avoid treating coded themes as prevalence?
Researchers should avoid treating coded themes as prevalence when participants can contribute multiple segments or when one segment can receive multiple codes. According to Tsai and Chen, the 128 gains assignments and 89 difficulty assignments were non-mutually-exclusive coding assignments rather than counts of unique respondents.
AI qualitative research systems should therefore report the unit of analysis clearly, distinguish documents from segments, and preserve the relationship between a theme and its supporting excerpts.
What limitations should guide AI-assisted nursing research?
AI-assisted nursing research should preserve uncertainty around self-report, measurement validity, sampling, and causality. According to Tsai and Chen, the adapted stress questionnaire did not support its prespecified two-factor structure, and the study could not model dependence among students grouped by classes, rotations, practicum units, campuses, and instructors.
The study also found that communication and biomedical-science quadrant classifications were retained in only 51.3% and 52.1% of bootstrap samples, respectively, in November 2026 reporting. A qualitative theme should be treated as evidence for interpretation and follow-up, not as proof of a clinical deficit or intervention effect.
How Evidano Helps With Qualitative Analysis of Nursing Student Feedback
Problem: Manual coding slows the review of open-ended responses
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. Evidano can organize nursing student comments into themes, content patterns, and frequency summaries while keeping the underlying text available for review.
Researchers can use Evidano’s survey analysis tools to bring open-ended questionnaire responses into a structured qualitative workflow. The workflow supports rapid first-pass synthesis while leaving researchers responsible for reviewing codes, exclusions, and interpretation.
Problem: Researchers need to compare feedback across student segments
Evidano supports cross-segment analysis, allowing researchers to compare themes across variables such as academic-achievement categories, practicum settings, demographic groups, or survey segments. This can help teams examine whether comments about stress, communication, or theory-practice integration appear differently across groups.
The approach complements the November 5, 2026 study’s quantitative comparisons, but it does not remove the need to assess sampling, measurement quality, confounding, and cluster dependence.
Problem: Teams need transparent evidence behind an AI-generated theme
Evidano provides AI chat over documents and analyses, along with visualizations such as hierarchical codes and subcodes, word clouds, and co-occurrence networks. These capabilities can help researchers move from a high-level theme to the excerpts and related concepts that support it.
Researchers can use Evidano’s AI chat to ask focused questions about practicum feedback, then verify the response against source excerpts before reporting findings. Evidano encrypts data and does not use customer data to train third-party models, a relevant consideration for sensitive educational and health-related research.
FAQ: Qualitative Analysis of Nursing Student Feedback
What is qualitative analysis of nursing student feedback?
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Qualitative analysis of nursing student feedback is the systematic examination of written or spoken student responses to identify themes, patterns, meanings, and differences across groups.
According to Tsai and Chen’s November 5, 2026 PLOS One study, thematic analysis of open-ended practicum responses added context to survey findings about nursing competencies and learning stress.
How many nursing students were included in the study?
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The final analysis included 699 nursing students. According to Tsai and Chen, 730 eligible students were invited between October and November 2023, 28 did not participate, and 3 incomplete questionnaires were excluded.
What themes appeared in the nursing student practicum feedback?
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The study’s qualitative findings concerned gains in communication and nursing skills and difficulties involving nursing-process assignments, theory-to-practice integration, and stress.
According to Tsai and Chen, the researchers generated 128 non-mutually-exclusive assignments for practicum gains and 89 for practicum difficulties on November 5, 2026 reporting.
Can AI determine whether nursing students are clinically competent?
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AI qualitative analysis cannot determine clinical competence from self-reported feedback alone. According to Tsai and Chen, the study measured perceptions and satisfaction rather than directly observed competence, safe practice, or patient outcomes.
AI can help organize evidence and identify areas for follow-up, while educators still need validated assessments, observation, and appropriate research designs.
Why is the number of coded themes not the same as the number of students?
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The number of coded themes is not the number of students when one participant can provide multiple meaningful segments or one segment can receive multiple theme assignments.
According to the November 5, 2026 study, the 217 total assignments represented coded segments and were not prevalence estimates for the 699 participants.
How can Evidano support this type of nursing education research?
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Evidano can support nursing education research by analyzing open-ended survey responses, generating thematic and frequency analyses, comparing segments, and helping researchers review source evidence through AI chat.
Researchers should use AI-generated outputs as an auditable starting point, review the supporting excerpts, document coding decisions, and report methodological limitations.
Conclusion and Next Steps
The November 5, 2026 PLOS One study shows why qualitative analysis is valuable in nursing education: student comments can add meaning to survey patterns about practicum competencies, stress, and theory-practice integration. The study also shows why responsible analysis matters, because coded segments are not prevalence estimates and self-reported experiences are not direct measures of clinical competence.
AI-enabled qualitative research can make this work more efficient by organizing large volumes of feedback, comparing student segments, surfacing recurring themes, and linking findings back to source text. Researchers remain responsible for reviewing evidence, protecting participant data, and distinguishing descriptive themes from causal conclusions.
Teams analyzing nursing student feedback can try Evidano for free to turn open-ended responses and research documents into reviewable qualitative insights.
Topics
- qualitative analysis of nursing student feedback
- AI qualitative research in nursing education
- nursing practicum feedback analysis
- thematic analysis of student feedback
- AI analysis of open-ended survey responses
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