Education researchers often have to compare lesson plans, open-ended questionnaires, and participant reflections before they can explain how a teaching approach works in practice. The 2026 PLOS One study of design thinking in mathematics-focused STEM lessons provides a useful example: the research combined 24 lesson plans with questionnaire responses from 55 senior pre-service mathematics teachers in Türkiye. AI qualitative research analysis can help researchers organize similar mixed qualitative datasets, identify recurring themes, compare planned practice with participant perspectives, and preserve the evidence behind each conclusion. This article explains what the study found, how its content-analysis workflow can inform AI-assisted research, and where human interpretation remains essential. The goal is not to replace qualitative judgment, but to make large collections of educational documents easier to inspect, compare, and audit.
Key Takeaways
The PLoS One study, “Pre-service mathematics teachers’ integration of design thinking in STEM lesson plans”, published on November 2, 2026, found that pre-service mathematics teachers used design thinking unevenly: Define, Ideate, and Prototype appeared frequently, while Empathy and Test were much less consistently implemented.
The study shows why AI qualitative research analysis is useful for education researchers: thematic coding, frequency analysis, and cross-document comparison can reveal where a framework appears formally in lesson plans and where participants actually describe implementation challenges.
- According to the November 2, 2026 PLoS One study, 55 senior pre-service mathematics teachers produced and reflected on mathematics-focused STEM lesson planning in a qualitative case study.
- According to the study, the dataset included 24 collaboratively developed lesson plans and 55 individual open-ended questionnaires.
- According to the study, Define appeared in 95.8% of lesson plans, Prototype in 91.7%, Ideate in 83.3%, Test in 54.2%, and Empathy in only 20.8%.
- According to the study, 43 participants believed mathematics teachers should be able to develop design-thinking lesson plans, while 30 participants identified time consumption as a disadvantage.
- According to the study, questionnaire coding reached 90% initial inter-rater agreement, while independent lesson-plan evaluation reached 93.1% initial agreement.
What the 2026 STEM Education Study Examined
The November 2, 2026 PLoS One study examined how senior pre-service mathematics teachers integrated design thinking into mathematics-focused STEM lesson plans, what influenced their planning decisions, and which challenges they experienced.
The study used a qualitative case-study design at a state university in Türkiye during a 15-week course in the 2021 to 2022 academic year. According to the study, participants had no prior experience with design thinking or STEM-focused coursework before the course.
According to the study, researchers analyzed two connected sources: 24 group lesson plans and individually completed open-ended questionnaires from 55 participants. The lesson plans were assessed for STEM features and the five design-thinking phases, while the questionnaire responses were analyzed through familiarization, initial coding, category development, and theme construction.
The study reported that researchers used methodological triangulation by comparing group-level lesson-plan evidence with individual reflections. The study also reported a codebook, an audit trail, expert validation, pilot testing with 8 pre-service mathematics teachers, and reconciliation between coders.
The study’s method is a useful model for AI-assisted qualitative research because it separates three analytical questions: whether a concept is present, how often it appears, and whether the concept is substantively implemented rather than merely named.
Snapshot: Design Thinking in Mathematics Lesson Plans
| Study metric | Reported value | What the finding suggests |
|---|---|---|
| Participants | 55 senior pre-service mathematics teachers | The study captured individual perspectives within one teacher-education cohort. |
| Lesson-plan artifacts | 24 group lesson plans | The study enabled comparison of planned instructional practice across artifacts. |
| Define phase | 95.8% of lesson plans | Problem definition was one of the most consistently represented phases. |
| Prototype phase | 91.7% of lesson plans | Construction and product development were strongly represented. |
| Ideate phase | 83.3% of lesson plans | Idea generation appeared in most plans. |
| Test phase | 54.2% of lesson plans | Evaluation and iterative refinement appeared in only slightly more than half of the plans. |
| Empathy phase | 20.8% of lesson plans | User-centered inquiry was the least represented design-thinking phase. |
| Initial questionnaire coding agreement | 90% | The study reported strong initial consistency across independent qualitative coding. |
| Initial lesson-plan rating agreement | 93.1% | The study reported strong initial consistency in independent artifact evaluation. |
What AI Qualitative Research Analysis Can Reveal
How can researchers distinguish framework compliance from meaningful implementation?
Researchers can distinguish framework compliance from meaningful implementation by analyzing the surrounding evidence, not only the presence of a label. The PLoS One study treated a design-thinking phase as robust when the phase was operationalized through mathematical reasoning, quantitative analysis, or evidence-based justification.
An AI qualitative analysis workflow can first identify every mention of Empathy, Define, Ideate, Prototype, and Test, then help researchers inspect whether each mention includes user needs, constraints, measurement, feedback, or refinement. Human researchers should make the final judgment about conceptual adequacy.
What patterns appeared between planned lessons and participant reflections?
The PLoS One study found that participants valued real-life relevance, student engagement, creativity, and mathematical modeling while also reporting problems with topic selection, discipline alignment, materials, timing, and assessment.
The study quoted PMT 25 saying, “The most challenging part was aligning the design with other disciplines and finding suitable learning outcomes.” The same pattern can be examined in AI-assisted analysis by linking questionnaire themes to the specific lesson-plan sections where interdisciplinary alignment was attempted.
Why are frequency counts useful in qualitative education research?
Frequency counts are useful because they show how widely a theme or design feature appears across a bounded dataset, while qualitative context explains what that theme means. The PLoS One study reported that 18 of 24 lesson plans included a math-focused feature and that 18 included a creative-application feature.
Frequency analysis should not be treated as a substitute for interpretation. Researchers need to inspect quotations, document context, coding definitions, and exceptions before claiming that a frequently occurring feature represents high-quality implementation.
Implications for Education and Qualitative Researchers
The November 2, 2026 PLoS One study suggests that teacher-education researchers should examine design-thinking phases as connected processes rather than as a checklist.
For researchers studying lesson design, the most important analytical contrast is between construction-oriented phases and feedback-oriented phases. The study found that Define, Ideate, and Prototype were dominant, while Empathy appeared in 20.8% of plans and Test appeared in 54.2% of plans, indicating less consistent attention to user needs, evaluation, and iteration.
For qualitative research teams, the study also demonstrates the value of combining artifact analysis with participant accounts. Lesson plans show what groups produced, while questionnaires show how individual participants understood the task and where they experienced constraints.
For AI-assisted analysis, a defensible workflow should preserve source excerpts, distinguish document-level frequency from participant-level frequency, compare themes across segments, and flag contradictions for human review. The PLoS One study’s use of a codebook, inter-rater checks, and reconciliation provides a useful standard for keeping automation accountable.
The study’s ethics statement also matters for education research: the author made anonymized questionnaire responses publicly available but withheld participant-generated lesson plans because of confidentiality and identification concerns. Researchers using AI tools should therefore evaluate privacy, access controls, and whether sensitive educational data can be used safely.
How Evidano Supports AI Qualitative Research Analysis
Problem: Researchers must compare many lesson plans and responses
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, academic papers, reports, and other documents.
Researchers can upload lesson plans and questionnaire responses, organize them as a single qualitative project, and use thematic, content, frequency, and cross-segment analyses to compare how design-thinking phases appear across documents. Researchers can learn more about the platform on Evidano’s features page.
Problem: A theme can appear often without being implemented deeply
Solution: Researchers can use thematic analysis and AI chat over their documents to examine the evidence surrounding each code. For a study like the PLoS One case, a researcher could compare nominal references to “Test” with passages describing measurement, feedback, evaluation, and design revision.
The resulting analysis should support, not replace, the researcher’s codebook and interpretive decisions. Researchers can use visualizations such as hierarchical codes and subcodes to separate design-thinking phases from indicators of substantive mathematical integration.
Problem: Group artifacts and individual perspectives may diverge
Solution: Cross-segment analysis can compare lesson-plan groups, questionnaire respondents, grade levels, topics, or other researcher-defined segments. The comparison can show whether participants’ stated beliefs about student engagement or mathematical modeling are visible in the lesson plans they created.
Evidano’s AI chat can help researchers ask traceable questions across uploaded documents, while researchers remain responsible for checking excerpts, context, and competing interpretations.
Problem: Education data may contain sensitive participant information
Solution: Researchers should use privacy-conscious workflows when analyzing questionnaires, classroom materials, or teacher-education records. Evidano provides encrypted data handling and states that customer data is never used to train third-party models; researchers can review the platform’s data security information before uploading sensitive materials.
FAQ: AI Qualitative Research Analysis
What is AI qualitative research analysis?
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AI qualitative research analysis uses artificial intelligence to help researchers organize, code, compare, summarize, and visualize text-based qualitative data.
The November 2, 2026 PLoS One study illustrates a suitable use case because it combined lesson-plan artifacts with 55 open-ended questionnaire responses and analyzed both themes and frequencies. AI can accelerate these steps, but researchers should retain responsibility for definitions, interpretation, and validation.
What did the STEM design-thinking study find?
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The study found that pre-service mathematics teachers integrated design thinking partially and unevenly into STEM lesson plans. According to the study, Define appeared in 95.8% of plans, Prototype in 91.7%, Ideate in 83.3%, Test in 54.2%, and Empathy in 20.8%.
The study also reported challenges involving topic selection, interdisciplinary alignment, materials, timing, assessment, and curriculum coverage.
How many participants and documents were included in the study?
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The study included 55 senior pre-service mathematics teachers and 24 collaboratively developed lesson plans. The study collected individual questionnaire responses from all 55 participants and analyzed the lesson plans as group-level artifacts.
Can AI replace human coding in education research?
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AI should not replace human coding decisions in education research because meaning depends on context, theory, ethics, and the study’s operational definitions. The PLoS One study used a codebook, independent coding, inter-rater agreement, reconciliation, and triangulation, which are practices that can guide responsible AI-assisted workflows.
Researchers can use AI to surface patterns and candidate excerpts, then validate those outputs against the original documents and research questions.
What should researchers analyze besides theme frequency?
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Researchers should analyze context, co-occurrence, differences between segments, negative cases, and the depth of implementation in addition to theme frequency. The PLoS One study distinguished between a design-thinking phase being named and the phase being substantively implemented through mathematical reasoning and iterative evaluation.
Conclusion and Next Steps
The November 2, 2026 PLoS One study shows how qualitative education research can combine lesson-plan artifacts, open-ended responses, thematic coding, frequency analysis, and triangulation to explain uneven design-thinking integration.
AI qualitative research analysis can make this workflow more manageable by helping researchers search large document sets, compare segments, identify recurring patterns, and connect themes to supporting excerpts. Human researchers must still define what counts as meaningful implementation, protect participant privacy, and validate AI-generated findings against the source data.
If your team is analyzing interviews, open-ended surveys, lesson plans, or research documents, Try Evidano for free and turn qualitative evidence into a clearer, auditable research workflow.
Topics
- AI qualitative research analysis
- qualitative analysis in education
- AI thematic analysis
- STEM education research
- design thinking lesson plans
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