Education researchers often need to compare lesson plans, questionnaire responses, and participant reflections before they can explain how an instructional approach works in practice. AI qualitative analysis in education research can help researchers organize those materials, identify recurring themes, and connect qualitative findings with structured counts. The November 2, 2026 PLOS One study by Rüveyda Karaman Dündar provides a useful example: 55 senior pre-service mathematics teachers in Türkiye created 24 design thinking focused STEM lesson plans, while researchers analyzed plans and open-ended questionnaire responses. This article explains what the study found, how its qualitative method worked, and how AI can support similar education research without replacing researcher judgment.
Key Takeaways
According to the PLOS One study on design thinking in mathematics STEM lessons, pre-service mathematics teachers integrated design thinking unevenly, with strong use of Define, Ideate, and Prototype phases but limited use of Empathy and Test in lesson plans published on November 2, 2026.
- The November 2, 2026 study analyzed 24 group lesson plans and questionnaire responses from 55 senior pre-service mathematics teachers at a state university in Türkiye.
- The study found that Define appeared in 95.8% of lesson plans, Prototype in 91.7%, and Ideate in 83.3% on November 2, 2026.
- The Empathy phase appeared in only 20.8% of lesson plans, while the Test phase appeared in 54.2% on November 2, 2026.
- The study reported that 43 participants believed mathematics teachers should be prepared to create design thinking lesson plans, while 30 participants identified time consumption as a disadvantage on November 2, 2026.
- The study’s qualitative analysis used familiarization, initial coding, category development, and theme construction, with a second researcher independently coding 14 of the 55 participants’ responses and achieving 90% initial agreement on November 2, 2026.
What the Education Study Found
The November 2, 2026 PLOS One study found that pre-service mathematics teachers valued design thinking but often treated it as a largely linear route from problem definition to product construction.
According to the study, participants completed a 15-week course with weekly two-hour sessions during the 2021 to 2022 academic year, then worked in groups of two or three to create mathematics-focused STEM lesson plans. Researchers analyzed 24 lesson plans and individually collected eight open-ended questionnaire responses from each participant.
The study used a qualitative case study design bounded by one cohort, one course, and one instructional context. The study combined lesson-plan analysis with inductive thematic analysis of questionnaire responses, allowing researchers to compare enacted planning practices with participants’ reported experiences.
The lesson-plan results showed a strong emphasis on mathematical content and creative application. According to the November 2, 2026 study, mathematics was central in 18 of 24 plans, creative application appeared in 18 plans, subject interdependence appeared in 15 plans, and essential STEM inclusion appeared in 14 plans.
The study identified a practical gap between naming a design thinking phase and implementing it meaningfully. The study’s coding protocol treated a phase as robust only when the lesson plan operationalized mathematical reasoning, quantitative analysis, or evidence-based justification rather than merely labeling an activity.
Participants described several barriers. According to the November 2, 2026 study, the most frequently reported challenge codes included choosing a topic with 18 mentions, difficulties with design thinking with 13 mentions, and planning and discipline alignment with 10 mentions.
One participant, identified as PMT 25, said, "The most challenging part was aligning the design with other disciplines and finding suitable learning outcomes." Another participant, identified as PMT 16, said, "I think design is necessary, but it should not take too much time because there is a program that needs to be covered."
Findings Snapshot: Design Thinking in STEM Lesson Plans
| Date | Metric | Value | Implication |
|---|---|---|---|
| November 2, 2026 | Study participants | 55 senior pre-service mathematics teachers | The findings describe one purposive cohort rather than all mathematics teachers. |
| November 2, 2026 | Lesson plans analyzed | 24 group lesson plans | The study examined planned instructional practice as a primary unit of analysis. |
| November 2, 2026 | Define phase | 95.8% of plans | Problem definition was present in nearly every lesson plan. |
| November 2, 2026 | Prototype phase | 91.7% of plans | Participants commonly translated ideas into products or models. |
| November 2, 2026 | Ideate phase | 83.3% of plans | Idea generation was substantially more common than user-centered inquiry. |
| November 2, 2026 | Empathy phase | 20.8% of plans | Most plans did not explicitly investigate user needs. |
| November 2, 2026 | Test phase | 54.2% of plans | Testing appeared in more than half of plans but was less common than construction phases. |
| November 2, 2026 | Questionnaire coding agreement | 90% before discussion | The researchers reported strong initial consistency between qualitative coders. |
Implications for Education Researchers Using AI Qualitative Analysis
Education researchers can use AI qualitative analysis to make uneven implementation visible across lesson plans, interviews, and open-ended questionnaires, while retaining human review of context and meaning.
The November 2, 2026 study shows why frequency alone is not enough. A design thinking phase can be mentioned in a lesson plan without being substantively implemented, so an analysis should distinguish nominal occurrence from evidence of mathematical reasoning, user inquiry, testing, and iteration.
- Researchers should create a code hierarchy that separates design thinking phases from evidence of mathematical integration, because the November 2, 2026 study evaluated both procedural presence and implementation quality.
- Researchers should compare documents with participant reflections, because the November 2, 2026 study triangulated group lesson plans with individual questionnaire responses.
- Researchers should preserve quotations alongside themes, because the November 2, 2026 study used participant language to explain barriers involving topic selection, assessment, materials, time, and disciplinary alignment.
- Researchers should analyze segments such as lesson-plan group, participant, grade level, or STEM topic, because the November 2, 2026 study found variation across shared plans and individual interpretations.
- Researchers should treat AI-generated themes as an auditable starting point rather than a final conclusion, because the November 2, 2026 study used independent coding, inter-rater comparison, reconciliation, and consensus.
The study’s results also suggest a specific analytical question for future education research: does a lesson plan include design thinking as a complete iterative cycle, or does it primarily use design thinking to support construction? AI-assisted thematic and cross-segment analysis can help researchers test that question across larger collections of plans and responses, but the researcher must define what counts as substantive evidence.
How Evidano Helps With AI Qualitative Analysis in Education
Problem: Researchers must synthesize lesson plans and open-ended responses
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, papers, reports, and other documents.
Researchers can upload lesson plans and questionnaire responses, then use thematic, content, frequency, and cross-segment analyses to examine where design thinking phases appear and how participants describe their implementation challenges. The Evidano features page explains the platform’s analysis capabilities.
Problem: A phase label does not prove meaningful implementation
Evidano can help researchers build hierarchical codes and subcodes, such as Design Thinking, Empathy, User Needs, Testing, Measurement, and Mathematical Reasoning.
Researchers can review coded evidence, compare code frequencies, and use visualizations such as hierarchical code views and co-occurrence networks to inspect relationships between design thinking phases and mathematical integration. Human researchers remain responsible for deciding whether a coded passage represents robust or superficial implementation.
Problem: Group plans and individual perspectives may diverge
Evidano supports cross-segment analysis, which can help researchers compare lesson-plan groups, questionnaire respondents, grade levels, or selected STEM topics.
This workflow mirrors the November 2, 2026 study’s embedded design, which compared group-level lesson plans with individual reflections. Researchers can then use AI chat over their documents and analyses to locate supporting passages and investigate differences between planned practice and reported experience.
Problem: Education data may contain sensitive participant information
Evidano provides transcription with PII redaction, encrypted data handling, and a policy that data is never used to train third-party models, which is relevant when education research includes identifiable interviews or questionnaire responses.
Researchers should still obtain appropriate consent, follow institutional ethics requirements, and remove identifying information according to their research protocol. More information is available on the Evidano data security page.
FAQ: AI Qualitative Analysis in Education Research
What did the 2026 study find about design thinking in mathematics STEM lesson plans?
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The November 2, 2026 PLOS One study found that pre-service mathematics teachers used Define, Ideate, and Prototype more often than Empathy and Test in their STEM lesson plans.
The study analyzed 24 lesson plans produced by 55 senior pre-service mathematics teachers and concluded that design thinking integration was often partial and unbalanced.
Why was the Empathy phase important in the education study?
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The Empathy phase was important because it represented explicit attention to users’ needs and perspectives, yet it appeared in only 20.8% of lesson plans in the November 2, 2026 study.
The study interpreted the low frequency as evidence that participants often replaced user-centered inquiry with curricular objectives or assumed user needs.
How can AI support thematic analysis of education research?
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AI can support thematic analysis of education research by organizing documents, suggesting recurring codes, grouping related passages, counting code occurrences, and comparing themes across segments.
The November 2, 2026 study used familiarization, initial coding, category development, and theme construction, so researchers should use AI to accelerate these activities while checking interpretations against the original data.
Can AI qualitative analysis distinguish between a mentioned phase and a robust phase?
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AI qualitative analysis can help distinguish a mentioned phase from a robust phase when researchers define explicit coding criteria and review the supporting text.
The November 2, 2026 study classified robust implementation using evidence of mathematical reasoning, quantitative analysis, or evidence-based justification, rather than relying on a phase label alone.
What were the main challenges reported by the pre-service mathematics teachers?
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The main challenges reported in the November 2, 2026 study were selecting appropriate topics, integrating design thinking across disciplines, aligning learning outcomes, managing materials and time, and assessing student performance.
The study reported that 30 participants identified time consumption as a disadvantage and that 10 participants mentioned materials or suitable environments as challenges.
Is AI qualitative analysis a replacement for education researchers?
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AI qualitative analysis is not a replacement for education researchers because researchers must define concepts, assess context, protect participants, and justify interpretations.
The November 2, 2026 study strengthened credibility through a second coder, 90% initial agreement, reconciliation, and triangulation, practices that illustrate why human oversight remains central.
Conclusion and Next Steps
The November 2, 2026 PLOS One study shows that qualitative education research can reveal more than whether design thinking appears in a lesson plan. The study identifies which phases are present, where mathematical reasoning is integrated, what participants find difficult, and how planned practice differs from participants’ reflections.
AI qualitative analysis can make that workflow more efficient by organizing documents, supporting thematic and frequency analysis, comparing segments, and preserving quotations for verification. Researchers should use AI as an auditable research assistant, not as an unsupported substitute for methodological judgment.
To analyze lesson plans, open-ended surveys, and education research documents with a qualitative research workflow, Try Evidano for free.
Topics
- AI qualitative analysis in education research
- qualitative analysis of education research
- AI thematic analysis for education
- design thinking STEM lesson plans
- qualitative case study analysis
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