Education researchers and teacher educators need to understand not only whether design thinking appears in STEM lesson plans, but also how deeply each phase is implemented and how mathematics shapes the design process. The qualitative analysis of design thinking in STEM can reveal gaps that simple keyword counts miss, including whether empathy reflects genuine user research and whether testing leads to measurable iteration. A November 2, 2026 study by Rüveyda Karaman Dündar examined 55 senior pre-service mathematics teachers in Türkiye, combining 24 lesson plans with 55 open-ended questionnaires. This post summarizes the study and shows how AI-enabled qualitative research can help researchers compare documents, code recurring themes, and connect quantitative frequencies with participant perspectives.
Key Takeaways
According to Pre-service mathematics teachers’ integration of design thinking in STEM lesson plans: Insights from a qualitative case study, design thinking appeared unevenly in lesson plans created by 55 senior pre-service mathematics teachers in Türkiye, with Define, Ideate, and Prototype dominant while Empathy and Test were less developed.
The study shows why qualitative analysis of design thinking in STEM should combine frequency analysis with thematic interpretation: counting phases identifies gaps, while lesson plans and participant reflections explain why those gaps occurred.
- In the study published on November 2, 2026, researchers analyzed 24 collaborative lesson plans and open-ended questionnaire responses from 55 pre-service mathematics teachers.
- Define appeared in 95.8% of lesson plans, Ideate in 83.3%, and Prototype in 91.7%, according to the November 2, 2026 PLOS One study.
- Empathy appeared in only 20.8% of lesson plans, while Test appeared in 54.2%, according to the study's lesson-plan analysis.
- The study reported 90% initial inter-rater agreement for a 25% questionnaire sample and 93.1% initial agreement between two raters evaluating lesson-plan STEM features.
- The study found that participants valued design thinking but reported challenges involving topic selection, discipline alignment, assessment, time, and materials.
What the Qualitative STEM Education Study Examined
The November 2, 2026 PLOS One study used a qualitative case-study design to examine how pre-service mathematics teachers integrated design thinking into mathematics-focused STEM lesson plans.
According to the study, the participants completed a 15-week course with weekly two-hour sessions during the 2021 to 2022 academic year. The researchers analyzed two connected data sources: 24 group-created lesson plans and individually completed open-ended questionnaires from 55 participants.
The study analyzed design thinking through five phases: Empathy, Define, Ideate, Prototype, and Test. The researchers also examined whether mathematics functioned as a meaningful analytical tool rather than appearing only as background content.
The researchers used bottom-up thematic analysis for the questionnaire data. The analysis moved through familiarization, initial coding, category development, and theme construction, while a second researcher reviewed a randomly selected 25% sample.
The study reported that the lesson plans were evaluated using a STEM feature rubric, while the design thinking phases were coded for robust implementation rather than simple mention. The researchers therefore distinguished between a phase being labeled in a plan and the phase being substantively connected to mathematical reasoning.
Snapshot: Design Thinking Phase Frequencies in November 2026
| Date | Metric | Value | Implication |
|---|---|---|---|
| November 2, 2026 | Study participants | 55 senior pre-service mathematics teachers | The findings describe one teacher-education cohort rather than a general population. |
| November 2, 2026 | Lesson plans analyzed | 24 collaborative plans | The study examined enacted planning artifacts alongside individual reflections. |
| November 2, 2026 | Define phase | 95.8% | Problem definition was present in nearly all analyzed lesson plans. |
| November 2, 2026 | Prototype phase | 91.7% | Construction activities were common in the lesson plans. |
| November 2, 2026 | Ideate phase | 83.3% | Idea generation was frequent, but not universal. |
| November 2, 2026 | Test phase | 54.2% | Fewer than three in five plans included testing, indicating limited iteration. |
| November 2, 2026 | Empathy phase | 20.8% | User-centered inquiry was the least represented design thinking phase. |
| November 2, 2026 | Initial questionnaire inter-rater agreement | 90% | Independent coding showed strong initial consistency before reconciliation. |
Implications for Researchers Analyzing STEM Lesson Plans
The November 2, 2026 PLOS One study suggests that researchers should analyze design thinking as a process, not merely as a checklist of named phases.
The study found that Define, Ideate, and Prototype formed the dominant procedural core, while Empathy and Test were often missing or less substantive. This pattern indicates that lesson-plan analysis should distinguish product completion from user-centered, iterative design.
The study also connected weak phase implementation with practical and conceptual barriers. Participants described difficulty aligning design activities with mathematics and other STEM disciplines, selecting suitable materials, developing assessments, and fitting design work into curriculum schedules.
For qualitative researchers, the study provides a useful mixed evidence model: document analysis shows what lesson plans contain, thematic analysis explains participants’ reasoning, and frequency analysis makes recurring patterns visible.
The study's participant evidence illustrates the value of preserving verbatim language during analysis. One participant said, "The most challenging part was aligning the design with other disciplines and finding suitable learning outcomes." Another participant said, "I think design is necessary, but it should not take too much time because there is a program that needs to be covered." These quotations connect coding categories to the lived planning constraints reported by participants.
The findings should be interpreted as research evidence from one state university cohort in Türkiye, not as a universal measure of mathematics teacher preparation. The study itself used a bounded qualitative case and reported that the participant-generated lesson plans were not publicly available because of confidentiality and ethics restrictions.
How Evidano Helps with Qualitative Analysis of Design Thinking
Problem: Researchers must compare lesson-plan documents with participant reflections
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, lesson plans, and research documents.
Researchers can upload lesson plans and questionnaire responses, then use thematic, content, frequency, and cross-segment analyses to compare intended design processes with reported experiences. The Evidano features overview describes the platform's document and spreadsheet analysis capabilities.
Problem: Phase frequencies do not show whether implementation is substantive
Evidano can help researchers organize hierarchical codes such as Design Thinking, Empathy, Define, Ideate, Prototype, Test, Mathematical Reasoning, Assessment, Materials, and Curriculum Alignment.
Researchers can use AI chat over uploaded documents and analyses to ask whether a phase is merely mentioned or connected to evidence such as user feedback, measurements, testing, or design refinement. Human researchers should review AI-generated interpretations against the source passages before publication.
Problem: Cross-group patterns are difficult to audit manually
Evidano can support cross-segment analysis across lesson-plan groups, grade levels, mathematical domains, or questionnaire themes. Researchers can compare whether plans with stronger mathematics integration also contain more evidence of testing and iteration.
Evidano's visualizations, including code hierarchies and co-occurrence networks, can help research teams inspect relationships among design thinking phases, mathematics, assessment, and reported constraints.
Problem: Sensitive education research requires careful data handling
Evidano encrypts data and states that uploaded research data is never used to train third-party models. Researchers working with participant responses should still apply informed-consent requirements, remove identifying information, and follow institutional ethics approvals. Details are available in Evidano's data security information.
FAQ: Qualitative Analysis of Design Thinking in STEM
What did the 2026 qualitative 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 integrated design thinking partially and unevenly across 24 STEM lesson plans.
Define, Ideate, and Prototype appeared most often, while Empathy appeared in 20.8% of plans and Test appeared in 54.2%.
Why was the Empathy phase uncommon in the study?
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The November 2, 2026 PLOS One study attributed the low representation of Empathy to limited operationalization of user-centered inquiry in mathematics-focused lesson planning.
The study reported that participants often treated curricular objectives as the starting point instead of gathering evidence about user needs and perspectives.
How did researchers analyze the qualitative questionnaire data?
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The November 2, 2026 PLOS One study analyzed questionnaire responses inductively through familiarization, initial coding, category development, and theme construction.
The researchers used a codebook, second-researcher review, reconciliation, and triangulation with lesson-plan findings to strengthen analytic credibility.
What challenges did the pre-service teachers report?
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The November 2, 2026 PLOS One study found that participants reported challenges with choosing topics, aligning mathematics with other disciplines, selecting materials, assessing student performance, managing time, and fitting design thinking into curriculum requirements.
One participant summarized the problem by saying, "It was challenging to find up-to-date questions for each DT step and to evaluate the originality of students’ designs."
Can AI replace human interpretation in qualitative STEM research?
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AI can accelerate coding, retrieval, comparison, and frequency analysis, but AI should not replace researcher judgment in qualitative STEM research.
The PLOS One study used explicit coding procedures, multiple researchers, inter-rater checks, and reconciliation, providing a model for keeping human oversight in an AI-assisted workflow.
Conclusion and Next Steps
The November 2, 2026 PLOS One study shows that design thinking can be visible in STEM lesson plans while remaining incomplete as a user-centered and iterative process. Qualitative analysis makes that distinction clearer by combining phase frequencies, document evidence, participant quotations, and thematic interpretation.
AI-enabled qualitative research can help education researchers move from manual document review to a more organized workflow for coding lesson plans, comparing participant perspectives, and tracing evidence across themes. Human review remains essential for judging context, meaning, ethics, and the difference between a named phase and genuine implementation.
Researchers studying design thinking, mathematics education, or teacher preparation can Try Evidano for free to analyze qualitative documents and open-ended responses in one workspace.
Topics
- qualitative analysis of design thinking in STEM
- AI qualitative research analysis
- design thinking lesson plan analysis
- qualitative STEM education research
- thematic analysis of lesson plans
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