Map STEM Gaps: Qualitative Analysis of Design Thinking

According to the PLoS ONE study, “Pre-service mathematics teachers’ integration of design thinking in STEM lesson plans”, pre-service mathematics teachers integrated design thinking into STEM lesson plans unevenly, with construction-oriented phases appearing more often than user-centered inquiry and iterative testing.

The November 2, 2026, study shows why qualitative analysis of design thinking should examine both observable phase frequencies and the meanings participants assign to curriculum, time, materials, and assessment constraints.

• The November 2, 2026, study included 55 senior pre-service mathematics teachers at a state university in Türkiye and analyzed 24 collaboratively developed lesson plans plus individual open-ended questionnaires.
• Define appeared in 95.8% of lesson plans, Ideate in 83.3%, and Prototype in 91.7% in the November 2, 2026, study.
• Empathy appeared in only 20.8% of lesson plans, while Test appeared in 54.2%, according to the November 2, 2026, study.
• The November 2, 2026, study reported that 43 participants believed mathematics teachers should be able to develop design-thinking lesson plans, while 30 participants identified time consumption as a disadvantage.
• AI-enabled qualitative research can help researchers connect frequency patterns with coded explanations, quotations, and cross-segment differences.

The November 2, 2026, PLoS ONE study found that pre-service mathematics teachers often treated design thinking as a mostly linear route from defining a problem to generating an idea and building a product.

The study used a qualitative case study design bounded by a 15-week course during the 2021 to 2022 academic year. Researchers examined 24 group lesson plans and 55 individual questionnaire responses, then triangulated lesson-plan evidence with participant reflections.

The study evaluated lesson plans through a STEM integration rubric and coded five design-thinking phases: Empathy, Define, Ideate, Prototype, and Test. The researchers distinguished between a phase being mentioned and a phase being substantively implemented with mathematical reasoning.

The study reported strong initial agreement between raters: 93.1% for the lesson-plan analysis and 90% for an independently coded 25% sample of questionnaire data. These reliability procedures matter because AI-assisted analysis should support an auditable chain from source text to code and theme, rather than replace methodological judgment.

One participant, PMT 25, described the central planning difficulty as: “The most challenging part was aligning the design with other disciplines and finding suitable learning outcomes.” PMT 16 added: “I think design is necessary, but it should not take too much time because there is a program that needs to be covered.”

The November 2, 2026, study implies that STEM researchers should measure design-thinking integration as a multidimensional process rather than as a yes-or-no label.

• Separate structural presence from substantive use: a lesson plan can name Empathy or Test without showing user research, measurement, feedback, or mathematical optimization.
• Compare planned practice with reported experience: the 24 lesson plans show what groups designed, while the 55 questionnaires show how individuals understood barriers and benefits.
• Track missing phases as analytic findings: the low Empathy frequency points to limited user-centered inquiry, while the lower Test frequency suggests constrained iteration and refinement.
• Preserve participant language: PMT 41 said, “It was challenging to find up-to-date questions for each DT step and to evaluate the originality of students’ designs.” Such quotations explain why a phase may be absent.
• Use cross-segment analysis carefully: researchers can compare lesson-plan scores with reported concerns about resources, curriculum alignment, assessment, or classroom management, while keeping the single-cohort limitation visible.

The November 2, 2026, study also reported positive expectations. Participants associated design-thinking lessons with active learning, contextual mathematics, creativity, collaboration, and mathematical modeling, but they simultaneously described practical constraints involving time, materials, classroom size, and assessment.

For AI-enabled qualitative research, the decision rule is straightforward: use AI to accelerate organization and comparison, then use researcher review to validate interpretations against the source excerpts and study design.

The November 2, 2026, PLoS ONE study shows that qualitative analysis of design thinking can reveal a meaningful gap between naming design-thinking stages and implementing a user-centered, iterative process. The study's combination of 24 lesson plans, 55 questionnaires, phase frequencies, thematic coding, and participant quotations provides a practical model for mixed qualitative evidence. AI-enabled qualitative research can make that workflow faster by connecting documents, codes, frequencies, comparisons, and excerpts, while researchers retain responsibility for interpretation and ethics. To analyze education research and open-ended data more efficiently, try Evidano for free.