AI Qualitative Analysis for Mixed-Methods Research

The PLoS ONE study found that information technology students reported substantial physical discomfort and limited occupation-specific training, while interviews helped explain gaps in curriculum resources and school-enterprise collaboration.

AI qualitative analysis is most useful in this type of mixed-methods research when it links recurring interview themes to survey findings without treating qualitative context as statistical proof.

• Published on November 10, 2026, the study retained 1, 924 questionnaires from 1, 863 students, 37 teachers, and 24 enterprise personnel after screening 2, 106 returned questionnaires.
• On November 10, 2026, the authors reported that 68.71% of students selected “Frequently” or “Occasionally” for physical discomfort after activities such as prolonged computer use or equipment debugging.
• The study reported 30 qualitative interviews, including 21 students, 6 teachers, and 3 enterprise personnel, to provide contextual explanations for the questionnaire findings.
• The study’s interviews identified three themes: student occupational-physical-fitness problems, insufficient institutional resources, and weak alignment between enterprise demands and school-based training.
• The authors proposed a preliminary framework of general physical fitness, occupation-specific physical fitness, and psychological fitness, but stated that the framework requires further validation.

The 2026 PLoS ONE study used a quantitative-dominant cross-sectional mixed-methods design in selected vocational colleges and information technology enterprises in Shanxi Province, China.

According to the study authors, the questionnaire was the primary method and semi-structured interviews were a supplementary explanatory method. The research team analyzed the quantitative and qualitative strands separately before integrating them during interpretation.

The study collected data between April 20 and May 29, 2025, and the article was published on November 10, 2026. The final quantitative sample included 1, 863 students, 37 teachers, and 24 enterprise personnel. The qualitative component included 30 interview participants.

The authors described the proposed framework as “general physical fitness–occupation-specific physical fitness–psychological fitness.” The 12 indicators covered general abilities such as endurance and flexibility, information-technology-specific demands such as visual load tolerance and sedentary tolerance, and psychological capacities such as sustained concentration and stress resistance.

The study authors reported that 44.50% of students selected dry eyes or blurred vision, 38.06% selected cervical or lumbar soreness, and 25.93% selected physical fatigue or insufficient endurance. The source treated these as self-reported questionnaire responses, not diagnoses or causal estimates.

The qualitative evidence added explanation to those numbers. According to the study authors, students and teachers described limited inclusion of visual relaxation, musculoskeletal relaxation, postural adjustment, and sedentary-behavior interruption in current educational practice.

The November 10, 2026 PLoS ONE study demonstrates how mixed-methods research can combine a large questionnaire sample with interviews that explain curriculum, training, and stakeholder constraints. AI qualitative analysis can make that workflow more manageable by organizing documents, comparing segments, finding recurring themes, and preserving a clearer path from evidence to interpretation. The strongest workflow treats AI as a research assistant, not as a replacement for methodological judgment, ethics review, or source verification. Researchers analyzing interviews, open-ended surveys, or mixed-methods datasets can try Evidano for free.