Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. This post explains the AIGC-assisted product design process from a qualitative-research perspective, using the PLOS One case study on AI-generated camping cookware to show methods you can replicate. The PLOS One study, published on August 17, 2026, combined GIOIA-based interview coding, Delphi expert review, FAHP weighting, Midjourney fuzzy generation, VIKOR decision ranking, Stable Diffusion refinement, and Fuzzy Comprehensive Evaluation to prioritize user needs and surface sustainability pathways.
Key Takeaways
AIGC-assisted product design can be structured around rigorous qualitative inputs and quantitative decision rules, as demonstrated by the PLOS One study (PLOS One) published on August 17, 2026. The PLOS One authors concluded that "AIGC can accurately reproduce design schemes, improve design efficiency and promote creativity generation, " and they used mixed qualitative and quantitative methods to reach that claim.
- The PLOS One study conducted 22 semi-structured interviews in 2025 to generate user requirement texts and visual stimuli for camping cookware.
- The PLOS One study collected 128 valid questionnaire responses in 2026 for Fuzzy Comprehensive Evaluation, producing a composite satisfaction score of 80.508 in August 2026.
- The PLOS One FAHP weighting results (published August 17, 2026) placed functional requirements at weight 0.348 and safety at 0.238, with experiential needs at 0.224.
What happened and how the study measured it
Answer: The PLOS One paper (published August 17, 2026) built a full AIGC-assisted concept design workflow starting from qualitative interviews and ending with user satisfaction validation.
The PLOS One research team collected 23 initial product images and then ran 22 semi-structured interviews in 2025 to extract user needs, as reported in the Methods section of PLOS One. The authors applied the GIOIA method to move from raw interview quotes to second-order themes clustered against Maslow’s hierarchy, and then used a 10-expert Delphi panel (5 university professors and 5 practicing designers) to validate dimensions, as described in the PLOS One article.
The PLOS One authors assigned weights using Fuzzy Analytic Hierarchy Process (FAHP) and reported that functional requirements (weight 0.348) and safety requirements (weight 0.238) were top priorities in August 2026. The authors then constrained AIGC inputs using prompt formula cards, ran Midjourney for fuzzy ideation, filtered options with VIKOR, refined visuals with Stable Diffusion Control Net, and validated the selected concept with 128 survey respondents using Fuzzy Comprehensive Evaluation.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 2024 | Global luxury camping market value | USD 3, 205 million | Context: growing glamping demand that motivates product innovation (PLOS One reference) |
| 2025 | Semi-structured interviews | 22 participants | Source data for GIOIA coding and theme extraction (PLOS One, published Aug 17, 2026) |
| August 17, 2026 | Overall FCE satisfaction score | 80.508 (satisfactory) | Validates human–AIGC workflow for concept-stage acceptance (PLOS One) |
| August 17, 2026 | FAHP weights (top four) | Functional 0.348; Safety 0.238; Experiential 0.224; Collaborative 0.111 | Design priority guidance used to sequence AIGC prompt inputs (PLOS One) |
Implications for product researchers and design teams
Answer: The PLOS One workflow shows that qualitative rigor plus structured weighting improves AIGC outputs and sustainability alignment.
Design teams should collect visual stimuli, record semi-structured interviews, and apply a transparent coding method such as GIOIA, as used in the PLOS One study, so that AIGC prompts are grounded in user language and priorities.
The PLOS One authors recommend using prompt formula cards (reference image + target product + main details + viewpoint + stylism + background + light + clarity + parameters) to standardize inputs across generative models, improving reproducibility and reducing wasted GPU cycles.
How Evidano helps researchers apply this workflow
Problem: Raw interviews and images are scattered and slow to synthesize
Solution: Evidano can ingest audio, transcripts, and image libraries and produce thematic and frequency analyses to surface first-order codes for AIGC prompt design.
Evidano supports transcription and redaction, which helps teams reproduce the PLOS One step where 22 interviews were transcribed and cross-referenced, and the platform can export structured outputs you can feed into prompt formula cards. See the Evidano features page for capabilities.
Problem: Prompt engineering needs structured, weighted inputs
Solution: Evidano generates weighted thematic summaries and cross-segment comparisons so designers can order prompt tokens according to FAHP-style priorities (for example, functional first, emotional later).
Evidano’s AI chat over your documents lets researchers iterate prompts quickly against the same coded evidence set, reproducing the PLOS One approach of sequencing prompt content by weight.
Problem: Validating concepts with mixed qualitative and quantitative evidence
Solution: Evidano supports integrated analysis of open-ended survey responses and structured ratings, enabling a Fuzzy Comprehensive Evaluation style validation at scale and segment-level comparisons similar to the 128-respondent FCE in PLOS One.
Evidano also links to transcription and speech-to-text capabilities for interview capture; see Evidano speech-to-text.
FAQ: aigc-assisted product design
What is an AIGC-assisted product design process?
Answer: An AIGC-assisted product design process is a human-guided workflow that uses generative AI to produce visual and conceptual options constrained by user-derived requirements.
The PLOS One study (published August 17, 2026) exemplifies this by combining qualitative interviews, GIOIA coding, Delphi expert review, FAHP weighting, prompt formula cards, Midjourney fuzzy generation, VIKOR ranking, Stable Diffusion refinement, and FCE validation.
How did the PLOS One study measure and prioritize user needs?
Answer: The PLOS One authors used the GIOIA method to code 22 interview transcripts and then applied a Delphi panel and FAHP to assign weights to 19 indicators across five dimensions.
Specifically, the FAHP weights reported on August 17, 2026 were: functional 0.348, safety 0.238, experiential 0.224, collaborative 0.111, and emotional 0.079, which guided the order of prompts fed to AIGC tools.
Can AIGC improve sustainability in product design?
Answer: AIGC can reduce material waste at the conceptual stage by enabling more iterations in silico, but lifecycle sustainability gains must be empirically measured in later stages.
The PLOS One authors caution (August 17, 2026) that their sustainability benefits are potential because the study stops at conceptual design and does not include quantified life-cycle assessment.
How can qualitative researchers reduce bias when using AIGC outputs?
Answer: Researchers should ground prompts in systematically coded user data, apply expert validation such as Delphi rounds, and use multi-metric evaluation like VIKOR and FCE.
The PLOS One workflow used image masking, representative sampling, GIOIA coding, and a 10-member expert panel to limit fixation and model-driven bias, as described in their Methods (PLOS One, published August 17, 2026).
Conclusion & Next Steps
The PLOS One case study (published August 17, 2026) shows a repeatable AIGC-assisted product design process that starts from rigorous qualitative coding and ends with decision-science evaluation tools.
Design teams and qualitative researchers can reproduce the study’s steps: collect visual stimuli, run semi-structured interviews, apply GIOIA coding, validate with Delphi and FAHP, use prompt formula cards for Midjourney fuzzy ideation, filter with VIKOR, refine with Stable Diffusion, and validate with FCE.
If you want to run the qualitative-to-AIGC pipeline in your team, Evidano can ingest interviews, generate thematic and weighted summaries, and accelerate prompt development and validation; learn more on our features page.
Get hands-on: Try Evidano for free.
Topics
- aigc-assisted product design
- AIGC-assisted design process
- AI qualitative research
- prompt engineering for design
- human-AIGC collaboration
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