AI-powered Qualitative Research: Participatory Evaluation

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One case study by Björnqvist et al. (2026), strategy mapping software was used in a four-workshop participatory evaluation with 15 healthcare professionals between April and June 2023 to generate ten participant-developed recommendations. The primary audience for this post is qualitative researchers and evaluation teams who want to use AI-enabled qualitative research to scale stakeholder workshops, translate and verify multi-language artifacts, and generate actionable recommendations without losing participatory ownership.

Key Takeaways

According to the PLOS One study (Björnqvist et al., 2026), a strategy mapping tool run across four workshops with 15 participants produced ten recommendations and, when followed up by ten interviews in spring 2024, showed that most recommendations had been used to some degree. PLOS One

15 participants took part in the workshop series held on 3 April 2023, 6 April 2023, 24 April 2023, and 12 June 2023, according to Björnqvist et al. (PLOS One, 2026).
The workshops resulted in 10 participant-developed recommendations, as reported in the PLOS One article published on 20 August 2026.
Approximately one year later, in spring 2024, the authors conducted 10 semi-structured follow-up interviews to assess perceived use and value of the recommendations (Björnqvist et al., 2026).
The authors report that the method had impact but recommend clearer facilitator involvement to improve recommendation clarity and actionability (Björnqvist et al., 2026).

What Happened: PLOS One case using strategy mapping

What happened: According to Björnqvist et al. (PLOS One, 2026), researchers used strategy mapping software (Strategyfinder) in four workshops with 15 employees of a Swedish regional unit for communicable disease and infection control between April and June 2023 to map risks from the COVID-19 response and produce recommendations.

The PLOS One study (Björnqvist et al., 2026) ran a blind gather on 3 April 2023, validation on 6 April 2023, recommendation generation on 24 April 2023, and a final in-person validation on 12 June 2023; the process produced ten recommendations which were later evaluated in ten follow-up interviews in spring 2024.

The PLOS One authors explicitly used a large language model (M365 Copilot, based on GPT-5) to translate Swedish statements into English and to recreate the visual structure of maps, with the authors reviewing and verifying the outputs against the originals (Björnqvist et al., 2026).

The study authors conclude that “strategy mapping methodology is a viable approach for conducting impactful participatory evaluations of crisis management efforts” (Björnqvist et al., 2026).

Findings Snapshot

Date	Metric	Value	Implication
3 April 2023	Workshop 1 (blind gather + causal mapping)	15 participants started	Generated initial risk system used for clustering and causal linking
6 April 2023	Workshop 2 (validation)	Subsystems created and rated	Validated map structure and guided focus for recommendations
24 April 2023	Workshop 3 (recommendation generation + ratings)	Recommendations drafted and rated for feasibility	Produced candidate recommendations for final validation
12 June 2023	Workshop 4 (in-person validation)	Finalized 10 recommendations	Recommendations packaged for reporting and follow-up
Spring 2024	Follow-up interviews	10 interviews conducted	Used to assess perceived use and value of recommendations
20 August 2026	Publication	PLOS One article published	Peer-reviewed documentation and dataset deposit instructions

Implications for qualitative researchers using AI-enabled methods

Answer: The PLOS One case shows that combining participatory strategy mapping with targeted AI support can scale multilingual synthesis while preserving stakeholder ownership.

According to Björnqvist et al. (PLOS One, 2026), using AI for translation (M365 Copilot, GPT-5) allowed the team to recreate maps and preserve statements’ meaning while the authors verified outputs against originals; the authors emphasize that the AI was not used for analysis or interpretation.

Practical implication 1: Use AI for mechanical tasks such as transcription, translation, and visual recreation to reduce facilitator workload and shorten multi-session processes; the PLOS One team ran a four-session sequence that some participants felt was too long (Björnqvist et al., 2026).

Practical implication 2: Define what “use” of recommendations means before follow-up, because Björnqvist et al. (PLOS One, 2026) found variation in participants’ definitions of implementation during the spring 2024 interviews.

Practical implication 3: Balance participatory ownership with facilitator editing: Björnqvist et al. (PLOS One, 2026) recommend increased facilitator involvement to improve clarity and actionability of participant-written recommendations.

How Evidano Helps

Problem: Long workshop outputs, manual synthesis

Answer: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

Solution: For workshop-based studies like the PLOS One case, Evidano ingests recordings and transcripts, applies thematic coding and frequency analysis, and produces cross-segment summaries to convert multi-session maps into prioritized action lists. See the platform overview at Evidano Features.

Problem: Translation and map recreation across languages

Answer: According to Björnqvist et al. (PLOS One, 2026), the authors used an LLM for translation and then verified output manually.

Solution: Evidano’s translation and custom dictionary features support verified translations and preserve domain terms during mapping, reducing a verifier’s workload compared to fully manual translation; see Evidano Translation.

Problem: Tracking recommendation use over time

Answer: Björnqvist et al. (PLOS One, 2026) recommended clearer definitions and follow-up to measure implementation.

Solution: Evidano’s cross-segment analyses and AI chat over documents enable quick follow-up queries across transcripts, meeting notes, and plans so evaluation teams can produce evidence logs showing whether a recommendation is planned, in progress, or implemented.

Problem: Transcription and PII concerns

Answer: The PLOS One study used Zoom-recorded interviews and a manual correction step for transcripts.

Solution: Evidano’s speech-to-text pipeline with custom dictionaries and PII redaction automates accurate transcripts while protecting privacy; see Evidano Speech-to-Text.

FAQ: AI-enabled qualitative research

What is AI-enabled qualitative research for participatory evaluation?

Answer: AI-enabled qualitative research uses machine-assisted tools to speed transcription, translation, coding, and synthesis while preserving human judgment in interpretation.

According to Björnqvist et al. (PLOS One, 2026), the study used an LLM (M365 Copilot, GPT-5) for translation and verification only, and the authors kept analysis and interpretation human-led to preserve participatory ownership.

Can large language models replace facilitators in strategy mapping?

Answer: No, LLMs can assist with tasks like translation and layout but cannot replace facilitator judgment in shaping actionable recommendations.

The PLOS One authors explicitly state the LLM was not used for analysis or interpretation and recommend active facilitator involvement to improve recommendation clarity (Björnqvist et al., 2026).

How should teams measure whether recommendations are used?

Answer: Define concrete, time-bound criteria for ‘use’ before follow-up and collect evidence aligned to those criteria.

Björnqvist et al. (PLOS One, 2026) found varied participant definitions of implementation during the spring 2024 interviews and recommend setting shared metrics such as planned, started, integrated into procedures, or completed.

How can Evidano fit into a strategy mapping workflow?

Answer: Evidano automates transcription, supports verified translation, produces thematic and frequency analyses, and provides visualizations that map to strategy-mapping outputs.

Teams can upload workshop transcripts and maps to Evidano, run thematic synthesis, and use AI chat over documents to produce draft, facilitator-reviewed recommendations quickly; see Evidano Features.

Conclusion & Next Steps

The PLOS One case study (Björnqvist et al., 2026) demonstrates that strategy mapping software used in a participatory evaluation produced ten actionable recommendations from a four-workshop sequence with 15 participants and that follow-up interviews in spring 2024 showed most recommendations were used to some degree.

Björnqvist et al. (PLOS One, 2026) also show that targeted AI support for translation and visualization is effective when outputs are verified by humans, and they recommend clearer facilitator involvement to improve clarity and actionability of recommendations.

If your team wants to run participatory evaluations with faster transcription, verified translation, and AI-assisted synthesis, try Evidano to accelerate workshop-to-action workflows and keep stakeholder ownership intact: Try Evidano for free.

Topics

AI-enabled qualitative research
AI qualitative analysis
participatory evaluation
strategy mapping software
qualitative data analysis

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