AI-assisted Participatory Evaluation: Strategy Mapping

Primary keyword: ai-enabled participatory evaluation. Evaluators and qualitative researchers struggle to turn post-crisis discussions into actionable change, and the PLOS ONE case study shows a concrete way forward. According to PLOS ONE, a multi-stage, workshop-based evaluation using browser-based strategy mapping produced ten participant-developed recommendations from 15 healthcare staff and was followed up with ten interviews in spring 2024. This post explains the method, the measurable outcomes reported by PLOS ONE, and how AI-enabled qualitative research tools can streamline the same participatory workflow for evaluation teams.

Key Takeaways

According to PLOS ONE, a 2026 case study applied strategy mapping software in four workshops with 15 healthcare professionals and produced ten participant-developed recommendations, with follow-up interviews of ten participants in spring 2024 (PLOS ONE).

15 participants joined the workshop series held on 3 April 2023, 6 April 2023, 24 April 2023, and 12 June 2023, resulting in ten recommendations reported in PLOS ONE on 20 August 2026.
Ten semi-structured interviews in spring 2024 were used to assess perceived use and value, and PLOS ONE reports that a majority of interviewees believed recommendations had been used.
PLOS ONE authors recommend clarifying recommendations and considering more active facilitator involvement to improve actionability after the participatory process.

What Happened: AI-enabled participatory evaluation using strategy mapping

Answer: PLOS ONE reports a multi-stage participatory evaluation that combined strategy mapping software with staged workshops to generate and validate recommendations for crisis management.

According to PLOS ONE, the study evaluated a communicable disease and infection control unit using the Strategyfinder browser software over four workshops between 3 April 2023 and 12 June 2023, with 15 staff participating and ten follow-up interviews conducted in spring 2024.

According to PLOS ONE, the process used blind gathers, open gathers, causal linking, centrality analyses, and participant ratings (importance, feasibility, effectiveness) inside the software to move from risk identification to ten final recommendations.

According to PLOS ONE, the authors used an LLM (M365 Copilot, GPT-5) only for translation and figure recreation, and they state explicitly that the model was not used for data generation, analysis, or interpretation.

Findings Snapshot

Date	Metric	Value	Implication
3 April 2023	Workshop 1	Risk mapping (15 participants)	Generated initial causal map in Strategyfinder
6 April 2023	Workshop 2	Subsystem validation	Refined five subsystems and rated importance
24 April 2023	Workshop 3	Recommendation development	Participants generated and rated recommendations
12 June 2023	Workshop 4	Recommendation validation (in-person)	Finalized ten recommendations for report
Spring 2024	Follow-up interviews	10 interviews (of 15)	Assessed perceived use and process value
20 August 2026	Publication	PLOS ONE case study published	Peer-reviewed evidence for method viability

Implications for qualitative researchers and evaluation teams

Answer: The PLOS ONE case shows that participatory, software-assisted mapping produces jointly owned recommendations and measurable follow-up data useful for qualitative evaluation.

According to PLOS ONE, stakeholder involvement increased the perceived usefulness and uptake of recommendations, but interview data in spring 2024 also showed variation in perceived implementation, pointing to the need for clearer definitions of what 'use' means.

According to PLOS ONE, the study found trade-offs: full participant authorship produced ownership, but some recommendations were seen as vague or confirming ongoing work, suggesting facilitators may need to edit recommendations for clarity while preserving ownership.

Practical steps for qualitative teams: (1) run an initial blind gather to surface diverse perspectives, (2) use causal linking and centrality measures to prioritize, (3) have participants rate importance and feasibility, and (4) schedule a short validation follow-up to refine recommendation wording, as recommended in PLOS ONE.

How Evidano Helps

Problem: long workshop transcripts and slow synthesis

Answer: Long qualitative workflows slow follow-up and dilute impact.

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

Evidano feature match: use Evidano transcription and thematic analysis to convert workshop Zoom recordings and Strategyfinder exports into coded transcripts, speeding synthesis of causal maps and recommendation drafts. See Evidano features.

Problem: unclear recommendation wording limits actionability

Answer: Ambiguous recommendations reduce implementation probability, as reported by PLOS ONE interviewees.

Evidano solution: use Evidano’s AI-assisted coding and report drafting to produce concise, actionable recommendation texts with traceable excerpts and frequency data so facilitators can produce clearer versions while preserving participant-authored language.

Problem: measuring use over time

Answer: PLOS ONE notes divergent views on whether recommendations were 'used', showing the need for structured follow-up metrics.

Evidano solution: export cross-segment analyses and timeline visualizations to track which recommendations appear in later meeting notes or policy texts, and combine Evidano’s transcription pipeline with surveys to capture structured adoption metrics.

FAQ: ai-enabled participatory evaluation

How did the PLOS ONE study run its participatory evaluation workshops?

Answer: The PLOS ONE study ran four structured workshops between April and June 2023 using browser-based strategy mapping and facilitator-led stages.

According to PLOS ONE, workshops included blind gathers, open gathers, causal linking, centrality analytics, and participant ratings, culminating in ten recommendations validated in an in-person session on 12 June 2023.

Did participants in the study use the recommendations that were produced?

Answer: The PLOS ONE authors report that a majority of interviewed participants believed recommendations had been used to some extent.

According to PLOS ONE, follow-up interviews in spring 2024 with ten participants revealed that many interviewees described implementation as 'started' rather than complete, and that some recommendations confirmed ongoing projects.

What role did AI play in the PLOS ONE study?

Answer: According to PLOS ONE, AI was used only for translation and visual recreation of maps, not for analysis or interpretation.

PLOS ONE states that the authors used M365 Copilot (GPT-5) to translate Swedish statements to English and recreate visual structures, and they verified those outputs against originals.

Can AI tools preserve participant ownership while improving clarity?

Answer: Yes, when used to assist editors rather than replace participant authorship.

According to PLOS ONE, facilitators may improve recommendation clarity post-production; AI-enabled platforms can help by generating draft rewrites tied to originating excerpts so participants can validate edits without losing ownership.

Conclusion & Next Steps

Answer: The PLOS ONE case study demonstrates that strategy mapping software plus participatory workshops can create actionable recommendations that participants view as useful, but clarity and follow-up definitions matter for implementation.

According to PLOS ONE, 15 participants across four workshops in April–June 2023 generated ten recommendations and ten follow-up interviews in spring 2024 supported the approach while pointing to needed refinements in facilitator involvement and recommendation wording.

If your team runs participatory evaluations and wants reproducible, AI-enabled synthesis of transcripts, recommendations, and adoption tracking, consider integrating tools that combine transcription, thematic coding, and traceable edits.

To try an AI-first qualitative workflow that supports participatory evaluation outputs, Try Evidano for free.

Topics

ai-enabled participatory evaluation
strategy mapping qualitative analysis
participatory evaluation crisis management
ai qualitative research tools

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