Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. The primary keyword for this post is AI-enabled qualitative research. According to the PLOS One article, published August 25, 2026, a mixed-methods Delphi process produced two usable definitions of climate change acceptance for undergraduate science students. This post is written for qualitative researchers and UX/education teams who want concrete, reproducible tactics to steward Delphi interviews, open responses, and multi-round surveys using AI-enabled qualitative research tools.
Key Takeaways
According to the PLOS One study, published August 25, 2026, expert consensus produced two operational definitions of climate change acceptance for undergraduate science students: the Anthropogenic definition and the Anthropogenic + Mechanistic definition (PLOS One).
The PLOS One study used a mixed-methods Delphi approach across four rounds and multiple data sources to validate the definitions and demonstrate how instrument variability obscures comparability in the literature.
- A scoping review conducted between October 2023 and February 2024 identified 523 relevant articles and extracted 3, 089 item measures, of which 2, 402 were unique (PLOS One, 2026).
- The PLOS One Delphi workflow ran from April 2024 through July 2025 and included iterative interviews and surveys with educators and students, culminating in multiple rounds with educator samples of n = 58 (Round 2), n = 26 (Round 3), and n = 111 (Round 4) as reported in PLOS One (2026).
- Round-level preferences shifted: in Round 2 the top choice (Anthropogenic + Mechanistic) was selected by 50.9% (n = 29), in Round 3 that mechanistic definition was chosen by 69% (n = 18), and in Round 4 the simpler Anthropogenic definition was preferred by 64% (n = 70) (PLOS One, 2026).
What happened: how the PLOS One Delphi study worked
What happened: the PLOS One study used a four-round Delphi process to derive and vet candidate definitions of climate change acceptance for undergraduate science students.
According to the PLOS One article, the research integrated three data sources: a scoping review (523 articles) between October 2023 and February 2024, a pilot survey of 15 undergraduate students in April 2024, and four iterative rounds of educator interviews and surveys conducted from May 2024 through July 2025 (PLOS One, 2026).
The PLOS One study reported that thematic analysis of interview transcripts and frequency counts of survey choices were combined to narrow 15 candidate definitions to two final definitions. The authors quote the final, revised wording as: "Climate change is happening, average temperatures and greenhouse gases are increasing globally, and human behaviors are causing it, " attributed to Duke and Holt, PLOS One (2026).
The PLOS One authors also report the alternative concise formulation: "Climate change is happening and human behaviors are causing it, " and they note participants’ tradeoffs between completeness and measurement simplicity. One educator participant told the authors, "I chose this definition because it is the most comprehensive, " (educator participant, PLOS One, 2026).
Findings Snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| Oct 2023–Feb 2024 | Articles identified in scoping review | 523 articles | Provided the 14-construct preliminary framework used to generate definitions (PLOS One, 2026). |
| Scoping review result | Items extracted | 3, 089 items (2, 402 unique) | Shows high instrument heterogeneity and motivates a consensus definition (PLOS One, 2026). |
| Apr 2024–Jul 2025 | Delphi rounds and participants | Four rounds; multiple educator/student samples (e.g., Round 2 n=58; Round 4 n=111) | Iterative expert feedback produced stability but bipolarity of opinion across rounds (PLOS One, 2026). |
| Round 2 (Dec 2024) | Top-selected definition share | 50.9% (n = 29) chose Anthropogenic + Mechanistic | Panel initially favored a more detailed mechanistic definition (PLOS One, 2026). |
| Round 4 (Jul 2025) | Top-selected definition share | 64% (n = 70) chose Anthopogenic (simpler) | Final round showed preference for a simpler operational definition for measurement practicality (PLOS One, 2026). |
| Aug 25, 2026 | Publication | PLOS One article published | Definitions and methodology made public for instrument development and classroom use (PLOS One, 2026). |
Implications for qualitative researchers and education teams
Implication headline: the PLOS One findings mean researchers must decide which operational definition of acceptance they measure and explicitly report it.
According to the PLOS One article, lack of a standardized definition has produced incomparable results across studies, because many instruments conflate belief, causation, mechanisms, concern, and policy support (PLOS One, 2026).
- Researchers designing instruments should state whether they target the Anthropogenic definition (Basic Belief + Human Causation) or the Anthropogenic + Mechanistic definition (adds Temperature + Greenhouse Gases), because the choice changes what students must 'know' to be scored as accepting (PLOS One, 2026).
- Education teams should match definition choice to learning goals: PLOS One (2026) recommends the simpler Anthropogenic definition for baseline assessment (e.g., incoming freshmen) and the mechanistic version as a target for progressed students.
- Qualitative teams should preserve and report the iterative rationale from Delphi panels (percent agreement and thematic stability), because the PLOS One study demonstrates that numerical consensus alone masks substantive disagreements.
Ethics note: this PLOS One study is education research, not clinical; researchers should protect participant confidentiality, follow IRB guidance, and treat sensitive open responses per data-sharing limits described by Duke and Holt (PLOS One, 2026).
How Evidano Helps
Problem: Large heterogeneous item pools from reviews → Solution: AI-assisted codebook synthesis
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano can ingest a scoping-review item pool and produce a thematic content framework, matching the PLOS One workflow that began with 3, 089 items reduced to 14 constructs (PLOS One, 2026).
Feature link: use Evidano features to map codebooks, compare construct overlap, and export frequency tables for Delphi reporting.
Problem: Time-consuming transcription and verification → Solution: Integrated speech-to-text
The PLOS One team transcribed interviews using Microsoft Word and hand-checked them, a process that took months across rounds (PLOS One, 2026).
Evidano supports automated transcription with a custom dictionary and PII redaction, which speeds up the Round 1 interview-to-code loop while preserving auditability. See Evidano speech-to-text.
Problem: Iterative Delphi rounds need stability metrics → Solution: cross-round frequency and AI summarization
The PLOS One study combined frequency counts with thematic coding to assess stability and consensus across rounds (PLOS One, 2026).
Evidano provides cross-segment analysis, automated frequency tables by round, and an AI chat over your documents to query why percent agreement shifted between rounds, supporting the "stability plus agreement" approach recommended by the PLOS One authors.
Problem: Translating results into classroom or instrument choices → Solution: evidence-backed visualizations and export
Educators in PLOS One selected different definitions for hypothetical first-year and final-semester students, illustrating the need to present results clearly (PLOS One, 2026).
Evidano creates hierarchical code maps and cross-segment visualizations that make it simple to show which constructs cluster with acceptance definitions and to export clean codebooks for instrument development.
FAQ: AI-enabled qualitative research
How can AI help analyze Delphi qualitative data quickly and rigorously?
AI-enabled analysis can speed coding, summarize rationale across rounds, and produce reproducible frequency counts for consensus metrics.
According to the PLOS One study, the Delphi process requires both percent agreement and thematic stability to claim consensus (PLOS One, 2026); AI can automate frequency counts and surface thematic stability signals while preserving raw responses for human review.
Which definition should researchers choose when measuring climate change acceptance?
The right choice depends on your population and learning goals: use the Anthropogenic definition for baseline belief, and the Anthropogenic + Mechanistic if you test mechanistic knowledge.
The PLOS One authors recommended choosing based on context and academic level because panels oscillated between the two definitions across rounds with substantive rationale attached to each choice (PLOS One, 2026).
Can AI replace human judgment in Delphi thematic coding?
No. AI can accelerate coding and propose themes but human experts must validate constructs and interpret nuance.
The PLOS One study combined human thematic coding with numerical summaries to capture expert rationale; that hybrid approach preserves trustworthiness and is best practice for Delphi-style consensus research (PLOS One, 2026).
How should researchers report consensus from Delphi rounds?
Report both percent agreement and qualitative stability, plus the dates, sample sizes, and recruitment windows for transparency.
The PLOS One article reported round-level n and percent choices and provided recruitment dates (e.g., Apr 2024–Jul 2025) which made their decision to retain two definitions traceable and reproducible (PLOS One, 2026).
Conclusion & Next Steps
The PLOS One Delphi study (published August 25, 2026) shows that rigorous, mixed-methods consensus can produce two defensible operational definitions of climate change acceptance for undergraduate science students, and that panelists weigh completeness against measurement simplicity (PLOS One, 2026).
Qualitative researchers can apply AI-enabled qualitative research methods to scale transcription, accelerate thematic synthesis, and compute cross-round stability metrics while keeping human experts in the loop.
If you want to prototype these workflows on your next Delphi or multi-round qualitative study, start by automating transcripts and codebook synthesis, then use round-level frequency tracking to report both agreement and stability.
Try these capabilities in your next project, or Try Evidano for free.
Topics
- AI-enabled qualitative research
- Delphi qualitative analysis
- climate change acceptance definition
- AI thematic analysis
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