Evidence map›Paper›PMID 42752696›Full record

ArticlePloS one2026

Generative AI as a cognitive support tool in early-stage design ideation: A human-AI co-ideation experiment.

Yu-Ju Lin

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Yu-Ju LinDepartment of Commercial Design and Management, National Taipei University of Business, Taipei, Taiwan.ORCID https://orcid.org/0000-0002-0848-3807

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence is increasingly used in creative practice, but controlled evidence on its role in early-stage design ideation remains limited. This study examined whether a combined text-and-image AI-assisted workflow differed from human-only brainstorming in expert-rated ideation outputs, preliminary design refinement, and subjective task experience. A counterbalanced within-subjects experiment was conducted with 32 undergraduate design students, each of whom completed both conditions using the same design brief. Ideation outputs were evaluated by three expert raters across six task-specific analytic criteria, participant-matched concept sheets were assessed across five design attributes, and participants completed a task-specific post-task questionnaire after each condition. After Holm-Bonferroni correction within each outcome family, AI-assisted outputs received higher ratings for problem framing, mechanism, creator perspective, and evaluator perspective, whereas human-only outputs received higher ratings for innovation and proposed solution strategies. No statistically robust condition differences were found for the five design attributes. Participants reported higher ratings for perceived cognitive processing, self-rated ideation skills, and affective response in the AI-assisted condition. These findings indicate different profiles of evaluated ideation output rather than a uniform advantage for either condition. Perceived support in the AI-assisted workflow did not translate into a uniform advantage across originality-related ratings or into superior expert-rated preliminary design refinement.

Indexed as

CognitionCreativityAdultFemaleGenerative Artificial IntelligenceHumansMaleSurveys and QuestionnairesYoung Adult

Identifiers

PMID42752696
PMCPMC13585246

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.