Evidence map›Paper›PMID 40826243›Full record

ArticleBehavior research methods2025

CAP: The creativity assessment platform for online testing and automated scoring.

John D Patterson, Jimmy Pronchick, Ruchi Panchanadikar, Mark Fuge, Janet G van Hell, Scarlett R Miller, Dan R Johnson, Roger E Beaty

Abstract read
In one paragraph

Article in Behavior research methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Lower engagement of cognitive control in children on ChatGPT.Cerebral cortex (New York, N.Y. : 1991) · 2026
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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

8 authors.

John D PattersonDepartment of Psychology, The Pennsylvania State University, 140 Moore Building, University Park, PA, 16802, USA. jpttrsn@psu.edu.ORCID http://orcid.org/0000-0002-7455-3535
Jimmy PronchickDepartment of Psychology, The Pennsylvania State University, 140 Moore Building, University Park, PA, 16802, USA.
Ruchi PanchanadikarSchool of Computing, Clemson University, Clemson, SC, USA.
Mark FugeDepartment of Mechanical and Process Engineering, ETH Zurich, Zürich, Switzerland.
Janet G van HellDepartment of Psychology, The Pennsylvania State University, 140 Moore Building, University Park, PA, 16802, USA.
Scarlett R MillerDepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA, USA.
Dan R JohnsonDepartment of Cognitive and Behavioral Science, Washington and Lee University, Lexington, VA, USA.
Roger E BeatyDepartment of Psychology, The Pennsylvania State University, 140 Moore Building, University Park, PA, 16802, USA.

Funding

National Science Foundation DGE-NRT-2125865National Science Foundation DRL-1920653National Science Foundation DRL-2400783National Science Foundation DUE- 2155070National Science Foundation DUE-2155071National Science Foundation DUE IUSE-1726811
6 · The paper itself

Abstract

Creativity is increasingly recognized as a core competency for the 21st century, making its development a priority in education, research, and industry. To effectively cultivate creativity, researchers and educators need reliable and accessible assessment tools. Recent software developments have significantly enhanced the administration and scoring of creativity measures; however, existing software often requires expertise in experiment design and computer programming, limiting its accessibility to many educators and researchers. In the current work, we introduce CAP-the Creativity Assessment Platform-a free web application for building creativity assessments, collecting data, and automatically scoring responses (cap.ist.psu.edu). CAP allows users to create custom creativity assessments in ten languages using a simple, point-and-click interface, selecting from tasks such as the Short Story Task, Drawing Task, and Scientific Creative Thinking Test. Users can automatically score task responses using machine learning models trained to match human creativity ratings-with multilingual capabilities, including the new Cross-Lingual Alternate Uses Scoring (CLAUS), a large language model achieving strong prediction of human creativity ratings in ten languages. CAP also provides a centralized dashboard to monitor data collection, score assessments, and automatically generate text for a Methods section based on the study's tasks, metrics, and instructions-with a single click-promoting transparency and reproducibility in creativity assessment. Designed for ease of use, CAP aims to democratize creativity measurement for researchers, educators, and everyone in between.

Indexed as

CreativityInternetSoftwareHumansMachine LearningAutomated creativity scoringComputational psychometricsDivergent thinkingOnline data collectionScientific creativityVisual creativity

Identifiers

PMID40826243
PMCPMC12361297

What OpenQuestion holds

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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.