Automated scoring of figural creativity using a convolutional neural network
Bibliographic Data
| ID | 21832457 |
|---|---|
| Authors | David H Cropley (0000-0002-7964-6538), Rebecca L Marrone (0000-0002-9218-4120) |
| Year | 2025 |
| Volume | 19 |
| Issue | 1 |
| Pages | 77-86 |
| Publication date | 2025-02-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Psychology of Aesthetics Creativity and the Arts (JOURNAL) |
| Journal identifiers | ISSN: 1931-3896 • E-ISSN: 1931-390X |
| Publisher | American Psychological Association (APA) (PUBLISHER) |
| DOI | 10.1037/aca0000510 |
| OpenAlex | W4283815673 |
| Language | EN |
| Citations received | 16 |
AB One of the abiding challenges in creativity research is assessment. Objectively scored tests of creativity such as the Torrance Tests of Creativity and the test of Creative Thinking-Drawing Production (TCT-DP; Urban & Jellen, 1996) offer high levels of reliability and validity but are slow and expensive to administer and score. As a result, many creativity researchers default to simpler and faster self-report measures of creativity and related constructs (e.g., creative self-efficacy, openness). Recent research, however, has begun to explore the use of computational approaches to address these limitations. Examples include the Divergent Association Task (Olson et al., 2021) that uses computational methods to rapidly assess the semantic distance of words, as a proxy for divergent thinking. To date, however, no research appears to have emerged that uses methods drawn from the field of artificial intelligence to assess existing objective, figural (i.e., drawing) tests of creativity. This article describes the application of machine learning, in the form of a convolutional neural network, to the assessment of a figural creativity test-the TCT-DP. The approach shows excellent accuracy and speed, eliminating traditional barriers to the use of these objective, figural creativity tests and opening new avenues for automated creativity assessment
Convolutional neural network · Creativity · Computer Science · Creativity in Education and Neuroscience · Psychology · Social Psychology · Artificial Intelligence
Using machine learning techniques for evaluating creativity in Graphic Combination Task (GCT) among Chinese college students
Automatic Scoring of Verbal Divergent Thinking Tests
How Does Narrow AI Impact Human Creativity
Investigating the Validity Evidence of Automated Scoring Methods for Divergent Thinking Assessments
Automatic Assessment of Mathematical Creativity using Natural Language Processing
The Evaluation and Selection of Creative Ideas in Educational Settings
Non-Linear Relationships Between Creativity Ratings of Musical Improvisations and Melodic Complexity
Virtual Creativity – Bibliometric Literature Review on Measurements and Factors That Influence Creative Virtual Teamwork
Fit-For-Purpose Creativity Assessment
The Paradox of Artificial Creativity
Creativity Assessment, Research, and Practice in the Age of Artificial Intelligence
What Makes Children's Responses to Creativity Assessments Difficult to Judge Reliably
Automated Scoring of Scientific Creativity in German
Automated Scoring of Figural Tests of Creativity with Computer Vision
Development and Validation of the Cebeci Test of Creativity
Envisioning the Future of Creative Thinking Assessment
| Unique citing works | 16 |
|---|---|
| Citations per year | 5.33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 16 |