JOURNAL ARTICLE

PISA 2022 Creative Thinking Assessment: Opportunities, Challenges, and Cautions.

  • Published In: Journal of Creative Behavior, 2025, v. 59, n. 1. P. 1 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Barbot, Baptiste; Kaufman, James C. 3 of 3

Abstract

The OECD's PISA program assesses 15‐year‐old students globally in key competencies every 3 years, providing influential data on education quality and spurring policy debates. In the latest cycle, the innovation domain focused on creative thinking, assessing over 140,000 students across 60+ countries, in the largest study of adolescent creativity to date. This innovation domain included a cognitive test covering multiple creative thinking processes (generating creative ideas, generating diverse ideas, and evaluating/improving ideas) and creativity domains (written expression, visual expression, social problem‐solving, and scientific problem‐solving), as well as an extensive survey on factors influencing creativity (such as openness, creative self‐efficacy, or growth mindset). While this dataset offers unprecedented research opportunities due to its scale and international scope, challenges arise from its aggregated scoring and complex sampling design. Missteps in using this data in secondary analyses could lead to fragmented and inconsistent findings. This paper provides an overview of the PISA 2022 creative thinking assessment's framework, methods, and findings, highlighting both the potential and the caution needed for impactful creativity research. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Creative Behavior. 2025/03, Vol. 59, Issue 1, p1
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2025
  • ISSN:0022-0175
  • DOI:10.1002/jocb.70003
  • Accession Number:186883856
  • Copyright Statement:Copyright of Journal of Creative Behavior is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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