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Assessing Creativity across Multi-Step Intervention Using Generative AI Models

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Title: Assessing Creativity across Multi-Step Intervention Using Generative AI Models
Language: English
Authors: Eran Hadas (ORCID 0009-0005-1531-2087), Arnon Hershkovitz (ORCID 0000-0003-1568-2238)
Source: Journal of Learning Analytics. 2025 12(1):91-109.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Peer Reviewed: Y
Page Count: 19
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Grade 9
High Schools
Junior High Schools
Middle Schools
Secondary Education
Descriptors: Creativity, Evaluation Methods, Computer Assisted Testing, Artificial Intelligence, Computer Software, Scoring, Behavior Patterns, Creative Thinking, Intervention, Grade 9, Correlation, Learning Analytics, Semantics, Efficiency, Scores, Creativity Tests, Longitudinal Studies
ISSN: 1929-7750
Abstract: Creativity is an imperative skill for today's learners, one that has important contributions to issues of inclusion and equity in education. Therefore, assessing creativity is of major importance in educational contexts. However, scoring creativity based on traditional tools suffers from subjectivity and is heavily time- and labour-consuming. This is indeed the case for the commonly used Alternative Uses Test (AUT), in which participants are asked to list as many different uses as possible for a daily object. The test measures divergent thinking (DT), which involves exploring multiple possible solutions in various semantic domains. This study leverages recent advancements in generative AI (GenAI) to automate the AUT scoring process, potentially increasing efficiency and objectivity. Using two validated models, we analyze the dynamics of creativity dimensions in a multi-step intervention aimed at improving creativity by using repeated AUT sessions (N=157 9th-grade students). Our research questions focus on the behavioural patterns of DT dimensions over time, their correlation with the number of practice opportunities, and the influence of response order on creativity scores. The results show improvement in fluency and flexibility, as a function of practice opportunities, as well as various correlations between DT dimensions. By automating the scoring process, this study aims to provide deeper insights into the development of creative skills over time and explore the capabilities of GenAI in educational assessments. Eventually, the use of automatic evaluation can incorporate creativity evaluation in various educational processes at scale.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1465584
Database: ERIC
FullText Text:
  Availability: 0
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  Data: Assessing Creativity across Multi-Step Intervention Using Generative AI Models
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Eran+Hadas%22">Eran Hadas</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-1531-2087">0009-0005-1531-2087</externalLink>)<br /><searchLink fieldCode="AR" term="%22Arnon+Hershkovitz%22">Arnon Hershkovitz</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1568-2238">0000-0003-1568-2238</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Learning+Analytics%22"><i>Journal of Learning Analytics</i></searchLink>. 2025 12(1):91-109.
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
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  Data: 19
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  Data: 2025
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  Data: <searchLink fieldCode="DE" term="%22Creativity%22">Creativity</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Testing%22">Computer Assisted Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software%22">Computer Software</searchLink><br /><searchLink fieldCode="DE" term="%22Scoring%22">Scoring</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Patterns%22">Behavior Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Creative+Thinking%22">Creative Thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+9%22">Grade 9</searchLink><br /><searchLink fieldCode="DE" term="%22Correlation%22">Correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Efficiency%22">Efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Creativity+Tests%22">Creativity Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+Studies%22">Longitudinal Studies</searchLink>
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  Data: 1929-7750
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Creativity is an imperative skill for today's learners, one that has important contributions to issues of inclusion and equity in education. Therefore, assessing creativity is of major importance in educational contexts. However, scoring creativity based on traditional tools suffers from subjectivity and is heavily time- and labour-consuming. This is indeed the case for the commonly used Alternative Uses Test (AUT), in which participants are asked to list as many different uses as possible for a daily object. The test measures divergent thinking (DT), which involves exploring multiple possible solutions in various semantic domains. This study leverages recent advancements in generative AI (GenAI) to automate the AUT scoring process, potentially increasing efficiency and objectivity. Using two validated models, we analyze the dynamics of creativity dimensions in a multi-step intervention aimed at improving creativity by using repeated AUT sessions (N=157 9th-grade students). Our research questions focus on the behavioural patterns of DT dimensions over time, their correlation with the number of practice opportunities, and the influence of response order on creativity scores. The results show improvement in fluency and flexibility, as a function of practice opportunities, as well as various correlations between DT dimensions. By automating the scoring process, this study aims to provide deeper insights into the development of creative skills over time and explore the capabilities of GenAI in educational assessments. Eventually, the use of automatic evaluation can incorporate creativity evaluation in various educational processes at scale.
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  Data: EJ1465584
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    Languages:
      – Text: English
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        PageCount: 19
        StartPage: 91
    Subjects:
      – SubjectFull: Creativity
        Type: general
      – SubjectFull: Evaluation Methods
        Type: general
      – SubjectFull: Computer Assisted Testing
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Computer Software
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      – SubjectFull: Scoring
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      – SubjectFull: Behavior Patterns
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      – SubjectFull: Creative Thinking
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      – SubjectFull: Intervention
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      – SubjectFull: Grade 9
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      – SubjectFull: Correlation
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      – SubjectFull: Scores
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      – SubjectFull: Creativity Tests
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      – SubjectFull: Longitudinal Studies
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      – TitleFull: Assessing Creativity across Multi-Step Intervention Using Generative AI Models
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