Unraveling Creativity Through Variability: A Comparison of LLMs and Humans in an Educational Q&A Scenario.
Saved in:
| Title: | Unraveling Creativity Through Variability: A Comparison of LLMs and Humans in an Educational Q&A Scenario. |
|---|---|
| Authors: | Braccini, Michele1 (AUTHOR) m.braccini@unibo.it, Aguzzi, Gianluca1 (AUTHOR) gianluca.aguzzi@unibo.it, Baldini, Paolo1 (AUTHOR) p.baldini@unibo.it |
| Source: | Technology, Knowledge & Learning. Jun2026, Vol. 31 Issue 2, p751-788. 38p. |
| Subject Terms: | *Variation in language, *Early childhood education, *Creative ability, *Science education, Semantics (Philosophy), Language models |
| Abstract: | Large Language Models (LLMs) have demonstrated a remarkable capacity for generating human-quality text across diverse applications. In light of this, the use of these models is becoming increasingly widespread. Education is no exception and so literature is starting to explore the potential of LLMs in educational contexts: ranging from teaching assistant chatbot to personalized tutoring. Although the versatility of these models is evident, the critical question arises whether they can truly replicate the creativity and nuances of human languages, giving students the variability and richness of expression necessary for effective learning, and for nurturing critical and divergent thinking. This is particularly important in the pre-school or primary education context, where novel and insightful content is crucial for effective engagement. Framing this problem from an abstract linguistic perspective, it translates into wondering how effectively the "creativity" of LLM-generated text can be quantified and how it compares to human one. So, recognizing the inherent ambiguity in defining creativity this paper investigates the variability of LLM outputs as a potential proxy, and compares it with observed text variability in a large human-authored dataset. Specifically focusing on early childhood education (ECE), we compare the semantic and syntactic diversity of responses from LLMs and humans prompted to answer like 5-year-olds. Our empirical results show LLMs have lower variability than humans, particularly on repeated questions, though the gap narrows for different questions. This limitation raises concerns for educational suitability, where diverse explanations of concepts are crucial, suggesting a need to enhance LLM expressive range and diversity. [ABSTRACT FROM AUTHOR] |
| Copyright of Technology, Knowledge & Learning is the property of Springer Nature 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.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: ehh DbLabel: Education Research Complete An: 195071863 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Unraveling Creativity Through Variability: A Comparison of LLMs and Humans in an Educational Q&A Scenario. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Braccini%2C+Michele%22">Braccini, Michele</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> m.braccini@unibo.it</i><br /><searchLink fieldCode="AR" term="%22Aguzzi%2C+Gianluca%22">Aguzzi, Gianluca</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gianluca.aguzzi@unibo.it</i><br /><searchLink fieldCode="AR" term="%22Baldini%2C+Paolo%22">Baldini, Paolo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> p.baldini@unibo.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technology%2C+Knowledge+%26+Learning%22">Technology, Knowledge & Learning</searchLink>. Jun2026, Vol. 31 Issue 2, p751-788. 38p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Variation+in+language%22">Variation in language</searchLink><br />*<searchLink fieldCode="DE" term="%22Early+childhood+education%22">Early childhood education</searchLink><br />*<searchLink fieldCode="DE" term="%22Creative+ability%22">Creative ability</searchLink><br />*<searchLink fieldCode="DE" term="%22Science+education%22">Science education</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics+%28Philosophy%29%22">Semantics (Philosophy)</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Large Language Models (LLMs) have demonstrated a remarkable capacity for generating human-quality text across diverse applications. In light of this, the use of these models is becoming increasingly widespread. Education is no exception and so literature is starting to explore the potential of LLMs in educational contexts: ranging from teaching assistant chatbot to personalized tutoring. Although the versatility of these models is evident, the critical question arises whether they can truly replicate the creativity and nuances of human languages, giving students the variability and richness of expression necessary for effective learning, and for nurturing critical and divergent thinking. This is particularly important in the pre-school or primary education context, where novel and insightful content is crucial for effective engagement. Framing this problem from an abstract linguistic perspective, it translates into wondering how effectively the "creativity" of LLM-generated text can be quantified and how it compares to human one. So, recognizing the inherent ambiguity in defining creativity this paper investigates the variability of LLM outputs as a potential proxy, and compares it with observed text variability in a large human-authored dataset. Specifically focusing on early childhood education (ECE), we compare the semantic and syntactic diversity of responses from LLMs and humans prompted to answer like 5-year-olds. Our empirical results show LLMs have lower variability than humans, particularly on repeated questions, though the gap narrows for different questions. This limitation raises concerns for educational suitability, where diverse explanations of concepts are crucial, suggesting a need to enhance LLM expressive range and diversity. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technology, Knowledge & Learning is the property of Springer Nature 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.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=195071863 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10758-026-09956-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 38 StartPage: 751 Subjects: – SubjectFull: Variation in language Type: general – SubjectFull: Early childhood education Type: general – SubjectFull: Creative ability Type: general – SubjectFull: Science education Type: general – SubjectFull: Semantics (Philosophy) Type: general – SubjectFull: Language models Type: general Titles: – TitleFull: Unraveling Creativity Through Variability: A Comparison of LLMs and Humans in an Educational Q&A Scenario. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Braccini, Michele – PersonEntity: Name: NameFull: Aguzzi, Gianluca – PersonEntity: Name: NameFull: Baldini, Paolo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22111662 Numbering: – Type: volume Value: 31 – Type: issue Value: 2 Titles: – TitleFull: Technology, Knowledge & Learning Type: main |
| ResultId | 1 |
