Perspective Chapter: Toward a Quantitative Framework for Evaluating AI-Generated Creativity: A Linguistic and Computational Analysis of Language Model Outputs
<p>As artificial intelligence (AI) language models advance rapidly, they develop new capabilities for generating human-like texts across various domains, including literature, historical narratives, and academic writing. These systems demonstrate impressive linguistic performance, yet researchers co...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2026
|
| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/1237280 |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1879164884535476224 |
|---|---|
| author | Emuna, Eden Elkayam, Ron Brill, Eyal |
| author_browse | Brill, Eyal Elkayam, Ron Emuna, Eden |
| author_facet | Emuna, Eden Elkayam, Ron Brill, Eyal |
| author_sort | Emuna, Eden |
| building | IntechOpen |
| collection | Acceso abierto |
| description | <p>As artificial intelligence (AI) language models advance rapidly, they develop new capabilities for generating human-like texts across various domains, including literature, historical narratives, and academic writing. These systems demonstrate impressive linguistic performance, yet researchers continue to debate how much of their output qualifies as “creative.” This study presents a structured analytical framework that evaluates the creativity of AI-generated texts using established linguistic and computational metrics, including lexical diversity (type-token ratio), readability (Flesch Reading Ease), and textual similarity (Jaccard similarity). The researchers used two AI language models (LLMs) to generate texts in different writing styles and analyzed them to assess linguistic richness, clarity, and originality. The findings address four key research questions regarding readability, innovation measurement, linguistic diversity, and comparative creativity in relation to human benchmarks, including the works of William Shakespeare. The study offers practical insights for organizations that integrate AI into creative tasks and contributes theoretically by advancing standardized methods for evaluating AI-generated creativity. This framework lays the groundwork for future empirical research and the development of tools that assess creativity in AI-driven language applications. Throughout this study, the term AI language models (LLMs) refers to large-scale generative text systems such as ChatGPT and Gemini.</p> |
| doi_str_mv | 10.5772/intechopen.1014250 |
| first_indexed | 2026-10-15T21:41:26Z |
| format | Book Chapter |
| fullrecord | <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><identifier>intech-1237280</identifier><datestamp>2026-02-09</datestamp>
<dc:title>Perspective Chapter: Toward a Quantitative Framework for Evaluating AI-Generated Creativity: A Linguistic and Computational Analysis of Language Model Outputs</dc:title>
<dc:creator>Eden Emuna</dc:creator>
<dc:creator>Ron Elkayam</dc:creator>
<dc:creator>Eyal Brill</dc:creator>
<dc:subject>Physical Sciences, Engineering and Technology</dc:subject>
<dc:description><p>As artificial intelligence (AI) language models advance rapidly, they develop new capabilities for generating human-like texts across various domains, including literature, historical narratives, and academic writing. These systems demonstrate impressive linguistic performance, yet researchers continue to debate how much of their output qualifies as &ldquo;creative.&rdquo; This study presents a structured analytical framework that evaluates the creativity of AI-generated texts using established linguistic and computational metrics, including lexical diversity (type-token ratio), readability (Flesch Reading Ease), and textual similarity (Jaccard similarity). The researchers used two AI language models (LLMs) to generate texts in different writing styles and analyzed them to assess linguistic richness, clarity, and originality. The findings address four key research questions regarding readability, innovation measurement, linguistic diversity, and comparative creativity in relation to human benchmarks, including the works of William Shakespeare. The study offers practical insights for organizations that integrate AI into creative tasks and contributes theoretically by advancing standardized methods for evaluating AI-generated creativity. This framework lays the groundwork for future empirical research and the development of tools that assess creativity in AI-driven language applications. Throughout this study, the term AI language models (LLMs) refers to large-scale generative text systems such as ChatGPT and Gemini.</p></dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2026-02-09</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/1237280</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.1014250</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-1-83634-269-4</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/1004748 ; Advances in Data Mining and Intelligent Analytics - Methods, Interpretability, and Cross-Domain Applications</dc:source>
</oai_dc:dc> |
| id | intech-1237280 |
| institution | IntechOpen |
| isbn | 978-1-83634-269-4 |
| language | English |
| last_indexed | 2026-10-15T21:41:26Z |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-12372802026-02-09 Perspective Chapter: Toward a Quantitative Framework for Evaluating AI-Generated Creativity: A Linguistic and Computational Analysis of Language Model Outputs Eden Emuna Ron Elkayam Eyal Brill Physical Sciences, Engineering and Technology <p>As artificial intelligence (AI) language models advance rapidly, they develop new capabilities for generating human-like texts across various domains, including literature, historical narratives, and academic writing. These systems demonstrate impressive linguistic performance, yet researchers continue to debate how much of their output qualifies as “creative.” This study presents a structured analytical framework that evaluates the creativity of AI-generated texts using established linguistic and computational metrics, including lexical diversity (type-token ratio), readability (Flesch Reading Ease), and textual similarity (Jaccard similarity). The researchers used two AI language models (LLMs) to generate texts in different writing styles and analyzed them to assess linguistic richness, clarity, and originality. The findings address four key research questions regarding readability, innovation measurement, linguistic diversity, and comparative creativity in relation to human benchmarks, including the works of William Shakespeare. The study offers practical insights for organizations that integrate AI into creative tasks and contributes theoretically by advancing standardized methods for evaluating AI-generated creativity. This framework lays the groundwork for future empirical research and the development of tools that assess creativity in AI-driven language applications. Throughout this study, the term AI language models (LLMs) refers to large-scale generative text systems such as ChatGPT and Gemini.</p> IntechOpen 2026-02-09 Chapter, Part Of Book https://www.intechopen.com/chapters/1237280 doi:10.5772/intechopen.1014250 en ISBN:978-1-83634-269-4 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/1004748 ; Advances in Data Mining and Intelligent Analytics - Methods, Interpretability, and Cross-Domain Applications |
| spellingShingle | Physical Sciences, Engineering and Technology Emuna, Eden Elkayam, Ron Brill, Eyal Perspective Chapter: Toward a Quantitative Framework for Evaluating AI-Generated Creativity: A Linguistic and Computational Analysis of Language Model Outputs |
| title | Perspective Chapter: Toward a Quantitative Framework for Evaluating AI-Generated Creativity: A Linguistic and Computational Analysis of Language Model Outputs |
| title_full | Perspective Chapter: Toward a Quantitative Framework for Evaluating AI-Generated Creativity: A Linguistic and Computational Analysis of Language Model Outputs |
| title_fullStr | Perspective Chapter: Toward a Quantitative Framework for Evaluating AI-Generated Creativity: A Linguistic and Computational Analysis of Language Model Outputs |
| title_full_unstemmed | Perspective Chapter: Toward a Quantitative Framework for Evaluating AI-Generated Creativity: A Linguistic and Computational Analysis of Language Model Outputs |
| title_short | Perspective Chapter: Toward a Quantitative Framework for Evaluating AI-Generated Creativity: A Linguistic and Computational Analysis of Language Model Outputs |
| title_sort | perspective chapter toward a quantitative framework for evaluating ai generated creativity a linguistic and computational analysis of language model outputs |
| topic | Physical Sciences, Engineering and Technology |
| url | https://www.intechopen.com/chapters/1237280 |
| work_keys_str_mv | AT emunaeden perspectivechaptertowardaquantitativeframeworkforevaluatingaigeneratedcreativityalinguisticandcomputationalanalysisoflanguagemodeloutputs AT elkayamron perspectivechaptertowardaquantitativeframeworkforevaluatingaigeneratedcreativityalinguisticandcomputationalanalysisoflanguagemodeloutputs AT brilleyal perspectivechaptertowardaquantitativeframeworkforevaluatingaigeneratedcreativityalinguisticandcomputationalanalysisoflanguagemodeloutputs |