An automated predictive model for evaluating narrative cohesion in children’s stories: a computational linguistic approach considering Gérard Genette’s narrative structure theory
This study develops a machine learning model to predict narrative cohesion in children’s stories, classifying cohesion as complete, partial, or absent, using Gérard Genette’s narrative structure theory as a framework. It analyzes both human-created and AI-generated stories, including those from Chat...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Adolescence and Youth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/02673843.2025.2500527 |
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| author | Jawharah Alasmari Mohammed Alzyoudi Masheal Alshehri Rana Alshammari Reyouf Aldakan |
| author_facet | Jawharah Alasmari Mohammed Alzyoudi Masheal Alshehri Rana Alshammari Reyouf Aldakan |
| author_sort | Jawharah Alasmari |
| collection | DOAJ |
| description | This study develops a machine learning model to predict narrative cohesion in children’s stories, classifying cohesion as complete, partial, or absent, using Gérard Genette’s narrative structure theory as a framework. It analyzes both human-created and AI-generated stories, including those from ChatGPT, by assessing linguistic, rhetorical, and stylistic elements such as narrative style, character development, time specification, event sequencing, and dialogue. The study employs a Decision Tree model to evaluate narrative cohesion, achieving optimal results with both recall and precision at 100%. These results demonstrate the model’s high accuracy in classifying narrative texts. By providing insights into narrative cohesion, the study enhances our understanding of children’s stories, offering a tool for better emotional comprehension and communication. Furthermore, it highlights the potential of AI and machine learning in analysing narrative structures. This research contributes to improving narrative text analysis and storytelling techniques, making it valuable for future applications in education, especially in enhancing the quality and coherence of children’s literature. |
| format | Article |
| id | doaj-art-a89b3d721f364db5ae7e522991ca4f29 |
| institution | Kabale University |
| issn | 0267-3843 2164-4527 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Adolescence and Youth |
| spelling | doaj-art-a89b3d721f364db5ae7e522991ca4f292025-08-20T03:52:47ZengTaylor & Francis GroupInternational Journal of Adolescence and Youth0267-38432164-45272025-12-0130110.1080/02673843.2025.2500527An automated predictive model for evaluating narrative cohesion in children’s stories: a computational linguistic approach considering Gérard Genette’s narrative structure theoryJawharah Alasmari0Mohammed Alzyoudi1Masheal Alshehri2Rana Alshammari3Reyouf Aldakan4Arabic Language Department, College of humanities and Social Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaHead of Education Department, Mohammed Bin Zayed University for Humanities, Abu Dhabi, United Arab EmiratesArabic Language Department, College of humanities and Social Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaArabic Language Department, College of humanities and Social Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaArabic Language Department, College of humanities and Social Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaThis study develops a machine learning model to predict narrative cohesion in children’s stories, classifying cohesion as complete, partial, or absent, using Gérard Genette’s narrative structure theory as a framework. It analyzes both human-created and AI-generated stories, including those from ChatGPT, by assessing linguistic, rhetorical, and stylistic elements such as narrative style, character development, time specification, event sequencing, and dialogue. The study employs a Decision Tree model to evaluate narrative cohesion, achieving optimal results with both recall and precision at 100%. These results demonstrate the model’s high accuracy in classifying narrative texts. By providing insights into narrative cohesion, the study enhances our understanding of children’s stories, offering a tool for better emotional comprehension and communication. Furthermore, it highlights the potential of AI and machine learning in analysing narrative structures. This research contributes to improving narrative text analysis and storytelling techniques, making it valuable for future applications in education, especially in enhancing the quality and coherence of children’s literature.https://www.tandfonline.com/doi/10.1080/02673843.2025.2500527Machine learningnarrative cohesionchildren’s storiesGérard Genette’s theorydecision tree |
| spellingShingle | Jawharah Alasmari Mohammed Alzyoudi Masheal Alshehri Rana Alshammari Reyouf Aldakan An automated predictive model for evaluating narrative cohesion in children’s stories: a computational linguistic approach considering Gérard Genette’s narrative structure theory International Journal of Adolescence and Youth Machine learning narrative cohesion children’s stories Gérard Genette’s theory decision tree |
| title | An automated predictive model for evaluating narrative cohesion in children’s stories: a computational linguistic approach considering Gérard Genette’s narrative structure theory |
| title_full | An automated predictive model for evaluating narrative cohesion in children’s stories: a computational linguistic approach considering Gérard Genette’s narrative structure theory |
| title_fullStr | An automated predictive model for evaluating narrative cohesion in children’s stories: a computational linguistic approach considering Gérard Genette’s narrative structure theory |
| title_full_unstemmed | An automated predictive model for evaluating narrative cohesion in children’s stories: a computational linguistic approach considering Gérard Genette’s narrative structure theory |
| title_short | An automated predictive model for evaluating narrative cohesion in children’s stories: a computational linguistic approach considering Gérard Genette’s narrative structure theory |
| title_sort | automated predictive model for evaluating narrative cohesion in children s stories a computational linguistic approach considering gerard genette s narrative structure theory |
| topic | Machine learning narrative cohesion children’s stories Gérard Genette’s theory decision tree |
| url | https://www.tandfonline.com/doi/10.1080/02673843.2025.2500527 |
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