Tailored Tales: Enhancing Children’s Reading Comprehension with Preference-Tuned Automatic Story Generation
Shankarnarayanan, Aadhith Syed, Taufiq Shapsough, Salsabeel Zualkernan, Imran Kochmar, Ekaterina
Shankarnarayanan, Aadhith
Syed, Taufiq
Shapsough, Salsabeel
Zualkernan, Imran
Kochmar, Ekaterina
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Reading comprehension is crucial for children’s academic success, but creating effective and engaging reading materials on a large scale is challenging. Traditional story creation methods are labor-intensive and often lack flexibility. Large Language Models (LLMs) have emerged as promising tools for automating content generation, but fine-tuning these models to produce high-quality children’s stories can be resource-intensive. Without fine-tuning, LLM-generated stories may not align with the needs of young readers, leading to issues with language, consistency, and educational value. This study explores the use of preference tuning as a cost-effective method for training LLMs to generate diverse, engaging, and age-appropriate stories that meet Early Grade Reading Assessment (EGRA) criteria. Building on previous work, which drew inspiration from classical tales like Arabian Nights and Brothers Grimm, the study uses a dataset of annotated short stories to refine the LLM’s output. The generated stories are evaluated for readability, coherence, and engagement through quantitative measures. Preliminary results show that preference-tuning significantly improves story quality and leads to better comprehension outcomes compared to baseline models.
Citation
A. Shankarnarayanan, T. Syed, S. Shapsough, I. Zualkernan and E. Kochmar, "Tailored Tales: Enhancing Children’s Reading Comprehension with Preference-Tuned Automatic Story Generation," 2025 11th International Conference on Computing and Artificial Intelligence (ICCAI), Kyoto, Japan, 2025, pp. 325-332, doi: 10.1109/ICCAI66501.2025.00057
Source
Proceeding of International Conference on Computing and Artificial Intelligence (ICCAI)
Conference
International Conference on Computing and Artificial Intelligence (ICCAI)
Keywords
EGRA, Reading Comprehension, Large Language Models, Preference Tuning, Children’s Stories
Subjects
Source
International Conference on Computing and Artificial Intelligence (ICCAI)
Publisher
IEEE