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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorGatt, A.
dc.contributor.authorDonošaite, Daniela
dc.date.accessioned2025-08-28T00:01:23Z
dc.date.available2025-08-28T00:01:23Z
dc.date.issued2025
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/50021
dc.description.abstractThis project investigates how AI technology can support children’s movement and creativity by developing an AI Camera Storytelling feature for the Monkey Moves Play app. The feature uses Multimodal Large Language Models (MLLMs) to generate personalized, creative, and age-appropriate stories based on the photos provided by users, encouraging physical activity and imaginative play. A central focus of the study is placed on the creativity of the generated content, given its crucial role in children’s development. GPT-4o and GPT-4.1 mini were utilized to create the stories through careful structured prompt engineering. Human evaluation was combined with large-scale LLM evaluation to assess language quality and creativity aspects. The results show overall satisfactory levels of creativity, rated on average 4.7/5 by LLM and 3.91/5 by human evaluators, as well as very limited correlation and agreement values between the two. The study shows how multimodal prompt engineering can guide creative and age-appropriate storytelling. Moreover, we show that LLM-as-a-judge provides scalable evaluation opportunities, but lacks in-depth understanding.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectDeveloping AI Camera feature that generates personalized movement-encouraging stories for children based on the photo of their surroundings. Exploring the creativity of the stories and the multi-modal LLMs that were used for the generation.
dc.titleMonkey Moves AI Camera: Investigating the Creativity of Multimodal LLMs for Photo-Based Children’s Storytelling
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsMLLMs; creativity; children storytelling; LLM-as-a-judge; human evaluation;
dc.subject.courseuuApplied Data Science
dc.thesis.id52786


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