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AI-Driven Personalization in Educational Dialogues : Microtool for Child Profile Building

Sandu, M.A. (2025) AI-Driven Personalization in Educational Dialogues : Microtool for Child Profile Building.

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Abstract:Robots have yet to reach the level of personalization necessary to foster long-term interactions with children. Existing systems struggle to establish the needed social connections as they depend on scripted content and rigid dialogue structures, not adjusting to individual users. To address this gap, this research implements a chatbot tool that, using a knowledge graph (KG) and strict prompt rules, creates an individualized conversational experience. It references each child's past data, personalizing questions and transitions throughout a structured process while filling 29 fields in a user model. The chatbot was tested in a within-subjects study where children aged between 9 and 11 interacted with both the chatbot and a personalized paper-based letter with integrated questions. Results show that the chatbot significantly increased data completeness, with the missing fields reduced by over 90% compared to the baseline, leading to more updated information, and was rated as more engaging by children. Qualitative feedback highlighted a preference for the chatbot’s interactive and conversational style, though both modalities were seen as personal in different ways. These findings support the use of AI-driven, memory-based interfaces for building rapport and gathering personalized data in child-robot interaction contexts.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
Link to this item:https://purl.utwente.nl/essays/107276
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