Our Coding Adventure: Using LLMs to Personalise the Narrative of a Tangible Programming Robot for Preschoolers

• URL: http://arxiv.org/abs/2506.20982v1
• Date: Thu, 26 Jun 2025 03:54:25 GMT
• Title: Our Coding Adventure: Using LLMs to Personalise the Narrative of a Tangible Programming Robot for Preschoolers
• Authors: Martin Ruskov,
• Abstract summary: We develop an early version of a formalised process to rapidly prototype game stories for Cubetto.<n>We document on one hand the process, the used materials and prompts, and on the other the learning experience and outcomes.<n>We believe our method is adequate for preschool classes and we are planning to further experiment in real-world educational settings.
• Score: 0.5099081649205313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Finding balanced ways to employ Large Language Models (LLMs) in education is a challenge due to inherent risks of poor understanding of the technology and of a susceptible audience. This is particularly so with younger children, who are known to have difficulties with pervasive screen time. Working with a tangible programming robot called Cubetto, we propose an approach to benefit from the capabilities of LLMs by employing such models in the preparation of personalised storytelling, necessary for preschool children to get accustomed to the practice of commanding the robot. We engage in action research to develop an early version of a formalised process to rapidly prototype game stories for Cubetto. Our approach has both reproducible results, because it employs open weight models, and is model-agnostic, because we test it with 5 different LLMs. We document on one hand the process, the used materials and prompts, and on the other the learning experience and outcomes. We deem the generation successful for the intended purposes of using the results as a teacher aid. Testing the models on 4 different task scenarios, we encounter issues of consistency and hallucinations and document the corresponding evaluation process and attempts (some successful and some not) to overcome these issues. Importantly, the process does not expose children to LLMs directly. Rather, the technology is used to help teachers easily develop personalised narratives on children's preferred topics. We believe our method is adequate for preschool classes and we are planning to further experiment in real-world educational settings.

Related papers

• Automatically assessing oral narratives of Afrikaans and isiXhosa children [15.669164862460342]
  We present a system for automatically assessing oral narratives of preschool children in Afrikaans and isiXhosa.<n>The system uses automatic speech recognition followed by a machine learning scoring model to predict narrative and comprehension scores.
  arXiv  Detail & Related papers  (2025-07-17T15:15:43Z)
• Applying LLM-Powered Virtual Humans to Child Interviews in Child-Centered Design [0.0]
  This study establishes key design guidelines for LLM-powered virtual humans tailored to child interviews.<n>Using ChatGPT-based prompt engineering, we developed three distinct Human-AI (LLM-Auto, LLM-Interview, and LLM-Analyze)<n>Results indicated that the LLM-Analyze workflow outperformed the others by eliciting longer responses.
  arXiv  Detail & Related papers  (2025-04-28T17:35:46Z)
• Benchmarking LLMs for Mimicking Child-Caregiver Language in Interaction [4.109949110722246]
  LLMs can generate human-like dialogues, yet their ability to simulate early child-adult interactions remains largely unexplored.<n>We found that state-of-the-art LLMs can approximate child- caregiver dialogues at the word and utterance level, but they struggle to reproduce the child and caregiver's discursive patterns, exaggerate alignment, and fail to reach the level of diversity shown by humans.
  arXiv  Detail & Related papers  (2024-12-12T14:43:03Z)
• Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure [36.83786872708736]
  One-to-one tutoring is one of the most efficient methods of teaching.<n>We develop StratL, an algorithm to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph.<n>As a case study, we create a prototype tutor for high school math following Productive Failure (PF), an advanced and effective learning design.
  arXiv  Detail & Related papers  (2024-10-03T16:15:41Z)
• Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
  We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring.<n>We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue.<n>We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners? [140.9751389452011]
  We study the biases of large language models (LLMs) in relation to those known in children when solving arithmetic word problems. We generate a novel set of word problems for each of these tests, using a neuro-symbolic approach that enables fine-grained control over the problem features.
  arXiv  Detail & Related papers  (2024-01-31T18:48:20Z)
• Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges [60.62904929065257]
  Large language models (LLMs) offer possibility for resolving this issue by comprehending individual requests. This paper reviews the recently emerged LLM research related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering.
  arXiv  Detail & Related papers  (2023-12-27T14:37:32Z)
• Next-Step Hint Generation for Introductory Programming Using Large Language Models [0.8002196839441036]
  Large Language Models possess skills such as answering questions, writing essays or solving programming exercises. This work explores how LLMs can contribute to programming education by supporting students with automated next-step hints.
  arXiv  Detail & Related papers  (2023-12-03T17:51:07Z)
• Democratizing Reasoning Ability: Tailored Learning from Large Language Model [97.4921006089966]
  We propose a tailored learning approach to distill such reasoning ability to smaller LMs. We exploit the potential of LLM as a reasoning teacher by building an interactive multi-round learning paradigm. To exploit the reasoning potential of the smaller LM, we propose self-reflection learning to motivate the student to learn from self-made mistakes.
  arXiv  Detail & Related papers  (2023-10-20T07:50:10Z)
• Character-LLM: A Trainable Agent for Role-Playing [67.35139167985008]
  Large language models (LLMs) can be used to serve as agents to simulate human behaviors. We introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc.
  arXiv  Detail & Related papers  (2023-10-16T07:58:56Z)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.