RoboBuddy in the Classroom: Exploring LLM-Powered Social Robots for Storytelling in Learning and Integration Activities
- URL: http://arxiv.org/abs/2508.16706v1
- Date: Fri, 22 Aug 2025 13:14:09 GMT
- Title: RoboBuddy in the Classroom: Exploring LLM-Powered Social Robots for Storytelling in Learning and Integration Activities
- Authors: Daniel Tozadore, Nur Ertug, Yasmine Chaker, Mortadha Abderrahim,
- Abstract summary: We implement an intuitive interface that allows teachers to create scenario-based activities using LLMs and social robots.<n>Our findings highlight the positive impact of integration policies perceived by the children and demonstrate the importance of scenario-based activities in students' enjoyment.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Creating and improvising scenarios for content approaching is an enriching technique in education. However, it comes with a significant increase in the time spent on its planning, which intensifies when using complex technologies, such as social robots. Furthermore, addressing multicultural integration is commonly embedded in regular activities due to the already tight curriculum. Addressing these issues with a single solution, we implemented an intuitive interface that allows teachers to create scenario-based activities from their regular curriculum using LLMs and social robots. We co-designed different frameworks of activities with 4 teachers and deployed it in a study with 27 students for 1 week. Beyond validating the system's efficacy, our findings highlight the positive impact of integration policies perceived by the children and demonstrate the importance of scenario-based activities in students' enjoyment, observed to be significantly higher when applying storytelling. Additionally, several implications of using LLMs and social robots in long-term classroom activities are discussed.
Related papers
- GIFT: Games as Informal Training for Generalizable LLMs [64.47890325824763]
Large Language Models (LLMs) struggle with "practical wisdom" and generalizable intelligence.<n>This gap arises from a lack of informal learning, which thrives on interactive feedback rather than goal-oriented instruction.<n>We propose treating Games as a primary environment for LLM informal learning, leveraging their intrinsic reward signals and abstracted complexity.
arXiv Detail & Related papers (2026-01-09T08:42:44Z) - The StudyChat Dataset: Student Dialogues With ChatGPT in an Artificial Intelligence Course [1.9116784879310025]
Large language models (LLMs) have significantly impacted education, raising both opportunities and challenges.<n>We introduce StudyChat, a dataset capturing real-world student interactions with an LLM.<n>We deploy a web application that replicates ChatGPTs core functionalities, and use it to log student interactions with the LLM.<n>We analyze these interactions, highlight usage trends, and analyze how specific student behavior correlates with their course outcome.
arXiv Detail & Related papers (2025-03-11T00:17:07Z) - Student-AI Interaction in an LLM-Empowered Learning Environment: A Cluster Analysis of Engagement Profiles [28.794946431719392]
This study explored diverse learner profiles within a multi-agent, LLM-empowered learning environment.<n>Students exhibit varied behavioral, cognitive, and emotional engagement tendencies.
arXiv Detail & Related papers (2025-03-03T16:08:28Z) - Vocal Sandbox: Continual Learning and Adaptation for Situated Human-Robot Collaboration [64.6107798750142]
Vocal Sandbox is a framework for enabling seamless human-robot collaboration in situated environments.
We design lightweight and interpretable learning algorithms that allow users to build an understanding and co-adapt to a robot's capabilities in real-time.
We evaluate Vocal Sandbox in two settings: collaborative gift bag assembly and LEGO stop-motion animation.
arXiv Detail & Related papers (2024-11-04T20:44:40Z) - Simulating Classroom Education with LLM-Empowered Agents [48.26286735827104]
Large language models (LLMs) have been applied across various intelligent educational tasks to assist teaching.<n>We propose SimClass, a multi-agent classroom simulation teaching framework.<n>We recognize representative class roles and introduce a novel class control mechanism for automatic classroom teaching.
arXiv Detail & Related papers (2024-06-27T14:51:07Z) - Surprising Performances of Students with Autism in Classroom with NAO Robot [2.1634090200833165]
This paper describes the design and implementation of a group experiment in a collective classroom setting mediated by the NAO robot.
Students in classrooms equipped with the NAO robot exhibited notably better performance compared to those in regular classrooms.
Our preliminary findings indicate that the NAO robot significantly enhances focus and classroom engagement among students with ASD.
arXiv Detail & Related papers (2024-06-27T01:04:58Z) - Empowering Large Language Model Agents through Action Learning [85.39581419680755]
Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error.
We argue that the capacity to learn new actions from experience is fundamental to the advancement of learning in LLM agents.
We introduce a framework LearnAct with an iterative learning strategy to create and improve actions in the form of Python functions.
arXiv Detail & Related papers (2024-02-24T13:13:04Z) - Disadvantaged students increase their academic performance through
collective intelligence exposure in emergency remote learning due to COVID 19 [105.54048699217668]
During the COVID-19 crisis, educational institutions worldwide shifted from face-to-face instruction to emergency remote teaching (ERT) modalities.
We analyzed data on 7,528 undergraduate students and found that cooperative and consensus dynamics among students in discussion forums positively affect their final GPA.
Using natural language processing, we show that first-year students with low academic performance during high school are exposed to more content-intensive posts in discussion forums.
arXiv Detail & Related papers (2022-03-10T20:23:38Z) - Educational Robotics in Online Distance Learning: An Experience from
Primary School [0.0]
This work presents the development of an Educational Robotics activity particularly conceived for online distance learning in primary school.
The devised activities are based on pen and paper approaches that are complemented by commonly used social media to facilitate communication and collaboration.
arXiv Detail & Related papers (2021-05-20T12:20:44Z) - RODE: Learning Roles to Decompose Multi-Agent Tasks [69.56458960841165]
Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles.
We propose to first decompose joint action spaces into restricted role action spaces by clustering actions according to their effects on the environment and other agents.
By virtue of these advances, our method outperforms the current state-of-the-art MARL algorithms on 10 of the 14 scenarios that comprise the challenging StarCraft II micromanagement benchmark.
arXiv Detail & Related papers (2020-10-04T09:20:59Z) - ChildBot: Multi-Robot Perception and Interaction with Children [43.08980479118157]
We present an integrated robotic system capable of participating in and performing a wide range of educational and entertainment tasks.
ChildBot features multimodal perception modules and multiple robotic agents that monitor the interaction environment.
arXiv Detail & Related papers (2020-08-28T19:07:28Z)
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.