From Keyboard to Chatbot: An AI-powered Integration Platform with Large-Language Models for Teaching Computational Thinking for Young Children

• URL: http://arxiv.org/abs/2405.00750v1
• Date: Wed, 1 May 2024 04:29:21 GMT
• Title: From Keyboard to Chatbot: An AI-powered Integration Platform with Large-Language Models for Teaching Computational Thinking for Young Children
• Authors: Changjae Lee, Jinjun Xiong,
• Abstract summary: We present a novel methodology with an AI-powered integration platform to effectively teach computational thinking for young children. Young children can describe their desired task in natural language, while the system can respond with an easy-to-understand program. A tangible robot can immediately execute the decomposed program and demonstrate the program's outcomes to young children.
• Score: 22.933382649048113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Teaching programming in early childhood (4-9) to enhance computational thinking has gained popularity in the recent movement of computer science for all. However, current practices ignore some fundamental issues resulting from young children's developmental readiness, such as the sustained capability to keyboarding, the decomposition of complex tasks to small tasks, the need for intuitive mapping from abstract programming to tangible outcomes, and the limited amount of screen time exposure. To address these issues in this paper, we present a novel methodology with an AI-powered integration platform to effectively teach computational thinking for young children. The system features a hybrid pedagogy that supports both the top-down and bottom-up approach for teaching computational thinking. Young children can describe their desired task in natural language, while the system can respond with an easy-to-understand program consisting of the right level of decomposed sub-tasks. A tangible robot can immediately execute the decomposed program and demonstrate the program's outcomes to young children. The system is equipped with an intelligent chatbot that can interact with young children through natural languages, and children can speak to the chatbot to complete all the needed programming tasks, while the chatbot orchestrates the execution of the program onto the robot. This would completely eliminates the need of keyboards for young children to program. By developing such a system, we aim to make the concept of computational thinking more accessible to young children, fostering a natural understanding of programming concepts without the need of explicit programming skills. Through the interactive experience provided by the robotic agent, our system seeks to engage children in an effective manner, contributing to the field of educational technology for early childhood computer science education.

Related papers

• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• Yell At Your Robot: Improving On-the-Fly from Language Corrections [84.09578841663195]
  We show that high-level policies can be readily supervised with human feedback in the form of language corrections. This framework enables robots not only to rapidly adapt to real-time language feedback, but also incorporate this feedback into an iterative training scheme.
  arXiv  Detail & Related papers  (2024-03-19T17:08:24Z)
• Anticipating User Needs: Insights from Design Fiction on Conversational Agents for Computational Thinking [10.363782876965221]
  We envision a conversational agent that guides students stepwise through exercises, tuning its method of guidance with an awareness of the educational background, skills and deficits, and learning preferences. The insights obtained in this paper can guide future implementations of tutoring agents oriented toward teaching computational thinking and computer programming.
  arXiv  Detail & Related papers  (2023-11-12T16:19:03Z)
• Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering Education [0.0]
  This paper explores the potential integration of large language models (LLMs) and chatbots into graduate engineering education. We develop a question bank from the course material and assess the bot's ability to provide accurate, insightful responses. We demonstrate how powerful plugins like Wolfram Alpha for mathematical problem-solving and code interpretation can significantly extend the bot's capabilities.
  arXiv  Detail & Related papers  (2023-09-09T13:37:22Z)
• Language-Driven Representation Learning for Robotics [115.93273609767145]
  Recent work in visual representation learning for robotics demonstrates the viability of learning from large video datasets of humans performing everyday tasks. We introduce a framework for language-driven representation learning from human videos and captions. We find that Voltron's language-driven learning outperform the prior-of-the-art, especially on targeted problems requiring higher-level control.
  arXiv  Detail & Related papers  (2023-02-24T17:29:31Z)
• Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance [71.36749876465618]
  We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks. Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples. experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
  arXiv  Detail & Related papers  (2022-12-19T22:50:40Z)
• PyBryt: auto-assessment and auto-grading for computational thinking [6.1345408064202696]
  We present a novel approach to providing formative feedback to students on programming assignments. Our approach uses dynamic evaluation to trace intermediate results generated by student's code and compares them to the reference implementation provided by their teachers.
  arXiv  Detail & Related papers  (2021-12-03T20:01:06Z)
• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation [80.29069988090912]
  We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. We propose to leverage offline robot datasets with crowd-sourced natural language labels. We find that our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%.
  arXiv  Detail & Related papers  (2021-09-02T17:42:13Z)
• Exploring a Handwriting Programming Language for Educational Robots [1.310461046819527]
  This study presents the development of a handwriting-based programming language for educational robots. It allows students to program a robot by drawing symbols with ordinary pens and paper. The system was evaluated in a preliminary test with eight teachers, developers and educational researchers.
  arXiv  Detail & Related papers  (2021-05-11T12:00:34Z)
• An Experience of Introducing Primary School Children to Programming using Ozobots (Practical Report) [10.213226970992666]
  A recent trend is to introduce basic programming concepts already very early on at primary school level. Schools and teachers are often neither equipped nor trained appropriately, and the best way to move from initial "unplugged" activities to creating programs on a computer are still a matter of open debate. We describe our experience of a small INTERREG-project aiming at supporting local primary schools in introducing children to programming concepts using Ozobot robots.
  arXiv  Detail & Related papers  (2020-08-28T07:36:07Z)
• A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits [91.3755431537592]
  Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neuro-robotics.
  arXiv  Detail & Related papers  (2020-03-23T14:55:00Z)

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.