From Keyboard to Chatbot: An AI-powered Integration Platform with Large-Language Models for Teaching Computational Thinking for Young Children
Project Overview
The document discusses an innovative AI integration platform named Spark, which aims to teach computational thinking to children aged 4-9 through an interactive conversational interface with a robot. By employing a hybrid pedagogy that merges top-down and bottom-up teaching strategies, Spark allows young learners to express programming tasks in natural language. The system then translates these tasks into executable code using a specialized Spark programming language, facilitated by a large language model (LLM) that enables semantic decomposition of tasks. This approach makes programming more accessible, eliminating the need for keyboard skills, and promotes deeper engagement with programming concepts. Through hands-on interactions with a tangible robot, the platform not only enhances understanding but also fosters enthusiasm for learning programming in early education. Overall, the findings highlight how generative AI can revolutionize teaching methodologies in education, making complex subjects like programming more approachable and enjoyable for young children.
Key Applications
Spark - AI-powered platform for teaching computational thinking
Context: Early childhood education (ages 4-9), specifically in programming and computational thinking skills
Implementation: Children interact with the system using natural language to describe tasks, which are processed by a chatbot and transformed into a Spark programming language code executed by a robot.
Outcomes: Improved engagement in learning programming concepts, reduced dependency on keyboarding, enhanced understanding of decomposition and sequencing in programming.
Challenges: Developmental readiness of young children, complexity of programming concepts, potential screen time concerns.
Implementation Barriers
Developmental Challenges
Young children may struggle with keyboarding and complex task decomposition, which are prerequisites for traditional programming education.
Proposed Solutions: Using natural language input and a conversational interface to bypass keyboarding requirements and simplifying the programming concepts.
Screen Time Concerns
Excessive screen time can be detrimental to young children's health.
Proposed Solutions: Integrating tangible interactions with a robot to minimize screen time while providing interactive learning experiences.
Project Team
Changjae Lee
Researcher
Jinjun Xiong
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Changjae Lee, Jinjun Xiong
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai