RoboBlockly Studio: Conversational Block Programming with Embodied Robot Feedback for Computational Thinking
Leyi Li, Chenyu Du, Jiafei Sun, Erick Purwanto, Qing Zhang
TLDR
RoboBlockly Studio combines block programming, conversational AI, and embodied robots to improve computational thinking education.
Key contributions
- Introduces RoboBlockly Studio, integrating block programming, conversational AI, and embodied robots.
- Designed to enhance learner agency, program transparency, and embodied task grounding in CT.
- Utilizes pedagogically grounded AI dialogue to scaffold student reflection on problem-solving.
- Evaluated with high school students, showing impact of robot/AI feedback on CT understanding.
Why it matters
This paper matters because it offers a novel, integrated approach to teaching computational thinking by combining tangible robot interaction with intelligent AI guidance. It addresses the challenge of connecting abstract programming to concrete outcomes, providing a practical framework for future interactive learning environments.
Original Abstract
Computational thinking (CT) is increasingly promoted as a core literacy, yet learners and teachers face challenges in connecting abstract program logic to meaningful outcomes. We design and evaluate RoboBlockly Studio, an integrated interactive system that combines block-based programming, a conversational AI teaching agent, and embodied robot execution. RoboBlockly Studio creates a tight iterative loop of authoring, running, observing, and revising. Informed by interviews with five programming teachers, the system was designed to support four goals: (1) preserving learner agency in computational thinking, (2) making program behavior transparent and interpretable, (3) grounding programming in embodied, classroom-aligned tasks, and (4) scaffolding reflection through pedagogically grounded AI dialogue. We deployed RoboBlockly Studio with 32 high school students, observing how robot and AI feedback influenced students' interactions with code, reflections on problem-solving strategies, and understanding of CT concepts. We discuss design insights and implications for creating interactive, embodied learning environments that integrate AI and robotics to support CT learning in computing education.
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