Date
Publisher
arXiv
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.
Study at a glance
- Question: How do physical robot feedback and conversational AI guidance affect novice students' programming and computational thinking?
- Setting and data: Classroom setting, local high schools, 32 students aged 15–18 with no prior programming experience.
- Method: Counterbalanced within-subject user study with pre- and post-tests, workload surveys, usability questionnaires, and interviews.
Why it matters
Novice learners often struggle to connect abstract programming code to physical outcomes, while educational robotics activities frequently lapse into trial-and-error without adequate scaffolding.
Key findings
- High school students' computational thinking problem-solving scores significantly improved from 1.28 out of 4 on the pre-test to 2.88 on the post-test.
- Adding a physical robot was associated with significantly lower student frustration, less perceived effort, and lower temporal demand compared to conversational AI alone.
- Thirty of 32 participants preferred the combined robot and AI system, using physical robot motion as observable evidence to guide more targeted AI questions.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
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Why use AI?
Study design
