Robots in Education — AI Ed Wiki

Robots in education (educational robotics) — the use of physical or simulated robots as tools for teaching and learning. Educational robotics spans a wide spectrum: from programmable kits that teach computational thinking and programming, to socially assistive and humanoid robots that tutor, tell stories, model sign language, or rehearse social skills. It is valued for fostering problem solving, critical thinking, creativity, and STEAM engagement, and for making abstract computing concepts tangible through embodied interaction. The wiki's robotics corpus spans curriculum-integrated programming, LLM-powered conversational tutors, socially assistive storytelling robots, and role-play for social-emotional learning.

Educational robotics is a distinct but closely related application of AI in education. Unlike software-only intelligent tutoring or LLM chatbots, robots add an embodied and often social presence — a physical agent that learners can see, manipulate, and (increasingly) converse with. This embodiment is central to their pedagogical value: it grounds abstract program logic in observable behaviour, and it can support relationship-building and emotional engagement that disembodied systems cannot. The field overlaps with Human Robot Interaction (the study of how people and robots interact) and Social Robots (robots designed to engage people socially).

How robots are used in education

Computational thinking and programming: Programmable robots (e.g., LEGO, block-based platforms) help learners connect code to real outcomes. Valls i Pou links computational thinking to secondary STEAM curricula, and RoboBlockly Studio combines block programming with a conversational AI agent and embodied robot feedback. EduSim-LLM lets beginners control simulated robots with natural language.

Tutoring and knowledge delivery: Knowledge-based design research and Teachy Mini develop LLM-powered generative social robots that tutor higher-education students, addressing risks like misinformation and overreliance. Research on trust shows that what a robot does (task context) shapes learner trust more than its appearance.

Storytelling and engagement: MotiBo and RoboBuddy use interactive, LLM-powered social robots for storytelling to boost motivation and engagement, while the iCub narrative HRI study explores co-creative storytelling between humans and humanoids.

Social-emotional learning and inclusion: REMind uses robot-mediated role-play to rehearse anti-bullying bystander intervention, and work with the Pepper robot explores robot sign-language communication to support Deaf learners.

Project-based and game-based approaches: Bots and Blocks presents a project-based robotics course, and a systematic review compares game-based learning and gamification in robotics education.

Embodiment and pedagogy

A defining theme is that robots are effective when they support genuine learning goals — not as isolated technical exercises. The value of a robot depends on the pedagogical context: teaching computational thinking (Computational Thinking), supporting STEAM, building programming skills, motivating learners (Motivation, engagement), or supporting Social Emotional Learning and inclusion. Robotics also connects to Project Based Learning, Game Based Learning, and Experiential Learning. Key design considerations include preserving learner Agency, building Trust, supporting Self Efficacy, and grounding learning in embodied interaction.