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Robots in Education

created 2026-08-13 · updated 2026-09-23

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 knowledge base's robotics corpus spans curriculum-integrated programming, LLM-powered conversational tutors, socially assistive storytelling robots, and role-play for social-emotional learning. It is underpinned by two closely related areas absorbed here: social robots (robots designed for social interaction and relationship-building) and human–robot interaction (HRI) (the study of how people perceive, trust, and learn with robots).

Questions to Consider

  • A robot in the classroom adds an embodied, social presence that a chatbot on a screen can't. What do you think the physical body and social cues of a robot change about how students learn, trust, and engage — and what might they distract from?
  • Social robots use human-like speech, gestures, and personality to teach, tell stories, or rehearse social skills. Is a robot that looks and acts human inherently better for learning, or could that social presence bring risks (misinformation, over-reliance, privacy) that software-only tools don't?
  • LLMs now let social robots converse fluently. If a robot can talk like a tutor, what still depends on its physical embodiment — and where does adding a 'body' really matter for learning versus just being a novelty?
  • Think of a time you learned something by physically manipulating an object or watching your actions produce a visible result. How might programming a physical robot ground abstract ideas (like program logic) more effectively than writing code on a screen?

Introduction

Educational robotics is a distinct but closely related application of AI in education. Unlike software-only intelligent tutoring or Large Language Models (LLMs) 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 behavior, and it can support relationship-building and emotional engagement that disembodied systems cannot.

Social robots and human–robot interaction

Two strands shape the social side of robotics in education.

Social robots are robots designed to engage people through social interaction, using human-like cues such as speech, gesture, facial expression, and personality to communicate, teach, assist, or accompany. In education, social robots (humanoids like iCub, Pepper, Reachy, and companion robots) are used for tutoring, storytelling, role-play, language support, and as study companions. Their social presence is the key differentiator from software-based AI agents, enabling relationship-building and emotional engagement. Advances in large language models have dramatically expanded what social robots can say and do, enabling fluent, adaptive conversational tutoring — while also introducing risks such as misinformation, over-reliance, and Privacy violations, motivating knowledge-based design approaches.

Human–robot interaction (HRI) is the interdisciplinary study of how people and robots interact, encompassing perception, communication, collaboration, and the social, cognitive, and ethical dynamics of that interaction. In education, HRI underlies how learners perceive, trust, and learn with robots — whether programming a robot, conversing with a tutoring robot, or rehearsing social scenarios. HRI research examines how robot appearance, behavior, task context, and embodiment shape user experience, trust, agency, and learning. Key concerns in educational HRI include preserving human Learner Agency, building Trust, supporting Self-Efficacy, and ensuring that interaction with robots supports rather than undermines autonomy and social learning. It connects robotics to Human AI Collaboration and Social-Emotional Learning.

How robots are used in education

Christ et al. (2026) add a role typology rather than a technology list: their five workshop-derived types of classroom robot differ by pedagogical function and abstraction level, not by hardware. Type a runs a non-interactive demonstration of generic social patterns (a scripted emotion theater followed by discussion of dynamics such as escalation or misunderstanding); type b is a touch-reactive interactive robot supporting participative physical theater, embodied learning, boundary awareness and emotion regulation; type c is a spoken-language partner that shows empathy and remembers interactions with a single pupil, creating a protected one-to-one setting for self-disclosure; type d is externally guided by a hidden specialist like a puppet with extra degrees of freedom, aimed at flattening social hierarchy; and type e is a non-interactive robot replaying actions recently observed in the school so pupils can reflect on situated behavior — the contrast with type a being exactly its context-specific rather than generalized abstraction. The typology is explicit about being an unvalidated design space grounded in one national mental-health program, so it is a menu for designing and evaluating robot roles, not evidence that any of them works.

  • 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, with the highest trust during instructional tasks.

  • 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. A scoping review maps Pepper's use in formal education.

  • Autonomy and agency: A systematic review synthesizes how HRI affects human autonomy and sense of agency, bridging design frameworks with regulatory demands (EU AI Act, IEEE Ethically Aligned Design). Social robots as study companions and robot–LLM integration in creative writing further explore robot roles.

  • 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.

  • Reinforcement learning and sim-to-real in a full robotics workflow. Dong, Cao and Wang (2026) convert an end-to-end research robotics workflow — assembly, electrical checks, simulation-based policy training, and physical deployment — into a high-school course built on one open humanoid (a ToddlerBot, reported parts cost under USD 6,000) across eight three-hour sessions. Pairs share one robot and train a walking policy in Simulation before deploying it to hardware, with safety gates (a passed standing test before walking) making the dependency order visible. Because a shared artifact rewards the team rather than the individual, the framework separates robot performance from individual understanding: students rotate roles, each submits a separate prediction and explanation at every checkpoint, and supported stepping is explicitly not treated as evidence of conceptual mastery — the authors' warning that passing a robot milestone is not understanding it.

  • Competition robotics as an ecosystem problem, not a kit problem. Jacobson et al. (2026) locate the binding constraint on K–12 robotics less in curriculum or hardware than in sustained local technical mentorship, and show it is distributed geographically: in Indiana, FIRST LEGO League participation collapsed in the 2020 remote season, urban participation gradually recovered, and rural participation did not, remaining near its post-2020 level through 2025–2026. Their ARC framework makes mentorship the engineered object — colleges run a credit-bearing course that prepares undergraduates as workshop mentors for nearby teams, and mature school programs become secondary hubs whose experienced students become peer mentors for further schools, so reach propagates beyond any university's catchment through a self-reinforcing loop. A one-university trial created three rural FLL teams and moved undergraduate community connection from 1.86 to 4.00 on a five-point scale (the largest of any measured shift, ahead of confidence teaching technical concepts at +1.29), while a spatially explicit Markov simulation of Indiana's 1,925 public schools projected 992 school programs after 40 years under moderate assumptions against 161 with no ARC. The evidence is feasibility-level — seven mentors and four parents, retrospective self-reports, no control group — but the framing is portable to any robotics program: what scales or fails to scale is mentorship capacity and hub geography, not the robot (Teaching AI, Robotics, & Community: A Hubs-Based K-12 Education Framework for Reaching Rural Schools).

  • Child development and young learners: AI-enabled toys and child development shifts the lens to commercial AI toys in early childhood, examining how AI-enabled playthings affect child development and play. This extends educational robotics beyond classroom robots to the consumer toys children encounter at home, raising questions about agents in play, trust calibration, Learner Agency, and Well-Being for the youngest learners — an area where design guidance is thinner than for school-age robotics curricula.

  • Tangible coding and social robots in pre-K AI literacy. Lee (2026) integrates unplugged play, tangible coding (Bee-Bot, Ozobot), and guided dialogue with a social AI robot in the Play With AI (PL-AI) curriculum for pre-K and kindergarten. The design-based research documents how these embodied, tangible robotics activities support children's emerging reasoning about AI concepts, with four design principles — embodied play, tangible coding, guided dialogue, and teacher co-design — offering a developmentally appropriate model for early childhood robotics and AI education.

  • Two paradigms for young learners: coding robots and generative social robots. Yang, Li and Lee (2025) frame early-childhood robotics as the pairing of two pedagogical paradigms, each with a distinct theoretical base. Coding robots (Bee-Bot, KIBO, Matatalab) descend from Papert's LOGO and embody constructionism — children learn by making and build computational thinking through tangible programming. Generative social robots, powered by generative AI, are grounded in social constructivism, acting as conversational peers or tutors who scaffold learning within the child's Zone of Proximal Development and support social-emotional development. Their five-step Creative Project Approach for integrating both robot types into the Project Approach keeps teachers as facilitators who guide child–robot interaction, balance automation with Creativity, and preserve child Learner Agency.

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 Learner Agency, building Trust, supporting Self-Efficacy, and grounding learning in embodied interaction. In Language Learning, meta-analytic evidence points to the effectiveness of embodied robot-assisted language learning.

  • Pathways to learning AI-powered robotics. A qualitative study of high school students in a robotics+AI curriculum found learning through real-world practice, designing, and playful creative expression (constructionist, epistemological-pluralist lens).

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Metadata

DisciplineStem Education, Cs Education
LevelK 12, Higher Ed
Learning and instructionEmbodied Learning
Technologies and techniquesRobots in Education
Foundations of AI in educationComputational Thinking
SourceHistoryEditChat

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