Humanoid Robots in Classrooms – Pros and Cons

Picture yourself this. A Year 6 class walks in, and next to the whiteboard stands a small humanoid robot. It introduces itself, asks students to repeat new vocabulary, turns its head towards whoever speaks. Phones stay in pockets. Eyes stay on the lesson.

Humanoid robots like NAO, Pepper or Furhat are already being piloted as language tutors, STEM partners, social–emotional training tools, and even AI-powered “co-teachers” in schools and universities across the world. A growing body of classroom studies and systematic reviews now gives a clearer view of what these robots do well, where they fall short, and what they demand from teachers and schools.

Studies so far report higher engagement and motivation, but small positive effects on learning outcomes, and also ethical, pedagogical, and practical concerns. The strongest body of evidence is in STEM / coding, language learning, and special education (especially autism).

In this article, especially directed at educators, I will present you a practical look at humanoid robots in classrooms.

What humanoid robots actually do now in real classrooms

The first thing you need to understand is that in research and pilot projects, humanoid robots rarely replace teachers. So teachers will not loose their jobs to automation, AI or robots. These humanoid robots usually take on three roles:

• Teaching assistant or tutor: The robot presents vocabulary, asks quiz questions, demonstrates movements in PE or science, or leads small group tasks. NAO-based vocabulary lessons at university level, for example, show that students often learn at least as well as with a human-only setup and report high enjoyment.
• Peer or learning companion: In some primary and lower secondary projects, robots act as “slightly older peers” who practice reading aloud, math facts, or problem-solving. A review of field studies on social robots in classrooms found that this peer role is one of the most common and tends to raise participation and on-task behavior.
• Special education support: For autistic learners and other students with special needs, robots provide predictable, repeatable interactions to train joint attention, turn-taking, or emotion recognition. Multiple systematic reviews report positive trends in engagement and some behavioral outcomes, though they warn about small samples and short interventions.

Robots act both as didactic tools and as social actors students respond to emotionally. That dual role is at the heart of both the promise and also the risks. If you look at more studies there are more detailed conclusions that emerge.

• A 2025 systematic review on humanoid robots in education looks at how they are used in formal education, which pedagogical approaches dominate, and what impact they have. It reports that most studies use constructivist or socio-cultural designs (problem-based learning, game-based learning) and that robots act both as didactic tools and as “social actors”. Benefits focus on engagement and short-term learning gains, while challenges include cost, technical reliability, teacher workload, and ethics.
• A meta-review on social robots in education (2023) concludes that robots can support learning and social interaction, but the overall effect sizes are moderate and the evidence base is heterogeneous (different ages, tasks, and measures).
• For special education, especially autism, a 2022–2024 line of work shows that social robots can help with attention, engagement, and some social behaviors, but should always complement human support, not replace it.

Classroom field studies with humanoid robots

Most of what we know about robots in education does not come from lab demos, but from ordinary lessons where teachers tried to make a robot fit into a timetable, a curriculum and a noisy room of children. The studies you can read about below show what happens when the Wi-Fi drops, when a robot mishears a pupil, or when the novelty wears off after a few weeks. They also capture the quiet moments that never make it into marketing videos: a shy student asking the robot to repeat a word, a teacher juggling behavior management and error messages at the same time.

Across primary, secondary and special education, these field trials reveal recurring patterns. Robots tend to boost attention and participation, especially in STEM and language activities. Teachers use them as tutors, co-presenters or social partners, often to structure repetition or small-group work. At the same time, every study surfaces practical trade-offs: extra preparation, technical fragility, questions about privacy, and the constant need to justify why the robot is in the room at all.

These classroom cases show humanoid robots as tools that either support or strain existing practice, depending on how they are introduced, supported and evaluated.

General / mainstream classrooms

• NAO and curriculum integration: Studies where NAO is embedded into lessons (e.g. programming, math, languages) show higher engagement and positive student attitudes compared to traditional lessons, with mixed but often positive learning gains. One 2025 paper directly compares robot-supported lessons with traditional lessons and measures post-test learning outcomes and student perceptions.
• Pepper as a teaching assistant: Recent work connects Pepper to large language models (LLMs) like ChatGPT and uses it to present new learning content to small groups in high school. Students generally find the robot’s presence meaningful and its explanations appropriate, although technical issues and novelty effects are noted.
• “Humanoid robots in the classroom” early study: Classic work with humanoid robots (NAO-type) in 7th-grade classes explored programming, language learning, ethics, and math. It showed that robots can act as “social objects” that trigger reflection, collaboration, and discussion, not just as gadgets.
• Usability and student experience (2025): A Norwegian Grade-6 study finds that students enjoy working with a humanoid robot on math and programming tasks, but learning effects and attitudes vary by gender and prior experience. It highlights that “robot = engagement” is not automatic; design and context matter.

Special education / inclusion

• A 2024 study with students on the autism spectrum reports that NAO can increase focus and classroom engagement and may support better learning performance and social behaviors, though the authors frame this as preliminary evidence.
• Systematic reviews on robots in special education underline promise for communication, emotion recognition, and routine-building, but stress ethical issues, teacher training needs, and the risk of over-relying on technology.

Teachers’ and students’ acceptance

Several recent studies have looked less at learning outcomes and more at whether people actually want robots in the classroom.

• A 2025 study on science teachers’ intentions to adopt humanoid robots uses the UTAUT-2 + TOE models. It finds that teachers’ intention is strongly driven by performance expectancy (do they see real value for learning?) and by organizational context (infrastructure, support).
• A 2024 paper on teaching with NAO focuses on teacher-users’ attitudes. It shows that teachers see potential, but worry about time, technical problems, unclear added value, and changes to their professional role.
• A 2025 acceptance study with 500+ undergraduates finds that perceived ease of use and perceived usefulness strongly shape attitudes and intentions to use humanoid robots. Novelty seeking and self-efficacy also play a role.

Engagement, motivation, and concrete learning

Engagement: students look up, not away

Across many classroom studies, the most consistent finding is simple: when a social or humanoid robot is part of the lesson, students pay more attention. A review of 23 field studies on social robots in classrooms concluded that robots are “feasible and effective tools” for raising engagement, especially in primary education.

The effect is strongest when:

• The robot has an active part in the task (asking questions, reacting to answers), not just standing in the corner.
• Activities fit the robot’s strengths: short dialogues, turn-taking, drills, storytelling, physical demonstrations.

Motivation: hesitant students step forward

Social robots can lower the barrier for participation. Language-learning studies using NAO as a vocabulary tutor show that some students, especially shy ones, prefer practicing aloud with the robot rather than with peers.

Special education studies report a similar pattern. In a 2024 classroom experiment with autistic students, a NAO robot mediating teacher-led activities helped students show more focus and fewer repetitive behaviors than in regular settings without the robot.

Active learning: robots make abstract STEM feel tangible

Programming and robotics projects turn abstract ideas – loops, sequencing, conditionals – into visible movement. A broad review of robotics education from 2010 to 2023 found that robots support problem-based and constructivist approaches, especially in STEM.

Humanoid robots add an extra twist: students often attribute intention and emotion to them. That can deepen engagement in activities like:

• Modelling a physics concept with gestures.
• Acting out historical dialogues.
• Simulating social situations in health or citizenship education.

Differentiation and repetition without exhaustion

Because robots never tire of repetition, they fit drill-heavy tasks: vocabulary, grammar practice, basic arithmetic, behavioural routines. Studies of NAO as a teaching assistant in vocabulary learning reported that students accepted repeated prompts and corrections from the robot without the frustration they might show towards a human.

This gives teachers an extra “channel” to personalise:

• Slower pace and extra explanations for some students.
• Optional stretch questions for quicker learners.

Inclusion and special education

For inclusive classrooms, robots can operate as structured, neutral partners. A 2023 scoping review on robots in inclusive classrooms concluded that robotics can address diverse learners’ needs, from attention and communication to physical interaction (with adapted interfaces), while stressing the importance of technical support and careful design.

Special education reviews for autism echo this: robots often help with joint attention, imitation, and social routines, yet they must be integrated into a broader therapeutic and pedagogical plan.

21st-century skills and AI literacy

Working with humanoid robots offers a direct way to explore:

• Human–machine interaction.
• Basic AI concepts (decision rules, limitations, bias).
• Ethics of automation and surveillance.

A 2025 review of social robots in education notes that robots act as a special form of intelligent tutoring system: embodied, social, and affective. Used well, they give students realistic experience with technologies that will appear in workplaces and public services.

What do we know about the impact of having humanoid robots in classrooms?

When you strip away the novelty and PR language, the key question remains: does having a humanoid robot in the room actually change what students learn and how they behave? The research so far gives a mixed, but useful, answer. Across dozens of classroom trials, robots boost attention and participation almost immediately. Students look up more, respond more, and stay with repetitive tasks longer when a robot asks the questions or models the activity.

The picture is more nuanced once you move from engagement to achievement. Meta-analyses and systematic reviews point to small to moderate gains in test scores for well-defined tasks – vocabulary, basic maths, procedural skills – especially when the robot acts as a structured tutor or practice partner. At the same time, many studies are short, involve small groups, and rarely follow students over months or years.

In other words, robots clearly change the atmosphere in the classroom and can improve learning in targeted areas, but they do not magically transform outcomes on their own. The impact depends on the role you give the robot, the quality of the lesson design, and the support teachers receive to integrate this extra “presence” into everyday practice.

Putting the big studies together we can detect these clear elements:

• Engagement & motivation: Consistently higher than in control conditions with “no robot”, especially for younger students, STEM, language learning, and storytelling tasks.
• Learning outcomes: Meta-analyses on educational robots in general report moderate positive effects on learning outcomes, but with big variation across studies. Many are small pilots with short interventions, so strong claims about long-term achievement are not justified yet.
• Best-supported domains
  • Programming / computational thinking
  • Basic math and science concepts
  • Vocabulary and language learning
  • Social skills and communication for students with special needs
• Conditions that matter
  • Clear pedagogical design (not “robot as gimmick”)
  • Teacher training and support
  • Reliable hardware/software
  • Thought-through roles: robot as co-tutor, peer, or drill partner, not vague “class mascot”

The hidden costs: money, infrastructure, and teacher workload

Budget: hardware is just the start

Humanoid robots cost far more than tablets or laptops. Beyond the purchase price, schools must plan for:

• Maintenance and repair.
• Software updates, licences, or cloud services.
• Accessories, chargers, protective storage.

Systematic reviews on robotics in classrooms repeatedly flag cost as a barrier and a driver of inequality: well-resourced schools can experiment, others cannot.

Infrastructure: robots do not like weak Wi-Fi

Most classroom robots depend on:

• Stable, low-latency Wi-Fi.
• Integrations with laptops, tablets, online platforms.
• Regular software updates and security patches.

Technical breakdowns are common in early deployments. A review of classroom field studies reported that technical glitches were among the most frequent obstacles and a major source of teacher frustration.

Teacher workload and expertise

Lesson planning with robots takes time. Teachers need to:

• Learn how to operate and reset the robot.
• Understand the scripting or programming environment.
• Design tasks where the robot adds more than entertainment value.

A 2023 study on robot-supported teaching noted that while children built positive relationships with the robot and achieved learning goals, teachers had to invest considerable effort to script scenarios and manage the technology.

Teacher acceptance studies confirm this. A 2025 UTAUT-2/TOE study on science teachers’ intention to adopt humanoid robots found that perceived usefulness and ease of use were decisive. Where teachers expected clear learning benefits and felt confident they could operate the robot, intention to adopt rose sharply; where they anticipated extra workload and technical stress, intention dropped.

Risks, criticism, and open questions

Researchers and educators raise recurring concerns:

• Ethics and privacy: Humanoid robots often collect audio, video, and interaction data. Recent experiments with “reading robots” in Australian primary schools triggered debate on children’s privacy, emotional data, and commercial platforms in classrooms.
• Over-reliance on machines: Some worry that children might bond more with robots than peers or teachers, or use them as an emotionally safe “shield” instead of learning to cope with human social complexity.
• Equity and cost: Humanoid robots are expensive and technically demanding. Schools with fewer resources may be left behind or forced into vendor lock-in.
• Pedagogical added value: Several reviews stress that robots only add value when they support well-designed activities. Without that, you just get novelty effects that fade quickly.

Humanoid robots in classrooms – Pros and cons

In order to see things more clearly about humanoid robots in classroom, I created the below table that handles all dimensions of having humanoid robots in classrooms. The legend to read this table is simple: ✅ = positive / desirable • ❌ = negative / risky .

Dimension	Point	Description	Verdict
Student engagement	Increased attention	Robots trigger curiosity, make lessons feel novel, and keep students focused longer.	✅
Motivation	Higher willingness to participate	Shy or disengaged students interact more when a robot leads an activity or supports group work.	✅
Active learning	Hands-on STEM practice	Programming or controlling a robot makes abstract STEM concepts concrete and visible.	✅
Differentiation	Personalized pacing	Robots can repeat instructions, drill exercises, or slow down for learners who need more time.	✅
Special education	Support for autism and SEN	Social robots help structure routines, train social skills, and offer predictable interactions.	✅
Multimodal teaching	Voice, gesture, movement	Robots combine speech, gestures, and movement, which helps some learners understand explanations better.	✅
Classroom climate	Fun and positive atmosphere	A robot used well can reduce tension, create shared experiences, and support group cohesion.	✅
Teacher role	New teaching scenarios	Teachers can design new activities, such as robot debates, simulations, or storytelling sessions.	✅
21st-century skills	Digital and AI literacy	Students gain early exposure to robotics, algorithms, and human–machine interaction.	✅
Inclusion	Alternative communication channel	Some learners feel safer asking questions or practicing oral skills with a robot than with peers.	✅
Assessment	Immediate feedback	Robots can give instant responses in quizzes, language drills, or math practice.	✅
Data for teaching	Learning analytics	Logged interactions can reveal patterns in errors, response times, or engagement over time.	✅
Curriculum innovation	New projects and modules	Schools can build interdisciplinary projects around robotics, coding, ethics, and society.	✅
Public image	Innovative school branding	Early adoption positions the school as tech-forward, which can help attract students and partners.	✅
Cost	High purchase and maintenance costs	Humanoid robots are expensive, require updates, repairs, and often extra licenses or services.	❌
Infrastructure	Technical complexity	Robots depend on stable networks, power, and compatible devices, plus regular software maintenance.	❌
Teacher workload	Extra preparation time	Designing good robot-based lessons requires planning, training, troubleshooting, and continuous adaptation.	❌
Equity	Risk of widening gaps	Well-funded schools gain access first, while others fall behind in robotics and AI skills.	❌
Pedagogy	Gadget-first teaching	Without a clear didactic design, robots become a gimmick that distracts from learning goals.	❌
Classroom management	Distraction and noise	Students may focus on the “cool robot” rather than the task, especially during the novelty phase.	❌
Reliability	Technical failures	Crashes, speech-recognition errors, or connectivity issues can derail a lesson in seconds.	❌
Data protection	Privacy and surveillance	Cameras and microphones raise questions about recording, storage, consent, and data ownership.	❌
Ethics	Emotional attachment and manipulation	Children may overtrust robots, blur boundaries, or respond strongly to praise, criticism, or nudging.	❌
Social development	Reduced human interaction	Over-using robots for practice or support risks displacing time with peers and teachers.	❌
Teacher autonomy	Dependence on vendors	Closed ecosystems and proprietary platforms can lock schools into specific companies and tools.	❌
Long-term evidence	Limited robust data	Many studies are small and short, so long-term learning gains and side effects remain uncertain.	❌
Sustainability	Hardware lifecycle	Production, energy use, and e-waste from frequent hardware refresh cycles raise sustainability concerns.	❌

Ethical and pedagogical fault lines

Novelty effect and “gadget-first” teaching

Robots attract attention. That can help start a lesson, but it also risks turning activities into performances centred on the machine rather than the concept. Without a solid pedagogical design, robots quickly become gimmicks.

There are a few questions you should ask for each proposed activity:

• Does the robot’s role directly support the learning objective?
• Could the same learning outcome be achieved more simply?
• Is the robot being used because it is available, or because it is the best tool for the job?

Data protection, surveillance, and AI integration

Humanoid robots often carry microphones, cameras, and sometimes cloud-based AI services. That raises immediate issues:

• What is recorded?
• Where is it stored, and for how long?
• Who has access (school, vendor, third parties)?

These concerns intensify when robots are linked to large language models for dialogue. A 2025 study exploring NAO connected to an LLM for teaching found promising interactions but stressed the need for clear data policies and transparency with students and parents.

In many jurisdictions, education data protection law is still catching up with AI-enhanced robots. Schools cannot treat these devices like ordinary classroom tools.

Emotional attachment and manipulation

Children often attribute feelings and intentions to humanoid robots. That can help learning, but it can also lead to:

• Over-trust in the robot’s feedback or explanations.
• Strong emotional reactions if the robot behaves “unfairly” or is removed.
• Vulnerability to subtle nudging (e.g. persuasive suggestions around behavior, choices, or even commercial content).

Ethically, the more human-like and conversational the robot, the more carefully educators need to monitor how it shapes students’ beliefs and behaviours.

Social development: human time vs machine time

Social robots can be especially appealing to students who struggle with peer interaction. For autistic learners, this can be helpful – robots provide predictable, low-pressure practice.

The risk: if robots become the default partner for difficult conversations or practice, students may lose chances to interact with peers and teachers. Reviews on robots in special education repeatedly emphasize that robots should complement, not substitute, human relationships.

Vendor lock-in and sustainability

Proprietary ecosystems are another, less visible risk. Once a school designs curricula, assessments, or behaviour programmes around one vendor’s robot and cloud platform, switching becomes hard.

On top of that, humanoid robots have a large material footprint: complex electronics, batteries, and plastics that need replacement or end up as e-waste. Reviews rarely quantify this yet, but sustainability is beginning to appear as a concern in broader discussions of assistive and educational technology.

What teachers and students actually think

Teachers are cautious curiosity

The 2025 UTAUT-2/TOE study of science teachers highlighted a few recurring themes:

• Performance expectation drove intention. If teachers believed robots would genuinely improve learning or engagement, they were much more willing to adopt them.
• Effort expectation mattered almost as much. Complex interfaces, unstable software, or lack of technical help reduced willingness.
• Organisational support (infrastructure, leadership backing, training time) strongly shaped adoption.

These findings mirror broader studies on AI in education: teachers are open to new tools when they see clear value and feel supported.

Students show enthusiasm with caveats

Student surveys in humanoid-robot projects usually report:

• Enjoyment of lessons with robots.
• Perception that robots make learning “more fun” or “more interesting”.
• Mixed views on whether robots explain better than teachers.

In higher education, a 2024 study on robot adoption found that perceived usefulness and task–technology fit heavily influenced students’ readiness to learn with robots, not just novelty or curiosity.

In short: students welcome robots, but they quickly notice when the technology feels like a distraction rather than genuine support.

A practical checklist for schools and educators

Before bringing humanoid robots into classrooms, it helps to treat them like any other major pedagogical decision: define the educational problem first, then choose the tool.

Use this checklist as a starting point.

1. Clarify the purpose

• Which concrete learning problems or goals will the robot address?
• Why is a robot better suited than existing tools (teacher-led instruction, tablets, peer work)?

Focus areas where the evidence is strongest:

• Targeted STEM and coding projects.
• Vocabulary and language learning with structured, repetitive practice.
• Special education and inclusion, embedded in a wider support plan.

2. Budget honestly

• Include maintenance, training, and replacement in the cost.
• Compare the investment to other options (more staff time, smaller classes, additional support teachers, non-robotic assistive technology).

3. Secure infrastructure and support

• Check Wi-Fi coverage in target classrooms.
• Assign technical support so teachers are not left alone to troubleshoot mid-lesson.
• Plan time for teachers to experiment, design, and test robot-assisted lessons.

4. Formalise ethics and data protection

• Map what data the robot collects (audio, video, logs, biometrics).
• Ensure compliance with local privacy regulations and school policies.
• Inform students and parents transparently about data use and rights.

If the robot uses cloud-based AI or large language models, insist on clear contractual guarantees about data handling and model training.

5. Protect the human core of teaching

Humanoid robots can do three things reliably:

• Repeat.
• Model movement or behaviour.
• Provide structured, rule-based interaction.

They cannot replace:

• Human judgement about context, equity, and wellbeing.
• Complex emotional support.
• The rich, messy social learning that happens between students and teachers.

Design activities so that the robot opens space for more human interaction, not less: free the teacher to observe, coach, and differentiate while the robot handles routine drills or demonstrations.

6. Start small, evaluate clearly

• Pilot in one class or subject with clear success criteria.
• Measure not only enjoyment, but also learning outcomes, teacher workload, and unintended side effects.
• Share findings with staff and students, and be ready to stop or rethink if the added value is thin.

Making choices is key

In the end, humanoid robots in education are less about shiny gadgets and more about the choices you make as an educator. They can lift engagement, unlock new ways of teaching STEM and languages, and offer structured support for learners who struggle in traditional settings. They can also drain budgets, add technical stress, raise hard questions about data and ethics, and quietly shift time and attention away from human relationships if you let them.

The research so far sends a clear message: robots work best when they serve a precise pedagogical purpose. As drill partners, demonstrators, or structured companions, they can free you to observe, coach, and differentiate. Used as vague “class mascots”, they quickly become a distraction with little impact on learning. The difference lies in lesson design, teacher training, and whether your school invests in support, not just hardware.

You also carry a new ethical responsibility. Robots with cameras, microphones, and AI backends change the data landscape in your classroom. Students may bond with these devices, trust them, confide in them. You need to know what happens to that data, who benefits from it, and how to protect learners from subtle pressure or over-reliance on machine feedback.

So the real question is not “robot or no robot”, but “on whose terms”. Humanoid robots can help you explore AI literacy, inclusion, and future skills in a concrete way. They can also lock your school into fragile, expensive systems that age quickly. Treat them like any other powerful tool: start from the learning problem, decide where human contact is non-negotiable, demand transparency from vendors, and be ready to walk away if the trade-off isn’t worth it.

Used on your terms, humanoid robots in the classroom can become one more instrument in a human-led learning environment – not the headline act, but a carefully scored part of the ensemble you already conduct every day.

Sources

• Humanoid Robots in Education: A Systematic Review of Pedagogical Approaches and Impact – Authors: Gordana Jugo, Boris Crnokić, Vlado Grubišić
• Social Robots in Education: Current Trends and Future Perspectives – Author: Georgios Lampropoulos
• The Use of Social Robots in Classrooms: A Review of Field-Based Studies – Authors: Hye Jeong Woo, Gerald K. LeTendre, Thao Pham-Shouse, Yuxin Xiong
• Robotic Roles in Education: A Systematic Review Based on a Proposed Framework of Learner-Robot Relationships – Authors: Weiqi Xu, Fan Ouyang
• Effectiveness of Social Robots as a Tutoring and Learning Companion: A Bibliometric Analysis – Authors: Rashmi Yogesh Pai, Ankitha Shetty, Tantri Keerthi Dinesh, Adithya D. Shetty, Namrata Pillai
• Social Robots for Education: A Review – Authors: Tony Belpaeme, James Kennedy, Aditi Ramachandran, Brian Scassellati, Fumihide Tanaka
• Humanoid Robots Go to School: What Teachers Think About Teaching and Learning with Education Robots – Author: Christine Chalmers
• A Humanoid Social Robot as a Teaching Assistant in the Classroom – Authors: (ArXiv preprint; child–robot interaction team, Pepper-type robot + LLMs)
• Integrating a Social Robot in Higher Education – A Field Study – Authors: Philipp Donnermann and colleagues
• Exploring Adoption of Humanoid Robots in Education: UTAUT-2 and TOE Models for Science Teachers – Authors: Burcu Ateş, Yasemin Polat (plus co-authors on some versions)
• Are Students Ready for Robots in Higher Education? Examining the Adoption of Robots by Integrating UTAUT2 and TTF Using a Hybrid SEM-ANN Approach – Authors: Faisal Suhail, Mouhand Adel, Mostafa Al-Emran, Adi Ahmad AlQudah
• Social Robots in Special Education: A Systematic Review – Authors: Georgios A. Papakostas, George Trougakos, Athanasios Karabatzakis, George Papadopoulos
• A Systematic Review on Robot-Assisted Special Education from the Activity Theory Perspective – Authors: Abdelghani Tlili, Hsiu-Ching K. Lin, Mei-Ling Chang
• People with Autism Spectrum Disorder Could Interact More Easily with a Robot Than with a Human: Reasons and Limits – Authors: Marie Dubois-Sage, Marie Simonnet-Laprune, Charlotte Beaudoin
• Social Robots in Education. Should We Prepare for Social Robots in Education? – Author: Merel van Steenis – Supervisors / editors: Duuk Baten, John Walker (SURF Responsible Tech)

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