The trial, registered as NCT06797050 and led by researchers at IRCCS Fondazione Stella Maris in Pisa together with the University of Florence and two additional Italian research hospitals, will enroll 80 children randomly assigned to either a social robot training program or a control group continuing standard care. The design is a waitlist randomized controlled trial, meaning the control group receives the same training after a waiting period, an ethically motivated arrangement that ensures no child is denied access to the experimental intervention. Assessments occur at baseline, after the intervention or control period, and again three to four months later.

The scientific rationale rests on a well-documented clinical reality. Children with neuromotor disorders frequently show intertwined difficulties in both motor planning and cognitive planning. Motor planning encompasses the anticipation of goals, the selection and temporal organization of movement sequences, preparation for execution, and online monitoring of outcomes, including components such as anticipatory control, motor programming, and motor imagery. These deficits ripple outward, compromising executive functions like working memory, cognitive flexibility, and inhibitory control, which in turn undermine academic skills, particularly reading comprehension, a multidimensional ability that integrates decoding, inferential reasoning, attention, and metacognitive self-regulation.

The intervention itself is deceptively simple in appearance but carefully engineered underneath. Over 20 to 25 sessions delivered two to three times per week, each lasting roughly an hour, children teach the robot to reorder picture-based stories displayed on a tablet interface. Complexity escalates along three dimensions: the number of images grows from three to seven, the required reasoning deepens from temporal to causal and inferential logic, and the number of characters and locations multiplies. A custom algorithm adapts the sequence of trials to each child’s performance, providing facilitations when errors occur, a structure consistent with principles from gamified learning environments where adaptive progression helps sustain engagement.

The story materials were developed with deliberate inclusivity. The clinical team at Stella Maris created a narrative script featuring characters from diverse ethnic backgrounds and with different types of disabilities, focusing on everyday activities that require specific motor sequences. Professional designers produced the graphics under clinical supervision, and each picture comes with a verbal description the child can replay on demand. Once a story is reordered, the child can listen to the full sequence before confirming the selection, adding a layer of self-monitoring to the task.

Two humanoid platforms from SoftBank Robotics and United Robotics Group will share the workload. Pepper stands 120 centimeters tall with 20 motors and a chest-mounted touchscreen, while NAO measures 57.4 centimeters with 25 degrees of freedom and relies on an external tablet. Both are programmable through the manufacturer’s Choreographe software and Python, and the University of Florence has built custom user interfaces, one for the child on the robot’s tablet and one for the therapist on a computer. Although the two platforms differ, the intervention protocol, session structure, and therapist-mediated procedures are standardized across centers, with robot type treated as a potential covariate in the statistical analysis.

What distinguishes this protocol most sharply from prior work is its multimodal measurement of engagement, a construct the authors acknowledge remains complex and methodologically contested. Emotional engagement will be captured through heart rate recorded by a wrist-worn Fitbit, expressed as a ratio relative to a three-minute pre-interaction baseline, and through facial action units analyzed with the OpenFace toolkit. Cognitive engagement will be estimated from eye-gazing metrics extracted offline using tools such as DeepGaze or Gaze360, tracking the proportion of attention directed to task-relevant stimuli, alongside gaze entropy, blinking rate as an index of cognitive load, and eyebrow and head-pose dynamics. Behavioral engagement will be derived from game logs, including error counts, replay requests, and task durations, with audio analyzed through OpenSmile. Therapists will additionally complete observational checklists after each session.

The primary outcomes are planning abilities measured through the Picture Arrangement subtest of the WISC-III and an ad hoc motor planning test developed at Stella Maris, in which children imitate short sequences of goal-directed actions presented through different model modalities, scored for execution and sequential accuracy. Secondary outcomes span a broad cognitive territory: executive functions via the Tower of London and NEPSY-II inhibition and sorting subtests, visuo-spatial memory through the Corsi block-tapping task, sustained attention on the Leiter-3, narrative production in a freely invented story task coded for structural complexity and psychological lexicon, visual-perceptual skills on the TVPS-4, and reading comprehension with standardized Italian MT test batteries. Parent questionnaires including the BRIEF-2 and ABILHAND-Kids will capture everyday functioning.

The statistical plan is appropriately cautious for a novel intervention. The a priori sample size of 77, rounded to 80, was calculated from paired-samples assumptions, and the authors candidly note it does not formally power the primary between-group comparison. Effects will therefore be reported as estimates with 95 percent confidence intervals rather than as definitive confirmations. Analyses follow the intention-to-treat principle using linear mixed-effects models with group, time, and their interaction as fixed effects, supplemented by per-protocol sensitivity analyses. The Holm-Bonferroni procedure will control for multiple comparisons, and exploratory mediation analyses will test whether engagement predicts or mediates intervention gains, an explicitly underpowered but conceptually important question.

The authors are forthright about limitations: the structured clinical setting may not generalize to daily life, allocation will not be concealed, there is no active technology-matched control condition to isolate the robot’s social presence from the task itself, and the ad hoc motor planning measure has not yet established inter-rater reliability. Yet the trial’s longitudinal, multi-session structure addresses a genuine gap, since most prior robot studies relied on single sessions or small single-case reports. If the children who teach Pepper and NAO show durable gains in planning, executive function, and reading comprehension, the study could establish a template for personalized, engagement-informed rehabilitation, and its sensor-based engagement framework may prove as influential as the intervention itself.

Subject of Research: A randomized controlled trial of social robot-mediated Learning by Teaching intervention for cognitive and motor planning in children with neuromotor impairments

Article Title: A story for Pepper and Nao: applying the learning by teaching paradigm in children with patterns of neuromotor impairment

Article References: Del Lucchese, B., Di Lieto, M. C., Scrocco, A., Matteucci, E., Fiorini, L., Pescini, E., Beani, E., Cotardo, A., Vanni, E., Conti, E., Menici, V., Camuncoli, F., Mangani, G., Maselli, M. V., Adelucci, E., Pani, J., Cavallo, F., Pecini, C., Diella, E., … Sgandurra, G. (2026). A story for Pepper and Nao: applying the learning by teaching paradigm in children with patterns of neuromotor impairment. Frontiers in Digital Health, 8, Article 1929794. https://doi.org/10.3389/fdgth.2026.1929794

Image Credits: AI Generated

DOI: 10.3389/fdgth.2026.1929794

Keywords: social robots, learning by teaching, neuromotor impairment, cerebral palsy, developmental coordination disorder, motor planning, executive functions, engagement, rehabilitation, randomized controlled trial, Pepper robot, NAO robot

Autism is a developmental condition that emerges in early childhood and persists across the lifespan, profoundly shaping social behavior and often making the acquisition of learning and social skills more difficult. According to data cited by the U.S. Centers for Disease Control and Prevention, autism spectrum disorder now affects a substantial and growing share of children worldwide, which has intensified the search for educational tools that can supplement traditional teaching. Over the past two decades, interactive technologies—from computer-based programs to tablet applications—have been explored as supports for autistic learners, and social robots in particular have attracted intense interest from researchers and clinicians alike.

The logic behind robot-assisted intervention rests on a striking observation: many children with autism engage more readily with machines than with people. Robots are predictable, their expressions are simplified, and their behavior is consistent and rule-governed. For children who find the rapid, ambiguous signals of human social interaction overwhelming, a robot can act as a social intermediary—demanding enough to practice attention and turn-taking, yet simple enough to feel safe. Numerous studies on autism intervention have highlighted the effectiveness of social robots in behavioral treatments, including work showing that the NAO robot can improve eye-gaze attention in children with high-functioning autism, and that long-term child-robot interaction can sustain attention and engagement over repeated sessions.

What has been missing, the researchers argue, is evidence from authentic classroom settings. Most robot-assisted autism studies have taken place in laboratories or clinics, in structured dyadic interactions between a single child and a single robot. But learning in schools is inherently social and collective: children must share a teacher’s attention, follow group instructions, and navigate the presence of peers. Reviews of field-based studies of social robots in classrooms confirm that genuine classroom integration remains sparse. The new study was designed to begin filling that gap by placing the NAO robot directly into a group teaching context at a special education school, with human teachers and the robot working side by side.

The experimental design was deliberately collaborative. Rather than replacing the teacher, the NAO robot functioned as a co-facilitator of classroom activities. Special education teachers led sessions in partnership with the robot, creating a triadic learning environment in which interactions flowed among teacher, robot, and students. This arrangement reflects principles from established autism education frameworks such as TEACCH, which emphasizes structured teaching, and applied behavior analysis, which underpins many technology-mediated interventions. By distributing instructional roles between a sensitive human professional and a predictable, engaging machine, the researchers sought to foster a dynamic learning environment that neither agent could create alone. The study, conducted at Changshu Special Education School, was explicitly framed as a foundational investigation—a proof of concept in anticipation of extended robot-assisted classroom sessions to be introduced at a later date.

The technical appeal of the NAO platform is central to the study. NAO is a 58-centimeter-tall humanoid robot developed by SoftBank Robotics, equipped with cameras, microphones, tactile sensors, and articulated limbs that allow it to gesture, dance, speak, and emulate human movement. Its child-sized stature and expressive but simplified face reduce the perceptual complexity that often challenges children with autism. Research on gaze perception has shown that decoding gaze direction from combined head and eye rotations is an integrative challenge that differs in autistic individuals; NAO’s exaggerated, unambiguous head turns and eye movements sidestep much of that ambiguity, making it an ideal cueing device for directing attention toward learning materials. Prior work has also demonstrated that robots can reduce delays in gesture production among preschoolers with autism, suggesting that the platform’s motor expressiveness carries direct pedagogical value.

The study’s data told a clear story. Students with ASD in classrooms equipped with the NAO robot exhibited notably improved performance compared to students in regular classrooms. The researchers’ preliminary findings indicate that the robot significantly enhanced focus and classroom engagement among students with autism—two behavioral pillars on which nearly all other classroom learning depends. Improved attention, in turn, appears to translate into better educational performance and potentially enhanced social functioning. These outcomes align with a broader literature: studies of robot-mediated group instruction and robot-assisted psychosocial interventions have reported gains in attention, imitation, and social responsiveness, while systematic reviews of robotics protocols for students with autism have called for exactly this kind of ecologically valid classroom evidence.

The implications extend beyond special education. Classroom bonding is widely recognized as a foundation for healthy development, and children with autism are at elevated risk of disengagement from school environments that feel socially punishing. If a social robot can lower the barrier to participation—making group instruction feel approachable rather than threatening—the technology could help close an achievement gap that has proven stubbornly resistant to conventional approaches. The findings also carry practical weight for teachers: the robot-assisted model tested here positions NAO as an assistant that amplifies, rather than supplants, professional expertise. The teachers retained authority over pacing, content, and behavior management, while the robot contributed attention capture, novelty, and motivational energy that human instructors alone often struggle to sustain across a full group of autistic learners.

Cautions remain. This was a foundational study conducted at a single special education school, and the authors themselves describe it as a first step toward extended robot-assisted classroom sessions. Questions about long-term novelty effects—whether children’s fascination with a robot fades after weeks or months—persist in the literature, even as some long-term engagement studies suggest sustained benefit. Sample sizes in robot-assisted autism research are typically modest, and generalizing from one school to diverse educational systems will require replication. Ethical safeguards, however, were carefully observed: the study received approval from the Institutional Review Board of Suzhou University of Technology, written consent was obtained from guardians and cognitively capable children, and video data were encrypted after encoding to protect privacy.

Even with those caveats, the study marks a meaningful shift in the trajectory of social robotics for autism. For years, the field has demonstrated that robots can capture the attention of autistic children in controlled settings; the harder question has always been whether that magic survives contact with real classrooms, real curricula, and real group dynamics. By showing that a teacher-and-robot partnership can measurably improve focus and performance among students with autism in an actual school, the researchers provide the most convincing answer yet that social robots belong not just in therapy rooms, but at the front of the class. As extended sessions begin, the world may be watching a small humanoid robot help rewrite what inclusive, effective education looks like for children on the autism spectrum.

Subject of Research: The use of the NAO social robot to improve classroom performance and engagement of students with autism spectrum disorder

Article Title: Classroom Performance of Students with Autism in Interaction with the NAO Robot

Article References: Feng, H., Yang, Q., Lu, H., & Gong, S. (2026). Classroom Performance of Students with Autism in Interaction with the NAO Robot. Frontiers of Digital Education, 3(2), Article 9. https://doi.org/10.1007/s44366-026-0083-1

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0083-1

Keywords: autism spectrum disorder, NAO robot, social robotics, robot-assisted classroom, special education, classroom engagement, human-robot interaction, educational technology, autism intervention, attention and focus, Classroom, Performance