The review, conducted by researchers at The Education University of Hong Kong and Dezhou University, set out to resolve a question that has divided the field: are embodied social robots, or ESRs, genuine social catalysts that stimulate children’s interpersonal growth, or merely scripted tutors that deliver adult-designed content in an engaging package? Social-emotional competence, often abbreviated as SEC, encompasses a child’s ability to understand and manage emotions, show empathy, build relationships, and navigate social conflicts. It is widely regarded as a foundation for later academic success and mental health, which is precisely why the prospect of robot-assisted SEL, or social-emotional learning, has attracted such intense interest from educators, technologists, and investors alike.

Methodologically, the review followed established scoping review frameworks, including the Arksey and O’Malley methodological tradition and the PRISMA extension for scoping reviews, to identify, screen, and chart the relevant literature. The authors organized the eighty-six child-level findings they extracted into six condensed domains of social-emotional competence and then examined how those outcomes varied according to measurement approaches, robot and intervention characteristics, and the practical conditions under which the interventions were implemented. This multi-layered mapping matters because, as the authors demonstrate, the apparent effectiveness of a robot intervention depends heavily on how researchers choose to measure its impact and how the technology is actually deployed in real classrooms.

The headline finding is one of mixed evidence. Positive results clustered most consistently around what the authors call proximal indicators of social-emotional functioning: children’s participation, engagement, and communication during robot-mediated activities. Preschoolers in the reviewed studies frequently interacted readily with robots, sustained attention during structured tasks, and showed increased verbal or behavioral engagement compared with baseline conditions. By contrast, broader or more complex SEC-related outcomes, such as durable gains in empathy, emotion regulation, or generalized prosocial behavior across settings, far more often showed no effect or inconsistent patterns. In other words, robots reliably captured children’s attention and got them talking, but the evidence that they durably reshaped deeper social-emotional capacities remains thin.

A particularly striking technical insight from the review concerns the role of measurement methodology itself. Behavioral observation and structured experimental tasks were substantially more likely to detect positive change than interviews or indicators extracted automatically from robot system logs. The authors also flag a serious psychometric problem: only about one-third of the reported findings were backed by documented reliability or validation procedures for the instruments used. Interviews and system-recorded data, in particular, were rarely supported by evidence that they actually measured what they claimed to measure. This means that some of the more enthusiastic claims in the literature may reflect measurement artifacts rather than genuine developmental change, a caution that applies well beyond robotics to the broader field of educational technology evaluation.

The review also paints a sobering picture of the technology as it currently exists in classrooms. Most interventions relied on low-autonomy, non-personalized robots embedded in adult-guided activities. In practical terms, the robots were typically scripted or remotely operated devices with limited capacity to perceive a child’s emotional state, adapt their behavior, or personalize interactions over time. This is consistent with a well-known issue in human-robot interaction research: many celebrated demonstrations involve hidden human control, the so-called Wizard of Oz paradigm, whose influence is often underreported. Far from being independent social agents, most classroom robots today function as animated props within a teacher-orchestrated lesson, and the review argues that acknowledging this reality is essential for honest interpretation of the evidence.

Implementation conditions emerged as another weak point. Reporting of technical stability, implementation fidelity, and teacher preparedness or involvement was uneven across the twenty-five studies. The authors note that when a robot malfunctioned mid-session, when an intervention deviated from its intended script, or when teachers received inadequate training, these details frequently went unrecorded, making it difficult to judge whether null results reflected genuine ineffectiveness or simply poor execution. This gap has practical consequences: schools considering robot investments currently have little reliable guidance about the staffing, training, and technical infrastructure required to replicate the conditions of successful trials. The review’s call for ecologically grounded classroom research, conducted in ordinary settings rather than carefully staged laboratory-like conditions, is a direct response to this problem.

What should educators and parents take away from all this? The authors recommend a cautious but not dismissive interpretation. The evidence supports using embodied social robots as bounded, teacher-mediated supports, tools that can enrich adult-guided activities, motivate engagement, and possibly create structured opportunities for practicing communication and cooperation, particularly for children who may find human-only interactions intimidating. Indeed, some prior work reviewed by the authors suggests that shy preschoolers may interact differently, and sometimes more openly, when learning with a robot rather than a human instructor. But the review firmly rejects the notion of robots as stand-alone solutions for social-emotional development. A machine that cannot reliably read a child’s frustration, model authentic empathy, or repair a social rupture cannot substitute for the responsive human relationships that developmental science identifies as central to early social-emotional growth.

The review also points toward clearer ethical safeguards as the field matures. Young children are uniquely vulnerable research participants and technology users, and questions about attachment to machines, data collected by robot sensors, and the appropriate framing of robots as social versus mechanical entities remain actively debated in the literature. The authors argue that future studies must pair stronger measurement reporting with explicit ethical frameworks, ensuring that enthusiasm for innovative technology does not outrun the field’s obligations to the children involved. Their conclusion is ultimately a call for scientific maturity: more rigorous and validated measurement, honest reporting of implementation realities, research grounded in genuine classroom ecologies, and a realistic framing of what robots can and cannot contribute. For now, the most defensible role for embodied social robots in early childhood education is that of a well-supervised assistant to human teachers, not their replacement, and the research community is only beginning to map, carefully and skeptically, where that assistance genuinely helps preschoolers flourish.

Subject of Research: The effects of embodied social robots on preschool children's social-emotional competence

Article Title: Social Catalysts or Social Tutors? Embodied Social Robots and Preschoolers’ Social-Emotional Competence: a Scoping Review

Article References: Qin, L., Wu, Y., & Li, H. (2026). Social Catalysts or Social Tutors? Embodied Social Robots and Preschoolers’ Social-Emotional Competence: a Scoping Review. Early Childhood Education Journal. https://doi.org/10.1007/s10643-026-02347-w

Image Credits: AI Generated

DOI: 10.1007/s10643-026-02347-w

Keywords: embodied social robots, social-emotional competence, preschool children, scoping review, early childhood education, social-emotional learning, human-robot interaction, educational robotics, measurement validity, classroom implementation, teacher-mediated learning, research ethics

Researchers Mireia Soler, Mariona Corcelles-Seuba and David Duran of the Universitat Autònoma de Barcelona examined whether reciprocal peer observation, or RPO, could strengthen the digital competence of in-service schoolteachers who were learning to implement educational robotics. Their quasi-experimental study, published in the Journal of New Approaches in Educational Research, followed 51 teachers from 13 public early childhood and primary schools in Barcelona’s Ciutat Vella district over a six-month training program running from November 2023 to May 2024, part of a project titled Robots for Peer Learning.

The design was elegantly simple. All participants completed the same core training: two self-paced online modules covering educational robotics devices and their pedagogical applications for promoting computational thinking, along with a requirement to implement robotics in their own classrooms and reflect on the results. But one group, 27 teachers, completed an additional third module requiring a full RPO cycle during their classroom implementation, while the remaining 24 teachers, the comparison group, completed the training without it. Because RPO inherently demands voluntary participation and active engagement, entire schools rather than individual teachers were assigned to each condition, with all teachers in a given school belonging to the same group.

RPO is a structured form of collaborative professional development in which pairs of teachers with similar levels of experience agree to observe specific pedagogical aspects of each other’s teaching. The cycle unfolds in four phases: a pre-observation meeting where the pair agrees on goals, indicators and data-collection methods; the observation itself, in which the observer gathers evidence; a feedback session built on a post-observation report, where constructive and exploratory dialogue identifies professional development objectives; and a final reflective synthesis written by the observed teacher. Crucially, the approach works only when it is voluntary, confidential and centered on mutually agreed objectives, supported by constructive feedback and a trusting environment.

To measure outcomes, the researchers used two complementary instruments. The first was a questionnaire drawing on validated items from SELFIEforTEACHERS, the European Commission’s self-reflection tool for teachers’ digital competence, covering four of the six areas defined by the European DigCompEdu framework: professional engagement, teaching and learning, empowering learners, and facilitating students’ digital competence. The second was an ad hoc competency-based case study, a formative practical test scored out of 20 points, aligned with B2-level indicators of Spain’s Teachers’ Digital Competence Benchmark. Participants completed the case study without access to training materials or the internet, both before and after the intervention, and scoring was performed by an educational robotics expert who was blinded to participants’ group assignments and assessment time points.

The results were striking. On self-perceived digital competence, the intervention group outperformed the comparison group significantly in two key areas. In teaching and learning, teachers who participated in the RPO cycle reported a mean improvement rate of 44.7 percent, compared with just 16 percent for the comparison group, a statistically significant difference. In empowering learners, the gap was even wider in relative terms: 43.9 percent versus 14.5 percent, roughly a threefold advantage, again statistically significant. Across overall perceptions of digital competence, the intervention group’s improvement rate of 32.3 percent was more than double the comparison group’s 13.7 percent, and while this fell just short of conventional statistical significance, the researchers suggest the trend could be confirmed with a larger sample.

Analyzing each group separately revealed an equally telling pattern. The comparison group showed no significant pretest-to-posttest gains in any specific area of self-perceived competence, only a modest improvement in overall perceptions, from a mean score of 2.32 to 2.54. The intervention group, by contrast, showed statistically significant improvements across every assessed area as well as overall perceptions, with effect sizes equal to or approaching 1, indicating a very large impact. In other words, the robotics training alone left most self-perceptions largely unchanged, while adding the peer observation cycle transformed how teachers viewed their own digital capabilities.

Perception, of course, is not the same as performance, and this is where the study makes a distinctive contribution. On the practical case study, both groups improved significantly from pretest to posttest in all areas, confirming that hands-on robotics training does build real skills. But the between-group comparison of absolute change showed statistically significant differences favoring the RPO group in every area and in the overall score. The area of empowering learners, which encompasses accessibility, inclusion, differentiation and active student engagement, stood out dramatically: the intervention group posted an average absolute percentage improvement of 307 percent, compared with 125 percent for the comparison group, with a large effect size of 0.55.

The authors are candid about the study’s limitations. With only 51 participants, statistical power is constrained, and although schools were randomized to condition, analyses were conducted at the teacher level, so the findings should be read as exploratory evidence rather than definitive causal estimates. The higher number of certified training hours in the intervention group, 30 versus 10, reflects institutional recognition of the structured observations and feedback inherent to the RPO cycle, which unfolded within the same six-month period rather than adding extra time. The researchers also note that qualitative analysis of the RPO documentation itself could deepen understanding of the mechanisms at work, and they call for future research with larger samples, longitudinal designs and, importantly, measurement of downstream effects on student outcomes.

Even so, the implications are considerable. International bodies from UNESCO to the OECD and the European Commission have flagged teacher digital competence as a policy priority, and the EU’s Digital Education Action Plan has funneled investment into robotics kits through the Recovery and Resilience Facility. Yet the TALIS report shows that despite 80 percent of surveyed teachers believing peer observation outperforms traditional training, actual participation in such collaborative practices remains low. This study offers rare experimental evidence that a structured, voluntary, feedback-driven observation cycle can amplify the returns on technology training, not only changing what teachers believe they can do, but measurably improving what they actually do in the classroom. As schools worldwide race to digitize, the message from Barcelona is that the cheapest, most human piece of professional development equipment may be a colleague standing quietly at the back of the room, notebook in hand.

Subject of Research: Reciprocal peer observation for enhancing in-service teachers' digital competence in educational robotics

Article Title: Reciprocal peer observation for enhancing teachers’ digital competence: insights from educational robotics implementation

Article References: Soler, M., Corcelles-Seuba, M., & Duran, D. (2026). Reciprocal peer observation for enhancing teachers’ digital competence: insights from educational robotics implementation. Journal of New Approaches in Educational Research, 15(1), Article 15. https://doi.org/10.1007/s44322-026-00064-1

Image Credits: AI Generated

DOI: 10.1007/s44322-026-00064-1

Keywords: reciprocal peer observation, teachers' digital competence, educational robotics, teacher professional development, DigCompEdu, SELFIEforTEACHERS, computational thinking, collaborative learning, peer feedback, Barcelona schools, classroom technology, quasi-experimental study

Computational thinking, a concept famously articulated by Jeannette Wing in 2006, refers to the ability to formulate and solve problems using models drawn from computer science. It encompasses abstraction, decomposition, generalisation, algorithmic thinking and evaluation, and it has been repeatedly linked to stronger problem-solving, pattern recognition and information selection. Because educational authorities now regard it as an essential competence, teacher educators face a pressing question: how do you cultivate computational thinking in people who will teach three- to six-year-olds, many of whom arrive at university with little or no programming background? The Spanish team behind StoryMat-Robot believed the answer might lie not in code alone, but in stories.

The premise of the project is deceptively simple. A StoryMat-Robot is a playful, interactive narrative starring a robot, which must move through a physical space, typically a mat decorated with settings, characters and obstacles drawn from an adapted story. The narrative must be coherently sequenced, synchronising the plot’s progression with the robot’s trajectory, while the robot overcomes playful challenges tied to the storyline. Participants in the study worked in groups of four or five to produce 38 such proposals, adapting works ranging from Hansel and Gretel, The Three Little Pigs and The Wizard of Oz to animated films such as Ratatouille, Up and Madagascar. Roughly 60 percent of the designs were based on children’s literary texts and 40 percent on films.

What makes the design process cognitively rich is the way it forces several distinct skills to operate simultaneously. Translating a plot into a visual, physical format demands abstraction: students must select the most relevant story elements, represent them graphically as a robot pathway, and adapt characters while preserving their defining traits. Breaking the storyline into narrative sequences, scalable challenges and structured robot movements exercises decomposition. Maintaining aesthetic and stylistic coherence with the original work, whether two-dimensional, three-dimensional or realistic, mirrors generalisation, and segmenting the story into curriculum-aligned educational challenges with clear objectives, feedback and timing draws directly on algorithmic thinking. Layered over all of this are transmedia competences, as students integrate digital resources, augmented reality applications and artificial intelligence tools into their mats.

To evaluate what the finished products revealed about their creators’ thinking, the researchers designed and validated a 24-indicator instrument organised into four dimensions and scored on a four-point rubric, from ‘not at all adequate’ to ‘very adequate’. Three independent raters, including an external researcher, assessed every StoryMat-Robot, achieving strong inter-rater reliability with an intraclass correlation coefficient of 0.874. An exploratory factor analysis confirmed the instrument’s validity, with three factors explaining 64.9 percent of the variance and a high internal consistency of alpha equal to 0.946. Statistical comparisons between literary-based and film-based designs employed the Mann-Whitney U test, complemented by effect sizes and confidence intervals.

The findings were encouraging across the board. Overall adequacy reached moderate-to-high levels, with decomposition scoring highest at 3.36, followed closely by algorithmic thinking at 3.34 and abstraction at 3.26, while generalisation lagged slightly at 2.98. The strongest single feature was the incorporation of educational challenges appropriate for early childhood education, rated at 3.69, suggesting that the trainee teachers excelled at embedding pattern-recognition tasks and age-appropriate problems into their narratives. In one adaptation of The Three Little Pigs, children were asked to select building materials; in the Ratatouille mat, they collected ingredients for the traditional dish. Students also showed a strong ability to adapt characters to the interactive format while preserving the essence of the original stories.

Interesting differences emerged between the two source materials. Designs based on literary texts proved more robust in spatial organisation: the logic of the robot’s pathways, the scaling of challenges, the alignment of tasks with the educational level and the overall mat design all scored significantly higher, likely because short, simple stories such as Hansel and Gretel often contain explicit routes that students could transfer directly onto the mat. Film-based designs, by contrast, were richer in digital integration, scoring higher on the use of artificial intelligence resources, the creation of augmented reality elements aligned with the narrative, the relevance of educational objectives, the functionality of digital tools and the inclusion of assessment procedures. The researchers attribute this to the greater multimodality of audiovisual discourse, which seems to prime designers toward technological enrichment and stronger pedagogical framing.

Not every dimension flourished equally. Generalisation proved the weakest component, and the integration of coding cards explaining the robot’s movement sequences was rated particularly low at 1.90 overall, revealing that trainee teachers struggle to make programming patterns explicit and transferable. The researchers also caution that the statistically significant differences between literary and film-based designs carried small or even negligible effect sizes, meaning neither format is categorically superior. Rather, the near-uniform presence of computational thinking skills across both groups suggests that the co-design process itself, grounded in Design-Based Learning, is the active ingredient. The study’s limitations are acknowledged candidly: it took place in a single institutional context, computational thinking was inferred from products rather than measured directly with pre- and post-tests, and students chose their own source works, a potential confounding factor.

The implications reach beyond one teacher-education classroom. The study reinforces a growing body of evidence that narrative creation, digital storytelling and educational robotics are powerful vehicles for computational thinking, and it demonstrates that these can be fused into a single, pedagogically meaningful activity rather than treated as separate strands. Because the resulting mats are ready-made classroom resources, the benefits flow in two directions: future teachers practise abstraction, decomposition and algorithmic sequencing, while the young children who eventually use the mats encounter the same skills through play. The research team now proposes testing the StoryMat-Robots with actual early childhood pupils aged three to six, to measure their influence on learning, motivation and computational thinking. If those trials succeed, the humble story mat may become a standard fixture in the effort to raise a generation that thinks computationally before it can even write its own name.

Subject of Research: Developing computational thinking in prospective early childhood teachers through robot-based transmedia storytelling design

Article Title: StoryMat-Robot: From Activating Narrative and Transmedia Competences in Prospective Teachers to the Development of Computational Thinking

Article References: StoryMat-Robot: From Activating Narrative and Transmedia Competences in Prospective Teachers to the Development of Computational Thinking. (n.d.). https://doi.org/10.1007/s13158-026-00544-7

Image Credits: AI Generated

DOI: 10.1007/s13158-026-00544-7

Keywords: computational thinking, educational robotics, transmedia storytelling, teacher education, early childhood education, narrative competence, augmented reality, artificial intelligence, algorithmic thinking, abstraction, decomposition, design-based learning