The study, published in the journal Large-scale Assessments in Education, was conducted by Markéta Žáková and Tomáš Lintner of Masaryk University in the Czech Republic. Drawing on PISA 2018 data from more than 600,000 fifteen-year-old students across 75 education systems, the pair set out to answer a deceptively simple question: does it matter which operational definition of academic resilience a researcher uses? The answer, they found, is a resounding yes, with consequences that ripple through prevalence estimates, country rankings, and conclusions about which factors help disadvantaged students beat the odds.

Academic resilience combines two ingredients: adversity, usually measured by socioeconomic status, and positive adaptation, usually measured by achievement. But researchers disagree about how to stitch those ingredients together. One common approach simply identifies students who land in the bottom slice of the socioeconomic distribution and the top slice of achievement, for example the poorest quarter who score in the top quarter. A second approach instead computes what each student’s achievement should be, statistically speaking, given their family background, and flags students who substantially outperform that expectation, an approach known as the residual method. A third treats resilience as a process rather than an outcome, predicting achievement as a continuous variable within the disadvantaged group and never labeling anyone resilient at all.

Žáková and Lintner compared these approaches systematically. They estimated a top-achiever definition, a residual definition computed within each country, and a residual definition benchmarked against an international standard, each at three different threshold levels, 20, 25, and 33 percent, spanning the range most commonly used in the published literature. All told, this produced nine binary operationalizations plus the continuous-outcome model, yielding nearly a thousand country-level analyses. Each estimate was pooled across ten plausible values for achievement and twenty multiply imputed datasets, with standard errors that fully accounted for PISA’s complex two-stage sampling design.

The headline finding is stark. The cross-country mean prevalence of academically resilient students ranged from 7.5 percent under the strictest definition to 35.3 percent under the most inclusive residual benchmark, a nearly fivefold difference computed from identical data. Part of this spread is arithmetic, since looser thresholds mechanically admit more students, but the deeper problem emerges when countries are ranked. Rankings were reasonably stable across thresholds within a given definition, yet they diverged sharply across definitions. The correlation between rankings produced by the top-achiever approach and the residual approaches fell as low as 0.51, and between the two residual variants, which differ only in whether the statistical expectation is local or global, it dropped to between 0.32 and 0.47. A country celebrated as an equity champion under one definition can rank unremarkably under another, and the discrepancy is itself informative about what its disadvantaged students actually do well.

The authors illustrate why with a thought experiment grounded in their results. A lower-performing system with a steep socioeconomic gradient may rank poorly on the top-achiever definition, because few of its disadvantaged students reach absolute excellence, yet rank highly on the within-country residual definition, because modest local expectations are easy to exceed. That same system may then sink again on the internationally benchmarked residual definition, since beating a weak local bar is not the same as meeting a global standard. The researchers argue that resilience rankings should therefore be reported under multiple definitions side by side, as complementary views of equity rather than competing estimates of a single quantity, a practice the OECD itself briefly adopted nearly a decade ago.

What about the protective factors that resilience research is meant to uncover? Here the news is more reassuring at the global level and more troubling at the level of individual countries. When the authors pooled effects meta-analytically across all 75 systems, three motivational constructs drawn from expectancy-value theory, students’ self-perceived reading competence, their enjoyment of reading, and their attitude toward learning, were consistently and positively associated with resilience under every operationalization and threshold, and girls consistently outperformed boys. Read that result alone, and the choice of definition seems inconsequential.

The country-by-country picture tells a different story. When each education system was analyzed separately, as is standard in PISA-based research, findings were consistent across all nine binary specifications in only 20 percent of systems for gender, 40 percent for attitude toward learning, 69 percent for reading enjoyment, and 72 percent for self-perceived competence. Just two of the 75 systems produced fully consistent results for all four predictors. Threshold level alone flipped conclusions in somewhere between 3 and 37 percent of countries depending on the factor. Much of this inconsistency reflects statistical power, since stricter thresholds shrink samples and smaller-effect predictors, notably gender and attitude toward learning, are the least stable. But the practical consequence does not depend on the cause: a researcher studying one country under one definition could legitimately conclude that a factor matters there when a defensible alternative would say it does not.

The most conceptually striking result came from a complementary interaction analysis that requires no thresholds at all. By modeling whether the socioeconomic gradient in achievement flattens at higher levels of each factor, the authors tested what the term protective factor actually implies. The findings complicate comfortable assumptions: the gradient was flatter, not steeper, among students with higher self-perceived reading competence and stronger attitudes toward learning, but it was significantly steeper, not flatter, among students who enjoyed reading more. In other words, reading enjoyment, though positively associated with achievement on average, was linked to wider rather than narrower socioeconomic gaps, a pattern that no threshold-based definition could ever detect. These moderation effects also varied in direction across countries, appearing in only a minority of systems individually.

The authors close with four practical recommendations: match the operationalization to the research question, report sensitivity analyses at a minimum of two thresholds, present cross-country rankings under multiple definitions, and document or pre-register operationalization decisions so findings can be interpreted in light of the choices that produced them. They caution that their analysis is cross-sectional and cannot establish causation, that their predictors were individual-level only, and that PISA’s socioeconomic index has itself attracted methodological criticism. Still, the broader message is hard to escape. Academic resilience, as measured in large-scale assessments, is not a fixed quantity waiting to be counted but a construct whose observed properties depend partly on how it is defined, and both researchers and policymakers ignore that dependence at their peril.

Subject of Research: How operationalization and threshold choices affect estimates of academic resilience and its protective factors across 75 education systems using PISA 2018 data.

Article Title: Academic resilience in 75 education systems: how operationalization and threshold choices shape findings on protective factors

Article References: Academic resilience in 75 education systems: how operationalization and threshold choices shape findings on protective factors. (n.d.). https://doi.org/10.1186/s40536-026-00318-6

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00318-6

Keywords: academic resilience, PISA 2018, socioeconomic status, educational equity, large-scale assessment, protective factors, operationalization, thresholds, student motivation, expectancy-value theory, educational measurement, cross-national comparison

The findings come from a scoping review conducted by Rosabel Martinez-Roig, María Aragonés-González and Miguel Cazorla of the University of Alicante in Spain, published in the Journal of New Approaches in Educational Research. The team searched Scopus, Web of Science, ERIC-ProQuest and Dialnet for peer-reviewed empirical studies published between the robot’s commercial launch in 2014 and 1 March 2026, following the PRISMA-ScR reporting guidelines. Out of 1,448 initial records, and after duplicates were removed and 345 full texts assessed, only 13 studies met all the inclusion criteria. That narrow yield is itself telling: despite more than a decade of commercial availability and enthusiastic marketing, rigorously documented classroom deployments of Pepper remain rare, recent and geographically concentrated.

The technical appeal of the platform is easy to understand. Pepper is a social humanoid designed for human interaction, equipped with cameras, sensors, mobility, speech, gestures and expressive non-verbal cues such as head movements and eye contact. Unlike tablets or desktop tutoring software, social robots embody interaction; they can be perceived by children as social agents capable of reciprocity, which is precisely why researchers have speculated they might serve as tutors, learning companions or even pupils in ‘learning-by-teaching’ scenarios where students consolidate their own knowledge by explaining content to the machine. But the review found a persistent gap between that theoretical versatility and what actually happens in schools, where Pepper’s effective autonomy proved limited in most documented settings.

Geographically, the evidence is dominated by Europe, with nine of the thirteen studies (69.2 percent) conducted there, particularly in Italy, Germany, the Netherlands and Sweden. Germany was the most frequently represented country, with work such as Donnermann and colleagues’ long-term study of adaptive robotic tutors supporting university students with exam preparation and motivation. Four studies were carried out in the United States, including an ethnographic project by LeTendre and Gray that followed adolescents through a project-based learning unit, documenting how students themselves came to understand both the possibilities and the hard limits of human-robot interaction. The first qualifying empirical study did not appear until 2018, four years after the robot’s launch, underlining how youthful this research area remains.

The review reveals a striking imbalance in where and with whom the robot is deployed. Five studies focused on primary education, with children aged six to twelve, while four took place in higher education and two in early years settings; only one examined secondary education. Three studies centred on pupils with special educational needs, particularly autism spectrum disorder, extending a line of research suggesting that the predictable, patient interaction style of social robots can support communication and participation in inclusive contexts. Methodologically, however, the field is dominated by qualitative approaches: eight studies used case studies or interaction analysis, three used mixed methods and only two were predominantly quantitative experiments. Eight of the thirteen studies involved fewer than fifty participants, and not a single large-scale longitudinal study was identified.

Three pedagogical configurations dominated the corpus. In six studies, Pepper acted as a tutor or teaching assistant, delivering guidance, structured feedback or gamified quizzes, including quiz-based university learning in Germany. In four, the robot was cast as a learning partner or tutored agent, a role exemplified by Swedish research in which children taught mathematics to the robot and, in doing so, rehearsed and revised their own reasoning. In three studies, Pepper served as a social mediator in inclusive settings, supporting storytelling and second-language learning among migrant children and pupils with special educational needs. Curricular coverage was diverse but thin: mathematics appeared most often, followed by STEM and programming activities, language learning, social-emotional development in early years, and even a sustainability-focused serious game in which the robot improved Italian children’s attitudes toward recycling.

The benefits reported are consistent across the corpus, though the authors caution against over-interpreting them. Twelve of the thirteen studies — 92.3 percent — documented increased motivation and engagement when the robot was present, with pupils frequently showing more enjoyment than with tablet-based equivalents of the same tasks. Five studies recorded improvements in social skills or communicative participation, including greater verbal engagement in collaborative mathematics. Four reported measurable academic gains, from improved university grades in robot-assisted tutoring to more positive attitudes toward recycling. Three studies noted enhanced sustained attention during structured tasks. Yet the reviewers stress that novelty effects, small samples, short interventions and children’s prior expectations may all inflate these apparent benefits, and that none of the outcomes can be attributed to the robot’s presence alone.

The obstacles documented are equally consistent, and in many ways more illuminating. Technical failures were the most frequently reported limitation, mentioned in ten of thirteen studies: voice recognition struggles with children’s voices and ambient noise, software glitches interrupt activity sequences, and programming demands exceed what most schools can sustain. In more than half the studies, teachers had to provide constant technical supervision, stepping in to repair interactions when the robot misheard a pupil or froze mid-lesson. Curricular integration was weak, with most deployments framed as one-off activities, pilots or limited experiments rather than embedded components of standard teaching. Organisationally, cost, device scarcity and reliance on technical staff constrained uptake, and a small number of studies raised ethical questions about privacy, data collection and the risk of ‘robotised’ teaching, as well as classroom competition over limited robot time.

Perhaps the review’s central insight concerns the teacher. In seven studies, educators served as technical supervisors; in six, they acted as pedagogical mediators who structured activities, allocated interaction turns and regulated participation. Only two studies involved teachers directly in designing the intervention from the start, and just three mentioned any specific training in the pedagogical use of the robot — a gap the authors identify as one of the field’s most significant blind spots. Situations of ‘trouble and repair’, where adult intervention rescued collapsing child-robot interactions, recur throughout the literature, and earlier work in a school for autistic children explicitly framed Pepper as a teaching aid external to the classroom rather than an autonomous instructor. The distinction between the robot’s technical autonomy and its pedagogical autonomy, the authors argue, is critical: its capacity for action is always embedded in human, technical and ethical decisions.

The overall verdict is measured rather than sensational. Pepper’s embodiment, humanoid appearance and multimodal interaction can spark initial interest, but they do not by themselves guarantee meaningful learning; outcomes hinge on instructional design, student profile, technical stability and teacher mediation. The authors call for the field to move beyond technological novelty toward longitudinal studies that test whether early enthusiasm endures, deeper investigation of teacher training and design involvement, and direct comparisons of the robot against tablets, virtual agents and hands-on materials to establish where, if anywhere, it adds unique value. Until then, the evidence suggests that the social robot in the classroom is best understood not as a replacement for teachers but as a demanding, engaging and highly dependent teaching resource — one whose success is written, lesson by lesson, by the humans standing beside it.

The scoping review’s methodological choices help explain both its strengths and its boundaries. By restricting the analysis to a single robotic platform, the authors avoided a common problem in educational technology research, in which reviews lump together devices with very different degrees of physicality, mobility, expressiveness and autonomy, blurring the conditions under which each actually functions. Pepper’s specific configuration, combining a wheeled mobile base with a chest-mounted tablet, speech and gesture, makes it a useful case study in how multimodal embodiment is translated into classroom practice.

The timing of the corpus is also noteworthy. Because the first qualifying study appeared only in 2018, the entire body of empirical evidence postdates the robot’s commercial launch by several years, suggesting that early adoption in schools lagged well behind the platform’s availability. The concentration of research in primary and higher education, with secondary schooling nearly absent, may reflect practical factors such as timetable flexibility, curricular pressure and the willingness of younger children to engage with novel devices, though the review does not establish causal explanations for this distribution.

For practitioners, the most actionable finding may be the recurring pattern of ‘trouble and repair’. Since technical interruptions were near-universal, schools considering deployment should plan for adult supervision as a structural requirement rather than an occasional contingency. The scarcity of teacher involvement in intervention design, noted in only two studies, likewise points toward a simple improvement: co-designing robot activities with educators from the outset, and embedding them within existing curricular units instead of isolated demonstrations.

The authors’ call for comparative research deserves emphasis. Without head-to-head comparisons against tablets, virtual agents or conventional materials, the field cannot determine whether Pepper’s embodied presence produces effects that cheaper technologies cannot replicate, or whether the observed gains reflect novelty and expectation rather than any unique pedagogical property of the robot itself.

Subject of Research: A scoping review of the implementation of the Pepper social robot in formal educational contexts

Article Title: Exploring the implementation of the Pepper social robot in formal education: a scoping review

Article References: Martinez-Roig, R., Aragonés-González, M., & Cazorla, M. (2026). Exploring the implementation of the Pepper social robot in formal education: a scoping review. Journal of New Approaches in Educational Research, 15(1), Article 22. https://doi.org/10.1007/s44322-026-00072-1

Image Credits: AI Generated

DOI: 10.1007/s44322-026-00072-1

Keywords: social robotics, Pepper robot, formal education, scoping review, educational technology, human-robot interaction, inclusive education, teacher mediation, primary education, student motivation, autism spectrum disorder, STEM learning