The study, published in Discover Education, was conducted by Hossein Isaee and Hamed Barjesteh of Islamic Azad University in Iran and Neda Fatehi Rad of Islamic Azad University’s Kerman branch. It examined a question that has received surprisingly little empirical attention: what happens when two of the most talked-about trends in language education, artificial intelligence and positive psychology, are integrated within a single instructional design rather than studied separately? Most previous research, the authors note, has either celebrated the technical affordances of AI or documented the emotional benefits of strengths-based pedagogy, leaving the interaction between the two largely unexplored, especially outside Western and East Asian settings.

To fill that gap, the research team recruited 300 undergraduate English as a Foreign Language learners aged 18 to 24 from twelve universities across Iran, Oman, and Türkiye, with four institutions from each country. Twelve intact classes, each taught by a different instructor, were assigned through cluster randomization to one of three conditions: AI-supported instruction enriched with positive psychology activities, AI-supported instruction alone, or traditional exam-focused teaching. The intervention ran for ten weeks, with two 90-minute sessions per week, and was standardized through a common implementation booklet, a centralized training workshop in Tehran for lead instructors, and follow-up online coordination sessions across all sites.

The AI component relied on ChatGPT, used as a guided pedagogical support tool rather than an autonomous teacher. Learners practiced conversation, vocabulary, and writing with the system, receiving immediate feedback on their language production. In the integrated condition, these tasks were interleaved with positive psychology exercises grounded in Seligman’s PERMA model of well-being, which identifies positive emotion, engagement, relationships, meaning, and accomplishment as pillars of human flourishing. Students kept gratitude journals, wrote reflectively about their strengths, set goals, and discussed their progress in class, while teachers framed feedback not only around linguistic accuracy but around effort, persistence, and resilience. The theoretical logic drew on Fredrickson’s broaden-and-build theory, which holds that positive emotions expand attentional and cognitive resources, making learners more persistent, more willing to take communicative risks, and more engaged.

Quantitatively, the results favored the integrated approach across every major measure. The researchers assessed language proficiency with an institutional examination modeled on TOEFL- and IELTS-style tasks, with productive skills rated by trained raters who were blind to group assignment. Proficiency rose by nearly 20 points in the AI-plus-positive-psychology group, compared with roughly 12 points in the AI-only group and about 7 points in the control group. Analyses of covariance confirmed that the group differences remained significant after controlling for baseline performance, and the AI-only group also outperformed traditional instruction, indicating that the technology itself contributed something real even without the emotional layer.

The affective outcomes told a strikingly parallel story. Learners completed the Foreign Language Enjoyment Scale, the PERMA-Profiler short form for well-being, and the Academic Buoyancy Scale, all carefully translated and back-translated into Persian, Arabic, and Turkish before use. The integrated group showed the largest increases in enjoyment and well-being, and a particularly pronounced advantage in academic buoyancy, the capacity to bounce back from everyday academic setbacks such as exam stress and corrective feedback. A repeated-measures analysis showed that enjoyment grew over time in all groups but grew far more sharply where positive psychology activities were present, suggesting that AI alone delivers only a modest emotional benefit.

Perhaps the most theoretically consequential finding came from the mediation analysis. Foreign language enjoyment partially explained the relationship between instructional condition and language achievement: the integrated program boosted enjoyment, and higher enjoyment in turn predicted stronger proficiency outcomes. Because the direct effect remained significant, emotion did not account for everything, but the pattern supports the idea that enjoyment functions as a proximal affective mechanism linking instructional design to achievement, rather than being a mere byproduct of successful learning. In exam-driven systems where language learning is often associated with pressure and anxiety, that mechanism may matter more than anywhere else.

The qualitative strand of the study, based on interviews with 27 students and all 12 instructors, weekly teacher journals, and classroom observations coded inductively with intercoder agreement above 85 percent, illuminated why the intervention worked. Three themes dominated. First, learners described the AI system as a supportive, patient, and nonjudgmental coach: one Omani student said the system corrected her calmly and that she stopped fearing mistakes, while an instructor in Türkiye reported that normally quiet students began participating once the fear of public correction was lifted. Second, positive psychology activities reframed the emotional climate of the classroom, with learners saying they began noticing small improvements instead of fixating on errors and looking forward to sessions rather than dreading them. Third, cultural adaptation and teacher mediation proved central: students initially found some reflection activities unfamiliar, even questioning whether they counted as serious academic work, and grew receptive only when teachers connected the tasks to locally meaningful values such as perseverance, self-improvement, and communication.

The authors are careful about causal language. Because intact classes rather than individuals were assigned to conditions, and each class had a single instructor who could not be rotated, the design is a cluster-based quasi-experiment, and the findings are framed as evidence of association rather than definitive causal proof. The AI-only condition controlled for exposure to the platform but not for the extra instructional variety and relational attention embedded in the psychology activities, and self-reported affective measures remain more vulnerable to expectancy effects than the blind-rated proficiency scores. The ten-week duration leaves open questions about whether the gains persist, and the sample of twelve universities in general English courses cannot represent the Middle East’s considerable diversity.

Even with those caveats, the study carries a message that is likely to resonate far beyond the three countries where it was conducted. Artificial intelligence in education is usually evaluated on technical sophistication, adaptivity, and efficiency. This research argues that such metrics miss the point: learners experienced the AI system as a relational and emotionally mediated presence, and its effectiveness depended on teacher mediation, cultural responsiveness, and the deliberate cultivation of positive emotion. Language programs, the authors suggest, should move beyond using AI merely for correction and drill, pairing AI tasks with brief reflective or strengths-based activities, and training teachers to interpret AI feedback in emotionally supportive terms. Future research will need active control conditions matched for time-on-task, longer follow-up periods, and possibly AI systems designed from the ground up around affective and motivational principles. But the headline finding is already clear: the smartest educational technology may be the kind that knows when to let human warmth lead.

Subject of Research: Integrating positive psychology principles with AI-supported English as a Foreign Language instruction in higher education

Article Title: A human-centered approach to integrating positive psychology and artificial intelligence in EFL instruction

Article References: Isaee, H., Barjesteh, H., & Rad, N. F. (2026). A human-centered approach to integrating positive psychology and artificial intelligence in EFL instruction. Discover Education, 5(1), Article 955. https://doi.org/10.1007/s44217-026-02151-z

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02151-z

Keywords: artificial intelligence in education, positive psychology, EFL pedagogy, foreign language enjoyment, human-centered AI, technology-enhanced language learning, PERMA model, academic buoyancy, teacher mediation, ChatGPT, language proficiency, learner well-being

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