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 research team argues that most current scholarship on artificial intelligence in the workplace has been captivated by performance metrics—productivity gains, cost reductions, service quality improvements—while treating the human cost as an afterthought. Their paper shifts the lens. By framing employee psychological wellbeing as a pillar of human sustainability, they contend that an organization cannot be truly sustainable if the mental health, motivation, and social connectedness of its workers deteriorate as algorithms take over more of the daily workload. The paradox emerges because the same adoption decisions that streamline operations can strip away precisely the experiences that make work psychologically nourishing.

To explain how this erosion happens, the authors build their framework on two well-established pillars of organizational psychology: self-determination theory and recovery theory. Self-determination theory holds that human flourishing at work depends on the satisfaction of three basic psychological needs—autonomy, competence, and relatedness. Autonomy is the sense of volition and control over one’s actions; competence is the feeling of effectiveness and mastery; relatedness is the experience of meaningful connection with others. Recovery theory, meanwhile, explains how workers replenish mental and emotional resources during and after demanding periods, and how chronic demands without adequate recovery produce strain, exhaustion, and psychological distress.

Applied to AI adoption, the theoretical machinery generates a sobering prediction. When intelligent systems automate decisions, standardize workflows, and monitor performance, employees may find their discretion shrinking—their sense of autonomy diminished as algorithmic recommendations dictate the pace and content of their work. Competence can suffer when the skills workers spent years honing are rendered obsolete or when success depends on opaque machine outputs they cannot fully understand. Relatedness may weaken as human interactions are replaced by automated transactions and as workers compete with, rather than collaborate alongside, machine agents. The cumulative result, according to the model, is unsatisfied psychological needs, which translate into psychological distress and a decline in what the authors term human sustainability.

Crucially, the framework does not treat this damaging trajectory as inevitable. The researchers identify two factors that shape how harshly AI adoption bears down on workers: employee AI literacy and the prevailing culture of AI within the organization. AI literacy—the knowledge and skills that allow workers to understand, evaluate, and work effectively with intelligent systems—can either amplify or buffer the paradox. Workers with low literacy may feel threatened, confused, and helpless in the face of AI, accelerating the erosion of competence and autonomy. But the relationship is not straightforward, and the authors emphasize that even high literacy alone does not guarantee positive outcomes if the surrounding organizational culture treats AI purely as a tool for surveillance and cost cutting.

This is where human-centered AI practices enter the model as moderators—variables that can soften or even neutralize the negative pathways. The study highlights four in particular. Transparency means that workers understand what the AI does, how it reaches recommendations, and what data it uses, reducing the anxiety born of opacity. Employee involvement means that staff participate in designing, selecting, and configuring AI systems, which preserves a sense of agency and ownership. AI literacy training equips workers with the competence to collaborate with intelligent tools rather than feel dominated by them. Human oversight ensures that meaningful decisions remain with people, keeping algorithmic systems in an advisory role rather than an authoritarian one.

When these practices are in place, the researchers suggest, the corrosive link between AI adoption and psychological need satisfaction can be tempered. An organization that deploys AI transparently, involves its employees, invests in their understanding, and retains human judgment at critical junctures can capture efficiency gains without paying the psychological price. Conversely, an organization that rolls out intelligent systems abruptly, opaquely, and without worker voice may find that short-term productivity is purchased with long-term burnout, disengagement, and turnover—a trade-off that ultimately defeats the goal of sustainability.

The practical implications the authors draw for managers are concrete. They call for participatory AI design processes in which employees help shape the systems that will share their workplaces. They urge organizations to preserve employee discretion, deliberately carving out spaces where human judgment remains authoritative. They recommend systematic assessment of employees’ psychological health and wellbeing as part of AI adoption programs, treating mental health indicators with the same seriousness as operational performance dashboards. And they argue that AI adoption strategies should be synchronized with the United Nations Sustainable Development Goals—specifically SDG 3 on good health and wellbeing, SDG 8 on decent work and economic growth, and SDG 9 on industry, innovation, and infrastructure—so that technological transformation aligns with, rather than contradicts, broader sustainability commitments.

The study also situates itself within the fast-growing literature on responsible AI, but with a distinctive reorientation. Whereas much of that literature concentrates on fairness, bias, privacy, and accountability of algorithms, this paper insists that the human experience of working alongside AI deserves equal analytical weight. Moving from an efficiency-focused lens to a human sustainability lens, the authors contend, is not merely an ethical nicety but a strategic necessity for organizations that intend to thrive over decades rather than quarters. Their conceptual model is explicitly designed as a foundation for empirical testing, and the researchers invite scholars to validate the framework across different industries, organizational types, and cultural settings—particularly relevant given the international composition of the team, which spans Gulf, Middle Eastern, and South Asian research institutions.

As artificial intelligence accelerates into every corner of the service economy, the message of this research lands with urgency. The paradox it identifies—efficiency gains that quietly hollow out the human core of work—will not resolve itself. Organizations that treat AI adoption as a purely technical project risk discovering, too late, that their most valuable asset, a psychologically healthy and motivated workforce, has been degraded in the process. The study’s framework offers a way forward: embed transparency, participation, literacy, and human oversight into the fabric of AI deployment, and measure success not only in output but in the wellbeing of the people doing the work. In an era when the race to automate is fierce, the research suggests that the true finish line is not the smartest machine, but the most sustainable human-technology partnership.

Subject of Research: The sustainability paradox of artificial intelligence adoption and its effects on workers' psychological wellbeing and human sustainability

Article Title: Sustainability paradox of artificial intelligence adoption for workers’ wellbeing

Article References: Sustainability paradox of artificial intelligence adoption for workers’ wellbeing. (n.d.). https://doi.org/10.1007/s43621-026-04671-y

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04671-y

Keywords: artificial intelligence, sustainability paradox, workers wellbeing, self-determination theory, recovery theory, AI literacy, human-centered AI, psychological need satisfaction, human sustainability, responsible AI, employee autonomy, organizational culture