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3 references
Seligman revises his earlier focus on authentic happiness into a plural theory of well-being. Well-being is not a single feeling and has no sole measure; it is built from five elements summarized as PERMA: positive emotion, engagement, relationships, meaning, and accomplishment.
People may pursue each element for its own sake, and different profiles can support flourishing. The book connects this framework to positive-psychology interventions, education, resilience training, psychotherapy, health, organizations, and public policy. Exercises involving gratitude, strengths, constructive responding, and meaning are presented as practices whose effects should be tested rather than accepted as inspiration alone. Seligman distinguishes relieving disorder from building capability and argues that institutions should assess and cultivate strengths as well as repair deficits.
Critics may question measurement and cultural assumptions, but the framework’s educational value lies in broadening success beyond mood or grades while keeping proposed practices open to empirical evaluation.
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Shi, Liu, and Hu investigate associations among AI literacy, self-regulated learning, perceived writing performance, and well-being in generative-AI-supported higher education. Survey responses from 257 university students in China were analyzed with structural equation modeling.
Both AI literacy and self-regulated learning positively predicted students’ perceived writing performance, with self-regulation showing the stronger association. AI literacy also had a positive relationship with generative-AI-related well-being, and writing performance partially mediated that relationship. The model brings technological competence and strategic learning behavior together rather than treating tool knowledge as sufficient on its own. For teaching, the results support developing students’ ability to plan, monitor, and reflect while also teaching awareness, effective use, evaluation, and ethics of AI.
Important limits temper the findings: measures were self-reported, the writing-performance scale had modest reliability, the sample came from one national context, and the cross-sectional design cannot establish causal effects. The study therefore indicates relationships worth supporting and testing, not proof that AI literacy or AI use automatically improves writing or psychological well-being.
Skaalvik and Skaalvik test a job-demands–resources model with survey data from 760 Norwegian teachers in grades 1–10. Demands include time pressure, discipline problems, low student motivation, value conflict, and role ambiguity; resources include autonomy, supervisory support, colleague relations, collective culture, and value consonance.
Structural equation analyses show that an overall demands factor strongly predicts lower teacher well-being, while resources more moderately predict higher well-being. Well-being in turn predicts greater engagement and less motivation to leave teaching. Among specific demands, time pressure has the strongest negative association with well-being. The findings distinguish conditions that consume sustained effort from conditions that help teachers reach goals, cope, and develop professionally.
Because the study is cross-sectional and based on self-report, causal direction cannot be established. Still, it directs school improvement beyond individual resilience: workload, role clarity, relational support, shared values, and professional autonomy are organizational conditions tied to motivation and retention.
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