Generative artificial intelligence (GenAI) is increasingly integrated into higher education, where it supports writing, feedback, problem solving, and research-related tasks while also raising concerns about cognitive offloading and learner dependence. This scoping review maps the literature on the relationships among GenAI, cognitive offloading, and learner agency in higher education. Following PRISMA-ScR guidance, searches were conducted on 24 June 2026 across six database sources: Web of Science Core Collection, Scopus, EBSCOhost ERIC, EBSCOhost Education Source, APA PsycInfo via ProQuest, and ProQuest Education Collection via ProQuest Social Science Premium Collection. English-language peer-reviewed or scholarly journal literature published from 2022 to 24 June 2026 was eligible for inclusion. Data were charted using a structured extraction matrix and synthesized through descriptive mapping and theory-informed configurative synthesis. After duplicate removal, 5327 records were screened by title and abstract, 691 full-text reports were assessed, and 123 studies were included in the final charting matrix and synthesis. The review shows that learner agency is conceptualized as a multidimensional construct involving self-regulation, reflective judgement, intentionality, and responsible action. Across the literature, GenAI is best interpreted through an interpretive dual-pattern account: agency-supportive patterns are associated with self-regulated learning, self-efficacy, feedback literacy, and reflective engagement, whereas agency-eroding patterns are associated with cognitive offloading, overreliance, dependence, uncritical uptake, and weakened judgement. These patterns should be interpreted as a configurative synthesis of heterogeneous evidence rather than as causal effect estimates. Overall, the findings suggest that the educational value of GenAI depends less on the technology itself than on how it is pedagogically embedded, with augmentation-oriented and scaffolded use being more supportive of learner agency than replacement-oriented use.
As artificial intelligence (AI) technologies become increasingly embedded in higher education, concerns have emerged regarding their psychological impact on university faculty. While existing research has largely focused on technological readiness and digital competencies, the social–psychological foundations of faculty well-being in AI-integrated teaching environments remain insufficiently explored. Drawing on social support theory and self-determination theory, this study proposes and tests a structural model of AI-related well-being among university faculty. A total of 523 faculty members in China participated in a cross-sectional survey measuring perceived social support, organizational support, AI literacy, technological self-efficacy, and AI well-being. Structural equation modeling (SEM) was used to examine the hypothesized pathways and mediating mechanisms. The results indicate that both social support and organizational support significantly and positively influence AI literacy and technological self-efficacy. In turn, AI literacy and technological self-efficacy significantly predict AI well-being and serve as key mediators. Among all factors, AI literacy exhibits the strongest total effect on AI well-being, followed by social support, organizational support, and technological self-efficacy. This study contributes to the theoretical understanding of faculty psychological adjustment during technological transitions and offers practical recommendations for institutions seeking to cultivate AI-ready and psychologically supportive academic environments.
With the development of digital technology and artificial intelligence (AI), numerous studies have focused on the applications and impacts of digital technology in the public sector. However, few studies have explored how frontline public service employees, the core subject of public organizations, can improve their proactive service performance. Based on the model of proactive motivation, this paper investigates the influence of digital leadership on employees' proactive service performance from a micro perspective, as well as the internal mechanisms and boundary conditions underlying this process. Through an analysis of three-wave questionnaire survey data from 234 employees, this study finds that digital leadership has a positive impact on public employees' proactive service performance through the serial mediation effects of AI service awareness and AI crafting. Furthermore, as an important boundary condition, employees' public service motivation strengthens the serial indirect effect of digital leadership on proactive service performance. This paper not only extends the literature on digital leadership by adopting a micro-level perspective within the context of public sector digital transformation but also identifies the individual and contextual antecedents of proactive service performance by examining the interactive effect of public service motivation and leadership. Furthermore, this paper offers valuable implications for the practice of digital transformation in public organizations.
Research on personality and behaviour has established that individuals exhibit relatively stable patterns of conduct across time, commonly described in terms of trait dimensions such as conscientiousness. At the same time, self-regulation and habit research have identified mechanisms involved in behavioural initiation, persistence, and automatization. Despite these advances, existing frameworks do not adequately specify the intermediate processes through which behavioural continuity is maintained across everyday contexts. This article introduces the concept of micro-discipline to address this gap. Micro-discipline refers to recurrent low-level acts of behavioural regulation that preserve continuity between intention and action under ordinary conditions of friction, including returning attention to a task, sustaining effort despite resistance, modulating minor impulses, and completing small obligations that might otherwise be deferred. The central claim is that these repeated regulatory acts constitute a distinct and temporally cumulative process through which behavioural patterns are stabilised and, over time, modified. Drawing on personality theory, self-regulation research, and related process-based approaches, the article develops a conceptual model explaining how such micro-regulatory processes bias the recurrence, persistence, and interruption of behavioural states, thereby contributing to trait stabilisation and trait change. By clarifying this intermediate process layer, the framework provides a more precise account of how local regulatory acts scale into durable patterns of behaviour. It further offers implications for understanding personality development, the maintenance of goal-directed behaviour, and the conditions under which intentional behavioural change succeeds or fails.
This study investigates how self-presentation and social skills relate to physical health among university students in Japan and Hong Kong, with particular attention to the mediating roles of social media use and social support. We conducted two online surveys; as a result, 452 participants in Japan and 289 participants in Hong Kong were analyzed using multi-group structural equation modeling. Across both samples, social skills were directly associated with increased physical symptoms, and this direct association was substantially stronger than the indirect association through social support, which was linked to fewer physical symptoms. Notable cross-cultural differences also emerged. In Japan, self-presentation was directly associated with increased respiratory-sleep symptoms (R-SSs), and this direct association was stronger than the indirect pathway through posting frequency and time spent on social media. In contrast, in Hong Kong, no direct association between self-presentation and physical symptoms was detected, although an indirect association through social media usage time-leading to increased R-SS-emerged at a marginally significant level. These findings highlight the dual and context-dependent effects of personal attributes in digital environments, demonstrating that characteristics typically associated with positive social functioning may produce unintended health consequences online. By integrating personal factors, social media behaviors, and cultural context, this study advances understanding of digital well-being and physical health in contemporary societies.
