Technology, AI and the mind

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1. 1

   Cognitive impacts of learning and using digital technology in older adults: A systematic review and meta-analysis.

   Psychology and aging (Zhang et al.)Published Sep 14, 2026Checked Sep 30, 2026

   “Fourteen studies met the inclusion criteria, of which 12 provided sufficient data to calculate effect sizes. All included studies focused on older adults (over age 60). Results of the random-effects models showed that providing digital technologies or digital technology training to older adults benefits their cognition (g = 0.11, 95% CI [0.03, 0.19]) compared with control conditions. These results suggest that digital exposure leads to cognitive benefits. However, the evidence is preliminary because the magnitude of the effect is small with substantial heterogeneity (95% Prediction interval [-0.31, 0.53]), and the underlying mechanism is unclear. Therefore, the practical implications of relying on digital exposure to prevent cognitive decline in the absence of structured programs or targeted interventions are limited. Future research needs to include granular, objectively quantified use metrics and incorporate psychosocial mediators to uncover the underlying mechanism of the observed benefit. (PsycInfo Database Record (c) 2026 APA, all rights reserved).”

2. 2

   Digital distraction and adolescent development: a narrative review of smartphone use, cognitive effects, and policy interventions.

   Frontiers in psychology (Colakoglu & van)Published Sep 8, 2026Checked Sep 30, 2026

   “Research across observational studies, experimental work, and systematic reviews suggests that excessive smartphone use is associated with modest but consistent relationships with sleep disruption, reduced attentional capacity, and increased internalizing symptoms among some adolescents. However, these effects are heterogeneous and appear to be moderated by factors including type of digital activity, timing of use, and individual vulnerability. Evidence examining the effects of school-based smartphone bans indicates improvements in classroom attention and engagement, although broader impacts on mental health and long-term digital habits remain unclear. Furthermore, adolescent views on smart phone bans are mixed, with students recognizing that there are positive and negative effects of both smartphones and bans. Together, these findings suggest that smartphone use represents a complex developmental exposure rather than a uniformly harmful or beneficial influence. Future research should examine how structured phone-free environments, including school and extracurricular settings, influence adolescents' social interaction, digital behavior, and well-being over time.”

3. 3

   AI information encounters and university students' digital burnout: the mediating role of cognitive flexibility and the moderating effect of AI learning trust.

   Frontiers in psychology (Li & Song)Published Sep 7, 2026Checked Sep 30, 2026

   “n encounters are significantly positively correlated with digital burnout; (2) cognitive flexibility partially mediates this relationship, as AI information encounters negatively correlate with cognitive flexibility, while cognitive flexibility negatively correlates with digital burnout; (3) AI learning trust negatively moderates the direct path between AI information encounters and digital burnout, meaning that higher levels of AI learning trust weaken their positive association; (4) AI learning trust also negatively moderates the link between cognitive flexibility and digital burnout, indicating that higher AI learning trust diminishes the negative association between cognitive flexibility and digital burnout. This study highlights the psychological costs associated with involuntary technology use and suggests that educators in higher education should be cognizant of the cognitive resource consumption resulting from AI information encounters, and the interactive effects of AI learning trust on this relationship. Additionally, educators should encourage students to develop reasonable AI learning trust and acknowledge the risk of digital burnout linked to AI information encounters.”

4. 4

   Digital governance pressure and digital burnout among grassroots public sector employees: the mediating roles of cognitive load and negative affect.

   Frontiers in psychology (Wang et al.)Published Aug 12, 2026Checked Sep 30, 2026

   “Based on survey data from 535 grassroots public sector employees, the study applies partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to examine both sufficient pathways and necessary-condition patterns. The PLS-SEM results show that technology-related pressures are associated with digital burnout through cognitive load and through the sequential pathway from cognitive load to negative affect. By contrast, regulation-related and context-related pressures are mainly associated with digital burnout through negative affect. The NCA results further suggest that, at high levels of digital burnout, the three forms of digital governance pressure, cognitive load, and negative affect exhibit threshold-like patterns, with cognitive load and negative affect showing relatively higher bottleneck values. These findings extend research on the unintended consequences of digital governance and provide empirical grounding for reducing digital burdens and protecting occupational health among grassroots public sector employees.”

5. 5

   Algorithmic governance at work: a legal-sociological and policy analysis of AI regulation, institutional power and labor rights.

   Frontiers in sociology (Olimid et al.)Published Aug 19, 2026Checked Sep 30, 2026

   “The results indicate that AI regulation across Europe, the Americas, and the Asia-Pacific is increasingly intersecting around human rights and personal data protection, risk-based governance, and accountability mechanisms, particularly in the employment environment. Legal frameworks emphasize transparency, non-discrimination, and the need for human oversight in AI-powered workplace decisions.ConclusionThe conclusions highlight that across Europe, the Americas and the Asia-Pacific, AI regulation is increasingly organized around six dimensions: protection of human rights and data privacy in the workplace; governance frameworks addressing reskilling, job transformation and adaptation to AI-driven work models; risk-based oversight of AI systems; AI civil liability regimes affecting employee rights and HR practices; promotion of ethical AI to foster trust and mitigate bias; and strengthened monitoring, accountability and non-discrimination mechanisms.”

6. 6

   The paradox of efficiency: institutional interfaces, residuals, and the erosion of innovation drivers in AI-augmented organizations.

   Frontiers in artificial intelligence (Yang)Published Sep 11, 2026Checked Sep 30, 2026

   “Under specific design conditions and viewed through the three nested interface lenses, AI systems tend to filter residuals across the cognitive, action, and value interfaces, systematically excluding potentially valuable innovations and impoverishing organizational cognitive diversity. This study articulates the conditions under which a self-reinforcing tendency toward efficiency lock-in emerges and theorizes the institutional features-non-optimization zones, outlier pathways, and paradox-balancing roles-that may counteract this drift. Critically, it further reveals the persistent and irreducible dilemmas facing such features (isolation, authority, and evaluation), arguing that they are tensions to be continually managed rather than problems to be solved. Advancing testable theoretical propositions, this article offers new analytical tools and design directions for innovation governance in AI-augmented organizations. It contributes directly to discussions on AI's role in business understanding and managerial decision support, while laying the conceptual foundation for a broader research program on the learning and governance of AI-augmented organizations.”

7. 7

   Artificial intelligence learning environments and educational psychology (2016-2025): a systematic bibliometric synthesis and review.

   Frontiers in psychology (Zhong & Simons)Published Aug 6, 2026Checked Sep 30, 2026

   “Longitudinal results reveal an unprecedented "generative turn" and a shift in methodological maturity, moving from exploratory computational modeling to rigorous experimental and quantitative validation. Despite these advancements, our analysis identifies a persistent "theoretical gap." While the synthetic literature advocates for deep psychological grounding, empirical studies remain overwhelmingly anchored in surface-level motivational outcomes, failing to adequately operationalize complex constructs such as metacognition, epistemic beliefs, and self-regulated learning (SRL), just to name a few. We conclude that for AI to foster effective, equitable instruction and learning, it must transcend being a mere content delivery mechanism and instead function as a "psychological catalyst." By prioritizing human-centric architectures, cognitive scaffolding, identity, and critical AI literacy, this synthesis provides a structural roadmap for stakeholders to align rapid technological strides with the foundational science of learning.”

8. 8

   Artificial intelligence and the transformation of education: a systematic narrative review towards adaptive epistemic ecosystems and post-linear pedagogy.

   Frontiers in artificial intelligence (Saparbaykyzy et al.)Published Sep 11, 2026Checked Sep 30, 2026

   “tional research; how it is associated with changes in pedagogy, curriculum, learning analytics, assessment, and governance; what recurring relationships connect adaptive feedback, curriculum sequencing, human-AI roles, epistemic validation, and institutional accountability; and what ethical, epistemological, and governance challenges accompany increasing human-AI participation. The synthesis indicates an uneven movement from isolated tool adoption towards more adaptive and data-informed arrangements, accompanied by substantial debate over technological determinism, platform power, teacher agency, language bias, data colonialism, and epistemic justice. Adaptive epistemic ecosystems and post-linear pedagogy are therefore advanced as synthesis-derived, integrative conceptual constructs rather than predetermined coding categories or universal empirical claims. Their contribution lies in specifying testable relationships amongst human-AI cognition, learning-data infrastructures, pedagogical adaptation, epistemic validation, governance, and contextual boundary conditions whilst preserving the distinction between observed patterns, interpretive synthesis, and future-oriented propositions.”