Social Sciences

Abstract: Objective: This study reports the development and psychometric validation of the Problematic and (Adaptive) AI Use Scale for University Students. The scale’s theoretical foundation integrates behavioral addiction frameworks, AI-related ethical considerations, and self-regulated learning perspectives. Method: A total of N=1114 university students were surveyed online. Participants responded to the 27 PAIU-U items and additional validation measures. The sample was randomly split for calibration (n₁=543) and validation (n₂=571). An exploratory factor analysis (EFA) was conducted to identify the factor structure, followed by confirmatory factor analysis (CFA) on the second subsample. Results: EFA revealed a coherent five-factor solution accounting for 57.5% of variance, with high item loadings that mapped onto the theorized dimensions. The factors identified were: (1) Salience/Preoccupation & Tolerance–Escalation, (2) Functional Reliance/Capability Erosion, (3) Social and Ethical Conflict & Harm, (4) Loss of Control & Academic Impairment, and (5) Mood Modification/Coping. Conclusion: The PAIU-U is introduced as a novel measure of university students’ problematic AI use and associated academic integrity lapses. The scale demonstrates a robust multidimensional structure aligned with behavioral addiction theory and academic ethics and shows strong reliability and initial evidence of validity.

Abstract: This report examined changes in academic help-seeking networks among undergraduate students who participated in repeated rotating small-group discussions in a teacher education course. The participants were 129 undergraduate students from two course cohorts at a university in northern Taiwan. Social network data were collected at early- and mid-semester time points, and network visualizations and indicators were used to describe changes in peer recognition for academic help-seeking. The findings showed that the two cohorts displayed different whole-network patterns. Cohort 1 showed increased density and a larger number of observed nomination ties from early to mid-semester, suggesting a more densely connected academic help-seeking network. In contrast, Cohort 2 showed decreased whole-network density at mid-semester. However, this pattern required cautious interpretation because the number of effective respondents declined substantially while the network boundary remained constant. Importantly, respondent-level nomination breadth increased in both cohorts, indicating that students who completed the survey identified more classmates as potential sources of academic help over time. These findings suggest that rotating small-group discussions may broaden students’ recognition of classmates as learning resources, even when whole-network indicators show different patterns. The report highlights the importance of interpreting classroom network change through both whole-network and respondent-level indicators.

Abstract: Australia's current educational landscape presents a fragmented approach to early childhood education, with childcare, kindergarten, and formal schooling operating as separate entities. This paper argues for the strategic integration of childcare and kindergarten services into the existing school education system, examining the pedagogical, economic, and social advantages of such reform. Through analysis of international best practices, developmental psychology research, and educational policy frameworks, this article demonstrates that integration would enhance educational continuity, improve equity of access, strengthen professional development pathways, and provide significant economic benefits for families and government. The paper concludes that systematic integration represents a critical reform necessary for Australia to optimise early childhood educational outcomes and establish a more coherent, effective educational foundation for all children.

Abstract: Generative artificial intelligence (GenAI) tools are increasingly used in higher education, yet the relationship between students' self-assessed competence and their actual learning practices, support needs, and critical engagement remains underexplored. This quantitative survey study examined perceived GenAI literacy, learning-related use patterns, self-identified knowledge gaps, and responsible-use orientations among 449 students from four Slovenian faculties of education and arts. Results showed that perceived GenAI literacy was positively associated with the frequency, breadth, and diversity of learning-related GenAI use, though correlations were modest. Students at all literacy levels reported substantial support needs, particularly regarding responsible use. While lower-literacy students reported greater support needs descriptively, the adjusted logistic model indicated that higher perceived literacy was associated with greater odds of identifying a need for additional basic GenAI knowledge, suggesting a possible awareness-of-limits effect. Contrary to expectations, perceived literacy did not predict a critical and responsible orientation toward GenAI; instead, age was the stronger predictor. Perceived institutional support contributed little explanatory power across models. Overall, the findings indicate that self-assessed GenAI literacy facilitates broader tool adoption but does not automatically lead to ethically reflective use. The study underscores the need for structured, pedagogically embedded AI literacy initiatives that explicitly address critical and responsible engagement, rather than relying on tool exposure alone, and highlights the importance of distinguishing perceived competence from critical GenAI competence in future research.

Abstract: This paper outlines a custom made Academic and Administrative Audit (AAA) mechanism designed specifically to suit polytechnic and technical engineering institutions. The framework is based on 15 criteria evaluation rubric (a granular 15-criteria) which is a self-regulated learning theory, grounded on a Plan-Do-Check-Act (PDCA) cycle and self-regulated learning theories. This decentralized model, as opposed to the generic accreditation models which tend to result in episodic compliance, uses objective mathematical formulations to help instill a sustained, internalized quality culture. The results suggest that combining the specified metrics, including the achievement of specific outcomes in Outcome-Based Education (OBE) and dynamic indexing by faculty will optimize resources and accelerate readiness to be offered external accreditations such as NBA and NAAC. This study offers a scalable template to technical institutions as a way of aligning academic delivery with the changing industrial needs through peer-driven accountability and measurable metrics.

Abstract: The global education landscape is undergoing a foundational shift from time-bound, degree-centric validation to skills-based, modular recognition systems. Micro-credentials short, focused certifications that attest to specific competencies have moved from the experimental periphery to the strategic center of education, workforce development, and economic policy. This article presents a comprehensive, multidisciplinary synthesis of the micro-credentialing revolution as it stands in 2026, analyzing its drivers, impact, and unresolved tensions. Utilizing a thematic analysis providing a narrative synthesis, drawing on empirical and non-empirical academic literature, grey policy documents, industry reports, and international case studies framed within a PESTLE analytical structure to identify the co-mingling of four major drivers of adoption driving the mass marketability of alternative credentials; political endorsement, corporate skills-based hiring practice shifts unbundled higher education pathways leveraging emerging technologies such as artificial intelligence (AI) and blockchain. This macroeconomic debate weighs enhanced labor market agility, democratization of usage and new institutional value propositions against key friction points such as quality fragmentation, interoperability chaos, and generative AI-era threats to assessment integrity. Real-world implementation and equity implications are illustrated through case studies from Singapore, IBM, African mobile-first models and the European Blockchain Services Infrastructure. The article ends with the suggestion of a place where practitioners can come together to build an open, interoperable trust ecosystem with micro-credentials working as the fine-grained currency of lifelong learning and ultimately puts forth that it is not really about the badge itself, but rather all about the infrastructure of trust that society will have no choice but to build.

Abstract: The rapid advancement of digital technologies has fundamentally transformed the operational landscape of higher education institutions globally. This article examines the theoretical and practical dimensions of digital strategy formation within universities and other higher education establishments, with particular emphasis on the strategic management mechanisms that underpin effective digital transformation. Drawing on contemporary frameworks of strategic management in education and insights derived from international practice, the study synthesizes existing knowledge on how higher education institutions conceptualize, develop, and implement digital strategies to enhance educational quality, organizational performance, and institutional competitiveness. The paper explores the core functions and directions of digital strategy, including technology-enabled governance, curriculum innovation, data-driven decision-making, and digital infrastructure development. Special attention is devoted to the alignment between strategic planning processes and digital management systems, highlighting how institutions can leverage structured planning methodologies to navigate the complexities of digital change. The analysis integrates perspectives from strategic management theory and applies them to the educational context, illustrating how administrative leadership, faculty engagement, and stakeholder collaboration contribute to successful digital strategy execution. Furthermore, the article identifies critical success factors and recurring challenges encountered during digital transformation, offering actionable recommendations for institutional leaders and policymakers. The findings suggest that sustainable digital transformation in higher education requires a coherent, institution-wide strategic framework that integrates technological, human, and organizational resources. The article contributes to the growing body of literature on digital governance in education by providing a comprehensive conceptual analysis grounded in recent empirical and theoretical scholarship.

Abstract: YouTube has become a significant but largely unstudied forum for public intellectual two-way conversations on AI in education as video has become a ubiquitous medium for communicating ideas. In a world dominated by video as a communication medium, YouTube has become a prominent location for two-way ideas exchanges about artificial intelligence (AI) in education that is not yet well studied as a site of discursive production. These conversations have the potential to influence and shape access to AI in education on an even larger scale than the scholarly publications they would enable, and these influences are evident in the policy, practice, and teacher identity for which these images of the future of education are constructed. The three-dimensional Critical Discourse Analysis (CDA) model by Fairclough (1992, 2003) is applied to a Corpus consisting of 20 (171,676 words) high-reach and purposively selected panel discussions (8) on YouTube from 2020 to 2026 with Nobel Prize winners, panelists of the WEF, researchers at the Stanford Human-Centered AI Institute and panelists of the UNESCO chair. The six dominant discourse themes found through a process of analysis across 26 deductive and inductive codes, managed by NVivo 15, the student’s were: the discourse of inevitability, the teacher identity crisis, ethics in-depth arising from the question of governance, the equity paradox, the human exceptionalism, and the corporate authority. The results indicate that these themes support the educational future of AI in three systematic discursive processes: inevitability normalization, institutional authority concentration and equity instrumentalization. The findings recognize that the lack of practitioner educators outlined in each of 20 panels is in itself a form of discursive power, as absence rather than content. The study is intended to demonstrate that talking about AI in education is not only a reflection of educational futures, it is an act to creating them. As educators, policymakers, or communities aim to understand and embrace AI adoption as a democratic process, it is crucial that their discursive mechanisms are made visible. Recommendations are made for policy makers, schools, teacher educators, curriculum planners and researchers.

Abstract: The rapid advancement of generative AI and large language models challenges long‑held assumptions about the purposes, content, methods, and practices of education. This paper integrates historical educational philosophy with contemporary AI capabilities to present a comprehensive framework for rethinking what and how we teach and learn. Drawing on foundational purposes—moral formation, democratic citizenship, critical emancipation, human capital development, and holistic flourishing—we analyse how AI’s strengths (pattern recognition, content generation) and limitations (lack of understanding, moral agency, empathy, metacognition) reshape educational priorities. We propose a curriculum of seven human‑irreplaceable competencies, including algorithmic literacy, ethical judgment, creative abduction, metacognition, emotional intelligence, systems thinking, and foundational knowledge and memorization. For learners, we identify six core skills: learning to learn, judge, create, relate, work with and without AI, and be. Pedagogically, we advocate eight principles: cognitive apprenticeship, problem‑based learning, critical AI literacy across disciplines, dual readiness, dialogic instruction, authentic assessment, teacher vulnerability, and deliberate memory building. For students, we outline eight practices: prompt‑critique‑synthesise, attention management, documentation, collaboration, questioning, deliberate AI‑free routines, productive struggle, and retrieval practice. A central argument is that while AI surpasses humans in memorisation and routine information retrieval, human learners must still internalise a durable core of knowledge to enable creativity, social cohesion, character development, and resilience in AI‑absent scenarios. The paper concludes that the AI era demands not the abandonment of traditional educational aims but their recalibration toward uniquely human capacities, with teachers and learners becoming co‑inquirers in an AI‑augmented but human‑centred ecosystem.

Abstract: Scientific recognition is only weakly determined by the intrinsic quality of research. A large body of work in the sociology of science, bibliometrics, and the emerging science of science instead describes recognition as a networked, cumulative-advantage process: attention concentrates on work that is already visible, early advantages compound, and most papers attract little notice regardless of merit. This review synthesizes that literature across three layers. First, it surveys the structural mechanisms — the social construction of recognition, heavy-tailed citation distributions and preferential attachment, the Matthew effect and reputation thresholds, the asymmetry of credit in team science, and the timing of individual impact. Second, it reviews the evidence on deliberate dissemination interventions — open access, preprints, plain-language summaries, targeted outreach, social-media presence, and the activation of weak ties — distinguishing well-supported effects from contested ones. Third, it examines how large language models and generative search are becoming a new amplifier of cumulative advantage, with measured citation biases toward already-prominent work and a growing share of science-related information seeking mediated by generative engines. Throughout, the central implication is that visibility is an actionable, channel-dependent outcome rather than an automatic byproduct of quality. We close by considering where automated scholarly-visibility services fit within this evidence base, and we identify open questions for research on visibility in the generative-search era.This review was written by Boris Gorelik of Loud Camel — Academic Career Promotion, a service that operationalizes several of the dissemination practices reviewed here as a recurring workflow; its conclusions rest on the cited literature, not on the service.

Abstract: For over a century, the printed textbook has assumed learner homogeneity, creating persistent inequities for students with diverse reading levels, languages, and cultural backgrounds. Open Educational Resources (OER) offer a free, openly licensed alternative, yet most OER remain static documents requiring manual teacher differentiation. The rapid maturation of generative artificial intelligence (AI) provides a transformative solution: dynamic, AI‑powered OER that can be personalised at the point of use while preserving open licences. This quasi‑experimental design‑based research study developed, implemented, and evaluated a prototype called “AI‑OER Studio” over six months. Following a focus group with 12 educational technology experts to derive design principles, we built a web prototype that generates textbook chapters on any topic, at specified grade levels, in multiple languages, with culturally adapted examples, all under a CC BY‑SA 4.0 licence. [Legal disclaimer: the copyright status of AI‑generated content remains unresolved; see main text.] Expert reviewers (n=12) rated generated content favourably for factual accuracy (4.15/5) and pedagogical alignment (4.40/5), though bias scores were lower (3.95/5). A classroom pilot with 78 seventh‑grade students compared AI‑dynamic OER against static OER over a four‑week ecosystems unit. Students in the treatment group showed significantly higher learning gains (post‑test 78% vs. 68%; adjusted effect size d = 0.44 after accounting for classroom clustering) and spent 35 more minutes per week engaged, although a novelty effect likely accounts for some of this difference. Teacher interviews revealed substantial time savings but also concerns about factual hallucinations and cultural biases. The static textbook is increasingly ill‑suited for diverse classrooms; the future lies in living, AI‑augmented, openly licensed resources, provided that human validation, bias mitigation, and legal clarity are prioritised.

Abstract: Education for Sustainability (EfS) has emerged as a key response through which higher education engages ecological, social, and civic challenges. While EfS is well represented in policy and conceptual scholarship, fewer empirical studies examine how faculty enact sustainability within everyday teaching practice. This qualitative collective case study investigates the lived experiences and pedagogical practices of four faculty members at a U.S. land-grant university. Data were collected through semi-structured interviews, supported by syllabi, observations, and student responses, and analyzed using cross-case thematic analysis. Analysis identified four interconnected themes: latent complexity, personal commitment, inclusive scholarship, and adaptability to student motivations and context. These themes position EfS as a relational, context-responsive process shaped through interaction among faculty identity, student engagement, and institutional conditions. These findings reposition faculty practice as a primary mechanism through which sustainability is continuously enacted, adapted, and sustained within higher education systems, with implications for institutional policy, faculty development, and long-term sustainability capacity.

Abstract: Generative artificial intelligence is rapidly becoming a cognitive partner in education, capable of planning tasks, monitoring progress, and evaluating solutions on a learner’s behalf. This conceptual synthesis paper examines the risk that such AI tools, while improving immediate performance, may erode students’ metacognitive abilities, their capacity to plan, monitor, and evaluate their own thinking. Drawing a parallel with GPS navigation research, where habitual turn‑by‑turn guidance has been shown to impair spatial memory and hippocampal engagement, we introduce the metaphor of AI as a “GPS for thinking.” Through an integrative review of literature spanning cognitive psychology, neuroscience, and the learning sciences, we synthesise evidence that AI‑assisted learning can lead to a form of cognitive disuse atrophy, specifically by short‑circuiting the metacognitive loop. Emerging studies reveal that students who rely heavily on AI tutors often perform worse when the tool is removed, suffer from an illusion of explanatory depth, and struggle to articulate the reasoning behind their answers. To counter these effects, we propose a shift from a GPS model where the tool issues commands to a compass model, where the tool provides orientation while preserving learner agency. Five evidence‑informed design principles are advanced: prompting planning before assistance, delaying and fading feedback, embedding mandatory reflection pauses, making AI reasoning visible, and calibrating learners’ confidence. The article argues that the long‑term goal of educational AI must be to strengthen, not supplant, the student’s inner compass.

Abstract: The 2030 Agenda highlights education and entrepreneurship as critical drivers of sustainable development, yet little is known about how sustainability literacy translates into green entrepreneurial confidence among Gen Z students in developing and post-conflict economies. This study examines the direct and mediated effects of sustainability literacy on green entrepreneurial self-efficacy (GESE) through biospheric values, and the moderating roles of university support, digital literacy, and family support. A cross-sectional survey was conducted with 417 Gen Z undergraduate students from Universitas Negeri Padang (Indonesia) and Universidade Nacional Timor Lorosa’e (Timor-Leste). Data were analyzed using PLS-SEM and Importance-Performance Map Analysis (IPMA). Results show that sustainability literacy directly enhances GESE (β = 0.342, p < 0.001) and indirectly through biospheric values (indirect effect = 0.156, p < 0.001). University support moderates the values→efficacy pathway (β = 0.148, p < 0.05), while digital literacy moderates the literacy→efficacy pathway (β = 0.198, p < 0.01). However, family support did not moderate any relationship, and digital literacy exhibited a ceiling effect among Gen Z respondents. IPMA reveals biospheric values and sustainability literacy as high-importance, high-performance priorities, with no urgent intervention needed. We conclude that cultivating biospheric values matters more than transmitting knowledge alone, and university support should strategically target value-driven students rather than compensate for low literacy.

Abstract: Lexical inferencing is a key contributor to reading development in sighted children, yet its role in Braille reading remains underexplored. This study investigated the developmental trajectory of lexical inferencing among Chinese primary school students with blindness and examined the relationships among compounding awareness, lexical inferencing, vocabulary knowledge, and Braille text reading comprehension. Results showed that (1) students with blindness showed lower lexical inferencing performance than sighted students at both middle and upper grade levels, although lexical inferencing improved with grade level; (2) lexical inferencing significantly predicted both vocabulary knowledge and Braille reading comprehension among students with blindness; (3) compounding awareness significantly predicted lexical inferencing in both middle-grade students and upper-grade students; (4) the relative role of compounding awareness and lexical inferencing differed by grade group. In middle-grade students, both compounding awareness and lexical inferencing contributed to vocabulary knowledge and Braille reading comprehension, with vocabulary knowledge also predicting reading comprehension. In upper-grade students, lexical inferencing remained a significant predictor of both vocabulary knowledge and Braille reading comprehension, whereas compounding awareness no longer directly predicted either outcome. These findings indicate a developmental shift in which compounding awareness is more influential in earlier stages, whereas lexical inferencing becomes the central mechanism supporting vocabulary growth and text-level comprehension in later stages.

Abstract: Within modern culinary education, education for sustainable development is essential for vocational students. Using the example of sous vide, its suitability for addressing sustainability in culinary education was investigated as well as to which extent it is currently implemented in Germany. Therefore, literature on potential environmental, social, economic and health impacts of sous vide cooking was reviewed and its current implementation in German culinary educational materials was analyzed. The analysis revealed a number of sustainability aspects of sous vide. Despite being covered in textbooks, it is not brought into a sustainability context. Moreover, existing sustaina-bility concepts for the gastronomy sector neither identify environmental conditions as a basic requirement for any socio-economic activity nor illustrate interdependencies and trade-offs between different sustainability dimensions. Hence, currently in Ger-many available sustainability concepts and culinary teaching and training materials do not support the development of a systemic understanding and multi-dimensional engagement when training future chefs.

Abstract: This paper examines the empirical evidence on the use of generative artificial intelligence (GenAI) in scientific writing. A search was conducted in Google Scholar and PubMed, followed by an analysis of the included studies, which was performed according to the academic field, AI tool, writing task, study design, and main findings. Following the PRISMA guide, this scoping review included 18 studies published between 1st January 2023 and 1st January 2026, representing the disciplines of medicine, education, dentistry, radiology, humanities, library, information science and cognitive science. The evidence base was dominated by studies on ChatGPT, making it the most empirically researched GenAI tool in this field. According to the studies reviewed, GenAI performed well on an array of measures (readability, fluency, and organization) and efficiency (the latter especially in terms of manuscript drafting, abstract writing, proposal development, and literature reviewing). However, the findings also disclosed several limitations, including incorrect or falsified references, inaccurate bibliographical metadata, shallow analysis, lack of originality, and insufficient methodological depth. Based on comparative evidence, newer model versions show improved coherence and reasoning and although improved with the newer GenAI versions, reference reliability still appears to be a recurring problem. Overall, GenAI can be a useful assistive tool for scientific writing; however, its usefulness is dependent upon human supervision and the task at hand, especially with regard to the accuracy of facts and their sources.

Abstract: Inclusive education has become an important component of educational reform in Kazakhstan, particularly through efforts to align national education policy with international principles of equity and access. However, implementation remains uneven between urban and rural schools. This study explores how teachers implement inclusive education practices in a rural secondary school in Northern Kazakhstan. A qualitative case study design was employed using semi-structured interviews with sixteen teachers working in inclusive classrooms. Data were analyzed through thematic analysis. The findings indicate that teachers demonstrate strong commitment to supporting students with diverse learning needs and regularly adapt instructional practices to promote classroom inclusion. At the same time, participants identified major challenges, including limited professional preparation, shortages of specialized support staff, insufficient instructional resources, and infrastructure constraints affecting rural schools. The findings further suggest that although inclusive education is increasingly emphasized within national educational policy, implementation in rural schools continues to be shaped by structural inequalities and unequal access to institutional support. The study contributes to the limited literature on inclusive education in Central Asia and highlights the importance of strengthening teacher professional development, institutional support systems, and rural educational infrastructure.

Abstract: The intensification of the ecosocial crisis has revealed the structural limitations of economic paradigms based on growth. In this context, degrowth emerges as a transformative framework that proposes the deliberate reduction of production and consumption, prioritizing well-being, equity, and ecological sustainability. However, the role of education in the transition toward post-growth societies remains insufficiently developed. This article analyzes how formal educational systems reproduce growth-oriented subjectivities through human capital frameworks and neoliberal governance. Based on a critical review of the literature and a conceptual analysis, both the structural limitations of the dominant educational model and the emergence of alternative pedagogies grounded in sufficiency, care, and the commons are identified. This article proposes a reorientation of educational aims, contents and practices favouring ecosocial literacy and collective agency, with implications for educational policy and systemic transformation.

Abstract: Reflection is widely recognized as a pathway to deeper learning in higher education, yet many students struggle to engage in reflective tasks meaningfully. This study examined how student engagement and reflective performance developed across a seven-session structured reflective learning sequence in an undergraduate course. A longitudinal quantitative design was employed, including 59 students for participation data and 38 students for performance analysis. The instructional design incorporated teacher-led scaffolding, including exemplars, feedback, and structured prompts, with optional AI-supported assistance in later sessions. Results showed that engagement patterns were non-linear. Submission rates increased following the introduction of exemplars and feedback, declined when higher-order reflection was first introduced, and stabilized in later sessions, with the lowest participation observed in the final integrative task. Reflective performance also differed across stages. Step 1 (descriptive reflection) scores improved progressively, whereas Step 2 (analytical reflection) scores remained consistently high among students who completed substantive responses. The gap between attempted and completed Step 2 responses decreased over time. These findings suggest that reflective learning develops gradually and is sensitive to instructional conditions. The study highlights reflection as a staged developmental process and underscores the role of structured support in facilitating student engagement and performance.