ABSTRACT: Higher education continues to face a persistent gap between academic outcomes and workforce demands, a problem intensified by the limited explanatory value of traditional transcripts. In response, digital badges and micro-credentials have emerged as tools for documenting verifiable competencies. However, the existing evidence base remains fragmented and methodologically constrained. To address this, this narrative review synthesizes recent empirical research on the integration, utility, and impact of digital credentials. A comprehensive search of EBSCOhost, PubMed, and Google Scholar was conducted, capturing literature published between 2017 and 2025. Following a structured screening protocol, 14 peer-reviewed empirical studies were selected and evaluated using a hybrid inductive and deductive thematic analysis. The synthesized findings demonstrate that well-designed badge systems extend beyond gamified incentives to function as sophisticated pedagogical tools supporting self-regulated learning and professional identity formation. Key results indicate a complex duality in learner motivation, where initial extrinsic engagement must transition into perceived intrinsic value for sustained persistence. Additionally, while stackable frameworks provide transparent alternatives to traditional grading, poorly integrated systems risk inducing cognitive overload and grade anxiety without active instructor mediation. Effective implementation requires balancing standardized labor market recognition with contextual flexibility through sustained collaboration between higher education institutions and industry stakeholders. The review concludes by highlighting the critical need for objective longitudinal and cross-cultural research to determine long-term pedagogical impacts and promote equitable educational outcomes.

ABSTRACT: Artificial intelligence (AI) is rapidly reshaping school leadership within Education 4.0, offering enhanced decision-making and organisational efficiency while intensifying ethical concerns regarding transparency, bias, and accountability. Existing research has largely treated these opportunities and risks as separate phenomena, overlooking the relational processes through which AI is enacted in practice. This paper advances a process-based conceptualisation by positioning trust as the central mediating mechanism in AI-enabled school leadership. It argues that AI does not produce outcomes directly; rather, its effects are contingent on how it is accepted, interpreted, and enacted within school contexts. The proposed framework shows that trust shapes whether AI leads to constructive outcomes, including ethical use, professional engagement, and improvement, or to disruptive consequences such as resistance and mistrust. Leadership is conceptualised as a key antecedent of trust, highlighting the centrality of relational governance in the effective and responsible integration of AI in schools.

ABSTRACT: As higher education faces the realities of technological advancements, institutions face the challenge of fostering high-level cognitive competencies while maintaining scalability. This paper proposes a holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning. While traditional IBL is pedagogical and resource-intensive, the proposed model utilizes AI as a scaffolding layer, transitioning its use from an output generator to a metacognitive coach. Utilizing a conceptual framework methodology, the study maps the approaches between the stages of inquiry and AI interactions. In addition, the paper explores the institutional implications for teacher training and technological governance, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills. This contribution provides a cohesive foundation for administrators and educators seeking to implement active pedagogies in this era of AI.

ABSTRACT: This study proposes an integrated, evidence-based model to explain the transition from artificial intelligence (AI)-related anxiety to acceptance and learning engagement in the context of Education 4.0. Drawing on a secondary data synthesis of four nationally representative South Korean datasets—KISTEP (2024), NIA (2024), KEEP (2024), and Statistics Korea (2024)—and grounded in Keller’s (1987) ARCS motivational model and Davis’s (1989) Technology Acceptance Model (TAM), the study develops a multi-layered framework linking AI anxiety, AI literacy, AI acceptance, and learning engagement. The analysis tests four hypotheses using a pseudo-SEM approach. Because the study relies on secondary data, proxy indicators are employed to operationalize key constructs, with the Digital Competency Index (DCI) used as a proxy for AI literacy. The findings show that AI anxiety is significantly and negatively associated with AI acceptance, whereas AI literacy exerts a direct positive effect and also weakens the anxiety-acceptance relationship, functioning as a cognitive buffer (moderating effect, β = .23, p < .05). In turn, AI acceptance emerges as a strong predictor of learning engagement (R² = .34). Most notably, this study reframes AI literacy not merely as a technical skill, but as a resilience mechanism that helps reduce technology-related anxiety. The theoretical contribution has direct implications for instructional design and age-sensitive policy interventions. In addition, the pseudo-SEM approach offers a replicable framework for building macro-level theory from national administrative data.