Keyword: South Korea

ABSTRACT: This study examines whether Big Five personality traits are associated with Korean adults' participation in non-formal adult education and their self-directed behavior at work. Personality has long been linked to learning and performance outcomes, yet its role in adult learning participation has rarely been examined with nationally representative, internationally comparable data, and almost never in the Korean context. Using the Korean public-use file from the second cycle of the OECD Survey of Adult Skills (OECD, 2024; N = 6,198), which for the first time included a short-form Big Five measure (BFI-2-XS), this study estimated weighted logistic and linear regression models with Fay's balanced-repeated-replication variance estimation. Openness to experience and emotional stability were positively associated with participation in non-formal education in the preceding twelve months (N = 5,766), net of education, employment status, parental education, age, and gender. Conscientiousness showed a positive association that approached but did not reach conventional statistical significance (p = .053). Among currently employed respondents (N = 4,268), conscientiousness, openness, and extraversion were each positively and significantly associated with self-directed work behavior, defined as the frequency of planning one's own activities and organizing one's own time. These findings extend dispositional theories of adult learning to the Korean context and suggest that human resource development interventions may benefit from attending to individual differences in personality alongside the structural and institutional barriers that have dominated prior explanations of Korea's comparatively low adult learning participation.

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.