Keyword: AI literacy

ABSTRACT: Artificial intelligence (AI), particularly generative AI, changes assessment by altering the evidential relationship among student work, learning, and professional judgment. Teachers must design tasks when students can readily obtain machine-generated responses, interpret automated feedback and analytics whose validity is conditional, and justify consequential decisions under uncertainty. This critical integrative review synthesizes scholarship on AI in education, assessment, teacher learning, and emerging professional-development interventions. It proposes Assessment-AI Knowledge (A-AIK) as an assessment-specific configuration of professional knowledge rather than a replacement for assessment literacy, AI literacy, or technological pedagogical content knowledge. A-AIK comprises three interdependent capacities: design competence, which establishes conditions for eliciting meaningful evidence; interpretive competence, which calibrates the meaning, uncertainty, and evidential weight of AI-mediated information; and justificatory competence, which supports transparent, fair, and reviewable decisions. Disciplinary authenticity, human agency, equity, transparency and data governance, and epistemic humility operate as cross-cutting commitments. The reviewed literature suggests that promising interventions use scenarios, authentic assessment redesign, structured critique, peer deliberation, and iterative classroom trials. Recurrent limitations include small samples, reliance on self-report, short duration, and measures of confidence or acceptance rather than demonstrated assessment performance. A four-phase professional-learning model—calibrate, redesign, enact, and justify, is proposed as a synthesis-informed design hypothesis requiring empirical evaluation. The article concludes with provisional performance indicators, institutional conditions, and a research agenda for responsible AI-mediated assessment.

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.

ABSTRACT: Understanding mental health literacy is essential in equipping students with the knowledge and confidence to manage mental health concerns effectively. A structured survey-based approach was implemented to examine key dimensions of mental health literacy among secondary and post-secondary students in Bukidnon, Philippines. The study involved 3,397 legally aged students from secondary and post-secondary institutions, with most participants enrolled in undergraduate programs. Using validated measurement procedures, the findings confirm that mental health literacy is shaped by awareness of resources, help-seeking behavior, perceived stigma, self-efficacy in managing mental health, and cultural influences. Each dimension demonstrated stable measurement properties and meaningful relationships within the overall model. The developed questionnaire provides a reliable and multidimensional tool for assessing literacy patterns within academic settings. By identifying strengths and gaps across these domains, the instrument offers a structured basis for designing interventions that strengthen awareness, reduce stigma, promote help-seeking, and support student well-being within similar educational contexts.