Rapid digital transformation demands educational approaches that effectively equip students with competencies crucial for addressing real-world sustainability challenges. This study introduces and evaluates a Living Lab-based collaborative problem-solving educational model explicitly designed to enhance collaborative problem-solving (CPS), computational thinking (CT), and collaborative communication (CC) within elementary informatics education. Aligned with South Korea’s 2022 revised curriculum, this quasi-experimental research involved 196 elementary students, divided into experimental and control groups. Both groups participated in pre- and post-tests measuring CPS, CC, and CT competencies. The experimental group actively engaged in structured, community-based tasks integrating informatics concepts with authentic, real-world problems, whereas the control group experienced traditional instruction methods. Statistical analysis demonstrated significant improvements in the experimental group’s CPS and CT competencies (e.g., applying problem-solving strategies increased from 3.44 to 3.93, p < 0.001; ICT usage from 3.40 to 3.82, p = 0.002). However, advancements in CC were comparatively modest (creative communication increased from 3.31 to 3.81, p = 0.006), highlighting the necessity for explicit and structured communication interventions within collaborative frameworks. This study confirms the effectiveness of the Living Lab-based collaborative problem-solving educational model in cultivating comprehensive competencies essential for sustainable development, while underscoring the need for further integration of targeted communication strategies to maximize educational impact. Future implementations should prioritize structured communication training to fully leverage the model’s interdisciplinary potential.

Artificial intelligence (AI) increasingly confronts children with ethical situations, yet AI ethics education has emphasized awareness, and no instrument measures children’s capacity to identify and prioritise ethically relevant considerations within AI dilemmas. Grounded in Rest’s conception of moral sensitivity, this study reports the development and preliminary validation of an AI Ethics Sensitivity Scale (AIESS) for elementary students. The 24-item instrument, a structured self-report measure using a difference-score format across three dilemmas, was content-validated by experts and administered to 147 fifth-graders, 134 of whom were matched at a second administration. The one-factor model was the only specification that yielded an admissible solution, indicating that AI ethics sensitivity is most defensibly represented as a single dimension rather than four separable sub-elements, which were strongly intercorrelated (r = .51–.64). The total scale was reliable (McDonald’s ω = .81; CR = .83), although average variance extracted was modest (AVE = .31). Longitudinal confirmatory factor analysis showed suboptimal configural fit (CFI = .799); after three loadings and one intercept were freed, partial-metric and partial-scalar models met the prespecified change-in-fit criteria, although absolute fit remained suboptimal. Scores increased following construct-aligned instruction using scenarios distinct from the assessment dilemmas (d_z = 1.06), but this increase is reported descriptively because the single-group design precludes causal claims. These preliminary results position the AIESS as a content-valid, AI-situated measure of structured ethical prioritisation; replication in larger, multi-site samples is a priority.

Rapid digital transformation demands educational approaches that effectively equip students with competencies crucial for addressing real-world sustainability challenges. This study introduces and evaluates a Living Lab-based educational model explicitly designed to enhance collaborative problem-solving (CPS), computational thinking (CT), and collaborative communication (CC) within elementary informatics education. Aligned with South Korea’s 2022 revised curriculum, this quasi-experimental research involved 196 elementary students actively engaged in structured, community-based tasks that integrated informatics concepts with authentic, real-world problems. Findings demonstrated significant improvements in CPS and CT competencies, including problem comprehension, algorithmic reasoning, abstraction, and ICT usage. However, advancements in CC were comparatively modest, highlighting the necessity for explicit and structured communication interventions within collaborative frameworks. The study confirms the effectiveness of the Living Lab-based educational model in cultivating comprehensive competencies essential for sustainable development, while underscoring the need for further integration of targeted communication strategies to maximize educational impact. Future implementations should prioritize structured communication training to fully leverage the model's interdisciplinary potential.

The integration of generative AI into education raises questions about how AI literacy should be organized, particularly as competencies may operate across different functions during human–AI interaction. This study examined the content and structural organization of AI literacy through a focused literature synthesis and a two-round Delphi study with 11 experts. An initial framework comprising three domains—AI Understanding, AI Application, and AI Integration—and nine competencies was developed and iteratively evaluated. Experts generally evaluated the competency content favorably, while their judgments showed greater heterogeneity regarding some aspects of structural representation. In Round 2, experts supported non-exclusive domain relationships (M = 4.45, SD = 0.52) and primary and secondary domain associations (M = 4.73, SD = 0.47), whereas the proposed representation of competency placement and cross-domain applicability showed greater dispersion (M = 3.73, SD = 1.19, IQR = 1.50). This descriptive pattern informed a theoretical interpretation of the three domains as functional rather than mutually exclusive categories. The resulting framework provides a basis for considering how AI literacy competencies may be enacted during human–AI interaction and related to curriculum, pedagogy, assessment, teacher roles, and AI technologies in generative AI-supported educational systems.