JuHyeong Ryu

Generative artificial intelligence (AI) has rapidly entered higher education classrooms, and AI literacy is now widely treated as a core competency for university learners. Yet university-produced and openly licensed AI literacy materials remain scarce, uneven across regions and disciplines, and often tied to a single commercial tool. This study addresses the supply problem through literature-informed rubric development. A narrative synthesis of a purposive corpus drawn from AI literacy frameworks, AI literacy pedagogy, open educational resource (OER)-enabled pedagogy, and recent AI-in-OER guidance was conducted to derive a seven-principle rubric for AI literacy OER design and review. Each principle pairs evidence with a concrete design and review cue: what an OER author can build and what a reviewer can inspect. The synthesis also identified five recurring topic domains that organize AI literacy OER across courses and institutions: tool-agnostic prompting, output evaluation, bias awareness, accessibility, and citation and integrity. The study contributes a rubric, a topic domain structure, and design and review cues that operationalize the rubric. It does not report empirical effectiveness data but is offered as a foundation for future learner outcome studies and independent rubric reapplication.

Purpose: Industry 4.0 transformed the manufacturing industry through automation, robotics, IoT, big data analytics and digital systems, and with it changed the workforce requirements, creating a critical skills gap between available capabilities and emerging technological demands. Design/methodology/approach: This study systematically analyzed 913 manufacturing job postings from LinkedIn using natural language processing (NLP) and unsupervised machine learning techniques (K-means clustering) to identify 269 distinct skills through thematic analysis, which clustered into four dimensions: technical and soft skills, domain knowledge, physical demands and workplace conditions. Findings: Key findings indicate that employer-stated requirements in LinkedIn job postings reflect demand for hybrid workforce profiles where traditional manufacturing competencies coexist with emerging digital requirements rather than being replaced. Communication skills and maintenance expertise dominate requirements, while substantial physical demands and workplace conditions persist despite technological advancement. Practical implications: For practitioners, the proposed competency framework offers actionable guidance on hiring practices, curriculum development, and workforce development policies that address both cognitive and physical aspects of Industry 4.0 manufacturing employment. By bridging theory and application, the findings indicate that manufacturing technology management can support workforce preparation by aligning traditional technical expertise with digital literacy, collaborative capabilities and physical competencies to enable technology adoption and operational competitiveness. Originality/value: This research contributes a scalable, data-driven methodology for real-time workforce intelligence, providing empirical and practical insight for manufacturing managers, human resource strategists and educators, while challenging technology-focused assumptions about manufacturing task transformation.

Objective: Fluoroscopic interventional procedures subject healthcare personnel to significant ionizing radiation exposure, necessitating the use of protective aprons. Protective aprons are made of varying materials and may be subjected to inadequate testing standards, leading to potential inaccuracies estimating effective dose when using single dosimeter methods. This study aims to develop an appropriate method to estimate effective dose for non-lead aprons using a single dosimeter worn outside the apron, thereby enhancing radiation protection and reducing musculoskeletal injuries to workers. Approach: A retrospective, secondary analysis of existing literature was conducted to derive a dataset suitable for algorithm development. Transmission of radiation through the protective apron at various thicknesses was modeled using exponential regression. The resulting equation incorporates the nominal lead equivalent thickness to estimate effective dose. Validation was performed by comparing algorithms using two-dosimeters, which do not rely on apron shielding assumptions. Main Results: The dataset comprised 205 dosimeter readings from 42 physicians across 8,243 procedures (Fetterly et al.). The derived equation, E=H_0 (0.13+1.02e^((-11.33x) ) ), demonstrated minimal underestimation, and a potential overestimation of up to 1.3-fold relative to the true effective dose, improving upon previous methods by 15%. Inclusion of a non-lead thyroid collar reduced effective dose by 17-30%. Use of accessory upper body shielding is required when using this method. Significance: This novel method provides a method to estimate the effective dose using non-lead aprons at varying nominal lead equivalent thicknesses, surpassing previous single dosimeter approaches. The findings support improved optimization of radiation protection during fluoroscopic interventional procedures, may inform radiation safety regulations, and support ergonomic improvements.

Background: In many countries, including Canada, employers have a legal obligation to provide training programs to the new workers to reduce the risk of musculoskeletal disorders (MSDs). However, current safety and health training modalities, including those specific to ergonomic and MSD prevention, have shown limited success in promoting safe motions patterns. As workers gain more experience, they develop the knowledge and skills necessary to consistently demonstrate safer and more productive performance in tasks within their areas of expertise compared to novices and apprentices. Training apprentices using expert work strategies is a potential intervention that can reduce MSD risk while balancing productivity needs. By understanding the perspectives of experts in the field, we investigate the specific needs of masonry workers and their employers to improve masons’ safety and health. Methods: This study conducted qualitative user interviews with eight masonry instructors with more than 20 years of experiences from the Ontario Masonry Training Centre. The eight instructors had an average of 23.9 years of experience as masons with a range between 10- and 43-years. As instructors, they had an average of 6.9 years’ experience with a range between 1.5- and 18-years. Results: Thematic analysis using template methodology was carried out on the data collected and identified six key themes: knowledge of muscle injury risks and prevention, safety in masonry, physical demands and MSD risk, the impact of physical demands, safety culture and attitudes, and the role of safety in apprentice training. The instructors’ exposure to high physical demands within masonry was a major theme during the interviews. Instructors discussed the high forces, repetition and awkward postures which take a toll on their bodies. Another large theme was about the safety culture and attitudes within the trade. Younger apprentices often think themselves invincible and show less concern towards musculoskeletal safety, whereas the older masons are more concerned. Conclusion: The findings highlight the need for apprenticeship training programs to include modules on safe lifting practices, ergonomic awareness, and long-term injury prevention. They also emphasize the importance of mentorship from experienced masons, structured rehabilitation support after injuries, and connecting ergonomic practices to productivity outcomes. Instructors’ perspectives provide valuable context to guide the development of ergonomic training systems that are both relevant to masonry work and tailored to the needs of apprentices and their employers.