https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/342 Sat, 04 Apr 2026 17:38:10 GMT 2026-04-04T17:38:10Z https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126597 Title: Development and validation of Generative AI Competence Scale (GenAIComp) among university students Authors: Lee, Seul Chan; Baby, Tiju; Vongvit, Rattawut; Lee, Jieun; Kim, Young Woo; Cha, Min Chul; Yoon, Sol Hee Abstract: The rapid development of Generative Artificial Intelligence (Generative AI) across several sectors underscores the need for a systematic tool to evaluate AI competence. Current digital literacy frameworks lack AI-specific competencies, resulting in inconsistencies in the assessment of AI competence. This study aims to establish a standardized assessment framework for Generative AI competence by identifying key skill factors and empirically validating a structured evaluation tool called the Generative AI Competence Scale (GenAIComp). The proposed GenAIComp has five essential factors: Information and Data Literacy, Communication and Collaboration, Digital Content Creation, Safety and Ethics, and Problem-Solving. A quantitative approach was employed, incorporating expert validation, pilot testing, and extensive empirical evaluation involving 1000 participants, principally university students. The factor analysis confirmed a robust 5-factor structure with strong psychometric properties. The final model demonstrated excellent fit indices, confirming its reliability and validity in assessing Generative AI competence across the five key factors. Research demonstrates that educational background considerably impacts AI competence, with individuals from technical disciplines showing a greater aptitude for problem-solving and content generation. Gender-based disparities were noted, with males achieving marginally higher scores in several factors, but with minimal effect sizes. Correlation analysis indicated that perceived AI expertise and frequency of AI utilization significantly influenced competence, especially in data literacy and problem-solving, and exhibited less correlation with ethical awareness. GenAIComp provides a reliable tool for assessing AI competence, helping educators, industry experts, and policymakers to design AI training programs and integrate AI literacy into curricula and thereby AI technology advancement in society. Future research should explore its applicability across cultures and include performance-based assessments to enhance AI competence. Sun, 01 Mar 2026 00:00:00 GMT https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/126597 2026-03-01T00:00:00Z https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/122039 Title: Ring-Opening Polymerization of Surface Ligands Enables Versatile Optical Patterning and Form Factor Flexibility in Quantum Dot Assemblies Authors: Lee, Yunseo; Shin, Jiyun; Shin, Seungki; Kim, Eun A.; Lee, Joon Yup; Gwak, Namyoung; Kim, Seongchan; Seo, Jaeyoung; Kong, Hyein; Yeo, Dongjoon; Na, Jina; Kim, Sungwon; Lee, Juho; Cho, Seong-Yong; Lee, Jaejun; Kim, Tae Ann; Oh, Nuri Abstract: The evolution of display technologies is rapidly transitioning from traditional screens to advanced augmented reality (AR)/virtual reality (VR) and wearable devices, where quantum dots (QDs) serve as crucial pure-color emitters. While solution processing efficiently forms QD solids, challenges emerge in subsequent stages, such as layer deposition, etching, and solvent immersion. These issues become especially pronounced when developing diverse form factors, necessitating innovative patterning methods that are both reversible and sustainable. Herein, a novel approach utilizing lipoic acid (LA) as a ligand is presented, featuring a carboxylic acid group for QD surface attachment and a reversible disulfide ring structure. Upon i-line UV exposure, the LA ligand initiates ring-opening polymerization (ROP), crosslinking the QDs and enhances their solvent resistance. This method enables precise full-color QD patterns with feature sizes as small as 3 mu m and pixel densities exceeding 3788 ppi. Additionally, it supports the fabrication of stretchable QD composites using LA-derived monomers. The reversible ROP process allows for flexibility, self-healing, and QD recovery, promoting sustainability and expanding QD applications for ultra-fine patterning and on-silicon displays. Sat, 01 Mar 2025 00:00:00 GMT https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/122039 2025-03-01T00:00:00Z https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/121416 Title: Insertion loss and polarization-dependent loss measurement improvement to enable parallel silicon photonics wafer-level testing Authors: Kim, Daehong; De Coster, Jeroen; Van Campenhout, Joris; Ban, Yoojin; Velenis, Dimitrios; Sar, Huseyin; Kobbi, Hakim; Magdziak, Rafal; Kim, Younghyun Abstract: We propose a measurement system that enables the rapid measurement of insertion loss and polarization-dependent loss using a parallel test setup with a fiber array, and the calibration procedure to be used within this system. By applying rough scan methods, we have developed a calibration algorithm that efficiently finds the accurately optimized state of polarization in minimal time. Through conducting on-wafer spectral optical power measurements, we compared conventional applications and our proposed algorithms. The results demonstrate that our method enables the almost simultaneous measurement of the spectral responses of multiple optical components. Moreover, the method enables to measure these responses with a well-defined input state of polarization (SOP) applied to each path individually. This novel approach holds promise for enhancing accuracy and cost-effectiveness in insertion loss and polarization-dependent loss measurements. © 2024 Elsevier Ltd Sat, 01 Mar 2025 00:00:00 GMT https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/121416 2025-03-01T00:00:00Z https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/121560 Title: Atomic Layer Deposition Strategies for Quantum Dot Displays: From Photolithographic Passivation Layers to Charge Transport Engineering Authors: 조성용 Wed, 04 Dec 2024 00:00:00 GMT https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/121560 2024-12-04T00:00:00Z