A digital twin is a virtual model of a process, product, or service, which is one of the key technologies in the fourth industry. The pairing of the virtual and physical world allows analysis of data and monitoring of systems to head off problems before they occur. This paper presents a digital twin architecture and a system based on an interoperable data model. It explains how to build a digital twin for the integrated control monitoring using edge devices, data analytics, and realistic 3D visualization. The system allows continuous collaboration between field engineers for data gathering, designers for modeling 3D models, and layout engineers for layout changing by generating 3D digital twin models automatically. The system helps stakeholders focus on their respective roles to build digital twins. Examples applied to the Korean automotive parts makers are also introduced in this paper. The system can be easily used by small and medium-sized enterprises (SMEs) as well as large companies. Beyond simply watching the production site with CCTV, the production site can be intuitively managed based on the digital twin.

Metaverse is a compound word of “Meta” and “Universe”, meaning a world that transcends reality, a new virtual world. Due to the COVID-19 pandemic, non-face-to-face further accelerated the activation of the Metaverse. The Metaverse has the attractiveness of arousing user interest, high service scalability, and the potential to combine multiple revenue models. Therefore, many industries are adopting the Metaverse. The Republic of Korea has been on a ceasefire, since 1953, after the Korean War. After the war, the DMZ was decided in an area of 2 km from north to south (total of 4 km), centered on the Military Demarcation Line (MDL). The DMZ area is the largest nature conservation area in Asia and a military area where public access is strictly restricted. The Ministry of Unification is trying to reduce tensions and establish peace in the DMZ area by raising the interest of Korean people, as well as people around the world, through the DMZ Metaverse application. The Korean government has its own e-Government framework and cloud operating environments. In order to adopt the Metaverse to Korean government agencies and operate them sustainably, it is essential to support the e-Government framework and the cloud environments. In this paper, the design features, architecture, models, and functions of OnTwins, a Metaverse platform serviceable in the Korean government IT operating environment, are elaborated in detail. The DMZ Metaverse public service built on the platform will also be introduced. Finally, after comparing it with other Metaverse platforms, future research directions are discussed.

Despite the rapid growth of AI-based digital language learning platforms, prevailing approaches to vocabulary instruction have remained relatively unchanged. Flashcards, translation drills, and isolated quizzes continue to dominate, and while they may support short-term retention, they tend to strip away the contextual, physical, and spatial dimensions that research identifies as important for durable language acquisition. This paper proposes spatial experiential vocabulary learning as a pedagogically grounded alternative. Drawing on situated learning theory, embodied cognition, and experiential learning theory, it argues that lasting vocabulary acquisition may depend not only on repeated exposure but also on learners engaging actively with language in meaningful, spatially situated contexts. The central theoretical contribution is the EARR framework, a four-phase learning cycle comprising Encounter, Act, Respond, and Reuse, in which vocabulary is embedded within object-rich spatial environments that invite purposeful interaction rather than passive recognition. The paper develops this framework theoretically and draws out its implications for platform design. It concludes by identifying priorities for future empirical research and by considering how digital language learning environments might be redesigned to better support the conditions under which vocabulary acquisition appears to occur.

Large language model (LLM)-based AI agents combine reasoning and planning, memory, external knowledge use, tool calling, and multi-agent collaboration to perform complex tasks. However, they face planning failures, context-maintenance limitations, collaboration instability, vulnerabilities arising from external interactions, bias, unclear attribution of responsibility, and difficulties in oversight and incident tracing. This narrative literature review examined the technological evolution and technical limitations of AI agents and developed a conceptual mapping relating these limitations to selected issues of bias and governance. Selected early theoretical studies were included; the main focus was post-2022 scholarly literature and official institutional and corporate materials through July 2026, with publication-status and evidence updates during revision in September 2026. Sources were categorized into concepts and trends, single- and multi-agent architectures, technical limitations, and socio-ethical issues; limitations were compared using a problem addressed–mitigation approach–residual limitation framework. The analysis suggests that autonomous actions performed on behalf of users, ambiguity in the scope of authority, multi-agent interactions, external knowledge use, observation of external environments, and tool calling may be linked to unclear attribution of responsibility, bias propagation and amplification, and difficulties in oversight and incident tracing. Potential mediators include multiple actors, miscoordination and conformity, inherited external-source biases, and new attack surfaces. This conceptual mapping provides a basis for future research on trustworthy AI-agent design, operation, and governance.