change management

New software engineers often struggle to comprehend large legacy systems, leading to prolonged onboarding periods. Method: This study introduces a systems-thinking training program grounded in Labelled Transition System (LTS) modeling and a structured understanding template—the first application of LTS modeling in software engineering onboarding education—featuring differentiated learning pathways across five sessions, integrating pedagogical best practices and pre-/post-assessment design. Contribution/Results: While overall comprehension gains were not statistically significant, learners with low initial proficiency showed a robust 15-percentage-point improvement (p < 0.05). Qualitative feedback indicated high engagement and perceived practical relevance. The framework offers a scalable, low-cost, reusable instructional model for cultivating software comprehension skills, addressing a critical gap in industry onboarding programs by introducing formal modeling techniques into foundational training.

Traditional DevOps and MLOps struggle to ensure runtime reliability of Agentic AI systems in non-deterministic, continuously evolving environments. This work proposes a novel operations paradigm tailored to the full lifecycle of Agentic AI, centered on the pioneering CHANGE framework—encompassing six core capabilities: Contextualize, Harmonize, Anticipate, Negotiate, Generate, and Evolve—to enable dynamic co-adaptation among agents, infrastructure, and human oversight. The AgentOps platform, built upon this framework, demonstrates its effectiveness in a customer service scenario by reliably supporting the continuous evolution of Agentic AI systems. This approach establishes an innovative architectural foundation for operating non-deterministic agent-based systems, addressing critical gaps in current AI运维 practices.

Digital Engineering (DE) transformation confronts complex, interdependent socio-technical barriers, yet existing research lacks a systematic understanding of their typologies, root causes, and alignment with U.S. Department of Defense (DoD) policy objectives. To address this gap, this study develops a novel six-dimensional socio-technical barrier taxonomy, uniquely integrating socio-technical systems theory into the DE transformation analytical framework and revealing cross-dimensional cascading effects among barriers. Leveraging a synthesis of literature review, theoretical modeling, and systems engineering principles, the study identifies critical risk nodes impeding policy implementation. The resulting operational risk diagnostic tool enables practitioners to precisely pinpoint bottlenecks, optimize strategic investment priorities, and refine change management pathways—thereby enhancing policy alignment and execution efficacy of DE transformation initiatives.

This study investigates how individuals’ dynamic perceptions of AI limitations influence organizational readiness for AI adoption. Method: Drawing on semi-structured interview data, the research applies the Gioia methodology and integrates organizational behavior theory with technology adoption frameworks. Contribution/Results: It advances the novel proposition that organizational AI readiness is a continuously evolving, cyclical learning process—where micro-level cognitive appraisals are translated into macro-level, sustainable adoption through two parallel pathways: (1) community-based support (e.g., peer networks, internal AI advocates) and (2) institutionalization mechanisms (e.g., formal policies, integration protocols). Findings indicate that experiential learning, social learning, and structured integration jointly recalibrate stakeholder expectations and strengthen trust in AI. Organizations that successfully institutionalize individual insights achieve more robust, scalable AI implementation. The study identifies a critical mediating mechanism—linking individual sensemaking to collective capability development—and offers actionable implications for designing adaptive, human-centered AI governance.

This study examines how Enterprise Architecture (EA) can be localized within Vietnamese government agencies operating under weak institutional foundations to support digital transformation. Addressing EA’s conceptual ambiguity and poor contextual fit, we propose a dual translation mechanism: “theoretical translation”—abstracting indigenous practices into generalizable concepts—and “contextual translation”—deconstructing EA into actionable, organizationally prioritized interventions. Drawing on a 15-year longitudinal case study and integrating mechanism-based analysis with sensemaking theory, we identify critical diffusion pathways for EA in institutionally immature environments. Our findings extend EA theory’s applicability to digital governance in developing countries and yield a reusable conceptual translation framework. This framework offers methodological guidance for digital capacity building across the Global South, bridging theory-practice gaps in public-sector digital transformation.

The widespread adoption of artificial intelligence is blurring organizational role boundaries and eroding “invisible work”—such as mentoring and feedback—that underpins professional development and cultural health. Through semi-structured interviews with 24 product professionals in technology firms and subsequent thematic analysis, this study systematically uncovers AI’s dual impact: while enhancing peer-level collaboration, it simultaneously weakens traditional mechanisms of career support. To address these tensions, the research introduces a strategic framework that renders invisible work visible and offers actionable interventions for organizations, leaders, and individuals. These measures aim to preserve cultural sustainability without compromising operational efficiency in AI-integrated workplaces.

Current LLM-assisted programming education systems lack a unified conceptualization of their assistance boundaries, implementation approaches, and control mechanisms, hindering education-oriented comparative analysis. Addressing this gap, this study conducts a scoping review and qualitative content analysis of 90 relevant systems, applying thematic coding to develop an innovative three-dimensional governance framework—PEA (Policy, Enforcement, Authority)—alongside a corresponding governance codebook. The framework reveals pervasive issues in existing systems, notably excessive centralization of authority and insufficient configurability. This work provides the first systematic mapping of governance design patterns in LLM-based programming support tools, establishing a theoretical foundation and a structured design vocabulary for developing next-generation educational tools that are goal-aligned, configurable, and accountable.

In the era of large language models, traditional record-centric data engineering struggles to meet the demand for organizational knowledge as executable infrastructure. This work proposes a novel paradigm—knowledge architecture—that systematically reimagines core data engineering mechanisms by upgrading ETL, data lineage, and catalogs into knowledge ingestion, change detection, provenance, and knowledge catalogs. It introduces knowledge views and a three-tier layered model (raw–refined–operational) to structure knowledge effectively. By integrating emerging standards such as LLM Wiki and Open Knowledge Format (OKF), this study formally defines knowledge architecture for the first time and establishes a theoretical framework that supports knowledge representation, governance, and operational delivery, enabling direct invocation of organizational knowledge by humans, agents, workflows, and models alike.

Generative AI, as an “arrival technology,” has entered classrooms before robust pedagogical evidence has matured, thereby challenging conventional models of STEM higher education reform that rely on stable empirical foundations. This study proposes a new institutional change framework tailored for the AI era, reconfiguring the logic of educational transformation across six dimensions—three technological (evidence base, pace of change, scope of application) and three human (faculty, change agents, students). The framework emphasizes humble, context-sensitive exploration; pedagogy-centered design; reconceptualizing change agents as facilitators of collective inquiry; and positioning students as partners in reform. Drawing on theories of educational change, analyses of generative AI applications, and illustrative case studies, the work constructs and validates an adaptive, collaborative, and dynamically evolving model that offers the first systematic response to the demands of transforming higher education within highly uncertain technological environments.

This study addresses the unclear adaptation patterns and impacts of large language model (LLM) agent skills when reused in downstream applications. Through an empirical analysis of 1,126 adaptation instances from six prominent skill repositories, the work systematically characterizes LLM skill adaptation behaviors and constructs a taxonomy comprising 46 patterns grouped into 13 families. The research uncovers critical phenomena including a “reuse paradox,” strong cross-component dependencies, and the introduction of security-sensitive content in nearly one-fifth of adaptations. It further identifies prevalent challenges such as logic rewriting, fixing discoverability issues, and cross-tool or cross-language translation. These findings offer new empirical insights and foundational support for improving skill design, standardizing interfaces, and enabling automated adaptation of LLM-based agents.