-- Joint activity describes when more than one agent (human or machine) contributes to the completion of a task or activity. Designing for joint activity focuses on explicitly supporting the interdependencies between agents necessary for effective coordination amon g agents engaged in the joint activity. This builds and expands upon designing for usability to further address how technologies can be designed to act as effective team players. Effective joint activity requires supporting, at minimum, five primary macroc ognitive functions within teams: Event Detection, Sensemaking, Adaptability, Perspective - Shifting, and Coordination. Supporting these functions is equally as important as making technologies usable. We synthesized fourteen heuristics from relevant literatu re including display design, human factors, cognitive systems engineering, cognitive psychology, and computer science to aid the design, development, and evaluation of technologies that support joint human - machine activity . Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) technologies have accelerated human - machine interactions progress ing from simple tool - based engagements to complex cognitive collaborations [1] . Machines are being designed to perform an increasing set of functions and are being expected to engage more deeply in the collaborative joint activit ies related to these functions. This shift in machine capabilities and expectations demands a corresponding re - evaluation and broadening of design and evaluation principles to support joint human - machine activity in ways that lie outside the boundaries of trad itional usability methods and models [2] . Traditional usability heuristics, such as those proposed by [3], provide a strong foundation focusing primarily on surface - level interactions such as enhancing the ease of use, efficiency, and satisfaction in human - machine interaction . These heuristics are primarily oriented towards actions and responses but offer limited support for the essential macrocognitive functions associated with effective teamwork including event detection, sensemaking, adaptability, perspective shifting, and co ordination, all of which are vital in the close collaboration of humans and machine s with joint activities [2], [4], [5], [6] . These heuristics are primarily oriented towards actions and responses but offer limited support for the essential macrocognitive functions associated with effective teamwork including event detection, sensemaking, adaptability, perspective shifting, and co ordination . A ll of these macrocognitive functions are vital in the close collaboration of humans and machines with joint activities in high - stakes and dynamic environments with little room for error [2], [5] . This reliance on macrocognitive functions is evident in domains where the ability to process complex information and adapt to changing conditions is crucial.

Today, products are no longer isolated artifacts, but nodes in networked systems. This means that traditional, linearly conceived life cycle models are reaching their limits: Interoperability across disciplines, variant and configuration management, traceability, and governance across organizational boundaries are becoming key factors. This collective contribution classifies the state of the art and proposes a practical frame of reference for SoS lifecycle management, model-based systems engineering (MBSE) as the semantic backbone, product lifecycle management (PLM) as the governance and configuration level, CAD-CAE as model-derived domains, and digital thread and digital twin as continuous feedback. Based on current literature and industry experience, mobility, healthcare, and the public sector, we identify four principles: (1) referenced architecture and data models, (2) end-to-end configuration sovereignty instead of tool silos, (3) curated models with clear review gates, and (4) measurable value contributions along time, quality, cost, and sustainability. A three-step roadmap shows the transition from product- to network- centric development: piloting with reference architecture, scaling across variant and supply chain spaces, organizational anchoring (roles, training, compliance). The results are increased change robustness, shorter throughput times, improved reuse, and informed sustainability decisions. This article is aimed at decision-makers and practitioners who want to make complexity manageable and design SoS value streams to be scalable.

Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.

This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is important to build, operate, and continuously improve dialogue systems correctly and efficiently. Accordingly, in addition to applying existing software engineering knowledge, it is becoming increasingly important to evolve software engineering tailored specifically to dialogue systems. In this paper, we enumerate the knowledge areas of dialogue systems engineering based on those of software engineering, as defined in the Software Engineering Body of Knowledge (SWEBOK) Version 4.0, and survey each area. Based on this survey, we identify unexplored topics in each area and discuss the future direction of dialogue systems engineering.

Engineering methodologies predominantly revolve around established principles of decomposition and recomposition. These principles involve partitioning inputs and outputs at the component level, ensuring that the properties of individual components are preserved upon composition. However, this view does not transfer well to intelligent systems, particularly when addressing the scaling of intelligence as a system property. Our prior research contends that the engineering of general intelligence necessitates a fresh set of overarching systems principles. As a result, we introduced the "core and periphery" principles, a novel conceptual framework rooted in abstract systems theory and the Law of Requisite Variety. In this paper, we assert that these abstract concepts hold practical significance. Through empirical evidence, we illustrate their applicability to both biological and artificial intelligence systems, bridging abstract theory with real-world implementations. Then, we expand on our previous theoretical framework by mathematically defining core-dominant vs periphery-dominant systems.

With the rapid development of more complex robots, Fault Detection and Diagnosis (FDD) becomes increasingly harder. Especially the need for predetermined models and historic data is problematic because they do not encompass the dynamic and fast-changing nature of such systems. To this end, we propose a concept that actively generates a dynamic system model at runtime and utilizes it to locate root causes. The goal is to be applicable to all kinds of robotic systems that share a similar software design. Additionally, it should exhibit minimal overhead and enhance independence from expert attention.

Multi-purpose Large Language Models (LLMs), a subset of generative Artificial Intelligence (AI), have recently made significant progress. While expectations for LLMs to assist systems engineering (SE) tasks are paramount; the interdisciplinary and complex nature of systems, along with the need to synthesize deep-domain knowledge and operational context, raise questions regarding the efficacy of LLMs to generate SE artifacts, particularly given that they are trained using data that is broadly available on the internet. To that end, we present results from an empirical exploration, where a human expert-generated SE artifact was taken as a benchmark, parsed, and fed into various LLMs through prompt engineering to generate segments of typical SE artifacts. This procedure was applied without any fine-tuning or calibration to document baseline LLM performance. We then adopted a two-fold mixed-methods approach to compare AI generated artifacts against the benchmark. First, we quantitatively compare the artifacts using natural language processing algorithms and find that when prompted carefully, the state-of-the-art algorithms cannot differentiate AI-generated artifacts from the human-expert benchmark. Second, we conduct a qualitative deep dive to investigate how they differ in terms of quality. We document that while the two-material appear very similar, AI generated artifacts exhibit serious failure modes that could be difficult to detect. We characterize these as: premature requirements definition, unsubstantiated numerical estimates, and propensity to overspecify. We contend that this study tells a cautionary tale about why the SE community must be more cautious adopting AI suggested feedback, at least when generated by multi-purpose LLMs.

Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-purpose Large Language Models (LLM) in supporting ME problem formulation tasks, specifically focusing on stakeholder identification. We identify a relevant reference problem, a NASA space mission design challenge, and document ChatGPT-3.5's ability to perform stakeholder identification tasks. We execute multiple parallel attempts and qualitatively evaluate LLM outputs, focusing on both their quality and variability. Our findings portray a nuanced picture. We find that the LLM performs well in identifying human-focused stakeholders but poorly in recognizing external systems and environmental factors, despite explicit efforts to account for these. Additionally, LLMs struggle with preserving the desired level of abstraction and exhibit a tendency to produce solution specific outputs that are inappropriate for problem formulation. More importantly, we document great variability among parallel threads, highlighting that LLM outputs should be used with caution, ideally by adopting a stochastic view of their abilities. Overall, our findings suggest that, while ChatGPT could reduce some expert workload, its lack of consistency and domain understanding may limit its reliability for problem formulation tasks.

As modern system of systems (SoS) become increasingly adaptive and human-centred, traditional architectures often struggle to support interoperability, reconfigurability, and effective human-system interaction. This paper addresses these challenges by advancing the stateof-the-art holonic architecture for SoS, offering two main contributions to support these adaptive needs. First, we propose a layered architecture for holons, which includes reasoning, communication, and capabilities layers. This design facilitates seamless interoperability among heterogeneous constituent systems by improving data exchange and integration. Second, inspired by principles of intelligent manufacturing, we introduce specialised holons-namely, supervisor, planner, task, and resource holons-aimed at enhancing the adaptability and reconfigurability of SoS. These specialised holons utilise large language models within their reasoning layers to support decision-making and ensure real-time adaptability. We demonstrate our approach through a 3D mobility case study focused on smart city transportation, showcasing its potential for managing complex, multimodal SoS environments. Additionally, we propose evaluation methods to assess the architecture's efficiency and scalability, laying the groundwork for future empirical validations through simulations and real-world implementations.

Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, the Systems Engineering approach is better equipped to facilitate the adoption of LLMs by prioritising the problems and their context before any other aspects. This paper introduces the challenges LLMs generate and surveys systems research efforts for engineering AI-based systems. We reveal how the systems engineering principles have supported addressing similar issues to the ones LLMs pose and discuss our findings to provide future directions for adopting LLMs.