Engineering education is an evergreen challenge. It is supposed to follow the scientific progression, aggregation of knowledge, development of technologies, industrial demands, social trends, personal interests, affordances of computerization, evolution of educational practices, and so forth. From time to time, it must renew itself to comply with the changing situations, growing complexities, and quality expectations. The presented work was driven by the conjecture that next-generation engineering education (NG-EE) cannot be designed and implemented without understanding it as a holistic problematics. Therefore, this article attempts to consider the whole of engineering education and make propositions concerning its probable future based on a survey of the current literature and the author's long-term experiences. It is structured according to five fundamental questions: (i) Why is innovation in engineering education a challenging problematics (again)?; (ii) What are the currently typical approaches to engineering education?; (iii) What can be utilized as enablers for NG-EE?; (iv) What can we expect from the offerings of generative artificial intelligence tools?; and (v) What sort of new mind-set is needed for NG-EE? The main findings of the literature survey are discussed in detail, and propositional answers are formulated to these questions. It is advocated that NG-EE (i) is becoming increasingly transdisciplinary, (ii) needs novel conceptual models, methodological frameworks, and management scenarios, (iii) should impose a holistic rather than a reductionist view on systems, (iv) should consider increased diversification of engineering jobs, (v) should equip with the competencies of autonomous learning, and (vi) should offer a constructive but critical attitude to using artificial intelligence technologies.

System knowledge and reasoning mechanisms are essential means for intellectualization of cyber-physical systems (CPSs). As enablers of system intelligence, they make such systems able to solve application problems and to maintain their efficient operation. Normally, system intelligence has a human-created initial part and a system-produced (extending) part, called synthetic system intelligence (SSI). This position paper claims that SSI can be converted to a new industrial asset and utilized as such. Unfortunately, no overall theory of SSI exists and its conceptual framework, management strategy, and computational methodologies are still in a premature stage. This is the main reason why no significant progress has been achieved in this field, contrary to the latent potentials. This paper intends to contribute to: (i) understanding the nature and fundamentals of SSI, (ii) systematizing the elicitation and transfer of SSI, (iii) exploration of analogical approaches to utilization of SSI, and (iv) road-mapping and scenario development for the exploitation of SSI as an industrial asset. First, the state of the art is surveyed and the major findings are presented. Then, four families of analogical approaches to SSI transfer are analyzed. These are: (i) knowledge transfer based on repositories, (ii) transfer among agents, (iii) transfer of learning resources, and (iv) transfer by emerging approaches. A procedural framework is proposed that identifies the generic functionalities needed for a quasi-autonomous handling of SSI as an industrial asset. The last section casts light on some important open issues and necessary follow-up research and development activities.

We live in an age in which new things are emerging faster than their deep understanding. This statement, in particular, applies to doing research and educating university students concerning next-generation cyber-physical systems (NG-CPSs). The fast evolution of this system paradigm would have expected a rapid and comprehensive paradigmatic change in research and education concerning this family of systems. However, this has not happened yet. Seeking a sufficient explanation, this paper reviews the current literature and attempts to cast light on the most significant recent developments in the field of NG-CPSs. The main assumptions of the authors are that research and education should appear in harmony in academic knowledge acquisition and distribution processes and that the academic education of NG-CPSs should be organized and conducted according to a defendable future vision. Combining the results of a broadly based study of the literature with prognostic critical thinking and personal experiences, this review-based position paper first discusses the current sociotechno-scientific environment, the involved stakeholders, and the demands and two approaches of truly systems-oriented education. Then, it concentrates on (i) the recognized limitations of mono- and interdisciplinary research, (ii) supradisciplinary organization of research, and (iii) transdisciplinary knowledge generation for NG-CPSs. As main contributions, the paper (i) identifies and analyzes the latest theoretical, engineering, and technological developments, (ii) reveals the major trends and their presumably significant implications, and (iii) presents several thought-provoking findings and makes propositions about the desirable actions.

Application-specific reasoning mechanisms (ASRMs) development is a rapidly growing domain of systems engineering. A demonstrative implementation of an active recommender system (ARS) was realized to support designing ASRMs and to circumvent procedural obstacles by providing context-sensitive recommendations. The specific problem for the research presented in this paper was the development of a synthetic validation agent (SVA) to simulate the decisional behaviour of designers and to generate data about the usefulness of the recommendations. The fact of the matter is that the need for the SVA was raised by the pandemic, which prevented involving groups of human designers in the recommendation testing process. The reported research had three practical goals: (i) development of the logical fundamentals for the SVA, (ii) computational implementation of the SVA, and (iii) application of the SVA in data generation for the evaluation of usefulness of recommendation. The SVA is based on a probabilistic decisional model that quantifies decisional options according to the assumed decisional tendencies. The three key concepts underlying the SVA are (i) decisional logic, (ii) decisional knowledge, and (iii) decisional probability. These together enable generation of reliable data about the decisional behaviours of human designers concerning the obtained recommendations. The completed tests proved the above assumption.

Conceptualization and design of intellectualized, socialized, and personalized cyber-physical systems (CPSs) need integration of existing knowledge across the involved disciplines, as well as exploration and synthesis of novel knowledge beyond disciplinary boundaries. The latter needs a combined use of interdisciplinary, multidisciplinary, and transdisciplinary research. Supradisciplinary research has emerged as a new doctrine of combining these research approaches from epistemological, methodological, and procedural perspectives. However, no methodology can be found in the literature that could facilitate the practical execution of supradisciplinary research programs. This position paper proposes a conceptual framework that can be used as a blueprint for operationalization of such undertakings. The framework rests on six generic pillars: (i) problematics, (ii) infrastructure, (iii) method, (iv) stakeholders, (v) operations, and (vi) knowledge. It specifies the major concerns that have to be taken into consideration in a systematic manner in developing executional scenarios for supradisciplinary research. The framework facilitates (i) management of research organization tasks, (ii) joint formation of shared research infrastructure, (ii) setting up concrete research program, (iii) academic partnering and public stakeholder involvement, (iv) process flow management and capacity/competence allocation, (v) a holistic knowledge synthesis, assessment, and consolidation, and (vi) development of tools supporting the preparation and execution of large-scale supradisciplinary research. In its current form, it does not cover the specific societal and personal issues of a successful organization of the inquiry at individual researchers, research teams, and research community levels. A community-based follow-up research may focus on the practical application and testing of the framework in concrete cases-a task that an individual researcher cannot address.

This Extended Editorial has been compiled by the members of the Editorial Board to celebrate the 25th anniversary of the establishment of the Journal of Integrated Design and Process Science, which operates as the Transactions of the Society for Process and Design Science. The paper divides in three parts. The first part provides a detailed overview of the preliminaries, the objectives, and the periods of operation. It also includes a summary of the current application-orientated professional fields of interests, which are: (i) convergence mechanisms of creative scientific disciplines, (ii) convergence of artificial intelligence, team and health science, (iii) convergence concerning next-generation cyber-physical systems, and (iv) convergence in design and engineering education. The second part includes invited papers, which exemplify domains within the four fields of interest, and also represent good examples of science communication. Short synopses of the contents of these representative papers are included. The third part takes the major changes in scientific research and the academic publication arena into consideration, circumscribes the mission and vision as formulated by the current Editorial Board, and elaborates on the planned strategic exploration and utilization domains of interest.

Cyber-physical systems (CPSs) are seen as one of the tangible results of the convergence of advanced information technology, nanotechnology, biotechnology, cognitive science, and social science in addition to conventional systems science, engineering, and technologies. Designing next-generation cyber-physical systems (NG-CPSs) is a challenging matter for abundant reasons. It is not possible to consider all reasons and to address their interplays simultaneously in one paper. Therefore, this position paper elaborates only on a selected number of topical issues and influential factors. The author claims that the shift of the paradigm of CPSs and the uncertainty related to the paradigmatic systems features of NG-CPSs are among the primary reasons. Since the future of CPSs will be influenced strongly by their intellectualization, adaptation/evolution, and automation, these aspects are also addressed. It is argued that interaction and cooperation with NG-CPSs should be seen from a multi-dimensional perspective and that socialization of NG-CPSs needs more attention in research. The need for aggregation, management, and exploitation of the growing amount of synthetic systems knowledge produced by smart CPSs is seen by the author as an important emerging concern.

Applicability testing of constructive computational mechanisms (CCMs) is a new challenge for both the academia and the industry. The overwhelming majority of the existing validation approaches focuses on the internal validity of CCMs (e.g. consistency, bias), while there is a shortage of efficient approaches for assessing the external validity (e.g. applicability, reusability). The objective of this paper is to clarify the concepts and criteria, and to develop an approach for a systematic evaluation of the applicability of a given CCM to cases that were not considered at design time. The approach is adapted from the validation square approach (VSA). The adapted methodology (A-VSA) makes it possible to evaluate CCMs from (a) theoretical structural, (b) empirical structural, (c) theoretical performance, and (d) empirical performance dimensions. Altogether eight indicators are introduced that support the evaluation process. The effectiveness of the A-VSA was confirmed through a case study, in which a specific CCM is considered and the strategy of the A-VSA was operationalized with three completely different application cases. As evidenced by the results, the proposed A-VSA establishes a tight coupling among the enablers embraced by a CCM and the aspects of theoretical and empirical validation, which approves the approach to be an efficient tool for defining the range and/or the extent of applicability. The advantage of the A-VSA is that it offers a way to transfer qualitative applicability evaluation into quantitative applicability assessment, which allows the use of both subjective statements and mathematical modeling in applicability testing. The results of the assessment can guide the adaptation work of a CCM when applied to an out-of-domain application.

Machlup used the words alpha, beta, and gamma to identify humanities, science, and social science as three distinct fields of academic learning and knowing, in addition to general knowledge. Gilles and Paquet identified a fourth type of disciplinary knowledge and labeled it as delta. This includes the knowledge of creative disciplines such as design, law, and economy. Since the time of these road-paving works, a lot has changed. In the last two decades, various concepts and manifestations of intellectualized engineered systems have appeared. A paradigmatic feature of these systems, exemplified by smart cyber-physical systems, is that they collect, infer, or extract massive amount of synthetic system knowledge (M-SSK) based on some pre-programmed human knowledge. The amount of this type of knowledge grows continuously. It can be aggregated on system level and on system of systems level. This paper argues that this aggregated M-SSK is not covered by the abovementioned four genres of knowledge. In fact, it represents a new genre. The conducted literature study underpins this claim. Therefore, the paper suggests dealing with it as a new genre, called epsilon-knowledge. Artificial intelligence, system engineering, cyber-physical systems, and knowledge engineering are the disciplines dealing with epsilon-knowledge. The paper refers to sympérasmology as the proper conceptual framework of studying this genre of knowledge.

Though they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them: (i) What way are smart design and smart systems enabled by artificial narrow, general, or super intelligence? and (ii) How can smart design be used in the realization of smart systems? and How can smart systems contribute to smart designing? A difficulty is that there are no exact definitions for these novel concepts in the literature. The endeavor to analyze the current situation and to answer the above questions stimulated an exploratory research whose first findings are summarized in this paper. Its first part elaborates on a plausible interpretation of the concept of smartness and provides an overview of the characteristics of smart design as a creative problem solving methodology supported by artificial intelligence. The second part exposes the paradigmatic features and system engineering issues of smart systems, which are equipped with application-specific synthetic system knowledge and reasoning mechanisms. The third part presents and elaborates on a conceptual model of AI-based couplings of smart design and smart systems. The couplings may manifest in various concrete forms in real life that are referred to as "connectors"in this paper. The principal types of connectors are exemplified and discussed. It has been found that smart design tends to manifest as a methodology of blue-printing smart systems and that smart systems will be intellectualized the enablers of implementation of smart design. Understanding the affordances of and creating proper connectors between smart design and smart systems need further explorative research.