Journal of Integrated Design and Process Science

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.

Software engineering education must be guided by developments in software engineering and aim for professional and educational development of students. This position paper explores the historical and conceptual evolution of software engineering as a discipline central to modern integrated design, software systems, and process science, tracing the trajectory from early structured models, through waterfall, to Agile and AI-augmented paradigms, and looks at how changes in application mix and modes of use, technological complexity, and business and enterprise needs have shaped development methodologies. Special attention is paid to the role of generative AI on the software engineering process and the role of the software engineer, altering workflows, collaboration, and enterprise architectures, and further enabling automation, digital transformation, and autonomous systems, shifting responsibilities and skill sets upwards. The article then considers corresponding shifts in software engineering education, highlighting the need for curricula that intertwine process thinking, systems theory, and ethical engagement, integrate with generative AI technologies, and place greater emphasis on “soft skills”, in courses that combine development of professional expertise with conceptual capabilities and an enhanced capacity for life-long learning. It then presents a set of requirements, options, and guidelines for software engineering education in the era of generative AI.

The essence and vital roots of scientific activities create theoretical propositions and models that provide unique integrity of the associated concepts, definitions, approaches and methodological components. Design engineering education has made great progress to date by integrating modern methods and tools into the existing process models focused on unique curriculum programs for students’ progress during various scientific and technical levels. Contemporary and integrated socio-scientific environments, however, are generally more productive and initiative for future success stories of engineering graduates with high qualifications and science/technology-motivated professional life. Today's global transition from the “industrial-digital age” to the “sustainable knowledge economy and digital society” opens a new path for design engineering education, enforcing a paradigm shift towards training creative professionals who develop and implement new knowledge in real-world social environments. This paper presents and discusses the models, methods and tools to establish a road-map into next-generation creative design engineering education paradigm.

Amidst the rapid evolution of intelligent technology clusters—including large language models, virtual reality, and educational big data—this paper proposes the imperative to enhance AI literacy among college teachers as a cornerstone of digital competence development. We propose a three-tiered competency framework, named ‘Tool Application → Behavior Transformation → Innovative Design’, to transition educators from technical operators to intelligent instructional designers. Current challenges include outdated perceptions of AI, uneven technical proficiency, inadequate training resources, and limited pedagogical integration. To address these, we construct a multidimensional AI literacy framework spanning five domains: 1) cognitive understanding of AI fundamentals, 2) pedagogical integration through instructional design, 3) discipline-specific applications via industry collaboration, 4) ethical governance, and 5) educational innovation. Implementation strategies emphasize blended training models, tiered skill development, hands-on tool practice, case-based learning, and ethical education. A dual-perspective evaluation system (teacher-student feedback loops) assesses training efficacy and instructional outcomes. The study establishes an innovative dual-core driven platform architecture comprising the Teacher Development Cloud Platform and the Subject-Specific Tool Box, aiming to enhance teaching capabilities and discipline-specific application competencies. The research provides actionable pathways for advancing educators’ AI competencies, supporting digital transformation in higher education, and fostering sustainable ‘AI + Education’ ecosystems. Findings underscore the critical role of structured competency development in bridging technological potential with pedagogical innovation.

Soft skills are an indispensable asset for engineers and industrial companies alike. Many studies have observed a growing gap between the soft skills of young people and those required by industrial companies and, more broadly, the business sector. At the same time, it is challenging to: (i) identify the soft skills currently expected of engineers in various industry segments; (ii) develop such skills intensively in education programmes; and (iii) provide learners with the means to develop their soft skills further. In the context of the current generation entering Engineering Education, our work has attempted to address these issues. Having grown up in a rapidly changing social and technological environment, young people have become increasingly dependent on virtual experiences rather than real-world experiences and personal interactions. We have created a novel Soft Skills Development Method (SSDM) and applied in various advanced engineering courses scheduled at the end of a mechatronics undergraduate programme. The SSDM focuses on personality development by simulating typical situations encountered in engineers’ everyday work. This paper reports on an exploratory case study, in which the SSDM was operationalised and tested. Individual interviews and focus group discussions were conducted, and thematic analysis was employed to evaluate and discuss the outcomes of applying the SSDM. It was concluded that limiting the application to a short period or a few specialised courses has a positive effect; however, it does not enable the desired improvement in soft skills properly. Therefore, we propose expanding the method to cover the entire undergraduate engineering training programme, including introductory courses such as Engineering Mathematics.

Concept maps have been used to assess knowledge acquisition, track learning, and reveal mental models. This study proposes and validates a computationally measurable coding scheme to overcome educational assessment limitations: time consuming, inconsistency in coding, and difficulty in measuring semantic and structural complexity. The three-step coding scheme includes (i) classifying vertices and edges using a guidebook, (ii) training coders through a standardized manual, and (iii) validating reproducibility via inter-rater reliability (IRR) using Fleiss’ Kappa. Results from 22 undergraduate researchers coding six student-generated maps yielded moderate to substantial agreement (Kappa = 0.67 for vertices, 0.45 for edges), supporting both the accuracy and consistency of the scheme. This coding scheme enables scalable, real-time analysis of student thinking and lays the groundwork for automated feedback systems, with potential applications in adaptive learning and tracking of engineering identity in students pursuing engineering education. Beyond enabling structural and semantic analysis of student thinking, the scheme supports automated translation of hand-drawn maps into a digital, analyzable format laying the groundwork for scalable, real-time feedback systems in engineering education. By aligning methodological precision with reflective assessment practices, this research introduces a tool for measuring how students organize and evolve their understanding within project-based curricula. Future directions include integrating AI for automated coding, with promising applications in adaptive learning environments, formative feedback mechanisms, and long-term identity tracking in engineering programs.

This paper offers a theoretical and historical reconstruction of threshold logic as a foundational model for understanding neural computation. Originally developed in the 1960s and largely forgotten in contemporary AI education, threshold logic provides a structurally transparent, spatially intuitive, and cognitively resonant framework for interpreting decision functions in artificial neurons. Rather than merely proposing a pedagogical technique, we argue for the epistemological value of reintroducing this model in the age of generative AI, where black-box abstractions increasingly dominate educational practices. Grounded in logic design, control theory, and cognitive models, threshold logic is revisited here not simply as a teaching aid, but as a epistemic bridge between symbolic and sub-symbolic paradigms. We show how its geometric representations — such as planes intersecting unit cubes — allow learners to engage with neural functions as intelligible structures rather than opaque algorithms. Drawing from problem-based learning and constructionist pedagogy, we illustrate how this approach can scaffold conceptual understanding across diverse learner populations. The originality of this work lies in recovering and recontextualizing a nearly abandoned approach, positioning threshold logic as both a cognitive anchor and a historical alternative to dominant code-centered instruction. While empirical evaluation remains a task for future work, the proposed framework offers a robust theoretical foundation for rethinking neural network education within an AI-native academic landscape.

The goal of this study is to advance the teaching and learning of uncertainty in conceptual design. The central research question is: Can a data-driven conceptual design course improve students’ ability to reason about aleatory and epistemic uncertainty? To investigate this, two aims were pursued: (1) constructing a data-driven conceptual design course with implementation and evaluation strategies, and (2) designing an educational flow that supports students’ engagement with uncertainty through structured tasks. Nine frameworks, grouped into five categories and supported by three discipline-based education research fields, were defined to ground the study and provide a foundation for addressing the research question. Using the backward design, a comprehensive conceptual design course was proposed, aligned with relevant ABET competencies and complemented by an educational flow and an educator's guide containing theoretical preparation materials, implementation tools, recommended programming libraries, and guidance for undergraduate and graduate-level instruction. A case study, based on bicycle frame design, demonstrated practical implementation through image preprocessing, dimensionality reduction, and clustering. The course was further contextualized to illustrate the applicability across multiple STEM fields, including mechanical, electrical/computer, and biomedical engineering. Overall, this study contributes a generalizable teaching-learning-assessment construct for supporting uncertainty reasoning in advanced engineering design courses.

Engineering education is undergoing a global transformation to bridge the longstanding gap between theoretical instruction and real-world competencies. This paper presents a simulation-driven, project-based learning (PBL) model centred on a multi-semester microdrone design project. The EU-funded DigitALL initiative supports this approach, which combines advanced simulation (CAD/FEA and control) with dedicated PBL spaces, BSc and MSc mentoring, and Faculty Learning Communities to align learning outcomes with the requirements of modern engineering practice. Positioned as a design-based research case, the work orchestrates a staged pipeline that spans ideation and modelling, through prototyping, testing, and optimisation. It specifies an a priori evaluation protocol that combines EUR-ACE-aligned performance rubrics with a brief pre- and post-concept test, engagement measures, and process analytics. The first implementation of the program shows positive results for task completion, iteration discipline, and self-reported systems thinking. However, the full outcome analysis, including learning gains, reliability, and retention, will be conducted in the following student groups. The contribution is a replicable orchestration that connects simulation and physical prototyping in a curriculum-wide loop, supported by a constructive-alignment matrix linking phases to intended competencies. The research demonstrates how microdrone-based simulation-driven PBL creates a scalable competency-based framework for engineering curriculum renewal, which can be adapted to other STEM fields. The proposed framework offers a transferable, evidence-based model for simulation-driven curriculum reform. It supports accreditation alignment and can inform engineering-education policy across European higher-education institutions.

The maritime sector is undergoing major changes driven by decarbonization targets, digitalization, and automation. In the Netherlands, the Maritime Masterplan addresses these through climate-neutral vessels, digital platforms, and a Human Capital program focused on learning communities and interdisciplinary collaboration. Education must shift from routine expertise to adaptive expertise, ensuring flexibility for new fuels and technologies, while also updating competencies beyond current standards. This position paper argues that deep understanding of fundamentals remains essential, illustrated by initiatives like a Ship Stability Game, which emphasize first-principles learning. The central proposition is that the development of fundamental understanding should be given a more prominent role, particularly within a multidisciplinary educational environment.