Design research has produced a rich set of theories explaining disparate aspects of design activity, including problem solving, contradiction resolution, conceptual expansion, and reflective practice. However, these theories often emphasize localized mechanisms and provide limited integration of why intervention becomes necessary, how it becomes possible, and why it remains constrained across domains. This paper introduces Designics, a scientific paradigm that defines design as intentional change (i.e., purposive environment transformation via intervention) in human-environment systems. Drawing on a 35-year synthesis of formal logic and empirical cognition, Designics is organized around a minimal unit of analysis-the Designer-Environment-Intervention (DEI) triad-and three governing laws that function as the first principles of the field. The Conflict Law states that conflict drives intervention: conflict (the mismatch between demands and resources) makes intervention necessary. The Perception Law states that perception enables intervention: perception forms the actionable perceived environment that makes intervention possible. The Capability Law states that capability constrains perception, and thereby constrains intervention: finite cognitive-affective capability constrains perception, rendering intervention effortful and recursive. Together, these laws explain design as a recursive process in which conflict makes intervention necessary, perception makes it possible, and finite capability makes it constrained, effortful, and open-ended. The paradigm integrates representation, reasoning, and empirical investigation through formal modeling principles and triangulated research instruments, including behavioral, physiological, and neurocognitive measures. By providing a stable unit of analysis, governing laws, and empirical pathways, Designics offers a scientific foundation for integrating existing design theories while enabling systematic investigation of intentional change across engineering, organizational, and socio-technical systems.
Adaptive Photovoltaic Shutter (APS) is an innovative shading device that integrates photovoltaic materials into its slats, enabling it to convert sunlight into electricity while adaptively adjusting frame and slat angles to optimize multiple performance objectives. Due to the complexity of its mechanical system and operation, an advanced digital twin (DT) is essential for effective monitoring and control. Current research integrates mixed reality (MR) with DT to provide a more immersive and intuitive management experience. An Arduino-based IoT system collects real-time data from the APS, uploads it to the cloud, and enables visualization through a MR application. Within the MR interface, users can observe the status of the physical APS and control it by interacting with its digital counterpart. A survey was conducted to evaluate the DT's usability and user engagement. The results suggest that the MR-DT can enhance user engagement and support anomaly detection tasks, although it is associated with longer task initiation time and perceived interaction latency. These findings highlight both the potential and limitations of MR-enhanced DT in APS operation.
Digital technologies offer significant economic, environmental, and societal benefits. However, a key challenge for implementing digital technology is determining which solution best addresses the needs of the organisation, its workforce, society, and sustainability. This paper uses the concept of legitimacy, the acceptance and normalisation of technology, to explore different stakeholder perspectives, helping to inform decisions about technology adoption. A legitimacy lens encourages critical questioning of the norms and values of societies, public bodies, or organisations. We outline a set of discussion cards designed to structure conversations about the legitimacy of implementing potential digital technologies into a specific workplace or industry setting. The cards simplify concepts from legitimacy theory into nine main themes and encourage discussions that explore both positive and negative aspects. This paper presents an early-stage prototype card tool, their piloting and redesign, and validation in an industrial setting with Rolls-Royce. The company used them in three on-line and in-person workshop sessions with 15 industry participants. Results support the usefulness of the legitimacy concept in holistic, guided conversations and the cards as a boundary object tool to help the industry operationalise theory effectively. Conclusions indicate the need to simplify the academic language used, ensure that topics for discussion are distinct, and enhance guidance or process for equity in workshop facilitation/participation.
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.
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 aviation industry continually strives to improve the safety, efficiency, and reliability of aircraft. With an increasing focus on novel aircraft configurations to reduce the environmental impact of aviation, aircraft have become even more complex than before. Model Based Systems Engineering (MBSE), Model Based Safety Assessment (MBSA), and Multidisciplinary Design Analysis and Optimization (MDAO) have thereby gained popularity over the document-centric approach in the past decade. The common integration strategy - extending the model within a single MBSE framework - inherently reduces the use of unique capabilities of specialized MBSA and MDAO platforms. This position paper proposes an alternative methodology utilizing model transformation techniques to develop a robust link between these domains. This is achieved via a custom script extracting and transforming relevant information from an MBSE model file into file formats required by MBSA/MDAO tools. The primary contribution is maintaining consistency through a novel iterative design process formalizing the model transformation. This approach ensures the preservation of extensive capabilities offered by each domain-specific tool. Formalism is supported by implementing QVTo mapping rules and strengthening the verification with OCL constraints and Python codes developed to ensure that bidirectional transformations occur without information loss or distortion. This systematic integration streamlines the design process by enabling parallel safety assessment from an early design phase and facilitating a comprehensive exploration of the design space, thereby fostering informed decision-making. The technical feasibility of this methodology is demonstrated through its application on a UAVcase study, establishing a foundation for future development and real aerospace applications.
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.
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.
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.
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.
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.
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.
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.
As automated retail environments continue to expand globally, their fa & ccedil;ade design has emerged as a critical factor influencing consumer perception, psychological comfort, and visual appeal. At the same time, current design practices often prioritize technological efficiency over visual wellness. This study proposes a generative AI-assisted design methodology grounded in the Environment-Based Design (EBD) framework. The approach emphasizes visual dimensions of the WELL Building Standard and integrates biophilic design principles to enhance fa & ccedil;ade aesthetics in automated retail contexts. This study has four research objectives: (1) to extract WELL-aligned visual design variables for fa & ccedil;ade design through literature, certification mapping, and case analysis; (2) to develop structured prompt strategies and ControlNet-based image generation workflows using Stable Diffusion XL; (3) to evaluate perceptual outcomes through Learned Perceptual Image Patch Similarity (LPIPS) metrics and expert scoring across wellness-relevant dimensions; and (4) to analyze design trade-offs and limitations, and identify opportunities for recursive improvement within the EBD framework. Eight fa & ccedil;ade images, consisting of the original and seven AI-generated variants, were evaluated by five experts using a 7-point Likert scale across five perceptual criteria. The results show that the "Material + Pattern" strategy received the highest ratings in perceived material quality and natural features; "Color + Material + Pattern" showed the most balanced overall performance. Perceptual similarity was quantitatively assessed using LPIPS, confirming that multidimensional interventions led to greater visual deviation from the original design. Expert comments emphasized the warmth and affinity created by natural textures, while cautioning against excessive decorative complexity. Open-ended feedback was subjected to thematic analysis, revealing nuanced perceptions of design richness, comfort, and realism. This study demonstrates the feasibility of operationalizing health-focused visual principles within an AI-assisted design pipeline. The proposed approach offers a scalable and reproducible method for enhancing the emotional and aesthetic quality of automated retail fa & ccedil;ades. Future research should extend the scope of visual dimensions, including form, signage clarity, and transparency, and incorporate multimodal user experience evaluations to better reflect real-world engagement.
Conventional research typically adheres to established disciplinary methodologies, whereas transdisciplinary research demands a more flexible and adaptable approach. This paper presents and discusses a work procedure developed and successfully implemented to support collaboration and progress in a large-scale 4-year research project. The work procedure integrates the overall principles from interactive research with the structure of the design research methodology (DRM) framework. The focus here is on the potential of the developed work procedure to support transdisciplinary engineering research. We specifically investigate to what degree the requirements of relevant transdisciplinary research processes are met, i.e., the ability to a) understand and manage complexity, b) incorporate diverse perspectives, c) link abstract and practice-oriented knowledge for implementation and d) develop descriptive, normative, and practical knowledge. Several principles essential to fulfilling the requirements and succeeding with transdisciplinary research were encouraged and supported. Therefore, integrating knowledge from collaborative research, industry-academia partnerships and engineering design could be a promising strategy to strengthen transdisciplinary engineering research. We propose that the suggested work procedure, together with the identified best practices, could serve as support in this context as a transdisciplinary engineering research framework.
Functional decomposition, essential yet challenging for novice engineers, particularly in identifying individual functions, often leads to negative perceptions. Despite extensive research on enhancing functional analysis in terms of quantity and quality, the experiential aspect for beginners remains underexplored. This study evaluates the NCFFA (Natural Cognitive Flow Functional Analysis) method, grounded in cognitive science and human-centered design, to enhance beginners' designer experience (DX) in functional decomposition. We conducted a controlled comparison to assess NCFFA's impact on the design process and outcomes against traditional methods. The findings indicate that NCFFA enhances the novice engineers' experience, yielding a better flow state without increasing perceived effort. In addition, it did not significantly affect the number of unique functions generated in functional diagrams. This research underscores the importance of integrating cognitive research and human-centered design principles into functional analysis and suggests new directions for enhancing the DX of the structured design process.
Developing trustworthy cyber-physical systems (CPS) demands cross-boundary collaboration among stakeholders with diverse disciplinary backgrounds, often creating challenges in crossing knowledge boundaries. Boundary objects, i.e., artifacts that enable shared understanding across domains, can help mitigate these challenges. This study examines the trustworthiness framework (T-Framework), as a boundary object, to support cross-boundary collaboration in trustworthy CPS development. Specifically, it addresses two research questions: (1) To what extent does the T-Framework, as a boundary object, support the crossing of knowledge boundaries in the development of trustworthy CPS? and (2) How can the T-Framework and its associated method be extended to address any identified gaps? Drawing on theories of knowledge boundaries and boundary objects, the study empirically evaluates the T-Framework through focus groups with CPS practitioners from academia and industry. Insights from these focus group sessions inform refinement of the T-Framework and its method to address identified gaps in its capability to support cross-boundary collaboration. In addition, the findings highlight the importance of incorporating structured mechanisms into both the design and utilization of architectural frameworks to promote shared understanding, address conceptual misalignment, and balance stakeholder influence during cross-boundary collaboration in the development of trustworthy CPS.
This paper investigates why it is so hard for engineering-based inclusive innovation (EII) to fit in mainstream scaling frameworks. Can new transdisciplinary design approaches build trajectories that are more inclusive and environmentally sustainable? There are a range of constraints on EII. These include the poverty of those who might benefit most, but also institutional barriers to the inclusion of some actors with knowledge and experience of scaling innovations towards the mainstream. This paper presents a review of the extant literature on conceptual understanding of scaling EIIs and a set of case studies of attempts to scale and mainstream innovations. We have gathered data from and analysed scaling-up case studies from different sectors and geographies. The paper advocates for the advantages of evolutionary approaches to development engineering that take account of institutional variety to bridge gaps in static, neo-classical and one-size-fits-all approaches of conventional frameworks for scaling. We propose the EII Scaling (EIIS) framework, incorporating an evolutionary, transdisciplinary approach to innovation that enables engagement with the complexities and challenges of scaling and delivering EII. We also illustrate that the engineering innovations not replicated on a large scale have not necessarily failed, as one size fits all does not apply to the scaling up of EIIs.
Background: The growth of unwanted weeds and shrubs in agricultural fields increased the use of special machines called brush cutters to cut them down. Due to the increased use of brush cutters among the farmers, many use the machine in various awkward postures. This may cause multiple work-related disorders. In this study, problems are identified, and a design intervention is proposed that aids in improving the farmers' posture and reducing health issues faced by them. Objective: This study aims to analyse existing brush cutter models and evaluate operator postures to identify ergonomic challenges. The research further focuses on developing and accessing an improved design using ergonomic evaluation techniques to enhance usability and reduce operator strain. Methods: Rapid Entire Body Assessment (REBA) tool was used for the postural analysis of the operators. Methodology chart and Pugh chart analysis was used to determine the design of the final concept. Results: The REBA analysis suggested a change in brush cutter design to improve the overall health of the operators and reduce injuries. This study presents an innovative brush cutter design aimed at reducing workload by redistributing the machine's weight for improved operator comfort. Conclusion: The new concept was designed using REBA, Pugh analysis, and a morphology chart to help mitigate pain and injuries experienced by operators due to prolonged use of existing brush cutters.
