The purpose of this paper is to evaluate the evolution of sustainability research in Additive Manufacturing (AM) by providing an overview of systematic literature reviews (SLRs) published between 2016 and early 2025. Nine SLRs were selected following the screening and filtering of 204 records identified through a structured search of the Scopus database. The following criteria were used to analyze and compare these articles: (i) their scope, (ii) data sources of literature considered, (iii) sustainability dimensions covered, and (iv) the main findings and key conclusions (e.g., significant patterns, recurrent themes, methodological advancements). The study highlights that AM is widely recognized as a transformative technology capable of fostering more sustainable, localized, and resilient production systems. However, realizing its full sustainability potential requires further research, methodological standardization, integration of lifecycle thinking, digitalization, and greater attention to social dimensions. Although AM demonstrates clear economic advantages, the social pillar of sustainability remains underexplored, with limited research addressing concerns such as occupational health risks and the demand for skilled labor in emerging economies. Both established and new AM technologies should be investigated in future studies, with a focus on promoting sustainable manufacturing using a multifaceted, interdisciplinary approach.

Artificial Intelligence (AI) systems are increasingly involved as interactive partners in design activities, influencing how users form intentions, interpret system output, and reflect on representational choices. In engineering design contexts, and particularly in product representation tasks, AI tools are now widely used to support the generation, refinement, and communication of conceptual representations. This study investigates Human–AI interaction in AI-mediated product representation tasks within undergraduate and graduate engineering programmes. Drawing on data from two survey instruments administered to a large, multi-year cohort of engineering students, existing measures of trust in AI, perceived output quality, conceptual depth, and cognitive effort are reinterpreted through a Human–AI interaction perspective. Cluster analysis identified three recurrent interaction profiles: Cognitive Adapters, who engage AI as a stimulus for exploration and reflective reasoning; Functional Executors, who primarily use AI to optimize task execution; and Skeptical Observers, who maintain low trust and limit AI integration. Structured laboratory observations qualitatively validated these profiles, revealing systematic differences in goal formation, interpretation of AI output, iterative representational strategies, and perceived control. Interpreted through Norman’s action cycle and Schön’s reflection-in-action, the findings provide a theoretical account of how generative AI appears to reshape intention formation, execution, and reflective evaluation during design representation. The study contributes empirically grounded interaction profiles and a human-centered framework for analyzing AI-supported representation, with implications for the design of interactive AI systems that support reflective use, calibrated agency, and adaptive transparency.

Considering the importance of the recent renewed interest in geometric specification management in industry and academia, along with the evolving landscape of educational models post-COVID-19, the authors aimed to investigate, through a literature review, the status of educational practices on these topics. In particular, the presence of educational initiatives dedicated to geometric and functional product specifications in mechanical or industrial engineering programs was investigated, with a specific focus on standards such as ISO GPS and ASME GD&T outside the Italian context. An analysis was then conducted to identify common teaching strategies and the development of specific tools to improve the understanding of these complex topics. The review of education and training experiences revealed significant trends and insights for future training efforts. In particular, the prevalence of active learning practices emerged, and detailed course descriptions demonstrate the effectiveness of hands-on approaches, often supplemented by specific tools, in facilitating student understanding. Leveraging these trends, educators can design more effective interventions to equip students with the knowledge and skills they need to succeed with geometric specifications.

The idea presented in this contribution stems from the authors' interest and previous experience in designing aids for individuals with disabilities in the field of Accessible Tourism - AT. Specifically, the focus has been directed towards devices utilized in the seaside sector, with an analysis conducted on beach wheelchairs. The study involved both a market analysis and an examination of the regulatory framework. Two main results were obtained. Firstly, the analysis of the regulatory framework revealed potential challenges in interpreting rules and regulations, particularly regarding design, safety, and testing requirements. Secondly, the market analysis demonstrated that all currently available devices necessitate users switching from their personal wheelchairs to beach wheelchairs. Considering these findings, a conceptual solution is proposed to adapt one's personal wheelchair for movement on sandy terrains. This solution entails a kit comprising beach wheels mounted on a lightweight frame, potentially incorporating a self-propelled device to replace the manual wheelchair's existing wheels. It is important to note that this solution is intended to supplement existing beach wheelchairs rather than replace them, thereby broadening the options available to individuals with mobility impairments.

The use of large language models (LLMs) is now spreading in several areas of research and development. This work is concerned with systematically reviewing LLMs’ involvement in engineering education. Starting from a general research question, two queries were used to select 370 papers from the literature. Filtering them through several inclusion/exclusion criteria led to the selection of 20 papers. These were investigated based on eight dimensions to identify areas of engineering disciplines that involve LLMs, where they are most present, how this involvement takes place, and which LLM-based tools are used, if any. Addressing these key issues allowed three more specific research questions to be answered, offering a clear overview of the current involvement of LLMs in engineering education. The research outcomes provide insights into the potential and challenges of LLMs in transforming engineering education, contributing to its responsible and effective future implementation. This review’s outcomes could help address the best ways to involve LLMs in engineering education activities and measure their effectiveness as time progresses. For this reason, this study addresses suggestions on how to improve activities in engineering education. The systematic review on which this research is based conforms to the rules of the current literature regarding inclusion/exclusion criteria and quality assessments in order to make the results as objective as possible and easily replicable.

The research described in this paper explores the integration of Large Language Models (LLMs), particularly ChatGPT, into engineering education, focusing at the end on mechanical one. Investigating the widespread adoption of generative AI tools, the study systematically reviews their applications, usage patterns, and overall impact on knowledge acquisition in higher education. The research delves into specific applications, highlighting ChatGPT role in product design, development, and innovation. The analysis, based on a systematic review of 67 papers, categorizes findings into general and subject-specific applications, revealing notable instances in computer science and mechanical engineering. The study addresses key research questions, elucidating the diverse applications of ChatGPT, especially in supporting knowledge extension and creative idea generation. The findings underscore the transformative potential of LLMs in engineering education, emphasizing the need for careful implementation to balance AI assistance and human engagement. As ChatGPT continues to evolve, this work contributes to the ongoing discourse on effectively harnessing LLMs in educational settings, laying the groundwork for future exploration and guideline development.

This research investigates the possible influence of students’ perceptions of emerging AI technologies on university courses, focusing on their knowledge and perceived usefulness within engineering design. An evaluation tool implemented in a Microsoft Excel workbook was developed and tested to perform the process of data collection through well-known questionnaires, data analysis, and the generation of results, facilitating attention to class compositions and measuring AI awareness and perceived usefulness. The study considers traditional aspects such as roles within design teams and the psychological factors that may influence these roles, alongside contemporary topics like Large Language Models (LLMs). Questionnaires based on well-established theories were administered during courses on product innovation and representation, assessing both primary and secondary design roles. Primary roles focus on technical skills and knowledge, while secondary roles emphasize problem-solving approaches. The Big Five questionnaire was used to characterize students’ psychological profiles based on the main personality traits. Students’ perceptions of AI involvement and usefulness in engineering design were evaluated using questionnaires derived from the consolidated literature as well. Data were collected via Google forms from both in-class and off-line students. The first results of the workbook adoption highlight some relationships between personality traits, perceived roles in design teams, and AI knowledge and usefulness. These findings aim to help educators enhance course effectiveness and align courses with current AI advancements. The workbook is available to the readers to collect data and perform analyses in different countries, education disciplines, and as time goes by, in order to add the longitudinal point of view to the research.

The advent of the COVID-19 emergency has affected all aspects of daily life, inevitably causing also significant changes at every education level. These changes have led to a sudden shift from face-to-face to online teaching, with a heavy impact on both interaction modes and methods and tools exploited meanwhile. The research described in this paper started by collecting data from university students about their perception of technical drawing teaching before and during/after COVID-19. The analysis of this students’ point of view, along with considerations about their performance (exam passing rates and marks, etc.) highlighted some suggestions to improve university courses, making them more effective and attractive.

The literature reports many evidences about the influence of personality on design activities. At the same time, Natural Language Processing - NLP - tools are gaining importance day by day in product innovation. This research investigates possible relationships among personality traits, ChatGPT usage and the generation of innovative design ideas. A Microsoft Excel workbook implementing the first release of a data analysis framework has been developed and is available for downloading. The reader can use it to carry on personal evaluations; in the near future, an updated release of the framework will allow sending the results to a cloud repository to build a large database and perform more robust statistical analyses. This will allow the relationships highlighted up to now gaining objectivity and discovering new ones.

The growing demand for innovative and user-centric product design has led to a growing need for effective idea generation methods. In recent years, natural language processing (NLP) tools such as ChatGPT have emerged as a promising solution for supporting idea generation in various domains. This paper investigates a framework for studying the role of ChatGPT in facilitating the ideation process in product design. This investigation measures the impact of ChatGPT on the generation of innovative concepts compared to the use of “classic” design methods. An overview of the state-of-the-art idea generation methods in product design opens the paper. Then, the paper highlights some hypotheses about the impact of ChatGPT on innovative product design, aiming for product augmentation by adding features. The paper then describes the design experience in which ChatGPT is used as a tool for concept generation. Finally, the paper analyzes the dataset, using precise metrics to characterize the participants’ performance and compare them. This analysis allows the paper to argue about the validation/rejection of the hypotheses. The paper concludes with a discussion of the implications of the findings and some suggestions for future research. Along with the paper, the Microsoft Excel workbook used to perform the data analysis is available to the readers to perform their own data collection and analysis. The workbook UX has been carefully studied and developed to make it usable by anyone. At the same time, it should be flexible enough to manage several situations characterized by different numbers of participants, product functions to implement, and generated concepts.

Day by day, more and more people get in touch with user interface (UI) development matters, since user experience (UX) and, as a direct consequence, user-centered design (UCD) gain importance in establishing the success of products on the market or, simply, because users feel the need to interact with products easier and more directly. In the last years, affordable UI development tools have appeared on the market, freeing apprentices from owning specific skill and knowledge and boosting interactive activities by allowing different competencies to work in synergy. This research exploits the experience where university students - the apprentices - use several affordable tools to develop the UI of a refrigerator. Pointing out the opportunities and criticalities encountered during the experience allows highlighting those variables to consider in selecting the most suitable tools already on the market as well as in developing new ones, all of this aiming at a sort of “discount UI development”.

The aim of this work is to describe some experiences of additive manufacturing - AM - for nuclear fusion applications. In this paper, a first case study is introduced concerning the realization of a scale prototype of an in-vessel component for tokamak nuclear fusion reactors, a wishbone of the deflector made in Ti-6Al-4V alloy. The 3D model of the wishbone component was designed, optimized with simulation, and then fabricated using AM in collaboration with the Laboratory for Advanced Mechatronics - LAMA FVG - and researchers at the University of Udine. For the construction of the prototype, a SLM machine using powder bed metal laser melting was used. The design, simulation and fabrication activities of the AM mock-up are presented in this paper, discussing the main limitations and possibilities arising from the 3D printing of titanium alloy. In addition, a further scale prototype of the wishbone was produced using conventional milling techniques, allowing an economic comparison and evaluation of the two manufacturing processes. The prototypes will then be used for a future evaluation of the mechanical properties of this material (Ti-6Al-4V), first on material samples and then on the mock-ups, under irradiations conditions, due to nuclear fusion applications.

This paper describes the development of a Hi-tech Cartesian cutting machine for non-metallic materials in the laser converting field. The challenge stands in developing a better machine than the existing ones by speeding up the cutting process, allowing more formats of the material to cut and increasing versatility to better respond to different applications. Since extremely high accelerations, specific materials, sophisticated component shapes, critical mechanical properties, etc., are involved and required, state-of-the-art design tools, belonging to the collaborative design paradigm, come in real help to actors owning different competencies. Generative design allows defining the components of the core of the machine and 3D printing helps in evaluating the results in terms of dimensions, assembly, workspaces, etc. Other than starting to reach the expected result, this study highlights the added value of the design tools involved as well as some limitations and related expectations about possible upgrades of them in the future.

This work is part of the historic collaboration between the design and methods research groups of the universities of Udine and Brescia. In particular, it presents the results of a survey on the perceptions of engineering students on the online teaching methods activated for the technical drawing courses during the COVID-19 pandemic. 111 students at the University of Udine participated in the survey and the results of the analysis of the responses showed that, in general, online teaching methods are not comparable to face-to-face ones; however, they have been appreciated as they allowed regular teaching during this critical period. Furthermore, opinions and scores from the new teaching methods were more than positive regarding both the availability of the recordings of the lessons and the introduction of generalized corrections of exercises. The authors planned to extend the survey also to the University of Brescia to collect further pieces of information in order to constantly improve this teaching paradigm.

Nowadays, User eXperience (UX) is one of the main concerns in developing innovative products. The literature already offers several UX design methods and tools. Among them, the interaction related Mental Models—based UX evaluation method appeared some years ago. Recently, the modular evaluation of key Components of User Experience questionnaire, a tool for quantifying products’ UX, integrated this method. The knowledge acquired thanks to this synergy suggested the way to define and exploit guidelines for interactive redesign of the UX of products. Because of their origin, these guidelines encompass all UX characteristics reputed as meaningful like usefulness, subjective feelings, motivational aspects, emotions, etc. Moreover, these guidelines are generic enough to be used in different situations and on different products; their structure and functioning make them independent from the methods and tools used to collect data and to implement them, widening in this way their applicability in almost any UX interactive redesign activity by almost any researcher/practitioner. The redesign suggestions offered by these guidelines, as well as the procedures to collect and analyze the data to define other guidelines and suggest the best ways to implement them, push different competencies (engineers, UX experts, psychologists, etc.) to work in synergy, just like an interactive design context requires. To date, this research defined ten guidelines; a first adoption in the field starts witnessing their goodness and effectiveness. The paper describes in detail how to define and exploit the guidelines, as well as the first adoption of them in a real redesign context.

This paper presents the results of a survey carried out with students enrolled in the first two years of the BS in Engineering at three Italian university locations. The study is part of a wider range of methods, tools and aids for the improvement of teaching and learning of technical drawing at university level developed by the University of Brescia, Udine, and Cassino and Southern Lazio. In particular, this work analyses the results of questionnaires related to the basic technical drawing outcomes, taking inspiration from previous research work in this field. What emerges is a positive picture that shows students’ interest in 3D CAD modeling topics such as part or assembly construction, but also their interest in more traditional subjects like sketching and dimensioning.

User eXperience (UX) gains more and more importance due to technological complexity, task specialization, widening of users range, etc. All of this become particularly true in medical contexts, where affordance, empathy with devices, natural interaction, dialogue reliability, real time response, etc., can make the difference for completely successful surgical activities. The PIRG - Product Innovation Research Group and the Clinic of Maxillo-facial Surgery of the University of Udine worked together for investigating about the UX during maxillo-facial surgery planning and implementation. The goal was double-faced: analyze the UX in order to highlight possible needs for improvements and test the evaluation process in terms of tools and activities performed. The outcome consists of suggestions to improve the UX and of a review of the evaluation process.

Advancements in additive manufacturing technology have made it possible to create machines that allow the use of a wider range of materials, even simultaneously in the production of a single piece. The production of heterogeneous objects allows to include multifunctionality within the domain by varying the composition in a gradual or net fashion. This paper analyzes the AM technologies that allow multi-material, emphasizing the constrains and the possible applications with the goal of identifying guidelines for design methods development. From the analysis we observe important innovations that permitted to easily process polymeric materials, especially with material extrusion and material jetting. However, the use of ceramic powders and metallic materials for the creation of heterogeneous objects requires the development of methods which remain very limited by the process conditions.

PERSEL (PERsonality SELector) is a personality-based selection tool to maximize UX redesign effectiveness by highlighting the best users to involve time by time in the activities due to the target the UX redesign of products aims at. To date, PERSEL knowledge base was populated using a top-down approach; the literature allowed highlighting relationships between personality traits and UX characteristics. Here, the bottom-up approach aims at generating fresh pieces of information to integrate PERSEL knowledge base by looking at experiences in the field. A questionnaire-based survey allows collecting data about the personality of the participants, together with their inclinations towards UX. The statistical analysis of the collected data highlights further relationships among personality traits, UX characteristics and using metrics like novelty, variety and usefulness of the users’ possible suggestions.