This study examines how Socratic and Generative AI tools can support inclusive instructional design and foster critical pedagogical engagement among educators. A two-phase intervention was conducted with 152 in-service teachers enrolled in the Italian national specialization program for inclusive education (TFA Sostegno), using the Socratic AI system EULER and the large language model ChatGPT. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study assessed teachers’ perceptions of pedagogical value, usability, and institutional feasibility. In the first phase, participants used EULER to design inclusive lesson plans through question-driven dialogue, promoting metacognitive reflection and instructional revision. In the second phase, teachers co-constructed lesson plans by combining EULER with ChatGPT, integrating critical interrogation with generative ideation. Across both phases, UTAUT scores improved, with participants reporting higher performance expectancy, effort expectancy, and social influence when using the dual-tool approach. Findings indicate that teachers perceived AI not as a passive assistant, but as an epistemic collaborator capable of deepening pedagogical reasoning and reinforcing professional identity. Qualitative data reveal a conceptual shift from viewing AI as a task-automation tool toward embracing it as a medium for dialogic and cognitive augmentation. Participants also identified institutional support, ongoing professional development, and equitable access as critical conditions for sustainable adoption. These results suggest that Socratic AI, when embedded within structured and ethically responsible educational contexts, can serve as a catalyst for reflective, inclusive, and professionally empowering pedagogical innovation.

Configure-to-Order (CTO) assembly systems are becoming pivotal in meeting the rising demand for mass customization. However, enabling rapid and cost-effective system reconfiguration remains a major challenge in manufacturing. Despite the flexibility of general-purpose machines, fixture design introduces significant bottlenecks, often requiring extensive manual effort to access, organize, and interpret fragmented or incomplete data. This leads to inefficient design pipelines that can compromise product functionality and result in costly redesign iterations, even when using advanced Computer-Aided Fixture Design (CAFD) tools. Although the Model-Based Definition (MBD) paradigm offers a promising response to these information-related challenges, its adoption within the CAFD domain remains limited. This work aims to support the broader adoption of MBD in this field through a multi-level contribution. First, a structured literature review identifies the essential semantic information required in an MBD dataset tailored to CAFD. Second, this content is structured in a machine-readable form using the ISO standard STEP AP242. Third, an MBD-driven CAFD framework is proposed. Finally, a proof-of-concept CAFD tool is developed by integrating established methodologies with commercial software to automate non-value-added tasks, such as configuring Computer-Aided Technologies (CAx). The results demonstrate the potential of this approach to streamline CAFD processes and establish a fully connected digital thread.

Modular product design has become a strategic enabler for companies seeking to balance product variety, operational efficiency, and market responsiveness, making alignment between modular architecture and manufacturing considerations increasingly critical. Modular Function Deployment (MFD) is a widely adopted method for defining modular product architectures, yet it lacks systematic support for assembly considerations during early concept and system-level development. This limitation increases the risk of delayed production ramp-up and life cycle inefficiencies. This paper proposes a set of enhancements to MFD that integrate Design for Assembly (DfA) logic into architectural synthesis. The extended method introduces structured heuristics, assembly-oriented module drivers, a coded interface taxonomy, and quantitative metrics for assessing assembly feasibility and automation readiness. These additions preserve compatibility with standard MFD workflows while enriching decision-making with traceable, production-informed reasoning. An illustrative case study involving a handheld leaf blower demonstrates the method's usability and effectiveness. The redesigned architecture shows reduced assembly effort, simplified interfaces, and increased automation potential. By supporting early-stage evaluation of architectural alternatives through an assembly lens, the method enables faster transition to efficient volume production and provides a foundation for continuous improvement throughout the product life cycle.

The engineering profession faces global challenges that require graduates with strong practical and cognitive skills, yet traditional curricula often lack real-world and interdisciplinary preparation. Immersive Reality (IR) technologies, including Virtual, Augmented, and Mixed Reality, are increasingly explored in engineering education to bridge this gap. This systematic literature review examines how IR supports learning outcomes, categorising studies by technology, instructional design, and assessment methods. Using Bloom's Taxonomy, the findings reveal that most interventions target the 'Apply' level, with limited focus on higher-order cognitive skills. A key gap identified is the limited use of pedagogical frameworks such as Bloom's Taxonomy and Constructive Alignment (CA), which hinders evaluation of instructional depth. To address this, the paper presents two illustrative designs that demonstrate how immersive Teaching and Learning Activities and Assessment Tasks (ATs) can be designed using CA principles. These cases offer practical guidance for embedding IR into engineering curricula in a pedagogically coherent and outcome-aligned manner.

Technology-driven manufacturing has been a transformative force, reshaping not only industrial processes but society at large. The integration of smart systems has enabled companies to reduce production costs, improve efficiency, and enhance product quality. Yet, competitive advantage increasingly depends on human capital, and many engineering graduates lack the competencies to operate effectively in digitally advanced environments. This study draws on a cross-sectional European survey capturing responses from 217 engineering students and 63 educators/mentors across multiple countries. Results reveal strong overall satisfaction with engineering education (60% satisfied, 35% neutral), but highlight systemic weaknesses: lack of hands-on training, limited industry collaboration, and passive delivery models. Students emphasised low engagement and motivation, while educators stressed barriers in content development and limited access to advanced technologies. Both groups recognised the potential of Mixed Reality (MR) to address these gaps, with over three-quarters of respondents reporting at least some familiarity with MR. However, adoption remains minimal, with only 22% of educators having integrated MR in their teaching. The findings indicate that MR-based Virtual Learning Environments could help bridge the persistent theory-practice divide by increasing engagement, reducing reliance on physical equipment, and enhancing inclusivity through flexible and multilingual design. These insights underscore the urgent need for scalable infrastructure and educator training to support integration. MR, when combined with gamification and microlearning, offers a promising pathway to align engineering education more closely with Industry 4.0/5.0 skill demands.

This study provides a comprehensive analysis of task planning paradigms in industrial automation, evaluating different active and reactive methods like: Finite State Machines (FSMs), Behavior Trees, Hierarchical Task Networks, Symbolic Planning (PDDL), and Reinforcement Learning. It assesses the trade-offs of each method concerning scalability, reactivity, and verifiability. The core focus is the transformative role of generative AI, not as a standalone solution, but as an enabling technology that augments existing frameworks. Key generative AI applications include generating plans from natural language and creating synthetic training environments. The conclusive statement of the study is that the future of automation lies in hybrid deliberative-reactive architectures, such as combining PDDL and BTs, where AI enhances human expertise under rigorous verification.

As corporate venture builders (VBs) gain traction in various industries, their adoption within the manufacturing sector remains largely underexplored. This scoping literature review investigates the hypothesis of a lack of scientific articles focusing on VBs in manufacturing. By reviewing several types of publications among multidisciplinary topics linked to manufacturing, we ensured ourselves to capture all the relevant literature. Key findings include the role of smart manufacturing technologies and the emerging trend of "shared factories,"where manufacturing tech VBs enable collaborative, cost-effective solutions. We highlight the advantages and challenges of VBs, with a particular focus on their potential to externalize RandD, drive innovation, and reduce costs. While promising, the adoption of VBs in manufacturing remains nascent, with opportunities for further research to explore their strategic impact. Our study underscores the transformative potential of VBs in accelerating manufacturing innovation and reshaping industry practices.

Artificial intelligence (AI) has demonstrated significant utility in education, particularly in providing feedback, personalised learning paths, and reducing costs [1]. Despite its potential [2, 3], there is a notable gap in the literature on AI's role in inclusive educational planning and IEP (Individualised Education Plan) compilation [4]. This study presents the results of interventions aimed at assessing the perceived usefulness of AI and the CONALI Ontology by trainee special education teachers during IEP compilation. Conducted with teachers across all educational levels, the study investigates whether AI and CONALI are beneficial for IEPs for students of all ages. The research verifies the effectiveness of AI combined with the CONALI Ontological framework by Maffei [5-7], guiding teachers to identify Specific, Measurable, Achievable, Realistic, and Timebound (SMART) educational goals. Exercises and validated questionnaires were administered to 391 teachers from early childhood, primary, lower secondary, and upper secondary education. Initially, teachers were introduced to the IEP's Section 5, emphasising SMART objectives. They then completed this section for a hypothetical student with disabilities. Next, the CONALI Ontology was introduced, showing its utility in aligning educational objectives with teaching activities and assessment methods. Teachers then completed a second IEP with CONALI support and evaluated their design experience via a questionnaire. Finally, they used the AI tool ChatGPT, in conjunction with CONALI, to compile a final IEP. Results showed that teachers perceived ChatGPT and CONALI as useful and effective. The Ontology was confirmed as a valuable tool for identifying SMART objectives aligned with intervention strategies and assessment methods. AI, supported by CONALI, effectively accelerated the IEP compilation process, enhancing engagement, motivation, and learning quality.

Artificial Intelligence (AI) is gaining importance in the field of education (Chen et al., 2020). Despite its widespread use to assist teachers (Pratama et al., 2023; Baidoo-Anu Ansah, 2023), there’s a gap in literature on AI’s role in instructional design, particularly for students with disabilities. This study explores the perceptions of 114 support teachers on using ChatGPT and the CONALI Ontology (Maffei et al., 2016) for creating Individualized Education Plans (IEPs). In the first phase of the study, teachers were trained on how to identify objectives, teaching and learning activities and assessment methods within the IEP, following the CONALI ontological framework and constructive alignment (CA) (Maffei et al., 2022). After verifying their perceptions of the experience through an initial validated questionnaire, a second exercise was conducted, in which the experimental group applied the CONALI framework in combination with ChatGPT to fill in the IEP. At the end of the exercise, a second questionnaire was handed out to collect participants’ perceptions of the experience and to assess the effectiveness of ChatGPT and CONALI in designing the IEP. Descriptive analysis revealed that the combination of ChatGPT and CONALI was perceived as useful in IEP design. Ontology was confirmed as a useful tool for identifying SMART training objectives and aligning them with intervention strategies and assessment methods in the IEP. AI, supported by CONALI, was seen as an effective tool for instructional design, speeding up IEP completion and promoting engagement, motivation, and learning quality.

There is a widespread fear that large language models (LLMs) produce content that is indistinguishable from an original human work. Can students use LLM-based tools for their exam essays without being spotted? We performed experiments on a publicly available dataset that we produced, containing thirty answers provided by ChatGPT and thirty-six answers from university students, for each of six anonymized exam essay traces from a scientific methodology course for engineers. We applied term frequency-inverse document frequency (TF-IDF), a state-of-the-art machine learning algorithm for natural language processing, and the cosine similarity metric, in order to produce two LLM detectors. One is based on one-class support vector machine anomaly detection, and the other is based on multi-class random forest classification. The results show that it is possible to spot when LLMs are involved, provided that the source LLM is known. Education institutions can follow our guidelines to prevent cheating and improve education.