Laboratories have played an integral part in science and engineering education for a long time. It makes sense that students have opportunities to put theoretical knowledge in a practical context. Another aspect of laboratory learning is to introduce student to aspects of professional practice, for example in relating to how tasks are completed in an engineering workplace.

The learning outcomes of laboratories have been much discussed, and there are several frameworks to support the analysis of their contribution to the educational objectives, such as the widely cited work of Feisal and Rosa (2005).

As the world is seeing immense change in the way that we interact with laboratory learning activities, and the capabilities of generative AI, it is timely to re-evaluate if our laboratory learning activities are still fit for purpose.

Previous work has shown that laboratory learning activities still have a place in a modern engineering curriculum (Kist et al, 2024).

When traditional laboratory learning activities were developed, it is likely that the learning objectives focused on the procedural nature of the activity. It was assumed that students learned things by completing the activities. As we move to virtual and remote activities, and as AI becomes more capable of performing procedural tasks, it is critical that we regularly review our approach.

In this paper, a framework will be introduced to assist educators in a review of existing laboratory activities, and the development of new laboratory learning activities, to ensure that the learning objectives focus on the real intent of the activity. This will include clearly identifying the purpose of the learning activities and also identifying where AI can be used within the activity.

The goal is to provide a tool to support regular review of learning activities to ensure that the integrity of the learning activities is maintained while fostering the requisite AI capabilities in students.

The development of artificial intelligence (AI) technologies has made it possible to build systems that can continuously evolve, in a way comparable to human education. This type of system can be trained to solve tasks in various fields, gradually advancing from a beginner to an advanced level of performance, autonomously adjusting to their complexity and requirements. The concept AI Virtual Student/Worker (AIV-S/W) promises not only the automation and optimization of processes, the elimination of human errors, the substitution of the human factor, respectively the improvement and personalization of education, offering innovative solutions and high-performance standards in various fields.

On the one hand it is impossible to underestimate the influence of generative artificial intelligence(AI) tools on the educational process. Students and learners by nature will always try to use techniques to limit their efforts to reach a goal. So educators are facing the problem of modifying the pedagogical approaches, so that they could include implementation of AI tools in an ethic way, helping student to reach learning outcomes.

On the other hand, project based learning (PBL) is a well-known didactic format in engineering education as it allows to provide students a reality based project environment. PBL is a student-centered model of teaching and learning by doing (student)projects. Projects are to be understood as complex tasks, based on challenging questions or problems. Projects typically run from 2 weeks to one semester. It involves learners collaboratively, in planning, problem solving, decision making and/or research activities. Students acquire autonomy and responsibility, develop overarching skills and apply knowledge. At the end of the projects students report and/or present their results. Combining the positive outcomes of the use of AI with well-defined project cases, will augment the learning experience, without suffering from the loss of original work for the students.

In the work authors research the possibilities of the retrieval augmented generation for the implementation in project based learning approach for the model based system engineering. This allows to increase the field of application of the existing equipment COCO and improve students motivation.

Although Artificial Intelligence (AI) is transforming the industry, many univer-sity programs prioritize generic AI principles above domain-specific applica-tions. This paper investigates an adaptive course format to overcome the gap to real-world use cases. The suggested concept expands on a two-semester pro-gramming course and introduces students to current AI approaches used in re-search and industry in mechanical engineering. The authors aim to address bu-reaucratic limitations in curriculum reform by focusing on modules that pro-mote flexible, problem-based learning. The course structure will include a lec-ture series with a high-level overview and in-depth workshops on specific ap-plications led by active AI researchers. Students will work on semester-long projects with instructors as clients, which specify the technical requirements to be fulfilled. This dual position requires learners to deal with not only AI tech-niques, but also project management, client communication, and interdiscipli-nary interaction.

The broad representation of AI applications is highlighted by providing a brief overview over the starting topics and goals of challenges set in different me-chanical engineering domains such as product development, control systems and thermodynamics.

This paper explores how smart technologies and artificial intelligence (AI) can contribute to building resilience and professional preparedness among social care workers responding to disasters. Drawing on the theoretical framework of resilience and the “help the helpers” vision, it investigates the ethical and pedagogical implications of integrating AI tools into disaster-related social work education. The study builds on doctoral research on Romanian frontline social care workers’ psychosocial responses to crises, emphasizing the shift from reactive coping to proactive learning in technology-mediated environments. The paper aims to discuss how digital training platforms and intelligent systems can support ethical decision-making, reflective practice, and emotional regulation in high-stress contexts. It proposes a framework for socially sustainable and ethically informed workforce development in the era of smart technologies.