The learning design, the role of generative AI as a support tool, and the value of real industrial challenges for automation education are discussed, and a positive perception of the activity is indicated.
Abstract
This paper presents an automation learning activity built around a real industrial problem proposed within a university-private sector collaboration, specifically the Michelin factory in Valladolid. The experience was conceived as a problem-based, competitive learning activity rather than as a conventional classroom exercise. Student teams had to analyse an authentic production-related challenge and prepare a technically grounded proposal involving robotics, computer vision, control engineering and artificial intelligence. The activity attracted 36 registered teams; 25 submitted a proposal, 4 reached the final defence stage, and one team won the competition. The finalist teams visited the industrial facilities, which helped students understand operational constraints, safety requirements and implementation feasibility. The educational impact was evaluated through a questionnaire completed by 30 participants. Results indicate a positive perception of the activity, with high scores for motivation, active involvement and teamwork. The paper discusses the learning design, the role of generative AI as a support tool, and the value of real industrial challenges for automation education.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026