Generative Artificial Intelligence (GenAI) is reshaping the educational landscape. A single GenAI prompt can generate a carbon footprint several times higher than that of a standard internet search. Yet, discussions of environmental sustainability remain limited within GenAI-in-education research, which primarily focuses on usability and ethics. This study examines the perspectives of K–12 students and teachers on the environmentally responsible use of GenAI. A questionnaire comprising 12 Likert-scale items was distributed to participants from 63 schools, yielding responses from 729 students and 66 teachers across 80 schools. The results were quantitatively analysed. The presented Structural Equation Modelling model demonstrated strong performance. Students and teachers generally expressed positive views toward environmentally responsible GenAI practices, although their perspectives differed in six aspects, primarily regarding understanding of computational power demands. The differences were statistically significant, as determined by t-tests.

Integrating symbolic AI planning with robotic execution remains a central challenge in cognitive robotics and robotics education. While automated planning provides powerful tools for high-level reasoning, students often struggle to connect symbolic models with concrete robot behavior. This pa-per presents the design, implementation, and evaluation of an educational workshop that bridges AI-based task planning and robotic manipulation through simulation in NVIDIA Isaac Sim. The workshop was developed as part of an advanced Cognitive Robotics course and combines symbolic plan-ning using the Unified Planning Framework (UPF) and PDDL with execu-tion of manipulation plans by a simulated Franka Emika Panda robotic arm. A Robot Manipulation Language (RML) was introduced to abstract gripper orientations and model pick-and-place actions symbolically, enabling stu-dents to reason about spatial affordances while maintaining executability. The workshop follows a two-stage workflow: modeling and planning manip-ulation tasks, followed by execution and validation in a high-fidelity simula-tion environment. Evaluation results based on student performance, reports, and questionnaires indicate that the workshop effectively supported under-standing of AI planning, robot affordances, and the planning–execution loop. The study demonstrates the educational potential of modern robotic simulation tools for making complex cognitive robotics concepts accessible and actionable.

This article presents nine years of initiatives dedicated to develop Bachelor-level students’ skills for the Industry of the Future. The work highlights how emerging industrial expectations—driven by automation, artificial intelli-gence, IoT, robotics, cybersecurity, and data processing—require new peda-gogical strategies centered on Project-Based Learning (PBL). Since joining the EduNet network in 2017, the University of Orléans has strengthened in-ternational collaboration and integrated professional-grade tools into its cur-riculum. Several major achievements illustrate the impact of this approach: finalist positions in the 2018 Xplore competition, a national victory in 2019, the creation of the world’s first “PLCnext for Bachelor Level” certification in 2021, and a third-place award in the Xplore 2023 contest for an AI-based food-waste reduction project. In 2026, the certification will be updated to re-flect evolving industrial needs and new partnerships. Emerging technologies such as Augmented Reality further enrich future pedagogical developments.

Deep Neural Networks (DNNs) are deep learning models inspired by biological neural networks, used for complex

pattern recognition, visual and auditory data processing, and multidimensional signal interpretation. They have

become fundamental in areas such as image recognition, natural language processing, time series prediction, and

industrial process optimization. Denoising represents a central application of DNNs, with the role of highlighting

hidden structures and increasing the accuracy of analysis. Noise can be introduced by external factors (lighting

conditions, mechanical vibrations, electromagnetic variations) or internal (measurement errors, instrumentation

limitations). Its reduction allows: identifying defects at a microscopic scale, improving the reliability of

communications, ensuring data security, and optimizing visual processing. The goal of using DNNs in denoising is to

exploit their generalization and adaptability to achieve high performance with low data collection costs.