ABSTRACT. As AI technology based on Generative Pre-trained Transformer (GPT) advances, an inferential model has emerged with GPT-4 that generates results by making inferences about prompts in a manner similar to human thinking. The inference model is designed to produce more accurate outcomes by making reasoning once from the user's input and then re-entering the result values. In this study, we compared the cognitive processes involved in team-based problem-solving within the engineering design process to the inferential results that individuals generate when using a GPT inference model. Halfin's codes were utilized to analyze the cognitive processes optimized for examining the engineering design process, and the findings will be assessed using ratio and sequential analysis methods. Through this study, we aim to determine the comparative strengths and weaknesses of AI thinking versus human thinking in addressing engineering design challenges and to discuss the proper use of AI in design and technology education.

ABSTRACT. Climate change is one of the greatest challenges of our time, which, according to institutional and political discourse, requires an imperative ecological and social transition (AFD, 2021). Therefore, it is crucial to prepare citizens for these new environmental challenges by equipping them with the skills needed to build a sustainable future. Technology education constitutes a strategic level of action, among others, to achieve this goal. In Tunisia, this dynamic is reflected in education, particularly through the introduction of renewable energy into the curriculum of the third year of technical sciences in high schools, with a particular focus on solar energy from 2022. This initiative aims to train a generation of citizens aware of environmental issues and equip them with the skills necessary to actively participate in the energy transition (Technology Curriculum, 2022). Our study focused on analyzing teachers' practices in response to this educational reform. Through an analysis of the Tunisian curriculum and the activities offered to students, as well as a semi-structured interview with 10 teachers, we qualitatively explored how educational tools are used to raise student awareness of environmental issues. The results reveal several challenges, including the lack of internet access in classrooms, forcing some teachers to simplify or circumvent planned digital activities, such as the use of PV6 software. This problem limits students' acquisition of targeted photovoltaic simulation skills. Furthermore, teachers emphasized that the complexity of the calculations related to the sizing of photovoltaic installations, presented in the textbook, is often unsuitable for students' level, distancing them from the educational objectives related to autonomy and engagement in the energy transition. These results highlight the importance of rethinking the integration of educational tools within an accessible framework aligned with local socioeconomic realities, in order to strengthen the role of education in the ecological and social transition.

ABSTRACT. In primary education, children with disabilities often face greater challenges in language acquisition compared to their peers without disabilities. This difficulty is compounded by the complexity of sign language and the limited ability of non-disabled parents to assist their children, as they are typically not proficient in sign language. This study proposes a device based on holographic projectors that simulates Arabic Sign Language, aimed at enhancing learning for deaf children. The device demonstrates the various letters and their shapes. Leveraging holographic projector technology, this solution is designed to be cost-effective and accessible for parents, providing an innovative tool to support children’s education at home.

ABSTRACT. This study presents an exploratory collaborative research on the role of educational robotics in developing mathematical thinking and the technology design process at elementary level. In collaboration with two techno-pedagogues (educational technology coordinators) and a mathematics education consultant, we are implementing a robotics-based project that explores the simultaneous variation of two quantities during the movement of a Lego Spike Essential robot. Our approach is inspired by Galileo's (1564-1642) study of motion.

We hypothesize that analyzing the robot’s within a mathematics and technology learning context – encompassing number sense, measurement, and data processing could offer rich learning opportunities for introducing the design process and foundational mathematical function concepts at elementary level, prior to their formal introduction in secondary education. . t. Our research aims to examine how elementary students' understanding the interdependence between two variables as it relates to a designed artifact, and to identify the registers of semiotic representations (Duval, 2006) that they use to express this relationship . The s study is being conducted in a multi-level class of 1st and 2nd cycles of a rural elementary school in Northern Quebec. . We will then elaborate on the appropriate theoretical framework and methodological approach adopted.

ABSTRACT. Technologies that use sensors and converters of physical quantities into digital signals are ubiquitous in everyday life. The functioning of an electronic thermometer can seem abstract and inaccessible (Sorey, Willard, & Kim, 2010). For science and technology teachers, this is problematic insofar as understanding how an instrument works allows them to assess the validity of experimental data (Fournier, 2001). This poster presentation aims to shed light on electronic measuring instruments as a learning object in technology education.

The general objective of our research was to develop a didactic intervention that would allow pre-service secondary science and technology teachers to build electronic measuring instruments in order to understand how they work.

This research is based on Raymond Duval’s theory of semiotic representation registers (1995, 2017) to explain how learners can understand the functioning of an electronic measuring instrument by mobilizing symbols and shifting from one semiotic representation register to another, coming from different STEM disciplines.

We adopted a research-development methodology (Harvey & Loiselle, 2009; Nonnon, 1993). First, we designed the didactic intervention while limiting ourselves to functional and empirical testing. Second, 27 pre-service teachers built four electronic measuring instruments in a computer-assisted experimentation environment, following a process that became progressively less guided, culminating in the autonomous construction of the manometer, which was observed and filmed. Third, the students completed a questionnaire to assess their understanding of how the manometer works.

A mixed thematic content analysis was performed on the verbatim transcription of each participant’s actions.

Students’ actions and questionnaire responses revealed a better understanding of how semiotic representation registers coordinate with non-semiotic representations.

This research provides insights into how the functioning of electronic measuring instruments can be introduced as a learning object in technology education for pre-service science and technology teachers.

ABSTRACT. Green hydrogen, produced by water electrolysis powered by renewable sources such as solar and wind energy, is emerging as a key solution to today’s energy and environmental challenges. It offers a clean, efficient way to decarbonize mobility and other sectors. This paper explores how green hydrogen, combined with fuel cell systems and supercapacitors, can be effectively integrated into engineering education to prepare students for the energy transition and the technologies shaping tomorrow’s mobility. We introduce the main principles of green hydrogen production and its role in powering fuel cell electric vehicles (FCEVs). The operation of fuel cells is discussed, with emphasis on how they convert hydrogen into electricity for zero-emission propulsion. In parallel, the role of supercapacitors is presented—they enhance system performance by delivering high power density and managing rapid load variations. A major focus of the paper is the intelligent control and optimization of hybrid fuel cell–supercapacitor (FC–SC) systems using Artificial Intelligence (AI). Techniques such as adaptive control, machine learning, and predictive optimization are highlighted for their ability to improve energy efficiency, extend component life, and ensure smooth energy flow under varying driving conditions. This pedagogical initiative is supported by a practical component: a hands-on experimental platform at the ASE-lab of ENSA Kénitra (Morocco), where students explore real FC–SC systems with AI-based control. The platform bridges theory and practice, reinforcing technical knowledge through experimentation. The approach is designed for undergraduate students, combining theoretical insights with practical experience in renewable energy, hydrogen technologies, and intelligent control. Beyond technical training, it aims to raise awareness of sustainable development and empower future engineers to contribute meaningfully to a more inclusive and environmentally responsible future.