Over 17 countries submitted entries to last year's competition, which addressed various local environmental issues, including city traffic, shoreline erosion, bushfire detection, honey bee endangerment, and more.

Teams will use Altium Upverter Modular PCB design software and the Arduino Portenta H7 to create prototype designs that will improve the environment in each team's respective local area. The teams will be challenged to tackle one or more environmental concerns, such as air pollution, water quality, and solar energy capture.

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Participating teams will enter the design challenge while harnessing Altium Upverter Education and the Upverter Modular tool. Altium features multiple educational initiatives designed to support high school STEM teachers and students, along with programs to support college students and industry professionals.

Open registration is available for the design challenge now via Upverter Education and runs through Monday, October 3. Teams must submit their designs online by Friday, November 18. Competition winners will be announced on Wednesday, December 14, followed by virtual presentations for the first place and runner-up entrants.

Altium, LLC (ASX:ALU) is a global software company headquartered in San Diego, California, accelerating the pace of innovation through electronics. For over 30 years, Altium has been delivering software that maximizes the productivity of PCB designers and electrical engineers. From individual inventors to multinational corporations, more PCB designers and engineers choose Altium software to design and realize electronics-based products.

The IPC Education Foundation creates awareness of the careers the electronics manufacturing industry has to offer students in high school and college by providing them with opportunities to access people, courses, and knowledge through key programs: 1) The IPC Student Chapter program provides scholarships, industry-standard education, industry connections, and access to hands-on competitions, especially with the support of industry experts and professionals; 2) IPC Video Subscription Libraries provide access to industry-relevant content to students in high school and college, and 3) a variety of engagement initiatives like in-person/virtual events, webinars, and classroom activities.

Arduino is the leading open-source hardware and software company in the world. Born to provide an easy-to-use platform for anyone making interactive projects, Arduino has reached a growing community and adapted to new needs and challenges, branching out into products for IoT, wearables, 3D printing, and embedded environments. As of today, the Arduino community included approximately 30 million active users.

Carnegie Mellon Robotics Academy studies how educators can use robots to teach Computer Science, Science, Technology, Engineering, and Mathematics (CS-STEM). Our mission is to use the educational affordances of robotics to create CS-STEM opportunities for all learners. We fulfill our mission by developing research-based solutions that are classroom-tested and foreground CS-STEM concepts.

As a world leader in robotics education, the Carnegie Mellon Robotics Academy and has trained thousands of teachers and coaches internationally. Our professional development courses equip educators with both the content and pedagogical knowledge needed to successfully implement robotics programs. All training and professional development courses allow educators to earn a Carnegie Mellon Robotics Academy Certification, simultaneously providing valuable continuing education credits and enabling them to offer our certifications to their students.

Coding and Computational Thinking with a VIrtual Robot features a brand-new bot: "VICE" (short for Virtual-Integrated Curriculum Environment). VICE packs a wide variety of sensors to detect its environment, outputs to communicate with you, and motors to navigate its environment and manipulate objects. Learn More >

The Carnegie Mellon Robotic Academy team is located within the National Robotics Engineering Center (NREC), where robots for business, government, and industry are designed, prototyped, and tested just outside our office doors. The NREC is part of the Carnegie Mellon University - Robotics Institute, a world-renowned robotics organization.

Students are exposed to AI every day. At young ages, many children have access to AI-capable devices, such as online search, personal digital assistants, automatic translations, and computer games. However, studies [1] indicate that children do not comprehend how their intelligent games and other applications utilise AI. Education and awareness are essential to dispelling student misconceptions about digital devices. Evangelista et al. [2] state that there is an increasing need to familiarise students of the 21st century with fundamental AI knowledge.

Machine learning is a subfield of AI based on the development of algorithms for training data models. Machine learning algorithms create models that make decisions or make predictions based on training data. Starting from the data itself, as shown in Fig. 1, and not some pre-existing theoretical model, using computational methods that are impossible to apply without a computer, the trained model is generated [7].

This method of utilising mathematical data models allows a computer to self-learn. It uses algorithms to identify patterns in the data and then uses those patterns to create a predictive model. In this context, machine learning is defined as the ability of a computing system to generate models or patterns from a dataset [8].

In addition, it is becoming increasingly vital that citizens are aware of the ethical and safety concerns that its use raises. According to the study [11], the demand for professionals with AI knowledge will increase significantly in the decades to come. Moreover, 67% of companies are already utilising machine learning, and 97% plan to use it within the next year. Following how humans learn, machine learning develops three types of learning: supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning is the process where the neural network learns to map input data to known-expected output data, with the ultimate goal of generalising inputs with unknown output [12]. The correct output value for the examined data is indicated by the system. Supervised learning algorithms use the data to train the model by analysing it and generating a model that can be used to characterise new input data.

Unsupervised Learning is the process where the algorithm constructs a model for a set of unsupervised inputs without knowing the desired outputs. It groups unclear information according to similarities, differences, and patterns that may exist without prior training in the data [12].

The benefits of teaching machine learning to students at all grade levels include introducing students to AI concepts, helping them develop foundational skills for life and work, adopting mental models, and inspiring the next generation of AI and machine learning researchers.

Introducing a simplified form of machine learning into education without the complexity of programming has a major learning outcome in understanding how important it is to develop reliable machine learning models for real-world applications and problems. A second crucial aim is for students to investigate and reflect by exchanging views on the possible effects on life and freedom of individuals from the improper use of AI [15]. Evangelista et al. [2] argue that machine learning should be taught to students at a young age because it is such a popular topic. Thus, they have a clearer understanding of what it means for a computer to self-teach and what tools are necessary for this to occur.

Although AI is a much more complex and challenging topic, Sakulkueakulsuk et al. [16], based on the results of their study, found that students had more fun, were more engaged, and cooperated more when implementing the activity in the lab than in the regular classroom. This shows that students can understand very complex concepts if the learning environment is well designed and organised. The majority of curricula for all levels of education in most countries suggest that machine learning and AI should be implemented in schools [15].

Whether referring to natural phenomena or socioeconomic and political issues, the study of a real problem provides a solid foundation for STEM. STEM epistemology addresses real-world, complex problems requiring interdisciplinary approaches [21]. When students are engaged in authentic STEM contexts and asked to solve problems they encounter on a daily basis, they are more motivated because the knowledge they are taught is relevant to their lives. In addition, solving authentic problems through STEM provides emotional outcomes, such as increased student engagement, perseverance, as well as crucial educational outcomes, such as critical and logical thinking and problem-solving skills.

Gao et al. [17] argue that one of the primary educational goals of STEM is to help students acquire content knowledge about one or more STEM disciplines. According to this strategy, priority is given to one or more specific cognitive STEM areas, while the other disciplines serve as a means of acquisition knowledge for these cognitive areas. Additionally, they note that another common learning goal of STEM education in the cognitive domain is to help students develop skills that transcend a single discipline. In other words, students should be able to use knowledge from various disciplines. The degree of knowledge for each discipline varies depending on the nature of the problem or situation, despite the fact that the significance of each discipline is treated equally (helping to understand the situation). Concerning the affective domain, students should focus their interest on, engagement, attitude, and motivation they acquire from STEM content and practises. Lastly, STEM education is anticipated to direct students to STEM professions and, by extension, to meet labour market demands. 152ee80cbc

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