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Auflistung Künstliche Intelligenz 35(2) - Juni 2021 nach Schlagwort "Artificial intelligence"
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- ZeitschriftenartikelEDLRIS: A European Driving License for Robots and Intelligent Systems(KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Kandlhofer, Martin; Steinbauer, Gerald; Lassnig, Julia; Menzinger, Manuel; Baumann, Wilfried; Ehardt-Schmiederer, Margit; Bieber, Ronald; Winkler, Thomas; Plomer, Sandra; Strobl-Zuchtriegl, Inge; Miglbauer, Marlene; Ballagi, Aron; Pozna, Claudiu; Miltenyi, Gabor; Alfoldi, Istvan; Szalay, ImreThis article presents a novel educational project aiming at the development and implementation of a professional, standardized, internationally accepted system for training and certifying teachers, school students and young people in Artificial Intelligence (AI) and Robotics. In recent years, AI and Robotics have become major topics with a huge impact not only on our everyday life but also on the working environment. Hence, sound knowledge about principles and concepts of AI and Robotics are key skills for this century. Nonetheless, hardly any systematic approaches exist that focus on teaching principles of intelligent systems at K-12 level, addressing students as well as teachers who act as multipliers. In order to meet this challenge, the European Driving License for Robots and Intelligent Systems—EDLRIS was developed. It is based on a number of previously implemented and evaluated projects and comprises teaching curricula and training modules for AI and Robotics, following a competency-based, blended learning approach. Additionally, a certification system proves peoples’ acquired competencies. After developing the training and certification system, the first 32 trainer and trainee courses with a total of 445 participants have been implemented and evaluated. By applying this innovative approach—a standardized and widely recognized training and certification system for AI and Robotics at K-12 level for both high school teachers and students—we envision to foster AI/Robotics literacy on a broad basis.
- ZeitschriftenartikelLearning by Enhancing Half-Baked AI Projects(KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Kahn, Ken; Winters, NiallWe have developed thirty sample artificial intelligence (AI) programs in a form suitable for enhancement by non-expert programmers. The projects are implemented in the Snap! blocks language and can be run in modern web browsers. These projects have been designed to be modifiable by school students and have been iteratively developed with over 100 students. The projects involve speech synthesis, speech and image recognition, natural language processing, and deep machine learning. They illustrate a variety of AI capabilities, concepts, and techniques. The intent is to provide students with hands-on experience with AI programming so they come to understand the possibilities, problems, strengths, and weaknesses of AI today.
- ZeitschriftenartikelNeural Network Construction Practices in Elementary School(KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Shamir, Gilad; Levin, IlyaThis paper describes an artificial intelligence (AI) educational project conducted with a small number of 12-year-old students. It is a preliminary step to add AI learning in a city-wide program consisting of elementary school students who learn computational thinking and digital literacy. Today children grow up in an age of AI which significantly affects how we live, work, and solve problems therefore AI should be taught in schools. Children usually employ AI models as black boxes without understanding the computational concepts, underlying assumptions, nor limitations of AI models. The hypothesis of this study is that to understand how machines learn, students should actively construct a neural network. To address this issue a dedicated curriculum and appropriate scaffolds were created for this study. It includes a programmable learning environment for elementary school students to construct AI agents. Findings show high engagement during the constructionist learning and that the novel learning environment helped make machine learning understandable.