Logo des Repositoriums

Künstliche Intelligenz 35(2) - Juni 2021

Autor*innen mit den meisten Dokumenten  

Auflistung nach:

Neueste Veröffentlichungen

1 - 10 von 16
  • Zeitschriftenartikel
    Learning by Enhancing Half-Baked AI Projects
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Kahn, Ken; Winters, Niall
    We 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.
  • Zeitschriftenartikel
    Three Interviews About K-12 AI Education in America, Europe, and Singapore
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Heintz, Fredrik
    As the impact and importance of artificial intelligence (AI) grows, there is a growing trend to teach AI in primary and secondary education (K-12). To provide an international perspective, we have conducted three interviews with practitioners and policy makers from AI4K12 in the US (D. Touretzky, C. Gardner-McCune, and D. Seehorn), from Singapore (L. Liew) and from the European Commission (F. Benini).
  • Zeitschriftenartikel
    Comparing 2 Years of Empowering Families to Solve Real-World Problems with AI
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Chklovski, Tara; Jung, Richard; Anderson, Rebecca; Young, Kathryn
    Over the course of 2 years a global technology education nonprofit engaged ~ 20,000 under-resourced 3rd-8th grade students, parents and educators from 13 countries in a multi-week AI competition. Families worked together with the help of educators to identify meaningful problems in their communities and developed AI-prototypes to address them. Key findings included: (1) Identifying a high level of interest in underserved communities to develop and apply AI-literacy skills; (2) Determining curricular and program implementation elements that enable families to apply AI knowledge and skills to real problems; (3) Identifying effective methods of engaging industry mentors to support participants; (4) Measuring and identifying changes in self-efficacy and ability to apply AI-based tools to real-world problems; (5) Determining effective curricula around value-sensitive design and ethical innovation.
  • Zeitschriftenartikel
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021)
  • Zeitschriftenartikel
    Why, What and How to Help Each Citizen to Understand Artificial Intelligence?
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Alexandre, Frédéric; Becker, Jade; Comte, Marie-Hélène; Lagarrigue, Aurélie; Liblau, Romain; Romero, Margarida; Viéville, Thierry
    A critical understanding of digital technologies is an empowering competence for citizens of all ages. In this paper we introduce an open educational approach of artificial intelligence (AI) for everyone. Through a hybrid and participative MOOC we aim to develop a critical and creative perspective about the way AI is integrated in the different domains of our lives. We have built and now operate a MOOC in AI for all the citizens from 15 years old. The MOOC aims to help understanding AI foundations and applications, intended for a large public beyond the school domain, with more than 20,000 participants engaged in the MOOC after nine months. This study addresses the pedagogical methods for designing and evaluating the MOOC in AI. Through this study we raise four questions regarding citizen education in AI: Why (i.e., to which aim) sharing such citizen formation? What is the disciplinary knowledge to be shared? What are the competencies to develop? How can it be shared and evaluated? We finally share learning analytics, quantitative and qualitative evaluations and explain to which extent educational science research helps enlighten such large scale initiatives. The analysis of the MOOC in AI helps to identify that the main feedback related to AI is “fear”, because AI is unknown and mysterious to the participants. After developing playful AI simulations, the AI mechanisms become familiar for the MOOC participants and they can overcome their misconception on AI to develop a more critical point of view. This contribution describes a K-12 AI educational project or initiatives of a considerable impact, via the formation of teachers and other educators.
  • Zeitschriftenartikel
    Cooperative Human Artificial Intelligence
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Ragni, Marco
  • Zeitschriftenartikel
    EDLRIS: 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, Imre
    This 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.
  • Zeitschriftenartikel
    Teaching Artificial Intelligence to K-12 Through a Role-Playing Game Questioning the Intelligence Concept
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Henry, Julie; Hernalesteen, Alyson; Collard, Anne-Sophie
    Although artificial intelligence (AI) is becoming increasingly important in the media environment (search engines, chatbots, home assistants, recommendation systems, etc.), the general audiences’ knowledge of it remains limited, which biases their representations. To compensate for this, some governments show an interest in teaching it from an early age. It appears that educational resources related to AI literacy in schools are most often focused on technical skills. However, the challenges of such education are also ethical and societal, requiring an interdisciplinary and critical approach. This research aims at developing a 10–14 years old curriculum questioning the concept of intelligence in AI systems, and crossing computer science education and media literacy education. Through a role-playing game, the children discover the basic concepts of machine learning. Beyond their initial representations, which they become aware that they are largely fueled by the media, they can realize that an AI system is the result of design choices and that it only works within the framework that has been defined for it. Moreover, the possibility for teachers to teach the curriculum themselves in their classes is also evaluated. To this end, the curriculum was taught to 60 future trainee teachers, 70 middle school pupils, and 12 elementary pupils. Interviews were conducted also with 5 teachers who had either observed the curriculum taught by a researcher or attempted to teach it themselves. The results show that the children’s representations have evolved towards representations that are more technically correct (although incomplete), but not very oriented towards aspects that open up critical questioning. The difficulties revealed in the implementation of the critical part are due in particular to the complexity of the IT concepts to be addressed, but also to the lack of teacher training. However, the data collected seems to confirm the interest and feasibility of crossing different disciplinary approaches to address certain aspects of AI. In conclusion, in addition to the curriculum, this paper describes a theoretical model of critical citizenship education in technology that integrates approaches to computer science education and media literacy education, and gives avenues for other designers and researchers to create AI critical educational experiences for K-12 learners.
  • Zeitschriftenartikel
    Education in Artificial Intelligence K-12
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Steinbauer, Gerald; Kandlhofer, Martin; Chklovski, Tara; Heintz, Fredrik; Koenig, Sven
  • Zeitschriftenartikel
    Neural Network Construction Practices in Elementary School
    (KI - Künstliche Intelligenz: Vol. 35, No. 2, 2021) Shamir, Gilad; Levin, Ilya
    This 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.