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Auflistung Künstliche Intelligenz 36(1) - März 2022 nach Schlagwort "Artificial intelligence"
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- ZeitschriftenartikelPrimary Mathematics Teachers’ Understanding of Computational Thinking(KI - Künstliche Intelligenz: Vol. 36, No. 1, 2022) Nordby, Siri Krogh; Bjerke, Annette Hessen; Mifsud, LouiseComputational thinking (CT) is often regarded as providing a ‘soft start’ for later involvement with artificial intelligence and, hence, as a crucial twenty-first century skill. The introduction of CT in primary mathematics curricula puts many demands on teachers, and their understanding of CT in mathematics is key to its successful introduction. Inspired by an information ecology perspective, we investigate how four primary school teachers understand CT in mathematics and how they go ahead to include CT in their mathematics teaching practice. Through observations and interviews, we find promising starting points for including CT, related to pattern recognition, problem solving and the use of programming activities. Our findings indicate that teachers’ lack of knowledge affects CT adoption in two ways: during its inclusion in the existing mathematics curriculum and as a new element focussed on programming and coding, leaving mathematics in the background. For the inclusion to be fruitful, we suggest there is a need to help teachers understand how CT can be used productively in mathematics and vice versa.
- ZeitschriftenartikelProgramming and Computational Thinking in Mathematics Education(KI - Künstliche Intelligenz: Vol. 36, No. 1, 2022) Tamborg, Andreas Lindenskov; Elicer, Raimundo; Spikol, DanielArtificial intelligence (AI) has become a part of everyday interactions with pervasive digital systems. This development increasingly calls for citizens to have a basic understanding of programming and computational thinking (PCT). Accordingly, countries worldwide are implementing several approaches to integrate critical elements of PCT into K-9 education. However, these efforts are confronted by difficulties that the PCT concepts are for students to grasp from purely theoretical perspectives. Recent literature indicates that the playful nature is particularly important when novices from both both early and higher education are to learn AI. These playful activities are characterised by setting a scene where PCT concepts such as algorithms, data processing, and simulations are meant to draw on to understand better how AI is integrated into our everyday digital life. This discussion paper analyses playful PCT resources developed around the game rock-paper-scissors developed in the UK and Denmark. Resources from these countries are interesting starting points since both have been or are in the process of integrating PCT as part of the K-9 curriculum. The central discussion raised by the paper, is the nature of the integration between mathematics and PCT in these tasks. These resources provide opportunities for discussion of how we may better integrate PCT and mathematics from the perspective of both subjects to build a solid foundation for a critical understanding of AI interactions in future generations.
- ZeitschriftenartikelSurvey: Artificial Intelligence, Computational Thinking and Learning(KI - Künstliche Intelligenz: Vol. 36, No. 1, 2022) Dohn, Nina Bonderup; Kafai, Yasmin; Mørch, Anders; Ragni, MarcoLearning is central to both artificial intelligence and human intelligence, the former focused on understanding how machines learn, the latter concerned with how humans learn. With the growing relevance of computational thinking, these two efforts have become more closely connected. This survey examines these connections and points to the need for educating the general public to understand the challenges which the increasing integration of AI in human lives pose. We describe three different framings of computational thinking: cognitive, situated, and critical. Each framing offers valuable, but different insights into what computational thinking can and should be. The differences between the three framings also concern the views of learning that they embody. We combine the three framings into one framework which emphasizes that (1) computational thinking activities involve engagement with algorithmic processes, and (2) the mere use of a digital artifact for an activity is not sufficient to count as computational thinking. We further present a set of approaches to learning computational thinking. We argue for the significance of computational thinking as regards artificial intelligence on three counts: (i) Human developers use computational thinking to create and develop artificial intelligence systems, (ii) understanding how humans learn can enrich artificial intelligence systems, and (iii) such enriched systems will be explainable. We conclude with an introduction of the articles included in the Special Issue, focusing on how they call upon and develop the themes of this survey.