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Title: | Games for artificial intelligence and machine learning education : review and perspectives |
Other Titles: | Non-formal and informal science learning in the ICT era |
Authors: | Giannakos, Michail Voulgari, Iro Papavlasopoulou, Sofia Papamitsiou, Zacharoula Yannakakis, Georgios N. |
Keywords: | Educational games Artificial intelligence Machine learning |
Issue Date: | 2020 |
Publisher: | Springer |
Citation: | Giannakos M., Voulgari I., Papavlasopoulou S., Papamitsiou Z., & Yannakakis G. (2020). Games for artificial intelligence and machine learning education : review and perspectives. In M. Giannakos (Ed.), Non-Formal and Informal Science Learning in the ICT Era (pp. 117-133). Singapore: Springer. |
Abstract: | Digital games have gained significance as a new paradigm in education. Digital games are accessible and affordable to anyone and provide opportunities for at-scale teaching and learning. In recent years, there has been increasing interest in digital games to support computational thinking and programming in pre-college (K–12) schools. Artificial Intelligence (AI) and Machine Learning (ML) are a rapidly developing field, attracting an increasing number of learners in the past few years. Although the confluence of digital games and AI/ML is an important and challenging field for teaching and learning researchers, a literature review has not yet been conducted in this area. The purpose of this work is to present a review of recent research into games to support AI and ML education. After a thorough search, relevant papers and games were selected and included in our qualitative content analysis. Based on this review, we present an overview of the relevant research papers and games, as well as showcased how different games provide a unique opportunity to teach a number of different concepts and topics in AI and ML. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/80910 |
Appears in Collections: | Scholarly Works - InsDG |
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Games_for_artificial_intelligence_and_machine_learning_education_review_and_perspectives_2020.pdf Restricted Access | 151.89 kB | Adobe PDF | View/Open Request a copy |
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