2022
DOI: 10.1609/aaai.v36i11.21554
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Smartphone-Based Game Development to Introduce K12 Students in Applied Artificial Intelligence

Abstract: This paper presents a structured activity based on a game design to introduce k-12 students in the topic of super-vised machine learning from a practical perspective. The activity has been developed in the scope of an Erasmus+ project called AI+, which aims to develop an AI curriculum for high school students. As established in the AI+ principles, all the teaching activities are based on the use of the student's smartphone as the core element to intro-duce an applied approach to AI in classes. In this case, a … Show more

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Cited by 7 publications
(2 citation statements)
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“…Although programming-focused curricular activities that leverage block-based programming environments Winters 2017, 2021;Kahn et al 2018;Lane 2021) notably take steps toward uncovering ML blackboxes, most rely on pre-trained models and APIs to demonstrate ML. A few notable exceptions include (Jatzlau et al 2019;Guerreiro-Santalla et al 2022) which present AI techniques such as Q-learning and clustering in a hands-on, project-based curriculum. Our work follows a similar approach but attempts to explore deeper into fundamental concepts like optimization then leverage this insight to inform rich discussions around cybersecurity, ethics, and bias.…”
Section: Background and Related Workmentioning
confidence: 99%
“…Although programming-focused curricular activities that leverage block-based programming environments Winters 2017, 2021;Kahn et al 2018;Lane 2021) notably take steps toward uncovering ML blackboxes, most rely on pre-trained models and APIs to demonstrate ML. A few notable exceptions include (Jatzlau et al 2019;Guerreiro-Santalla et al 2022) which present AI techniques such as Q-learning and clustering in a hands-on, project-based curriculum. Our work follows a similar approach but attempts to explore deeper into fundamental concepts like optimization then leverage this insight to inform rich discussions around cybersecurity, ethics, and bias.…”
Section: Background and Related Workmentioning
confidence: 99%
“…Although these models provide a lot of innovation to the field, they need high computational power due to their complexity and the high number of required parameters [25]. Moreover, due to the wide proliferation of mobile devices in daily life (e.g., home monitoring healthcare [26], natural psychological evaluations [27], education [28]), a crucial challenge arises, for low power consumption and computing, in implementing lightweight and performance-oriented CNNs [29]. In this context, these models gained an essential role in using mobile devices in real-life scenarios.…”
Section: Introductionmentioning
confidence: 99%