Learn to Machine Learn: Designing a Game Based Approach for Teaching Machine Learning to Primary and Secondary Education Students

Abstract

With the ubiquitous role of Artificial Intelligence (AI) in everyday applications such as smartphones and social media, children need digital literacy skills to navigate the digital world, critically view, and reflect on the social and ethical implications of the design and architecture of AI systems. To address this increasing need for AI literacy skills, particularly for younger students, this paper presents the rationale of the LearnML project which aims to develop a framework and game-based educational material for promoting AI literacy among primary and secondary education students. We also describe the design and initial assessment of the game "Art-Bot", developed as part of the LearnML project. We review existing literature, discuss the educational game design and development of "ArtBot" and describe the initial feedback of students and teachers. Our goal is to provide insights and suggest guidelines for the implementation of game-based learning environments for supporting AI literacy skills of students.

Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review

“…Besides articles focusing specifically on High School level, some courses are designed for a wider scope of educational stages, such as elementary or middle school to High School (Zimmermann-Niefield et al, 2019;Kandlhofer et al 2016;Rodríguez-Garcia et al, 2021;Voulgari et al, 2021). We also observed that some instructional units are specifically targeting girls aiming at the inclusion of minorities (Vachovsky et al, 2016).…”

“…(Norouzi et A workshop aligned with the K-12 AI curriculum from (Van Brummelen 2019), in which students develop conversational agents using an interface in MIT App Inventor. (Voulgari et al, 2021) ArtBot: a game-based approach for teaching Machine Learning to primary and secondary education students.…”

“…References Students demonstrated a gain in understanding of what ML is, basic concepts and how ML works, enabling them to understand, discuss and explain how ML systems work. (Bhatia 2020;Chua et al, 2019;Zhu, 2019;Lao, 2020;Voulgari et al, 2021;Wan et al, 2020;Burgsteiner et al, 2016) Students were enabled to design an ML system as a result of some courses . (Chua et al, 2019;Bilstrup et al, 2020) Students understood the importance of data for ML especially through courses that cover data science to support the teaching of AI/ML.…”

“…(Bhatia, 2020;Kaspersen et al, 2021;Sperling and Lickerman, 2012;Van Brummelen et al, 2020;Santana et al, 2018;Wan et al, 2020;Zimmermann-Niefield et al, 2019) High-order thinking tasks help in engaging students with technology. (Voulgari et al, 2021) Students enjoyed project-based learning, building AI systems and had positive feelings about their projects. (Huang et al, 2021;Van Brummelen, 2019) Applying problem-based learning, successfully allowed students to work on powerful, purposeful real-world applications in their communities, engaging students to recognize constraints in the real world and the inexistence of one perfect solution to problems.…”

“…(Wan et al, 2020;Zimmermann-Niefield et al, 2019; Students enjoyed game-based learning and 73% (n=95) of the students ( indicated that the game helped them to understand ML principles and processes. (Voulgari et al, 2021)…”