Designing AI Recommender System Curriculum to Innovate Pedagogical Practices

Brief Paper (Asynchronous) ID: 62040
  1. George Liu
    Massachusetts Institute of Technology
  2. Nathan Blumofe
    Massachusetts Institute of Technology

Abstract: With the growing popularity of web-based entertainment content such as TikTok, YouTube, and Netflix among children, recommender systems have become one of the most visibly influential forms of Artificial Intelligence (AI) K-12 student’ communities. The study implemented a curriculum based on students creating a music recommender system through a Python Spotify API. The curriculum provides a background on APIs, recommender systems, and computer algorithms design, before engaging students in building a content and score-based recommender system. The curriculum also encourages students to consider the ethical concerns of recommender systems. The study conducted a mixed method approach to understand students' attitudes and knowledge towards AI throughout the curriculum implementation. The results showed the effectiveness of the curriculum to increase students’ interest in AI and their understanding of the role AI plays in their lives. However, the individual variation among students’ ethical understanding of the recommender system might be related to their prior knowledge and explicit exposure to the nuances of AI in classroom learning. The study further con-tributes to AI literacy education that provides the con-tent material and facilitates the discussion for educators to design curriculum across age groups with real-world connection to the social and ethical implications of recommender system.

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