Visions of a Discipline: Analyzing Introductory AI Courses on YouTube

• URL: http://arxiv.org/abs/2407.13077v1
• Date: Fri, 31 May 2024 01:48:42 GMT
• Title: Visions of a Discipline: Analyzing Introductory AI Courses on YouTube
• Authors: Severin Engelmann, Madiha Zahrah Choksi, Angelina Wang, Casey Fiesler,
• Abstract summary: We analyze the 20 most watched introductory AI courses on YouTube. Introductory AI courses do not meaningfully engage with ethical or societal challenges of AI. We recommend that introductory AI courses should highlight ethical challenges of AI to present a more balanced perspective.
• Score: 11.209406323898019
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Education plays an indispensable role in fostering societal well-being and is widely regarded as one of the most influential factors in shaping the future of generations to come. As artificial intelligence (AI) becomes more deeply integrated into our daily lives and the workforce, educational institutions at all levels are directing their focus on resources that cater to AI education. Our work investigates the current landscape of introductory AI courses on YouTube, and the potential for introducing ethics in this context. We qualitatively analyze the 20 most watched introductory AI courses on YouTube, coding a total of 92.2 hours of educational content viewed by close to 50 million people. Introductory AI courses do not meaningfully engage with ethical or societal challenges of AI (RQ1). When \textit{defining and framing AI}, introductory AI courses foreground excitement around AI's transformative role in society, over-exaggerate AI's current and future abilities, and anthropomorphize AI (RQ2). In \textit{teaching AI}, we see a widespread reliance on corporate AI tools and frameworks as well as a prioritization on a hands-on approach to learning rather than on conceptual foundations (RQ3). In promoting key \textit{AI practices}, introductory AI courses abstract away entirely the socio-technical nature of AI classification and prediction, for example by favoring data quantity over data quality (RQ4). We extend our analysis with recommendations that aim to integrate ethical reflections into introductory AI courses. We recommend that introductory AI courses should (1) highlight ethical challenges of AI to present a more balanced perspective, (2) raise ethical issues explicitly relevant to the technical concepts discussed and (3) nurture a sense of accountability in future AI developers.

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