Deep learning goes to school: toward a relational understanding of AI in education

TLDR

Machine learning employs neural networks to train computers to perform tasks without human intervention. The article questions whether machine‑learning methods are suitable for educational contexts. The authors examine recent data‑science attempts to embed deep‑learning components in platforms such as Khan Academy and ASSISTments, using Science and Technology Studies to analyze the resulting scholarly work. Their analysis reveals that deep‑learning applications in education link flawed data, opaque algorithms, narrow pedagogical assumptions, and reductionist data‑science rhetoric, creating a controversy that questions AI’s objectivity and exposes economic interests.

Abstract

In Applied AI, or ‘machine learning’, methods such as neural networks are used to train computers to perform tasks without human intervention. In this article, we question the applicability of these methods to education. In particular, we consider a case of recent attempts from data scientists to add AI elements to a handful of online learning environments, such as Khan Academy and the ASSISTments intelligent tutoring system. Drawing on Science and Technology Studies (STS), we provide a detailed examination of the scholarly work carried out by several data scientists around the use of ‘deep learning’ to predict aspects of educational performance. This approach draws attention to relations between various (problematic) units of analysis: flawed data, partially incomprehensible computational methods, narrow forms of educational’ knowledge baked into the online environments, and a reductionist discourse of data science with evident economic ramifications. These relations can be framed ethnographically as a ‘controversy’ that casts doubts on AI as an objective scientific endeavour, whilst illuminating the confusions, the disagreements and the economic interests that surround its implementations.