Quantifying the Academic Quality of Kids' Videos using Machine Comprehension
By Sumeet Kumar, Mallikarjuna T., Ashique Khudabaksh
Informs Journal of Computing | 2025
Citation
Kumar, Sumeet., T., Mallikarjuna., Khudabaksh, Ashique. (2025). Quantifying the Academic Quality of Kids' Videos using Machine Comprehension Informs Journal of Computing .
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Informs Journal of Computing, 2025
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Abstract
YouTube Kids (YTK) is one of the most popular kids' applications used by millions of kids daily. However, various studies have highlighted concerns about the videos on the platform, like the over-presence of entertaining and commercial content. YouTube recently proposed high-quality guidelines that include `promoting learning' and proposed to use it in ranking channels. However, the concept of learning is multi-faceted, and it can be difficult to define and measure in the context of online videos. This research focuses on learning in terms of what's taught in schools and proposes a way to measure the academic quality of children's videos. Using a new dataset of questions and answers from children's videos, we first show that a Reading Comprehension (RC) model can estimate academic learning. Then, using a large dataset of middle school textbook questions on diverse topics, we quantify the academic quality of top channels as the number of children's textbook questions that an RC model can correctly answer. By analyzing over 80,000 videos posted on the top 100 channels, we present the first thorough analysis of the academic quality of channels on YTK.

Sumeet Kumar is an Assistant Professor of Information Systems at the Indian School of Business (ISB). He studies problems at the intersection of technology and society. He is interested in analysing user behaviour, quantifying polarisation on online forums , and finding advertisements disguised as regular content on online platforms. His current focus is on identifying implicit or hidden advertisements in videos posted on children’s platforms such as YouTube Kids.

Additionally, Professor Kumar has conducted research in software design and development, with particular emphasis on user experience. He has investigated the use of mobile phone sensors during emergencies to improve situational awareness. His study on the Wireless Emergency Alerts (WEA) service in the United States addressed several issues of critical importance to emergency alerts effectiveness and adoption. Notably, some of his research recommendations was included in the US Federal Communications Commission (FCC) proposed changes to WEA.

He completed his undergraduate education at Indian Institute of Technology (IIT) Kanpur. He holds two Master’s degrees—in Software Engineering and in Machine Learning--both from Carnegie Mellon University, where he also earned his doctorate degree.

Sumeet Kumar
Sumeet Kumar