Adverse effects of information personalization on human learning

Many online content providers use “personalization” algorithms to generate recommendations that are individually fine-tuned for users’ interests. However, these algorithms have also been criticized because tailoring content to specific users necessarily restricts the diversity of content, leading to reinforcement of existing beliefs. Here, we investigated the degree to which personalization can hinder learning in the context of learning about novel categories. Our results show that personalized learners developed inaccurate representations about the categories and reported inflated confidence about incorrect decisions for what they have never or rarely studied before relative to non-personalized learners. The results suggest that personalization algorithms may contribute to distorted representations of information, which in turn causes inaccurate generalizations, such as stereotypes.

Ryan Watkins

Professor at George Washington University

I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.