The Effect of Recommendation Source and Justification on Professional Development Recommendations for High School Teachers
Lijie Guo, Christopher Flathmann, Reza Ghaiumy Anaraky, Nathan J. McNeese, Bart P. Knijnenburg · 2022
This paper describes a study conducted in the process of building a recommender system that provides personalized professional development pathways for high school teachers seeking to increase their disciplinary knowledge and/or their teaching skills. A controlled experiment (N = 190) was conducted to study the effects of the presented justification for the recommendations (teachers’ needs vs. their interests) and the presented source of the recommendations (a human expert vs. an AI algorithm) on users’ perceptions of and experience with the system. Our results show an interaction effect between these two system aspects: users who are told that the recommendations are based on their interests have a better experience when the recommendations are presented as originating from an AI algorithm, while users who are told that the recommendations are based on their needs have a better experience when the recommendations are presented as originating from a human expert.