Examining the role of automation transparency in learning with intelligent tutoring systems
Date
2023
Journal Title
Journal ISSN
Volume Title
Abstract
In the present study, a training system that either assigned restudy of concepts based on learner performance (adaptive instruction) or provided a set amount of restudy (static instruction) was designed to investigate whether adding automation transparency into an intelligent tutoring system would improve learning outcomes in an assembly task. Participants received instruction on the assembly process of 8 unique shapes. They were provided with error sensitive feedback that served the transparency manipulation, where some participants received explanations of why they were receiving restudy or were given generic feedback. Findings indicate that adaptive instruction may be most beneficial to learning when automation transparency provides learners with an understanding of how the system is responding to their performance. Findings and implications to be discussed.
Description
Rights Access
Subject
automation
learning
adaptive training
transparency
intelligent tutoring systems