Reactive AI feedback Improves task performance over time

Author's Department

Computer Science & Engineering Department

Find in your Library

https://doi.org/10.1016/j.cogsys.2025.101361

All Authors

Jacquelyn H. Berry

Document Type

Research Article

Publication Title

Cognitive Systems Research

Publication Date

9-1-2025

doi

10.1016/j.cogsys.2025.101361

Abstract

What is the best way to give feedback to improve task performance? Informing someone of their success after the fact, which they can often plainly see, is effective for simple tasks. However, for complex, ecologically-based tasks with multiple subskills such as piloting a helicopter, remotely operating a robot arm, or playing Tetris, this type of feedback may be less effective. Some research suggests that certain types of feedback given during task performance maybe preferred for complex tasks rather than feedback given after the fact. This question was addressed by this pilot study which compared performance across sessions in the video game Tetris. Novice Tetris players were provided Reinforcement-based feedback, Instructive feedback, or a combination of the two. Results suggest that Instructive feedback, followed by combining the two, was most effective for improving performance over time.

Link to Full Text
Find this item online

Share

COinS