Learning from machines: How negative feedback from machines improves learning between humans

• Main Authors: ZOU, Tengjian, ERTUG, Gokhan, ROULET, Thomas
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Learning; failure; machine-human interaction; Educational Methods; Graphics and Human Computer Interfaces; Strategic Management Policy; Technology and Innovation
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/7521; https://ink.library.smu.edu.sg/context/lkcsb_research/article/8520/viewcontent/1_s2.0_S0148296323007762_pvoa_cc_by.pdf
• Institution: Singapore Management University

Prior studies on learning from failure primarily focus on how individuals learn from failure feedback given by other individuals. It is unclear whether and how the advent of machine feedback may influence individuals’ learning from failures. We suggest that failure feedback provided by machines facilitates learning in two ways. First, it focuses individuals’ attention on their failures, leading them to learn from these failures. Second, it serves as a catalyzer, motivating individuals to learn more from failure feedback given to them by other individuals as well. In addition, this catalyzing effect is stronger if the failure feedback from machines and by other individuals pertain to related tasks. Using a dataset of 1.5 million observations from an online programming contest community, we find support for our predictions. We contribute to the learning literature by demonstrating both the direct effect and the catalyzing effect of machine failure feedback on individuals’ learning.