Online-Computer-Mediated Interviews and Observations: Overcoming Challenges and Establishing Best Practices in a Human-AI Teaming Context

Abstract

Prior research has established the feasibility of conducting online interviews and observations, yet there is limited guidance in how to interact with participants when conducting fully mediated research with screen-sharing and video. This study, conducted during early phases of COVID-19, included 15 volunteer tweet-annotators working with an emergency response organization.This method contribution uses cues-related and surveillance theories to reveal challenges and best practices when asking research participants to share their screen, be on video, and participate in a multiple-interview study. The findings suggest that researchers conducting online-mediated research should be prepared to provide technical support for the devices and interfaces participants use during the study, find ways to "see" beyond what is on the mediated screen, and consider ethical issues not often discussed. In addition to these findings, an output of this research is two brief training videos useful for other researchers embarking on conducting fully mediated research.

Involvement of Parents in Remote Learning of Children amid COVID-19 Crisis in the Philippines: A Transcendental Phenomenology

“…Because of the restrictions caused by the current COVID-19 situation, personal interviews were not possible, thus an augmented form of interview called online interview was done individually with the participants. An online interview is a research method conducted using communication mediated by computers (Salmons, 2014;Stephens, 2021). Just like the personal interview, the online interview asked participants to describe what they think or feel about an aspect, which in this case, is the involvement of parents in the remote learning of their children amid the COVID-19 crisis.…”

Converging Human Intelligence with AI Systems to Advance Flood Evacuation Decision Making

“…Previous studies on the concepts similar to HAC have focused on the interaction and effectiveness of human teams with AI working in robotic swarms (Seeber, et al, 2020) with landed aircraft perimeter security (Madni & Madni, 2018) in collaborative games (Ong, et al, 2012), to identify risky human behavior (Stephens et al, 2023), and how various factors such as a person's understanding of the limits or mistakes of a machine learning system might affect team performance in a HAC implementation (Bansal, et al, 2019;Liang et al, 2019;). These studies have led to increased growth in HAC literature, where humans and AI data meet at a point to work together in collaboration and carry out complex tasks as an integrated unit.…”