What Will Affect the Diffusion of AI Agents?
Author:
Open Access
- 1 October 2021
- journal article
- research article
- Published by Communication and Social Robotics Labs in Human-Machine Communication
- Vol. 3, 83-89
Abstract
For billions of people, the threat of the Novel Coronavirus SARS-CoV-2 and its variants has precipitated the adoption of new behaviors. Pandemics are radical events that disrupt the gradual course of societal change, offering the possibility that some rapidly adopted innovations will persist in use past the time period of the event and, thus, diffuse more rapidly than in the absence of such an event. Human-machine communication includes a range of technologies with which many of us have quickly become more familiar due to stay-athome orders, distancing, workplace closures, remote instruction, home-bound entertainment, fear of contracting COVID-19, and boredom. In this commentary I focus on Artificial Intelligence (AI) agents, and specifically chatbots, in considering the factors that may affect chatbot diffusion. I consider anthropomorphism and expectancy violations, the characteristics of chatbots, business imperatives, millennials and younger users, and from the user perspective, uses and gratifications.Keywords
This publication has 15 references indexed in Scilit:
- In the shades of the uncanny valley: An experimental study of human–chatbot interactionFuture Generation Computer Systems, 2019
- Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a ChatbotJournal of Communication, 2018
- Diffusion Of Innovations Theory, Principles, And PracticeHealth Affairs, 2018
- Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversationsComputers in Human Behavior, 2015
- Feeling robots and human zombies: Mind perception and the uncanny valleyCognition, 2012
- Virtual humans elicit socially anxious interactants' verbal self‐disclosureComputer Animation and Virtual Worlds, 2010
- Personalizing e-Learning. The Social Effects of Pedagogical AgentsEducational Psychology Review, 2010
- A probabilistic multimodal approach for predicting listener backchannelsAutonomous Agents and Multi-Agent Systems, 2009
- Modelling social action for AI agentsArtificial Intelligence, 1998
- Portraying the New: Communication Between University Innovators and Potential UsersScience Communication, 1994