Are Conversational AI Agents the Way Out? Co-Designing Reader-Oriented News Experiences with Immigrants and Journalists

TL;DR

This paper explores how conversational AI can be co-designed with immigrants and journalists to improve reader-oriented news experiences, addressing unique engagement challenges faced by immigrant readers.

Contribution

It introduces four metaphors for designing conversational AI agents that facilitate collaborative news reading among immigrants and journalists, based on co-design research.

Findings

Identified an 'unaddressed-or-unaccountable' paradox in news engagement.

Proposed four metaphors for AI design to support immigrant readers.

Provided insights into shared responsibilities in AI-assisted news reading.

Abstract

Recent discussions at the intersection of journalism, HCI, and human-centered computing ask how technologies can help create reader-oriented news experiences. The current paper takes up this initiative by focusing on immigrant readers, a group who reports significant difficulties engaging with mainstream news yet has received limited attention in prior research. We report findings from our co-design research with eleven immigrant readers living in the United States and seven journalists working in the same region, aiming to enhance the news experience of the former. Data collected from all participants revealed an "unaddressed-or-unaccountable" paradox that challenges value alignment across immigrant readers and journalists. This paradox points to four metaphors regarding how conversational AI agents can be designed to assist news reading. Each metaphor requires conversational AI, journalists, and immigrant readers to coordinate their shared responsibilities in a distinct manner. These findings provide insights into reader-oriented news experiences with AI in the loop.