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

2026-01-26

AI Summary

This study addresses the significant barriers immigrant readers face in mainstream news consumption and the longstanding neglect of their needs, which has led to a misalignment of values between readers and journalists. Employing a human-centered co-design approach, the research conducted qualitative interviews and participatory workshops with 11 immigrant readers and 7 local journalists in the United States to explore how conversational AI can foster reader-centered news experiences. The findings reveal a core paradox of “unanswered or unaccountable” communication and propose four accountability coordination models as design metaphors for conversational AI. These models offer a novel framework for tripartite collaboration among AI systems, journalists, and readers, advancing more inclusive and responsive journalistic practices.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• immigrant readers
• reader-oriented news
• news engagement
• value alignment
• conversational AI

Innovation

• conversational AI agents
• co-design
• reader-oriented news
• value alignment
• immigrant readers