Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2026

Abstract

Intergenerational conversations often break down when differences in tone, language, or expectations lead participants to feel dismissed or misunderstood. In this work, we explore how people envision AI-driven chatbot interventions for addressing communication problems in text-based intergenerational family chat. We conducted a scenario-based design interview with 10 pairs of family members from different generations, in which participants designed chatbot interventions that varied in intervention target and timing. Our findings show that participants expect chatbots to perform multiple themes of intervention, including mediating understanding, providing emotional support, offering evaluative commentary, and guiding interaction through behavioral suggestions. These expectations varied systematically across intervention contexts, giving rise to distinct chatbot roles such as neutral mediators, message coaches, repair facilitators, and emotion regulators. Across these roles, participants positioned chatbots as moral advisors that evaluate communicative appropriateness and exercise varying degrees of moral authority. Rather than prescribing specific system behaviors, this work offers a conceptual and exploratory account of AI-mediated intervention in intergenerational communication, and articulates key design tensions that arise when chatbots are imagined as socially and morally involved actors in intimate family interactions.

Keywords

AI-mediated Communication, Intergenerational Communication, Communication Accommodation Theory, Human-AI Interaction

Discipline

Artificial Intelligence and Robotics

Research Areas

Information Systems and Management

Areas of Excellence

Digital transformation

Publication

DIS '26: Proceedings of the 2026 Designing Interactive Systems Conference, Singapore, June 13-17

First Page

2709

Last Page

2724

ISBN

9798400725630

Identifier

10.1145/3800645.3813010

Publisher

ACM

City or Country

Singapore

Additional URL

https://doi.org/10.1145/3800645.3813010

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