Localizing Content: The Roles of Technical & Professional Communicators and Machine Learning in Personalized Chatbot Responses

Abstract

Purpose: This study demonstrates that microcontent, a snippet of personalized content that responds to users' needs, is a form of localization reliant on a content ecology. In contributing to users' localized experiences, technical communicators should recognize their work as part of an assemblage in which users, content, and metrics augment each other to produce personalized content that can be consumed by and delivered through artificial intelligence (AI)-assisted technology. Method: We use an exploratory case study on an AI-driven chatbot to demonstrate the assemblage of user, content, metrics, and AI. By understanding assemblage roles and function of different units used to build AI systems, technical and professional communicators can contribute to microcontent development. We define microcontent as a localized form of content deployed by AI and quickly consumed by a human user through online interfaces. Results: We identify five insertion points where technical communicators can participate in localizing content: • Creating structured content for bots to better meet user needs • Training corpora for bots with data-informed user personas that can better address specific needs of user groups • Developing chatbot user interfaces that are more responsive to user needs • Developing effective human-in-the-loop approaches by moderating content for refining future human-chatbot interactions • Creating more ethically and user-centered data practices with different stakeholders. Conclusion: Technical communicators should teach, research, and practice competencies and skills to advocate for localized users in assemblages of user, content, metrics, and AI.