How different mental models of AI-based writing assistants impact writers’ interactions with them
Shalaleh Rismani, Su Lin Blodgett, Alexandra Olteanu, Q. Vera Liao, AJung Moon · 2024
AI-based writing assistants are being integrated into a range of products and platforms. While direct users—who use AI writing assistants for various writing tasks—often get to decide how they integrate these systems in their writing process, the final outcomes (e.g., the writing artifact) will be influenced by the users’ understanding of how these systems work, and by whether their generated output (e.g., suggestions) is accurate and matches their expectations. In this write-up, we examine the types of controls users believe they have—based on their understanding of the system—when using AI-based writing assistants (e.g., getting personalized suggestions) and whether they can effectively use these controls to minimize possible negative outcomes (e.g., poor writing quality). To do so, we examine users’ mental models of writing assistants and how these models affect users’ ability to intervene appropriately. We argue that more work is needed to examine the connection between different mental models and a user’s ability to control these systems to minimize potential negative outcomes. To this end, we discuss an illustrative case study design where participants are asked to use an AI-based writing assistant to write a cover letter. We discuss how the results from this study could help us understand the impact that different mental models have on user reliance on AI-based writing assistants.