Interaction-Required Suggestions for Control, Ownership, and Awareness in Human- AI Co-Writing (2025)

Abstract: This paper explores interaction designs for generative AI interfaces that necessitate human involvement throughout the generation process. We argue that such interfaces can promote cognitive engagement, agency, and thoughtful decision-making. Through a case study in text revision, we present and analyze two interaction techniques: (1) using a predictive-text interaction to type the agent’s response to a revision request, and (2) highlighting potential edit opportunities in a document. Our implementations demonstrate how these approaches reveal the landscape of writing possibilities and enable fine-grained control. We discuss implications for human-AI writing partnerships and future interaction design directions.

## introduction current chatbot interfaces for large language models like chatgpt, claude, and gemini limit interaction to a turn-taking conversation, even though the underlying models could support more versatile interactions, especially for writing tasks. in this paper, we begin to explore the design space of interactions that people can have with model outputs, focusing on the potential opportunities presented by interactions where human initiative is required for completing a task. although these interactions are, by construction, less efficient at producing plausible outputs, we aim to explore the potential benefits they might offer in control, ownership, visibility of the solution space, and feedback for model tuning. we present two interaction techniques for revision in writing: predictive-text and opportunity highlighting. the first technique adapts the familiar predictive-text interaction (top-k suggestions or free typing) found on mobile devices to allow people to type the assistant's response word by word. the second technique visualizes alternative figure 1 : predictive text interaction repurposed to type the assistant's response (and sometimes divergent) choices for revising text according to a writer-specified goal. we propose interaction-required approaches that fundamentally shift this dynamic by necessitating ongoing human involvement throughout the generation process. our approach is guided by three design principles that emphasize cognitive partnership between humans and ai systems: prioritize cognitive engagement over efficiency although ai assistance can speed task completion, using it without cognitive engagement can lead to overconfidence @xcite , errors @xcite , and skill stagnation (gajos and mamykina, 2022). interactions can instead be designed to encourage writers' thoughtful participation rather than optimizing solely for speed or ease. this principle addresses how ai systems can support authentic self-expression, ownership, and accountability in writing, which many writers desire @xcite . the literature on explainable ai systems for decision-making suggests cognitive engagement as valuable goal @xcite . enable granular control rather than offering only coarse accept/reject options for completed ai outputs, interfaces could instead allow writers to influence the progress of generation. granularity could enable just-in-time feedback that shapes the direction of ai assistance, providing a way for users to clarify their goals without having to engage in prompt refinement or writing examples. reveal the landscape of possibilities interactions should make visible the alternatives available at each decision point, helping writers understand the range of options and make more informed choices. prior work has explored contextual suggestions of alternative words or phrases at targeted points (e.g., @xcite al. (2023); gero and chilton (2019) ), but some authors have explored interfaces for navigating through the tree of suggestions in a narrative generation context @xcite . ## a case study in revision we will present two interaction designs that embody these design principles for the purpose of revision. as a running hypothetical example, suppose alex is a wikipedia editor who wants to revise the introduction section for the article on "six thinking hats", as it was on 2025-02-25: "six thinking hats was written by dr. edward de bono. "six thinking hats" and the associated idea of parallel thinking provide a means for groups to plan thinking processes in a detailed and cohesive way, and in doing so to think together more effectively." we will use a revision instruction generated by claude.ai: "rewrite this document to be more comprehensive, structured, and engaging." ## discussion so far these interaction designs have only been evaluated informally; empirical studies with writers are needed to determine how interaction-required suggestion interfaces affect writers' sense of ownership, control, and awareness of alternatives. anecdotally, however (from use by the authors and a few others), both have been useful in low-level editing (trimming and clarifying wording), the predictivetext interface has been helpful for initial drafting (e.g., based on an outline), but neither are useful for larger-scale revision because they focus attention on localized choices; other tools are needed to address those needs (e.g., @xcite ; @xcite ; @xcite ). although we described a case study in revision, predictive text could be used in any assistant response. we are particularly curious about how it might have different effects across different types of tasks: open-ended tasks such as ideation, analytical tasks such as review generation, and closeended tasks such as refactoring code. the straightforward application of predictive text to typing the assistant's response, as we propose in section 3.1, presents opportunities to increase cognitive engagement and control over the status quo of accepting complete generated responses. yet it is still possible to use the chatbot's words uncritically by accepting suggestions rapidly. (should the interface be designed to allow larger-block acceptance?) and even cognitive engagement with the suggestions could still lead to a reduced sense of ownership over the result @xcite and influence on human opinions @xcite . additional exploration of the interaction design (e.g., how alternatives are visualized and navigated) is needed. the additional control afforded by predictive text (effectively prefilling the assistant's response) affords some additional risks for users to jailbreak the model @xcite . however, since prefilling is part of many commercial llm apis, we doubt that this interaction design presents significant marginal risk. predictive text can be viewed as an interactive visualization of high-probability local alternatives within a sequence of categorical choices (e.g., figure 1 shows two-token predictions to provide awareness of where each suggestion could be going. 1 from this perspective a wide range of interactive visualization techniques are possible, such as the dasher text entry system @xcite (which may have accessibility benefits as well). design dimensions of these visualizations include the granularity of suggestions (words, phrases, or larger units such as copy-pasted text from a writer's other drafts) and how interacting with the suggestion affects the surrounding text. the effects of these design decisions might vary by stage of the writing process. the opportunity highlighting interface explored an extreme design position of being minimally prescriptive in ai help, but relaxing that extreme could yield a range of alternative interaction designs. for example, it could incorporate an interactive visualization where the writer could navigate through contextual alternatives at any point. both of these models presuppose autoregressive (left-to-right) language modeling, but additional types of interaction might be enabled by emerging model types based on out-of-order modeling or diffusion llms @xcite . although prior work has explored the effects of generating different kinds of content with llms on writer reactions @xcite @xcite , this work keeps the task for the llm unchanged and explores the kinds of interactions that people can have with the inference process. interaction-required suggestions are a source of rich feedback data for reward-based language model training and personalization. unlike static documents, the interaction logs with a conversational predictive text system would include what suggestions were made but not taken, providing a fine-grained human feedback signal. these feedback signals can be used for updating a language model @xcite . conclusion with continuously increasing capabilities of llms, the difference between augmenting and replacing human thinking is a question not of system capabilities but of interaction design. the interaction-required approaches we've presented demonstrate how small shifts in interface design can fundamentally change the nature of human-ai partnership in writing. by prioritizing cognitive engagement, enabling granular control, and revealing the landscape of possibilities, we can design ai writing interfaces that help us think not less but better-maintaining human agency while still benefiting from ai capabilities. the highlighting interface constructs a pseudoconversation by where the user message is the revision prompt concatenated with the original document and the assistant message is the original document repeated unchanged. rather than generate additional tokens, we simply compute the next-token distributions for all tokens in the "assistant" message corresponding to the user's document. the frontend highlights the tokens where the model gives a higher score to a token other than the one in the original document. mouseover hovers show an alternative token; for tokens where the argmax prediction matched the original document (which are typically the majority of tokens), the hover shows the 2nd highest-scored option.