Interaction-Required Suggestions for Control, Ownership, and Awareness in Human-AI Co-Writing
This paper addresses the problem of passive user engagement and lack of process control in generative AI writing. To remedy this, we propose the “Interaction-Required” design paradigm, embedding real-time human-AI collaboration throughout the entire text generation pipeline. Methodologically, we develop a predictive input interface that automatically detects and highlights edit points using semantic and structural awareness, and introduce a human-AI collaborative state synchronization mechanism to enable fine-grained intervention and visualization of the writing possibility space. Empirical evaluation demonstrates significant improvements in users’ sense of control over AI outputs, clarity of revision intent, and trust in collaboration. This work is the first to systematically establish that mandatory, continuous human interaction enhances cognitive engagement, perceived autonomy, and situational awareness. It provides both theoretical grounding and an empirically validated design framework for explainable, intervenable generative AI writing tools.