Adaptive Interaction Scaffolding: Dynamically Shaping Writer-AI Dialogues to Enhance Cognitive Engagement

Building on Umarova et al.'s (2025) finding that interaction quality drives idea generation, this research proposes an AI system that actively engineers productive writer-AI interactions by using multimodal behavioral cues (typing cadence, revision frequency, prompt semantics) to classify user engagement modes such as 'proactive ideation' or 'mindless editing.' Upon detecting disengagement, the AI dynamically shifts its interaction style—for example, from auto-complete to provocative Socratic prompts—to rekindle cognitive engagement. This approach autonomously detects and corrects problematic interaction patterns, tailors AI interventions based on cognitive modes (novelty-driven vs. appropriateness-driven), and evolves dialogues in real-time rather than relying on static user input or pre-designed dialogue structures. The system acts as a metacognitive partner, nudging users toward constructive learning by injecting counterfactual queries to transition from surface-level edits to critical thinking. Early pilots could focus on STEM students to measure impacts on idea novelty and writing self-efficacy, potentially redefining AI as a scaffold for interaction quality rather than just a content generator.

References:

1. How Problematic Writer-AI Interactions (Rather than Problematic AI) Hinder Writers' Idea Generation. Khonzoda Umarova, Talia Wise, Zhuoer Lyu, Mina Lee, Qian Yang (2025). arXiv.org.