Alignment Games: A Framework for Conceptual Repair in Human-AI Collaboration

2026-09-28

AI Summary

This study addresses the problem of concept misalignment arising from contextual discrepancies in human-AI collaboration by introducing a novel alignment game framework grounded in situated conceptualization. By defining concept frames—such as attributes and constraints—and alignment actions that intervene in reasoning processes, the proposed framework translates runtime conceptual divergences into composable repair sequences, thereby enabling dynamic concept alignment. The effectiveness of these repair sequences is empirically validated in application scenarios such as educational content generation. Furthermore, this work establishes design principles that support real-time concept alignment, offering a new paradigm for human-AI synergy.

Abstract

The meaning of a concept in use is shaped by the situation, task, goals, and prior knowledge. For example, a request to make a poster"visually appealing for a five-year-old"might evoke bright colors and cartoon imagery for one collaborator, but less text, bold shapes, and visual simplicity for another. We call such task-relevant differences conceptual misalignment. We introduce Alignment Games, a framework for making these differences visible and repairable during human-AI interaction. Drawing on theories of situated conceptualization, we characterize task-specific conceptual frames in terms of relevant attributes, values, relations, constraints, and priorities. We then define alignment moves that intervene on the situation, the reasoning used to interpret it, or the resulting frame. Through examples from educational content generation, creative coding, and argumentative writing, we show how these moves can be composed into repair sequences and derive design principles for supporting task-sufficient conceptual alignment at runtime.

Problem

• conceptual misalignment
• human-AI collaboration
• conceptual repair
• situated conceptualization

Innovation

• Alignment Games
• Conceptual Misalignment
• Situated Conceptualization
• Human-AI Collaboration
• Conceptual Repair