Balancing Evidence and Interpretation: Historical Grounding Ratio as a Design Parameter for AI-Generated Urban Storytelling

Abstract: Location-aware generative systems can now select historical archives and real-time contextual information based on a user's surroundings to automatically generate narratives for urban heritage walks. Yet when multiple sources jointly inform generation, existing systems provide neither a clear representation of how much content from each source actually appears in the output nor an operational means of measuring it. We introduce the Historical Grounding Ratio (HGR), defined as the proportion of claim-bearing information units in a generated narrative that are supported by historical archives. HGR turns the realized share of historical evidence in a narrative into a directly measurable design parameter. In GeoDrama, a mobile narrative system, we created three conditions that used a common retrieval procedure and comparable evidence-bundle sizes while varying the allocation of information from different sources during generation. We evaluated how changes in HGR affected narrative experience through a within-subject walking study with 18 participants. Increasing HGR significantly strengthened the perceived relevance between narrative content and the specific location. However, historical understanding, integration with the visible scene, appropriateness of the amount of information, and intention to explore further did not increase monotonically with HGR; all four measures were highest in the intermediate, balanced condition. These findings show that designing location-aware generative interfaces involves not only retrieving relevant material but also determining how information from different sources composes the final output. HGR offers an operational measure for comparing information-allocation strategies and their experiential consequences.