How Does Users' App Knowledge Influence the Preferred Level of Detail and Format of Software Explanations?

2025-02-10

AI Summary

This study investigates how to dynamically adapt the level of detail and modality (e.g., text vs. video) of software explanations based on users’ domain knowledge to reduce cognitive load and improve usability. Method: A web-based survey with 58 participants was conducted, employing descriptive statistics and correlation analysis to examine relationships among domain knowledge, demographic variables, self-reported confidence, and explanation preferences. Contribution/Results: Findings indicate that domain knowledge alone does not directly predict explanation preferences; instead, demographic factors—particularly gender—indirectly moderate preferences by influencing both knowledge acquisition and perceived self-efficacy. Participants consistently preferred concise, medium-detail textual explanations over highly detailed or multimodal alternatives. Critically, the study demonstrates that effective explanation adaptation must integrate psychological (e.g., confidence) and sociodemographic (e.g., gender) dimensions—not merely objective knowledge metrics—thereby providing empirical grounding for designing user-adaptive explanation systems in interactive software.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Context and Motivation: Due to their increasing complexity, everyday software systems are becoming increasingly opaque for users. A frequently adopted method to address this difficulty is explainability, which aims to make systems more understandable and usable. Question/problem: However, explanations can also lead to unnecessary cognitive load. Therefore, adapting explanations to the actual needs of a user is a frequently faced challenge. Principal ideas/results: This study investigates factors influencing users' preferred the level of detail and the form of an explanation (e.g., short text or video tutorial) in software. We conducted an online survey with 58 participants to explore relationships between demographics, software usage, app-specific knowledge, as well as their preferred explanation form and level of detail. The results indicate that users prefer moderately detailed explanations in short text formats. Correlation analyses revealed no relationship between app-specific knowledge and the preferred level of detail of an explanation, but an influence of demographic aspects (like gender) on app-specific knowledge and its impact on application confidence were observed, pointing to a possible mediated relationship between knowledge and preferences for explanations. Contribution: Our results show that explanation preferences are weakly influenced by app-specific knowledge but shaped by demographic and psychological factors, supporting the development of adaptive explanation systems tailored to user expertise. These findings support requirements analysis processes by highlighting important factors that should be considered in user-centered methods such as personas.

Problem

• User preferences for software explanation detail
• Influence of app knowledge on explanation formats
• Demographic impact on explanation system adaptation

Innovation

• Adaptive explanation systems
• User-centered explanation formats
• Demographic influence on preferences