Bridging the Dual Nature: How Integrated Explanations Enhance Understanding of Technical Artifacts

2026-03-25

AI Summary

This study investigates how integrating explanations of a technological artifact’s architecture (structure) and relevance (purpose) enhances user understanding, with a specific focus on two dimensions: comprehension (knowledge-based understanding) and enabledness (practical application capability). Using a between-subjects experimental design, the research compares three conditions—explanations of structure only, relevance only, and an integrated combination of both. The findings provide the first empirical evidence that while the integrated approach does not significantly improve comprehension, it significantly enhances users’ enabledness (η²ₚ = .045, p = .030), thereby demonstrating the critical role of synthesizing structural and purposive perspectives in fostering effective technological understanding.

Technology Category

• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• 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: Bridging structured and unstructured data

Abstract

Purpose: Understanding a technical artifact requires grasping both its internal structure (Architecture) and its purpose and significance (Relevance), as formalized by Dual Nature Theory. This controlled experimental study investigates whether how explainers address these perspectives affects explainees' understanding. Methods: In a between-subjects experiment, 104 participants received explanations of the board game Quarto! from trained confederates in one of three conditions: Architecture-focused (A), Relevance-focused (R), or Integrated (AR). Understanding was assessed on comprehension (knowing that) and enabledness (knowing how). Results: The A and R conditions produced equivalent understanding despite different explanation content. The AR condition yielded significantly higher enabledness than the focused conditions combined $\mathrm{F}(1, 102) = 4.83$, $p = .030$, $η^2_p = .045$}, while no differences emerged for comprehension. Conclusion: Integrating Architecture and Relevance specifically enhances explainees' ability to apply their understanding in practice, suggesting that fostering agency with technical artifacts requires bridging both perspectives. This has implications for technology education and explainable AI design.

Problem

• technical artifacts
• Dual Nature Theory
• explanation
• understanding
• enabledness

Innovation

• Integrated Explanation
• Dual Nature Theory
• Enabledness
• Explainable AI
• Technical Artifact Understanding