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

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.

Figures (2)

• Figure 1: Illustrative examples of dual nature annotation timelines for one representative dyad per condition. Each bar represents the normalized dialog timeline (0--1), with segments colored by coding category: Architecture (blue), Relevance (orange), and uncoded content (white), with different shading per speaker. Dyads were selected as the closest to their condition mean in Euclidean distance on Architecture and Relevance proportions (A: arch = 92.4%, rel = 7.6%; R: arch = 36.5%, rel = 63.5%; AR: arch = 55.5%, rel = 44.5%).
• Figure 2: Mean Architecture proportion across dialogue thirds by condition, averaged over both EX and EE utterances ($\pm$ SEM). The A and AR conditions open with Architecture-dominant content, while the R condition is Relevance-oriented from the outset ($M_\text{arch} = .33$ in the first third). The AR condition shifts toward more Relevance-oriented content across the dialogue, converging with R in the second third ($p=.998$) and remaining statistically indistinguishable from R in the third ($p = .100$). The A condition remains Architecture-dominant throughout, declining gradually from $M = .96$ to $M = .82$.