Inclusive Design Insights from a Preliminary Image-Based Conversational Search Systems Evaluation

TL;DR

This study evaluates image-based, text-based, and mixed conversational search systems, revealing that mixed systems maximize user engagement and have potential benefits for individuals with intellectual disabilities.

Contribution

It provides a comparative evaluation of different conversational search modalities, highlighting the advantages of mixed systems and exploring accessibility implications.

Findings

Mixed systems achieve highest user engagement.

Text-based systems reduce user confusion.

Image-based systems aid individuals with disabilities.

Abstract

The digital realm has witnessed the rise of various search modalities, among which the Image-Based Conversational Search System stands out. This research delves into the design, implementation, and evaluation of this specific system, juxtaposing it against its text-based and mixed counterparts. A diverse participant cohort ensures a broad evaluation spectrum. Advanced tools facilitate emotion analysis, capturing user sentiments during interactions, while structured feedback sessions offer qualitative insights. Results indicate that while the text-based system minimizes user confusion, the image-based system presents challenges in direct information interpretation. However, the mixed system achieves the highest engagement, suggesting an optimal blend of visual and textual information. Notably, the potential of these systems, especially the image-based modality, to assist individuals with intellectual disabilities is highlighted. The study concludes that the Image-Based Conversational Search System, though challenging in some aspects, holds promise, especially when integrated into a mixed system, offering both clarity and engagement.