WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakers

AI can help bridge communication gaps for non-native English speakers in professional settings. WordDecipher addresses a real problem: non-native English speakers often struggle with subtle language nuances in high-stakes workplace communications like emails and messages, where misunderstandings can have serious consequences due to the lack of face-to-face cues.

Explainability matters for language learning and trust. Rather than simply suggesting corrections, WordDecipher explains the differences between similar expressions and shows users why one phrasing might be better than another—helping users understand the "why" behind suggestions rather than just accepting them blindly, which supports both learning and informed decision-making.

AI writing tools should account for user intent and cultural context. The tool can infer users' intended meaning either from numerical descriptions of their social goals or by analyzing their native language input, recognizing that direct translation often fails and that understanding what someone is trying to communicate is essential for good suggestions.

Responsible AI design requires considering diverse user needs. This work highlights how general-purpose AI writing tools may not adequately serve non-native speakers, demonstrating the importance of designing AI systems with specific user populations in mind rather than assuming one-size-fits-all solutions work for everyone.