Autistic individuals often experience difficulties in conveying and interpreting emotional tone and non-literal nuances. Many also mask their communication style to avoid being misconstrued by others, spending considerable time and mental effort in the process. To address these challenges in text-based communication, we present TwIPS, a prototype texting application powered by a large language model (LLM), which can assist users with: a) deciphering tone and meaning of incoming messages, b) ensuring the emotional tone of their message is in line with their intent, and c) coming up with alternate phrasing for messages that could be misconstrued and received negatively by others. We leverage an AI-based simulation and a conversational script to evaluate TwIPS with 8 autistic participants in an in-lab setting. Our findings show TwIPS enables a convenient way for participants to seek clarifications, provides a better alternative to tone indicators, and facilitates constructive reflection on writing technique and style. We also examine how autistic users utilize language for self-expression and interpretation in instant messaging, and gather feedback for enhancing our prototype. We conclude with a discussion around balancing user-autonomy with AI-mediation, establishing appropriate trust levels in AI systems, and customization needs if autistic users in the context of AI-assisted communication

翻译：自闭症个体在传达和解读情感语调及非字面细微差别方面常面临困难。许多人还会掩饰自身的沟通风格以避免被他人误解，在此过程中耗费大量时间与精力。为应对文本交流中的这些挑战，我们提出了TwIPS——一款基于大语言模型（LLM）的原型短信应用，该应用可协助用户实现以下功能：a) 解读接收消息的语调与含义；b) 确保自身消息的情感语调符合表达意图；c) 为可能被他人误解或产生负面接收效果的消息提供替代措辞方案。我们通过基于人工智能的模拟对话脚本，在实验室环境中对8名自闭症参与者进行了TwIPS评估。研究结果表明：TwIPS为参与者提供了便捷的澄清求助途径，提供了比语调指示器更优的替代方案，并促进了对写作技巧与风格的建构性反思。我们还探讨了自闭症用户在即时通讯中如何运用语言进行自我表达与信息解读，并收集了改进原型的反馈意见。最后，我们围绕以下议题展开讨论：在人工智能辅助交流背景下，如何平衡用户自主性与AI中介作用、建立对AI系统的适度信任水平，以及满足自闭症用户的定制化需求。