  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
