Building an Emphatic AI Coach & Agent
Dev Vora, Dev Shah, Tanish Roy, Mehtab Cheema, Saadullah Shahzad · Journal of Computing Data and Exploration · 2025
Traditional AI models, including ChatGPT, primarily respond with verbose answers rather than engaging in natural, interactive conversations. Human dialogue thrives on clarification—asking the right questions to refine understanding—yet current AI systems leave the burden of re-prompting on the user. Our project aims to shift AI towards a more open and empathic approach by training models to ask clarifying questions rather than merely generating responses. To achieve this, we have built a pipeline of models that assess ambiguity, intent, and sentiment in user queries. These outputs, alongside the original prompt, are processed by an AI agent that determines whether a clarifying question is necessary before generating a response. This approach fosters steerable AI behavior, making models more agentic, adaptable, and human-like in conversation. By developing a dataset of high-quality clarifying questions—both synthetically generated and human-validated—we pave the way for next-generation AI assistants that actively seek to understand user intent rather than passively respond. This project has implications for instruction tuning, AI alignment, and conversational AI, ultimately making models more effective collaborators in human-AI interactions