Imagine a world where AI automatically refactors your code, making it cleaner, more efficient, and less buggy. That's the promise of AI-assisted refactoring using Large Language Models (LLMs). However, there's a catch: can developers truly trust AI to make these critical changes without introducing new problems? This is the central question explored in recent research. The study emphasizes that simply having AI tools isn't enough; developers need to trust them. This trust isn't about blind faith, but a calibrated confidence based on the AI's proven trustworthiness. The research focuses on how to build this trust within the familiar environment of IDEs. One key is developing robust safeguards that validate the AI's refactoring suggestions, acting like a safety net to catch potential errors before they hit the codebase. Equally important is designing intuitive user interfaces that clearly communicate the AI's reasoning and build confidence in its decisions. This requires a delicate balance. Too much trust can lead to developers blindly accepting flawed changes, while too little trust can cause them to dismiss valuable suggestions, hindering the adoption of this powerful technology. This research tackles the challenge head-on, proposing an action research approach within real-world development environments. The goal is to continuously refine both the AI's safeguards and the user experience, creating a virtuous cycle where increased trustworthiness leads to greater trust, ultimately paving the way for widespread adoption of AI-powered refactoring. The future of coding might be automated, but only if developers feel confident enough to let AI take the wheel.