Contemplative researchers frequently use qualitative interviews and written responses to explore human experience. However, analyzing large amounts of text is time-consuming and often limited by researchers’ theoretical perspectives. Recent advances in AI, particularly large language models (LLMs), offer the potential to automatically detect a wide range of relevant constructs in text, from equanimity and mystical experiences to anxiety and the five hindrances in Buddhism. Nevertheless, AI remains underused in contemplative research. One challenge is that AI models have clear cultural biases and blind spots when interpreting nuanced experiences. At the same time, our work suggests that AI can not only accelerate qualitative coding but also reveal researchers’ own blind spots by suggesting alternative constructs and perspectives. This project will develop a user-friendly digital toolbox that enables researchers to use AI for qualitative research, providing tools to: compare AI and human coding, highlight AI biases, discover alternative constructs, and offer expert-curated codebooks. It will be tested and refined using data from meditation and psychedelic studies and shaped by guidance from contemplative researchers, ensuring attention to ethics, reflexivity, and enactivism. By making advanced AI more accessible and trustworthy, our project aims to foster deeper, more diverse insights in contemplative science and beyond.