Background: Mixed-method studies require adaptation to the era of big data in quantitative research, seeking scalable approaches that can analyze extensive qualitative datasets while preserving the depth and nuance inherent in the study of consciousness on a broader scale. Objective: This study aimed to leverage ChatGPT, renowned for its descriptive generation proficiency, to perform a phenomenological analysis. Methodology: Our research followed four key stages: (1) Preparation of Phenomenological Data, where transcriptions were refined to align with the research question; (2) Individual Analysis, where ChatGPT highlighted experiential nuances from each participant; (3) Global Analysis, synthesizing insights from individual narratives temporally and transversally; and (4) Structure of the Experience, which synthesized the elemental components of shared experiences. Custom prompts, tailored for each stage, ensured alignment and precision in capturing the experience dimensions. Results: ChatGPT showcased a sophisticated processing capability of human experiences, effectively organizing themes that reflect the intensity of sensations and variations in empathetic encounters. The tool’s proficiency in thematic organization provided a phenomenologically-grounded processing of data, highlighting how individuals engage with and are affected by stimuli. Discussion: Our findings highlight ChatGPT’s potential in consciousness studies, transforming raw input into detailed phenomenological accounts. ChatGPT combines precision with scalability, making it a compelling tool for researchers exploring the intricacies of human experiences. Further research is essential to better understand AI’s capacity in phenomenological analysis and to strengthen the methodological framework, ensuring it effectively captures the nuances and depth of phenomenological inquiry.