<p>In the ever-evolving realm of artificial intelligence, Chat-generative pre-trained transformer (ChatGPT) stands out as a significant achievement in the advancement of scientific research. This paper aimed to evaluate the potential of ChatGPT in the examination of autobiographical memory through two studies. In Study 1, ChatGPT was presented with a scale designed to measure specificity along with various autobiographical memories. It was then requested to analyze the memories for specificity, and its evaluation was subsequently compared to that of an independent rater. The results of ChatGPT’s analysis were correct, matching the assessment provided by the independent rater by 100%. In Study 2, we assigned ChatGPT the task of analyzing self-defining memories (SDMs), which are identified as a particular subtype of autobiographical memory that play an important role in the composition of narrative identity. We provided both ChatGPT and a human rater with 31 SDMs. Tasking both ChatGPT and the human rater with the analysis of achievement, relationship, contamination, integration, and specificity of SDMs, we observed high levels of agreement between ChatGPT and the human rater for each dimension, with higher levels of agreement observed for specificity. Collectively, our findings underscore the potential of ChatGPT as an effective tool for coding autobiographical memory. The application of ChatGPT in the analysis of autobiographical memory may allow a higher volume of memory collection and analysis, while also offering the potential for reliable and accessible clinical applications.</p>