This study provides an empirical approach to utilizing an Artificial Intelligence (AI)-based system for identifying students' university choice factors that impact their matriculation decision. We created an AI-based chatbot that gathered both qualitative and quantitative data from nearly 1200 participants worldwide. The entire human-AI interaction process was managed autonomously by the AI without researcher intervention. We analysed all data collected by the AI and identified relevant matriculation decision factors and themes. The AI collaboration demonstrated remarkable efficacy in streamlining the research workflow by consistently adhering to predefined criteria, eliminating variations and human-induced biases, establishing rapport with participants, and amplifying not just the efficiency and scalability of data collection but also the reliability and generalizability of the research findings. Acquiring such insights into students' university choice factors from such a diversified and large sample may potentially empower policymakers to make informed decisions that synch higher education policies with students' preferences, expectations, and needs, ultimately aiding institutions to improve recruitment and retention strategies leading to better overall performance and outcomes for both students and the institution itself. From an economic perspective, gaining this insight can foster closer alignment between higher education and the job market. By understanding students' aspirations and the influences driving their decisions, institutions can tailor programmes that not only enhance graduates' job prospects but also proactively contribute to economic growth and the development of a highly skilled and committed workforce.