Exploring the Use of Generative Artificial Intelligence (GenAI) to Maximize Doctoral Learning Experiences

This exploratory research utilized an interdisciplinary approach to investigate the use of generative artificial intelligence (GenAI) to personalize and maximize learning experiences for graduate DPA and DCJ students. Limited research had been conducted specifically on using GenAI in higher education dissertation research and its potential to enhance student engagement, dissertation development success, and influence on the extent of faculty presence needed in graduate learning. Forty-one DPA and DCJ (17 DPA and 24 DCJ) students were recruited to complete a brief learning module that utilizes a GenAI reinforcement algorithm. Students then completed a short survey regarding their experiences and insights. Results indicated that the GenAI tool, Dissertation And Research Assistant (DARA), was helpful in several ways. Regarding the usefulness of the DARA tool, the vast majority students (37 of 41, or 90%) indicated that the module helped them in developing and/or aligning their problem, purpose, and research question(s), with it being most helpful in defining the research problem. A strong majority of students (86%) indicated that use of the DARA tool was more useful than traditional faculty feedback. Potential future implications include enhancing current GenAI learning capacity by the further cultivation of student engagement and success, maximization of student learning experiences, and evaluation of the potential influence of a GenAI presence for more consistent feedback and dissertation research progress to maximize effective use of faculty, thus providing insights into potentially what role GenAI may play in future learning curriculum and dissertation development as well as impact on course staffing.