Pixel International Conferences

The field of AI has exploded in the last decade, and innovation has come from both industry and academia.  Like many fields that are technology driven, the rapid growth, availability of resources and widespread use have outpaced policymaking¹.  Ironically, this has also been the case in academia where discussions on fair usage, ethics and quality control have taken place but no clear, definitive answers are apparent with many organizations trying to cobble together best practices on-the-fly².  Such discussions are often ill informed, relying on anecdotal expertise, and may lack data to back up “gut” feelings or simply be an extension of current policies.  This type of approach will always lag the unique challenges presented rather than echo current usage. Additionally, the use of AI is not an endpoint in and of itself.  AI should be used to increase productivity.  In academia, productivity can be measured in terms of both volume and quality³.  Specifically, ‘productivity’ is defined in the novel context of Four Pillars of Academic Productivity (4PoAP).  In defining the 4PoAP Framework in the context of education, particular attention will be given to topics in the use of Generative AI. It is the aim of this study to provide a perceptual framework in which comparisons may be made on the productive use of AI by comparing usage by students and faculty (both technical and non-technical users).  While the use of AI is ever-evolving, some comparisons may persist over the longer term.  Our methodology will take a quantitative approach to gauging student performance (coop acquisition, course completion rates, raw scores) and confirm using a mixed method approach (faculty confirmation by survey and interview).