This paper argues that generative AI has become ubiquitous in academia, making irrelevant the debates about whether or not to allow it. Instead, the author advocates for transparency and accountability frameworks that acknowledge AI's presence while preserving academic integrity. The paper examines the challenges educators face: unreliable…

Purpose: This study will evaluate the organizational effectiveness of an artificial intelligence (AI)/machine learning (ML) recommender system at a higher education institution. It will determine the positive or negative net benefits (i.e., organizational effectiveness) of utilizing the D&M ISSM. Background: Identifying the value and efficacy…

Among community college leaders and others interested in reforms to improve student success, there is growing interest in adopting machine learning (ML) techniques to predict credential completion. However, ML algorithms are often complex and are not readily accessible to practitioners for whom a simpler set of near-term measures may serve as…

Objective: This study examines how accurately a small set of short-term academic indicators can approximate long-term outcomes of community college students so that decision-makers can take informed actions based on those indicators to evaluate the current progress of large-scale reform efforts on long-term outcomes, which in practice will not be…