<p>There is a need for an updated understanding of academic integrity and misconduct with Artificial Intelligence (AI). We conducted a systematic review by searching the SCOPUS and Web of Science and identified 320 studies for review. Of these, 57 duplicates were removed, and 263 studies remained for screening. Following the inclusion/exclusion criteria, the authors screened and assessed the studies for eligibility until a consensus was reached. A total of 82 studies were included in the review. The studies were coded and analyzed using established frameworks for the application of AI, the dimensions of AI employed, the level of AI integration, and the focus of AI. For application, establishing a conducive environment for fostering academic integrity is studied the most (36/82, 43.90%), followed by using Generative AI (GAI) detection tools (19/82, 23.17%), utilizing GAI tools for learning support (12/82, 14.63%), educating students and faculty about how to use GAI reasonably (11/82, 13.41%), and designing assignments that require innovative thinking (4/82, 4.88%). For dimension, the operational dimension of AI was studied the most (35/82, 42.68%), followed by pedagogical (26/82, 31.71%), and governance (21/82, 25.61%). For level, the mega dimension was studied the most (38/82, 46.34%), followed by micro (20/82, 24.39%), macro (13/82, 15.85%), and meso (11/82, 13.42%). For focus, the investigation of AI was studied the most (54/82, 65.85%), followed by detection (17/82, 20.73%), prevention (8/82, 9.76%), and follow-up (3/82, 3.66%). This work identifies the current state and offers novel implications and future work. We further conduct a detailed synthesis of the 82 reviewed studies and present implications and future considerations on a per-study basis, divided into three groups: higher education teaching and learning, assessment and detection, and administration and management.</p>