Artificial Intelligence (AI) has been around for nearly a century, yet in recent years the rapid advancement and public access to AI applications and algorithms have led to increased attention to the role of AI in higher education. An equally important but overlooked topic is the study of AI teaching and learning in higher education. We wish to examine the overview of the study, pedagogical outcomes, challenges, and limitations through a systematic review process amidst the COVID-19 pandemic and public access to ChatGPT. Twelve articles from 2020 to 2023 focused on AI pedagogy are explored in this systematic literature review. We find in-depth analysis and comparison of work post-COVID and AI teaching and learning era is needed to have a more focused lens on the current state of AI pedagogy. Findings reveal that the use of self-reported surveys in a pre-and post-design form is most prevalent in the reviewed studies. A diverse set of constructs are used to conceptualize AI literacy and their associated metrics and scales of measure are defined based on the work of specific authors rather than a universally accepted framework. There remains work and consensus on what learning objectives, levels of thinking skills, and associated activities lead to the advanced development of AI literacy. An overview of the studies, pedagogical outcomes, and challenges are provided. Further implications of the studies are also shared. The contribution of this work is to open discussions on the overlooked topic of AI teaching and learning in higher education.