Artificial Intelligence in Higher Education: Opportunities and Challenges

While AI is often presented as a panacea for the challenges facing higher education, there is limited empirical evidence supporting its effectiveness in improving student learning and institutional performance. This gap between expectation and reality emphasizes the need for rigorous research, realistic goal-setting, and careful planning to ensure that AI technologies deliver on their promises in higher education. This study contrasts the potential utilization of AI technologies in Higher Education from the literature, against actual utilization in universities. The study also investigates the key barriers of AI implementation in higher education. This study uses a mixed-research methods approach, including case study analysis, literature review, surveys, interviews, and direct observations. The study shows that universities are still in the primary phases of exploring and using AI. This significant contrast between perceived and actual value is a result of the lack of a dedicated vision, strategy, governance, and plan, that'd show how AI can be infused within universities deliverables. The success of AI use at the personal level in everyday' s life is slowly but surely leading to a positive perception, and higher expectations, that go beyond limited tools such as chatbots, and plagiarism detection tools, into areas such as students' communication, advising, academic support, and adaptive learning. The study also shows that AI in higher education is faced with barriers, several of which, are like those in the literature, while some are unique to the setting of higher education, especially those related to Edtech student information system, and learning management system. Moreover, the priority of adoption barriers appears to place the technical limitations, and environmental settings.