Adoption of Artificial Intelligence (AI) in Higher Education: Changing Roles and Identities in the Current Scenario.

Artificial intelligence is evolving rapidly and integrating more and more with everyday life, a trend that will inevitably shape societal development in the future. Higher education is currently using this technology. The emergence of AI has altered conventional education paradigms and approaches. College education is moving more and more toward intelligence and informationization as a result of AI technologies. AI research and implementation is a crucial and essential way for administrators to advance their future endeavors. To prepare students for the upcoming era of intelligence, it is more crucial to teach them how to take autonomous self-learning tests and to emphasize the application of social skills.The research paper's objective is to provide insight on how AI is currently being used in higher educational institutions. This research article uses a sociological framework to investigate the implementation of AI in higher education. It talks about how AI is changing teaching and learning techniques and examines its societal and academic consequences for higher education institutions. In addition, it talks about how the application of AI technology is transforming how humans and machines interact. The perceptions and attitudes of the beneficiaries in higher education on the use of artificial intelligence in academic settings are examined in this study using the method of surveys. The survey approach is used in the qualitative research article to obtain the data. Using a structured questionnaire that was sent via Google Form to academic staff, students, and administrators at several universities.The article explores the advantages of AI technology, but it also focuses on the disadvantages and challenges of AI in higher education, including biases in databases and algorithms, copyright violations, micromanagement of staff and students, innovative and critical thinking, behavior manipulation, over-reliance on AI, low or non-explainability, transparency of AI's decisions, skill loss, and privacy and ethical issues.