THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE TEACHING PROCESS IN HIGHER EDUCATION

Source abstract

The rapid rise of artificial intelligence (AI), especially generative AI, is transforming how higher-education institutions design instruction, support learners, evaluate knowledge, and structure academic work. In this paper, we review the implications of AI for teaching in universities, drawing upon contemporary scholarship, international guidance and select technical studies of service-oriented architectures, process modeling, database design and graph-based analytics. We performed a structured narrative review guided by a priori concepts of AI-supported personalization, tutoring, assessment, educator workload, academic integrity, privacy, bias and institutional governance. Evidence suggests that artificial intelligence can help improve access to timely feedback, adaptive support, course design and reduce routine workload. But access to a general-purpose model does not necessarily produce learning: benefits depend on pedagogical scaffolding, course alignment, verification, and ongoing supervision by humans. The key risks are: generated or biased outputs, cognitive offloading, unequal access, opaque data practices, and unreliable authorship judgments. The paper concludes that AI should be deployed as an augmentative educational infrastructure, not as a replacement for teachers. To ensure that technological efficiency benefits real learning and academic principles, universities need to combine AI literacy, redesigned assessment, privacy-by-design, transparent policies, interoperable systems, and ongoing assessment.