Artificial Intelligence as a Lecturer's Assistant: Personalizing Feedback and Assessment in Higher Education

Abstract

Objective. The objective of the article is to substantiate the transformative potential of artificial intelligence as a lecturer's assistant in higher education for personalizing feedback and optimizing assessment.

Methods. To achieve the stated objective, a comprehensive approach was used, including the analysis of scientific publications and research on the application of artificial intelligence in education. Specifically, methods of theoretical analysis were applied to study the concepts of personalized learning, formative, and summative assessment. Synthesis methods were used to generalize data on the capabilities of AI (Natural Language Processing, Machine Learning, Computer Vision, predictive analytics) in the context of analyzing student work, automating assessment, and providing adaptive recommendations. Comparative analysis methods allowed for the identification of unresolved parts of the general problem and the outlining of ethical challenges associated with the implementation of AI.

Results. The study found that artificial intelligence is capable of significantly improving feedback and assessment processes in higher education. AI systems effectively identify grammatical, syntactic, and stylistic errors in written assignments, perform plagiarism checks, analyze the logic of presentation and argumentation, and provide specific suggestions for improvement. The automation of testing and numerical task assessment using AI ensures quick and objective verification of large volumes of work, identifying problematic topics for students. Predictive analytics based on AI allows for early identification of students who need additional support and the development of preventive strategies. It has been determined that the role of the lecturer evolves from a simple "knowledge transmitter" to a facilitator, mentor, and manager of digital tools.

Conclusions. Artificial intelligence is a powerful assistant for lecturers, enabling them to enhance the quality and individualization of learning, freeing up time for more creative and mentoring activities. However, its effective implementation requires considering ethical aspects, such as algorithmic transparency, bias avoidance, data confidentiality, and fostering a culture of academic integrity. The symbiosis of human and artificial intelligence opens a new era in higher education, making learning more effective, accessible, and personalized.