Personalized Learning Based on Artificial Intelligence: How Ready Are Modern Students for New Educational Opportunities

Abstract

One of the key advantages of integrating artificial intelligence (AI) technologies into education is the creation of conditions for the implementation of a personalized learning model – a system developing individual potential, in which the learner is the main subject of the educational process and, in accordance with individual abilities, interests and needs, selects the educational content and chooses methods, techniques, and means of teaching, determines the pace of mastering the educational material and takes responsibility for the process and outcome of the learning. At the same time, the readiness of students to use a personalized form of learning will largely determine its effectiveness. The goal of the study is to determine the readiness of Russian university students for personalized learning based on AI tools. Based on the analysis of academic literature, the following semantic components of personalized learning were proposed: a) student subjectivity; b) partnership; c) dominance of problem-solving assignment; d) pace; e) adaptability, and f) An online survey was conducted to determine the students’ readiness for personalized learning. The respondents were 1,211 students from 38 Russian universities. The results of the questionnaire survey indicate that at the present stage about 50% of students use AI to solve various educational tasks. The opinions of students were divided regarding their readiness for personalized learning. About 45–60% of respondents expressed their readiness for such learning, 25–30% of respondents were neutral and 5–10% had a negative attitude on most issues. The data obtained indicate that at the present time, personalized learning cannot be widespread. Not all students of Russian universities fully understand the essence and potential of personalized learning, are ready and willing to act as subjects of the educational process, bearing full responsibility for the process and outcome of learning. Personalized learning places new demands on teachers, whose function is to prepare students for interaction with AI, adequately assess their abilities, formulate the goal of learning, check feedback materials from generative AI, build an individual learning trajectory, determine the pace of learning, select the content, means and methods of teaching, reflection, etc.

Keywords

• artificial intelligence
• personalized learning
• higher education
• learner’s autonomy
• student readiness