This study is devoted to examining and analysing the roles of university teachers (professors and lecturers) in the AI world. Today, AI is increasingly prevalent and ubiquitous in our lives. In such conditions, the role of the lecturer changes. The lecturer is no longer just a generous giver of knowledge or the verifier, but also a provider of information, sometimes a moderator, or a teammate. These are only three roles out of many (we characterised seven lecturers{\textquoteright} roles) that exist and, in our view, differentiate lecturers{\textquoteright} functions in the current era of AI. A mixed-methods approach (a combination of qualitative and quantitative strategies) was chosen and applied for the research procedure. Based on an analysis of previously published scientific literature on the use of AI in teaching by university teachers, the authors proposed seven roles that teachers can perform in the educational process using AI: a leading teacher, a mentor, a teammate, a provider of information, an assistant, an instructor, and an explorer. An analysis of the surveyed data and a Pearson correlation analysis (Pearson{\textquoteright}s r) were conducted. Findings confirm that lecturers primarily value AI as a tool for simulating learning and teaching tasks that are difficult to complete, allowing students to practice assignments and lecturers to provide their wards with multiple attempts to develop professional abilities and practice skills during tutorials. In their view, this role is more in demand in AI. The role of the lecturer in the era of AI is primarily to provide information, while AI serves as a helpful tool. Moreover, the results of the questionnaire analysis allowed us to note that the roles of an instructor, a mentor, and a leading teacher are also highly valuable in an AI-supported process of delivering knowledge to the mentee. A comprehensive Pearson{\textquoteright}s r and processing of survey data showed that lecturers (this is slightly more than one-third / 7% “always” = 27.9% “often” = 34.9% / Pearson{\textquoteright}s r = 0.44) who regularly incorporate AI into the teaching and learning process tend to have a more positive assessment of AI{\textquoteright}s potential and affirm its use in enhancing the quality of higher education. However, opinions varied. Pearson{\textquoteright}s r also revealed that some lecturers utilise AI in their work. Still, they are generally sceptical about the value of AI in general and its suitability for educational purposes (7% of respondents hold this view / Pearson{\textquoteright}s r=0.06).
Osadcha, K, Osadchyi, V, Proshkin, V & Shumeiko, N 2025, 'Artificial intelligence and the transformation of teaching roles: insights from lecturers’ experiences', CEUR workshop proceedings, vol. 4096, pp. 87-107. <https://ceur-ws.org/Vol-4096/paper7.pdf>
Osadcha, K., Osadchyi, V., Proshkin, V., & Shumeiko, N. (2025). Artificial intelligence and the transformation of teaching roles: insights from lecturers’ experiences. CEUR workshop proceedings, 4096, 87-107. https://ceur-ws.org/Vol-4096/paper7.pdf
@article{777b4529e5614608907ca4e11bd526d0,
title = "Artificial intelligence and the transformation of teaching roles: insights from lecturers{\textquoteright} experiences",
abstract = "This study is devoted to examining and analysing the roles of university teachers (professors and lecturers) in the AI world. Today, AI is increasingly prevalent and ubiquitous in our lives. In such conditions, the role of the lecturer changes. The lecturer is no longer just a generous giver of knowledge or the verifier, but also a provider of information, sometimes a moderator, or a teammate. These are only three roles out of many (we characterised seven lecturers{\textquoteright} roles) that exist and, in our view, differentiate lecturers{\textquoteright} functions in the current era of AI. A mixed-methods approach (a combination of qualitative and quantitative strategies) was chosen and applied for the research procedure. Based on an analysis of previously published scientific literature on the use of AI in teaching by university teachers, the authors proposed seven roles that teachers can perform in the educational process using AI: a leading teacher, a mentor, a teammate, a provider of information, an assistant, an instructor, and an explorer. An analysis of the surveyed data and a Pearson correlation analysis (Pearson{\textquoteright}s r) were conducted. Findings confirm that lecturers primarily value AI as a tool for simulating learning and teaching tasks that are difficult to complete, allowing students to practice assignments and lecturers to provide their wards with multiple attempts to develop professional abilities and practice skills during tutorials. In their view, this role is more in demand in AI. The role of the lecturer in the era of AI is primarily to provide information, while AI serves as a helpful tool. Moreover, the results of the questionnaire analysis allowed us to note that the roles of an instructor, a mentor, and a leading teacher are also highly valuable in an AI-supported process of delivering knowledge to the mentee. A comprehensive Pearson{\textquoteright}s r and processing of survey data showed that lecturers (this is slightly more than one-third / 7% “always” = 27.9% “often” = 34.9% / Pearson{\textquoteright}s r = 0.44) who regularly incorporate AI into the teaching and learning process tend to have a more positive assessment of AI{\textquoteright}s potential and affirm its use in enhancing the quality of higher education. However, opinions varied. Pearson{\textquoteright}s r also revealed that some lecturers utilise AI in their work. Still, they are generally sceptical about the value of AI in general and its suitability for educational purposes (7% of respondents hold this view / Pearson{\textquoteright}s r=0.06).",
keywords = "artificial intelligence, lecturer, Higher education, survey, role of lecturers, correlation",
author = "Kateryna Osadcha and Viacheslav Osadchyi and Volodymyr Proshkin and Natalia Shumeiko",
year = "2025",
language = "English",
volume = "4096",
pages = "87--107",
journal = "CEUR workshop proceedings",
issn = "1613-0073",
publisher = "CEUR Workshop Proceedings",
}