AI-Supported Supervision of Master’s Theses (TFM)
Uses, Perceptions, and Needs among Spanish University Lecturers
DOI:
https://doi.org/10.4151/S0718-09342026012201463Keywords:
artificial intelligence, academic tutoring, master's thesis, digital teaching competence, writing competenceAbstract
The integration of artificial intelligence (AI) in academia, especially to support master's thesis writing, raises several questions: whether and how teachers use AI as a teaching tool, how they perceive its role in the supervision process, what training needs they have, and what implications its use has for supporting students academically. This empirical study, with a descriptive and exploratory approach, addresses three fundamental questions: first, how university teachers use and perceive AI tools and what their needs are in order to provide constructive and appropriate feedback to students in the writing of their TFMs. Second, it identifies the possible influences of certain sociodemographic characteristics of teachers (gender, age, teaching experience) on their use, perception, and needs with regard to AI. Third, it defines digital and writing competence profiles to determine whether these have a significant influence on teachers' perceptions and practices, as well as on their expectations regarding the role of AI in academic writing. A validated questionnaire was administered to 66 master's thesis supervisors from six private and public universities in Madrid (Spain). The results indicate that teachers do not use AI tools extensively to provide feedback on master's theses and that the B1 Integrator and B2 Expert digital competence profiles predominate, compared to the C1 Leader and C2 Pioneer written competence profiles. Regarding to training needs, most teachers express a strong demand for training in the use of AI. Based on these results, the study suggests the value of developing critical AI literacy initiatives tailored to the diversity of teacher profiles.
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