Augmenting Philological Textual Criticism with Generative Artificial Intelligence in Higher Education

Abstract

This study examines how Generative Artificial Intelligence (GenAI) can be applied in philological textual criticism in higher education to enhance efficiency and accuracy in textual analysis, manuscript transcription, and translation. Conventional approaches to philology are time-intensive and demand considerable expertise and knowledge, which GenAI can improve by automating repetitive procedures, including recognizing textual variants and proposing contextually-appropriate translations. The experiment uses quantitative data through the use of pre-test scores and post-test scores to identify the differences in the ability of the students to analyze texts and translate ancient languages with the help of AI. The qualitative data of case studies offer additional information about the experiences of users in using AI tools in the classroom. Early evidence shows that AI applications can greatly enhance the accuracy and efficiency of students, particularly in activities such as variant identification and translation, and some students have shown an increase of up to 54.5% in textual analysis. The study also shows the ethical issues connected with AI, especially in the context of academic integrity and the threat of homogenization of interpretations. The results indicate that though GenAI can assist philologists to make textual criticism quicker and more accurate, it must not substitute the human experience. The research concludes that with effective ethical guidelines, GenAI can potentially transform the higher education field of philological studies by increasing not only the research output but also the learning outcome, and maintaining the scholarly focus.