Jul 2026· Mokslo taikomieji tyrimai / Applied Research· Vol 1, pp. 113-120· 0 citations· 10 references
TL;DR
How generative text reshapes the understanding of creative activity and academic ethics in educational and research contexts is analyzed to highlight the need to reconceptualise academic integrity as a reflective process-oriented practice and to develop educational frameworks that promote ethical literacy, transparency, and responsible authorship.
Abstract
Generative artificial intelligence (AI) tools based on large language model (LLM) technology are transforming processes of creation, writing, and learning in higher education, raising questions about authorship, originality, and academic responsibility. Despite the growing body of research, existing studies mainly focus on regulation and plagiarism, while paying less attention to broader transformations in creativity and academic ethics. The aim of this article is to analyse how generative text reshapes the understanding of creative activity and academic ethics in educational and research contexts. The study adopts a qualitative, theoretical-analytical approach combining hermeneutic and discourse analysis. The analysis is based on three illustrative cases from language learning, translation practice, and academic writing, which are examined as analytical instances to explore emerging ethical and cultural tensions. The findings indicate that the key challenges associated with generative AI are primarily cultural and pedagogical rather than technological. Generative systems redistribute creative agency between human actors and algorithmic tools, transforming the role of the author into that of an editor, coordinator, and ethical decision-maker. While AI enhances productivity and linguistic accuracy, unreflective use risks diminishing interpretive depth, personal voice, and value-based reasoning. The results highlight the need to reconceptualise academic integrity as a reflective process-oriented practice and to develop educational frameworks that promote ethical literacy, transparency, and responsible authorship. The study contributes to the field by offering an integrative perspective that positions generative AI as a catalyst for rethinking creativity, authorship, and ethical responsibility in contemporary higher education.
It is concluded that academic integrity cannot be reduced to rule compliance or technological surveillance, but must be understood as a constitutive dimension of educational experience, linked to intellectual honesty, responsibility, and critical formation.
Empirical evidence is contributed from educational action research showing that structured pedagogical interventions can promote the critical, ethical, and responsible use of Generative Artificial Intelligence in education.
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