Generative text as a challenge to creativity and academic ethics

Abstract

Generative artificial intelligence (AI) is rapidly transforming academic writing and creative practices, raising fundamental questions about authorship, originality, responsibility, and academic integrity. The central problem addressed in this study concerns the changing boundaries between human and algorithmic contribution and the ethical implications of AI-mediated text production. The study aims to reveal how the use of generative text reshapes the understanding of creative activity and academic ethics in educational and research contexts. A qualitative analytical methodology is employed, integrating research in academic ethics, philosophy of language, and digital culture with the analysis of practical cases from language learning, translation, and academic writing. The findings demonstrate that generative AI enhances linguistic accuracy, coherence, and productivity but may simultaneously weaken individual authorial voice, emotional authenticity, and reflective engagement. The analysis further reveals that authorship is shifting from the model of an independent creator toward a form of human–AI co-creation in which the human increasingly acts as an editor, coordinator, and critical evaluator of algorithmic contributions. Despite this redistribution of creative agency, ethical and intellectual responsibility for the final text remains with the human author, making transparency about AI use a central principle of academic integrity. The study identifies a broader cultural shift from prohibition-oriented approaches toward a culture of accountability based on disclosure, reflection, ethical literacy, and process-oriented authenticity. The originality of the study lies in integrating ethical theory, linguistic philosophy, and case-based analysis to conceptualise generative AI as a catalyst for epistemic and cultural transformation rather than merely a technological tool. It is concluded that contemporary academia requires a renewed understanding of authorship and creativity, supported by transparent institutional guidelines, ethical literacy, and assessment practices that recognise both human agency and the realities of AI-assisted knowledge production.