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.
Rodrigo Florencio da Silva· Information· 1 citation
Examination of how South African ODeL students describe and rationalise their use of large language model tools in academic writing indicates that students consistently frame paraphrasing as an ethical practice aligned with institutional expectations, even when their engagement with AI involves varying degrees of automation.
M. Ngoveni, M. Graham, Mathelela Steyn Mokgwathi· Journal of Education and Tra...· 0 citations
A ten-step teaching framework for AI-supported creative interactive content design is proposed, aimed at fostering pedagogical innovation while preserving critical thinking, creativity, and student authorship.
Belén Mainer, Ana Pérez-Escoda· Education sciences· 0 citations
The adoption of Artificial Intelligence (AI) in academic writing has been widely discussed in terms of its impact on writing performance and efficiency, with little attention given to how students actively negotiate its place in process-oriented pedagogies. To fill this gap, this paper investigates how university students negotiate autonomy, ethical responsibility, and cognitive engagement in the context of integrating AI into a Process-Based Approach (PBA) to academic writing. A qualitative-dominant mixed-method design was used. Data were collected from 103 students from eight universities in Indonesia using four Likert-scale questionnaires and open-ended responses, and analysed using descriptive statistics and thematic analysis. Findings suggest that AI is perceived as a form of cognitive support for lower-order writing processes, such as grammar, vocabulary, and idea generation, while students aim to retain control. However, support also produces tensions of overreliance, reduced critical participation, and challenges to academic integrity. Students are beginning to understand the ethical limits, especially when it comes to AI being a tool that helps them with their own writing rather than replacing it. The findings indicate that AI-mediated writing is not only a matter of tool use but a site of negotiation, in which learners negotiate efficiency, autonomy, and authenticity.
Background: The rapid emergence of generative artificial intelligence (GAI) is having a substantial impact across numerous areas of higher education, including academic writing. Its use as a linguistic assistant occupies an ethically ambiguous position between legitimate academic support and practices that may foster technological dependence or compromise academic integrity.
Objective: Examining the use of GAI among university students in a specific academic scenario: its use as a linguistic assistant to improve the writing quality and clarity of essays previously produced by the student, without substantially altering their content. The objective is to analyze how different ethical judgments shape students’ adoption of this practice, which occupies an intermediate position between legitimate academic support and potential risks of technological dependence or academic fraud.
Methods: The study draws on the Multidimensional Ethics Scale, considering four moral dimensions: justice, relativism, consequentialism, and deontology. It also incorporates sociodemographic and academic variables, including gender, employment status, and perceived academic performance. Methodologically, fuzzy-set qualitative comparative analysis is applied to identify causal configurations associated with both the use and nonuse of GAI.
Results: Acceptance does not depend on a single ethical dimension but on specific combinations of moral judgments. Consequentialism emerges as the most relevant condition in the pathways leading to use and is often combined with favorable perceptions of justice and relativism. In contrast, rejection shows a more fragmented structure and lower explanatory coverage, particularly when unfavorable ethical assessments, especially consequentialist assessments, are combined with sociodemographic factors.
Conclusion: Students’ acceptance and rejection of GAI-supported essay editing follow asymmetric configurational logics, showing how different ethical judgments combine to explain academic technology use in a morally ambiguous context. The findings highlight the need for clear institutional rules, ethical training, and transparent criteria for academic GAI use. Universities should distinguish between acceptable linguistic support and practices that may compromise academic integrity, while helping students develop responsible and reflective uses of GAI.
Jorge de Andrés-Sánchez, Antonio Pérez-Portabella, Mario Arias-Oliva et al.· Review of Artificial Intelli...· 0 citations