TL;DR
The key findings reveal a detection process that synthesizes textual and contextual evidence by combining objective textual indicators with subjective pedagogical assessments, and highlight the significant role of subjective contextual intuition and the ethical and emotional challenges educators face.
Current practices related to AI use are examined, focusing on LLM-based ghostwriting and the reliability of disclosed interactions as evidence of authentic use, and the possibility of mimicking authentic interactions, which raises concerns about the effectiveness of current approaches.
The analysis reveals that reflective essays written by students highly dependent on artificial intelligence display a distinct “conceptual emptiness” in which surface-level fluency coexists with inner superficiality, and concrete linguistic criteria enabling educators to perceive patterns of AI intervention in student assignments is offered.
The rapid diffusion of generative artificial intelligence (GenAI) tools has opened unimagined avenues for disrupting higher education, enabling professionals, especially researchers, to produce expert-seeming outputs and claim “expert-level status” without formal training in artificial intelligence. Yet, despite its popularity, concerns about AI’s effects on labor and expertise have largely overlooked a deeper categorical crisis: the collapse of the distinction between AI tool proficiency and genuine AI expertise and the consequences this has for professional identity and institutional decision-making. This positional paper interrogates the boundary between AI use and AI expertise, arguing that access to a tool that simulates expert output creates conditions under which the distinction between literacy and expertise becomes nearly impossible to perceive. Drawing on AI literacy frameworks, expertise theory, and professional identity. The paper maps this crisis by introducing simulated contributory expertise as a construct to name this condition at both individual and collective levels. The paper raises pressing implications for how higher education certifies expertise, designs AI literacy curricula, and governs institutional decision-making in an era where the professional identity, AI expertise, and its substance have become difficult to pinpoint.
This article examines the relationship between undergraduate students’ choice of generative artificial intelligence (AI) tools and the cognitive operations mobilized in scientific research, drawing on Vygotsky’s cultural-historical theory and the concept of the Zone of Proximal Development. It is based on an exploratory diagnostic study, with a mixed-methods approach, conducted with 166 undergraduate students at the Federal University of Mato Grosso do Sul, through a questionnaire with closed questions and open fields, treated by descriptive statistics and qualitative reading. The results indicate intensive use of AI — 72.3% of respondents use these tools weekly or daily — yet concentrated in general-purpose generative systems: among those who use AI in any research phase, 95.6% rely exclusively on generative or language-revision systems, with residual presence (2.4%) of AI-layered scientific databases. The open questions reveal concerns about reliability, plagiarism, impoverishment of one’s own learning, and the lack of institutional guidelines. The mismatch between intensity of use and functional adequacy of the tool is discussed as evidence of an unmediated zone of development, in which students build inadequate scaffolds on their own. The article concludes that AI should be understood as a mediating instrument, not as a more capable peer — a role that remains reserved for the teacher, who must mediate the very choice of the tool. An analytical mediation matrix is proposed to guide this teaching practice.
This paper argues that the core problem extends beyond academic dishonesty to a deeper misalignment between assessment practices and the learning outcomes they are intended to measure, and highlights the need for alternative assessment models that emphasize process over product.
The massive spread of Generative Artificial Intelligence (Gen AI) into language classrooms has reopened a currently relevant question related with the human teacher’s role, and in some quarters revived the worry that the instructor who once stood as the “Sage on the Stage” has become obviously less important. This narrative literature review asks what actually happens to the roles and professional identities of language teachers as Gen AI has massively intervened their work, with particular attention to English for Specific Purposes (ESP). Following a protocol-guided search of academic databases, thirteen peer-reviewed studies published between 2022 and 2026 were synthesized, read alongside a smaller body of abundant literature on general language teaching. Taken together, the studies point away from displacement and toward reinvention. Teachers, ESP practitioners in particular, are taking on the work of prompt design, drawing on what one study terms AI-pedagogical knowledge to translate tacit expertise into instructions a model can follow. Since Gen AI can produce credible but incorrect content in specific fields such as engineering, medicine, law, etc., ESP teachers also find themselves play roles as domain validators and as guides to critical AI literacy. What emerges is less an authoritative source of knowledge than a facilitator who decides when to lean on automation and when to rely on judgment, context, and rapport that a model cannot supply.
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