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...
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.
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.
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.
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).
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
