From Cheating to Competence: A Pedagogical Framework for Reimagining Machine Translation in Higher Education

The rapid advancement of Neural Machine Translation (NMT) and Large Language Models (LLMs) has sparked a crisis of academic integrity in Higher Education, particularly in Foreign Language departments. Traditional pedagogical models often categorize the use of tools like DeepL or ChatGPT as "cheating," a view that increasingly conflicts with the demands of the global professional market.

This paper proposes a paradigm shift from prohibition to Machine Translation Literacy (MTL). Using a "Human-in-the-Loop" (HITL) instructional design within English Linguistics and Practical Language courses, this study explores how the student’s role evolves from a passive learner to a critical evaluator.

The proposed framework replaces traditional rote translation with a three-tiered educational process: collaborative drafting, diagnostic post-editing (error taxonomy), and sociolinguistic critique. Preliminary observations indicate that this approach promotes highlevel metacognitive skills, as students must justify linguistic choices that the AI fails to make.

Rather than diminishing rigor, the integration of AI into the curriculum fosters "translingual competence," transforming the classroom into a laboratory for critical thinking. The paper concludes that by embracing AI, educators can move beyond the "cheating" narrative to cultivate the sophisticated analytical skills essential for the modern researcher.