I WROTE IT MYSELF: A CASE STUDY ON HUMAN AUTHORSHIP AND AI MISCLASSIFICATION

Abstract

The rapid spread of AI writing tools has complicated how academic authorship is recognized and evaluated, especially in Translation Studies, where the translator’s voice and presence have always been central concerns. Building on Venuti’s (1995) concept of the translator’s invisibility and posthumanist views of human–machine entanglement (Hayles, 1999; Haraway, 1991), this study examines how human authorship becomes difficult to prove in academic translation contexts. Awareness of AI detection software has introduced a new anxiety: even genuine human writing may be flagged as machine-produced simply because it is fluent and well-structured—the very qualities traditionally encouraged in translator education. To explore this tension, the study collects Discussion/Conclusion sections from three published translation studies and creates parallel AI-generated counterparts on the same thematic focus. A comparative analysis of stylistic rhythm, lexical specificity, stance, and pedagogical implications shows that while AI produces smooth and confident prose, it tends to flatten nuance, avoid risk, and generalize outcomes. In contrast, human writing displays hesitation, contextual awareness, and interpretive judgement. The findings suggest that evaluating translation work should emphasize reflective justification and decision-making processes rather than relying solely on final-text fluency or automated detection tools.