Learner cognition and behavioral engagement in GenAI-mediated professional language education: a critical integrative review
TL;DR
It is argued that the central educational challenge is not whether GenAI improves single-task language performance, but how learners develop calibrated trust, critical judgment, self-regulated feedback use, and professional agency in human–AI language-learning environments.
Abstract
Generative artificial intelligence (GenAI) is rapidly changing professional language education, yet the behavioral and cognitive mechanisms through which learners engage with GenAI-mediated language tasks remain insufficiently synthesized. This critical integrative review reframes translation and interpreting education as a high-cognitive-load case of professional language learning in which students must evaluate AI output, regulate feedback use, and remain accountable for meaning across languages. A documented evidence-selection workflow was applied to an author-curated bibliographic corpus and a supplementary source-expansion corpus comprising 690 potentially relevant records, from which 21 core studies were selected for focused synthesis. The included studies directly addressed GenAI, translation/interpreting education, AI-generated feedback, post-editing, learner revision, interpreter assessment, or AI literacy. The synthesis is theoretically grounded in cognitive load theory, self-regulated learning, feedback literacy, trust in automation, and social-cognitive accounts of agency. Across the reviewed evidence, learners’ engagement with GenAI involves cognitive load redistribution, trust calibration, feedback uptake, metacognitive monitoring, affective responses, and behavioral revision decisions. The review proposes a behavioral model that links technological mediation, cognitive appraisal, self-regulated behavioral engagement, pedagogical regulation, observable process evidence, and professional agency. We argue that the central educational challenge is not whether GenAI improves single-task language performance, but how learners develop calibrated trust, critical judgment, self-regulated feedback use, and professional agency in human–AI language-learning environments. The framework may also inform adjacent AI-mediated language-learning contexts where learners must evaluate feedback, revise language, and justify communicative choices.