Speaker
Description
Undergraduate students taking mandatory English classes can use generative artificial intelligence to perform an assignment, receive a grade, and learn the language all at once. Seeing task completion and language acquisition as competing goals overlooks the possibility that these goals could enhance each other or change in importance within a specific task. This conceptual study looks into the relationship between these goals for undergraduate students not majoring in English. By means of a purposeful synthesis, the study draws upon sociocultural mediation, self-determination theory, and the research on language-task engagement in conjunction with the latest research on AI delegation. The analysis distinguishes the task set by the teacher, the learner's goals, motivational regulation, and the actual engagement. It proposes alignment and conflict as potential relationships between the goals of task completion and language learning, while considering reorientation as a change process rather than a third kind of learner. The core argument states that AI affects the options for completing the task and, consequently, the relevance and feasibility of engaging with the language. Three conditional propositions consider completion requirements, possible support, and opportunities to reflect on delegation. Examples of situations constructed for an oral task demonstrate these propositions. The resulting design principles focus on meaningful completion criteria, manageable support, and flexible learner decisions. The framework allows one to explore engagement in mandatory classes without assuming that goal-oriented participation is necessarily shallow and that stated learning goals automatically result in continued engagement.
Keywords: generative artificial intelligence, learner engagement, task completion, motivational regulation, pedagogical mediation