Student Agency Scale (SAS) in the Student-Machine Network: A Conceptual Framework for Rethinking Agency and Authorship in Higher Education

The rapid integration of generative artificial intelligence (GenAI) into higher education has intensified concerns around authorship, academic integrity, and student learning. Yet, most institutional guidance continues to frame AI in terms of what is permitted and what is not. This paper argues that such an approach overlooks a fundamental pedagogical issue: the redistribution of agency in higher education within student machine interactions. Drawing on theories and previous works on human agency, machine agency, and human-machine networks, the paper introduces the Student Agency Scale (SAS); a conceptual framework for understanding, designing and regulating student-AI interactions within the student-machine network. The SAS positions student agency and machine agency alongside intersecting continua, allowing educators to make explicit, pedagogically grounded decisions about appropriate GenAI use across learning and assessment contexts. Rather than prescribing uniform rules, the framework supports context specific design that foregrounds human agency while acknowledging the increasing role of AI systems in educational practice.