Using Generative Artificial Intelligence as a More Knowledgeable Other to Scaffold Learning Within Vygotsky’s Zone of Proximal Development

This study examines the pedagogical role of Generative Artificial Intelligence as a More Knowledgeable Other within Vygotsky’s Zone of Proximal Development in a higher education setting, teaching Inductive Thematic Analysis. Using an autoethnographic methodology situated within an enabling research design program at a leading Australian university, this study analyses the integration of GenAI tools as instructional scaffolds to model initial coding practices and support student learning. GenAI was employed to generate preliminary, explicit codes from students’ own research data, which were then subjected to structured critique and extension by students. Findings indicate that this AI supported scaffolding enhanced student engagement, confidence, and interpretive skill development by providing concrete exemplars while preserving space for higher order analysis. Students demonstrated rapid progression through the ZPD, using AI outputs as provisional reference points rather than authoritative analyses. The findings are interpreted through the lenses of the Technology Acceptance Model (TAM) and connectivism. TAM helps explain students’ willingness to engage with GenAI based on perceived usefulness and ease of use, while connectivism frames learning as the development of students’ capacity to evaluate, manage, and mobilise AI within their broader learning networks. Rather than replacing disciplinary understanding, GenAI functioned as a pedagogical resource that supported critical engagement. The study contributes to educational technology scholarship by illustrating how GenAI, when embedded within structured, theory informed pedagogy, can support complex analytical skill development in higher education.