Paper Type
Complete
Paper Number
PACIS2026-1725
Description
Generative AI is now embedded in higher education, yet research often treats adoption as a static metric. This study examines how students actively appropriate AI in a post-adoption landscape, reconceptualising traditional adopter categories as dynamic identity resources. Using a mixed-methods design, we analysed 188 reflective essays (framed as curated identity performances) from postgraduate Information Systems students via qualitative content analysis and K-medoids clustering. We propose the generative DCI Framework, bridging macro-level social diffusion with micro-level AI competence and learner identity to explain GenAI adoption as intellectual governance. Findings reveal distinct cross-layer mechanisms: while most identify as Early Adopters or Early Majority, they differ sharply in how they legitimise use through verification, boundary-setting, and identity protection against career-readiness pressures. The study contributes empirically informed typologies to guide stakeholder-specific AI literacy initiatives and process-oriented assessments.
Recommended Citation
Truong, Vi; Chow, Winn; Chester, Andrew; Peng, Xiang (Stella); Li, Zoey Ziyi; Atiq, Arzoo; and Holden, Eun-Jung, "From Metrics to Meaning: A Three-Layer DCI Framework of Generative AI Adoption and Intellectual Governance" (2026). PACIS 2026 Proceedings. 9.
https://aisel.aisnet.org/pacis2026/is_education/is_education/9
From Metrics to Meaning: A Three-Layer DCI Framework of Generative AI Adoption and Intellectual Governance
Generative AI is now embedded in higher education, yet research often treats adoption as a static metric. This study examines how students actively appropriate AI in a post-adoption landscape, reconceptualising traditional adopter categories as dynamic identity resources. Using a mixed-methods design, we analysed 188 reflective essays (framed as curated identity performances) from postgraduate Information Systems students via qualitative content analysis and K-medoids clustering. We propose the generative DCI Framework, bridging macro-level social diffusion with micro-level AI competence and learner identity to explain GenAI adoption as intellectual governance. Findings reveal distinct cross-layer mechanisms: while most identify as Early Adopters or Early Majority, they differ sharply in how they legitimise use through verification, boundary-setting, and identity protection against career-readiness pressures. The study contributes empirically informed typologies to guide stakeholder-specific AI literacy initiatives and process-oriented assessments.
Comments
04-DigitalLearning