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Integrating Generative Artificial Intelligence into Applied Business Statistics (Practice Report)
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Integrating Generative Artificial Intelligence into Applied Business Statistics (Practice Report)

John Haw, Badri Prasad Bhattarai, Nahid Islam, Silva Wei and Sharen Nisbet
SSRN, Vol.Series Paper No. 58
Southern Cross University Scholarship of Learning and Teaching Research Paper Series, Elsevier
16/06/2026
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Abstract

GenAI assessment design student engagement AI literacy
Generative Artificial Intelligence (GenAI) is fundamentally reshaping curriculum and assessment design in higher education. This Practice Report details the transition of a postgraduate business statistics unit from a traditional, instructor-led model to a self-directed, GenAI-enabled framework. Across six scaffolded modules, responsibility for learning was progressively shifted from structured academic guidance to independent use of AI tools. The intervention was evaluated using a mixed-methods approach across two sequential cohorts (2025, n = 31; 2026, n = 32), drawing on academic performance data, student satisfaction surveys, and qualitative feedback. The findings reveal a compelling paradox. Following the redesign, the mean unit grade point average (GPA) decreased by 1.38 points on a 7-point scale. However, overall student satisfaction increased by .23, to 4.67 on a 5-point scale, alongside elevated ratings for cognitive engagement and task relevance. Rather than indicating poorer learning outcomes, the decline in GPA is argued to reflect reduced grade compression and enhanced assessment integrity achieved by disrupting unverified AI use. These outcomes demonstrate that intentional GenAI integration can increase cognitive demand and validly differentiate student performance without compromising the student experience.

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