From Black Box to Glass Box: GenAI in Non-STEM Computing Education

Abstract

The rapid integration of Generative AI into higher education challenges traditional assessment integrity. This quasi-experimental study analyses its impact on web development skills within an Advertising and Public Relations degree. Drawing on academic performance data, transparency logs, and student perception surveys across four distinct cohorts, the research traces the evolution from a pre-AI baseline through unregulated adoption to a regulated Glass box intervention. Quantitative analysis reveals that while unregulated adoption led to mark inflation, the Glass box protocol stabilised performance. Granular data exposes a dual impact: Generative AI hindered accuracy in foundational tasks due to unverified corrections, yet eliminated resubmissions in complex tasks. The paper contributes evidence-based guidelines for designing non-STEM assessments, demonstrating how Chain-of-Thought documentation converts GenAI from a threat into a verifiable auxiliary tool.