Human-Centred Acceptance and Selective Reliance in AI-Assisted Decision Support for Higher-Education Quality Assurance
Selecting a machine-learning model for higher-education quality assurance (QA) is a governance decision that requires defensible trade-offs. This paper examines post-exposure acceptance, trust, and intended reliance in a staged multi-criteria AI decision-support workflow after repeated within-session use across six scenarios. Using a primary analytic subset from a controlled study (N = 38 participants, 228 completed scenarios), we distinguish perceived helpfulness, trust, policy acceptance, and intended reliance rather than collapsing them into a single attitude measure. The workflow was viewed as helpful (M = 4.05/5) and moderately trustworthy, while self-reported intended reliance remained moderate (M = 3.34/5). Scenario-level counterfactual trust indicated a heterogeneous pattern: 50.0% unchanged, 36.8% lower without the workflow, and 13.2% higher. An exploratory participant-level correlation pattern links reliance-related outcomes to evidence inspection rather than to recommendation uptake alone. A broader completed-all comparison set (N = 90) showed a directionally consistent profile. The observed pattern is more consistent with selective reliance than with undifferentiated recommendation uptake, although the correlational design does not permit strong causal claims. Analysis scripts and anonymized data for the locked snapshot are available in a public reproducibility package [15].