Abstract
Artificial intelligence (AI)-assisted software quality engineering is increasingly concerned with reducing the cost of defect identification while improving the efficiency with which limited testing resources are allocated. This research develops a conceptual framework for AI-assisted defect detection and test prioritization by synthesizing the methodological and technological patterns evident in the provided literature. Although the supplied studies are predominantly situated in augmented reality (AR), simulation-based learning, engineering visualization, embedded monitoring, and technology-enabled experimentation, they collectively demonstrate principles relevant to intelligent quality assurance: automated observation, simulation, data-driven classification, prioritization of critical conditions, and continuous feedback. The proposed framework integrates data acquisition, defect-feature extraction, AI-assisted classification, risk-aware test prioritization, execution feedback, and iterative model refinement. Particular attention is given to the distinction between defect detection and test prioritization, since accurate identification alone does not guarantee efficient testing. The analysis further positions project-risk prediction as a complementary decision-support mechanism, emphasizing how predictive reasoning can improve prioritization decisions (Philip, 2024). The resulting framework provides a conceptual basis for incorporating AI into modern software quality engineering while recognizing limitations associated with heterogeneous data, domain transfer, model interpretability, validation, and the absence of empirical software-testing datasets within the supplied literature.