AI TESTING FOR SMART LEARNING APPLICATIONS—A CASE STUDY

Abstract

The increasing adoption of artificial intelligence (AI) in smart learning environments has heightened the need for systematic, reliable testing of AI-driven educational applications. Existing studies primarily rely on benchmark accuracy, manual testing, or user-based assessment, offering limited insight into robustness, coverage, and failure behavior. These limitations are driven by the lack of standardized intelligence quality criteria, inadequate test automation support, complex diversity in Q&A tasks, and the difficulty of automatically validating test results in smart learning applications. This paper investigates model-based AI testing for Q&A-based smart learning applications, using ChatGPT (GPT-5) as a case study to evaluate its intelligence quality in college algebra question answering tasks that support student learning. A three-dimensional (3D) AI testing framework structures testing along input, context, and output dimensions to enable model-driven test generation, controlled contextual variation, and consistent validation. College algebra problems selected from a standard undergraduate textbook are used to construct representative test cases. Controlled image-based data augmentation and structured similarity-based validation mechanisms are employed to support automated test execution and result analysis. Empirical results demonstrate that the proposed approach improves intelligence quality coverage and provides more diagnostic insight than ad hoc evaluation methods.