Contextualization of Secondary School Student’s Test Performance in a Large-Scale Assessments for Learning using Human-Centered AI Approach

Large-scale educational assessments often rely solely on final scores, overlooking the rich process data that capture students’ problem-solving behaviors and cognitive engagement. This study introduces a Human-Centered Artificial Intelligence (HCAI) framework—Explainable Adaptive Learning System (X-ALS)—to interpret such contextual data from secondary school students’ performance in Mathematics and Agricultural Science in Akwa Ibom State, Nigeria. Using process data (response times, item attempts, and problem-solving steps), the framework combines machine learning with human expert judgment to generate interpretable profiles that reflect student learning strategies. A descriptive survey design was employed with 2,500 Senior Secondary Two (SS2) students across ten schools. Three research questions and four null hypotheses were formulated to guide the study. A mathematics and Agricultural science achievement test was used for data collection. The instrument was face and content validated by three specialists. Kuder-Richardson (K-R20) was used to determine the internal consistency of the test items and a coefficient of 0.89 and 0.85 was obtained. Mean and standard deviation was used to answer the research questions, PPMC was used to test hypotheses one and two while Regression Analysis was used to test hypotheses three and four. Findings revealed that the HCAI framework enabled efficient data annotation, improved prediction accuracy over traditional AI models, and produced actionable feedback for educators. The results underscore the potential of contextualized AI tools in supporting adaptive teaching strategies, especially in large-scale assessment environments. The study recommends integrating human-centered AI into digital learning systems for real-time, personalized feedback to enhance educational outcomes.