AI-Driven Multi-Dimensional Course Assessment and SHS Strand Recommendation System

Abstract

Senior High School (SHS) students often face challenges in selecting the most appropriate academic strand due to limited assessment tools and the continued reliance on traditional guidance practices. These methods frequently lack objectivity and comprehensive evaluation, resulting in mismatches between students’ competencies, interests, and their chosen academic pathways. Such misalignment may affect students’ academic performance, educational direction, and future career readiness. This study aimed to develop an AI-Driven Multi-Dimensional Course Assessment and SHS Strand Recommendation System designed to objectively evaluate student competencies and generate strand recommendations that align with learners’ abilities, interests, and career potential. The system intends to support students, teachers, and guidance personnel by providing data-driven insights that can assist in making informed academic decisions. The research employed a quantitative–descriptive design integrated with Explainable Artificial Intelligence (XAI). The system was developed using an Input–Process–Output (IPO) conceptual framework combined with Agile Software Development Methodology, enabling iterative system improvement and structured data processing. The platform utilizes a 30-dimension assessment model measuring cognitive skills, literacy and communication abilities, technical aptitude, and personality-interest indicators. Student responses are processed through a structured scoring model and analyzed using an AI-powered evaluation module to generate strand recommendations, competency profiles, and career-related insights, which are presented in a downloadable evaluation report. The results are presented in a comprehensive evaluation report that users can download and use for academic guidance and decision-making. Evaluation results showed strong system performance and user acceptance. Based on the ISO/IEC 25010 software quality model, IT experts gave the system an overall mean score of 3.95, indicating a high level of software quality and functionality. System users rated the platform with an overall mean score of 4.00, interpreted as Highly Acceptable, reflecting positive user experience and effectiveness. This finding clearly demonstrates that the system can provide data-driven guidance in SHS strand selection, reduce academic-career mismatches, and support better educational alignment and long-term career readiness among students.