AN AI-ENABLED CONCEPTUAL FRAMEWORK FOR EVALUATING CURRICULUM-INDUSTRY ALIGNMENT IN THE STUDENT INDUSTRIAL WORK EXPERIENCE SCHEME (SIWES)

The Student Industrial Work Experience Scheme (SIWES) is intended to connect classroom learning with practical training in Nigerian tertiary institutions. However, the assessment of students’ industrial activities remains affected by reliance on handwritten logbooks, irregular supervision, delayed feedback and placements that may not provide experiences relevant to departmental learning outcomes. Existing web-based internship systems improve record keeping and monitoring, but the studies reviewed in this research do not sufficiently address the assessment of curriculum-task relevance. This paper presents an AI-enabled conceptual framework for evaluating curriculum-industry alignment through the analysis of students’ SIWES logbook entries. The framework is derived from the SIWES assessment challenges, literature on digital internship management and intelligent educational assessment, and Prosser’s principles of vocational education. It brings together digital logbook submission, departmental curriculum competency records, natural language processing for identifying tasks, tools and skills, and semantic similarity analysis for comparing reported activities with expected competencies. A weighted relevance scoring component is proposed to calculate a Field Relevance Percentage, identify weak competency areas and support recommendation generation. Supervisor review and feedback are incorporated to maintain human oversight of the assessment process. The framework provides a structured basis for moving beyond the collection of training records towards curriculum-aligned relevance evaluation. Its contribution is a proposed integration of language-based activity analysis, competency mapping and feedback within SIWES monitoring; implementation and empirical validation are required to establish its accuracy and practical usefulness.