A Framework for the Integration of Mobile Technology and Artificial Intelligence with the Aim of Evaluating the Quality of Teaching in Higher Vocational Education

Against the backdrop of global digital transformation in vocational education, the practice-oriented and skill-centered nature of higher vocational education poses challenges for teaching quality evaluation, including fragmented data collection across diverse instructional settings such as theoretical courses, practical training, and internship placements and the lack of effective methods for capturing unstructured operational data. Skill assessments also remain dependent on subjective human judgment, limiting objectivity and quantifiability of performance indicators such as the compliance of electrical wiring tasks. Although mobile technology and artificial intelligence (AI) offer potential solutions, existing approaches lack a systematic evaluation paradigm aligned with vocational education needs. Current research remains limited by insufficient scenario adaptability and misalignment between technological functions and pedagogical requirements. To develop a teaching quality evaluation system for vocational education that integrates mobile technology and AI, the core research questions include the construction of a system framework adaptable to diverse instructional scenarios, the implementation pathways of key technical modules, and the verification of the system’s effectiveness. A four-dimensional framework—data acquisition, intelligent analysis, feedback optimization, and management coordination—was established, and a prototype system featuring full-scenario mobile data capture and AI-based skill quantification was implemented. Quasi-experimental studies in three vocational institutions of varying types demonstrate the system’s ability to address contextual and technical bottlenecks. The proposed scenario–technology–education alignment paradigm provides a reusable technical solution and empirical basis for quality assurance in vocational education.