A Practical Study on Enhancing the Learning Efficiency of Basic Photoshop Skills among Secondary Vocational School Students through Artificial Intelligence Tools

Abstract

With the rapid advancement of artificial intelligence technology, its potential for application in the field of education has become increasingly prominent. Secondary vocational schools have long faced challenges in Photoshop skill instruction, including significant disparities in student foundational knowledge, extended skill acquisition periods, and difficulties in fostering creativity. Grounded in constructivist learning theory and personalized learning theory, this study explores the theoretical basis and feasibility of using AI tools to enhance Photoshop skill acquisition. It constructs a teaching model centered on "human-machine collaboration, data-driven decision making, and dynamic adaptation," and designs specific implementation pathways from three perspectives: technology integration, task design, and assessment optimization. By establishing a multi-dimensional learning efficiency evaluation system, the study systematically analyzes learning outcomes following the integration of AI tools. The findings demonstrate that the introduction of AI tools such as Adobe Sensei and Remove.bg significantly improves the learning efficiency of secondary vocational students in areas including operational proficiency, workflow optimization, and creative expression. This research provides a transferable framework for innovating digital skill instructional models in vocational education.