Volume 10,Issue 8
Research on the Teaching Practice of Generative AI Practice and Application Course in Higher Vocational Colleges Based on the Task-Driven Approach
Aiming at the problems existing in the course “Generative AI Practice and Application” of higher vocational colleges, including teaching contents easily affected by tool iteration, students focusing on imitation rather than task transfer, lack of verification for generated outputs, and ambiguous evaluation basis, this paper constructs a task-driven teaching model centered on “task traction, human-AI collaboration, evidence verification and transfer application.” The model consists of six links: professional scenario introduction, task analysis and prompt design, human-AI collaborative practice, output verification and iteration, achievement display and multi-dimensional evaluation, as well as transfer reflection. Teachers’ guidance, students’ practice, and generative AI support are embedded into the same teaching process. Progressive tasks are set around prompt design, AI text generation, AI-aided programming, and other content. Two intact classes are taken as research objects to carry out teaching practice, and data are collected through comprehensive tasks, course scores, questionnaires, and classroom observation. The results show that the compliance rate of comprehensive projects in the task-driven group rises from 59.3% at the beginning of the semester to 87.0% at the end, while the compliance rate of the control group reaches 70.6% at the end. The task-driven group also achieves higher scores in comprehensive performance, attendance, and classroom participation. The study proposes that the teaching focus of generative AI courses should shift from tool operation to task analysis, prompt iteration, result verification, and responsibility awareness, and that process records should be adopted to improve the interpretability of evaluation.
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