INSTRUCTIONAL DESIGN OF A WORKPLACE-ORIENTED GENERAL AIGC COURSE IN THE ERA OF DIGITAL INTELLIGENCE

Abstract

In the era of digital intelligence, artificial intelligence generated content (AIGC) has become deeply integrated into workplace workflows. However, a growing gap has emerged between corporate demand for AIGC-driven productivity and the lag in talent development. Current introductory AIGC courses suffer from fragmented content, superficial assessment methods, and limited adaptability. To address these challenges, this study proposes a holistic instructional design framework based on the "structural reconstruction, capability building, and closed-loop evaluation" model, featuring an 8-hour modular curriculum: prompt engineering (2 hours) to establish foundational concepts for cross-domain transfer, core tool mastery (4 hours) covering four mainstream AI platforms (ERNIE Bot, DeepSeek, WPS AI, and Doubao), and role-specific applications (2 hours) offering tailored practical exercises across five key professions (finance, education, software development, copywriting, and design). The framework employs a hybrid assessment model combining self-evaluations, instructor feedback, and AI-driven evaluations to target real-world problem-solving skills and adaptability to new tools, while a data-driven feedback loop enables continuous optimization of both course content and personalized learning interventions. Ultimately, this study provides a scalable, iterative system framework for empowering today's AI-driven workforce.