Abstract:

In recent years, generative Artificial Intelligence (genAI) has begun to reshape various learning processes, including in-campus courses. Prior research indicates that genAI-supported instructional practices can enhance learning performance and student motivation; however, persistent challenges such as surface learning, content inaccuracy, equity, and ethics require careful consideration within instructional design processes. Although research on genAI in education is rapidly growing, further efforts—particularly in the form of instructional design recommendations—are still required to address these challenges. To respond to this need, this study examines the integration of genAI into in-campus courses through a review of recent genAI literature and insights from ongoing course-level implementations. Building on both literature-based evidence and practical implementation experiences, the study proposes instructional design recommendations to address current challenges in genAI-supported instruction. These recommendations are organized according to a role-based implementation framework proposed by Xu and Ouyang (2022), which conceptualizes genAI as an assistant, a mediator, and an instructional agent. The proposed recommendations address key issues related to content quality, cognitive presence, assessment, equity, technical support, and ethical assurance. By providing a structured design perspective grounded in both theory and practice, the study aims to support effective, responsible, and context-sensitive adoption of genAI in in-campus courses.

Keywords:

Generative Artificial Intelligence, GenAI-Supported Learning, Instructional Design Recommendations, Higher Education, Role-Based Implementation.