Introductory artificial intelligence (AI) courses present significant learning challenges due to abstract concepts, mathematical complexity, and students' diverse technical backgrounds. While active and collaborative pedagogies are often recommended, implementation can be difficult at scale due to large class sizes and the intensive design effort required of instructors. This paper presents a quasi-experimental case study examining the redesign of in-class instructional time in a university-level Introduction to Artificial Intelligence course. Inspired by CS Unplugged approaches, we redesigned the summer offering, integrating embodied, unplugged simulations, collaborative programming labs, and structured reflection to provide students with a first-person perspective on AI decision-making. We maintained identical assignments, exams, and assessments as the traditional lecture-based offering. Using course evaluation data, final grade distributions, and post-course interviews, we examined differences in student engagement, experiences, and traditional learning outcomes. Quantitative results show that students in the redesigned course reported higher attendance, stronger agreement that assessments measured their understanding, and greater overall course effectiveness, despite no significant differences in final grades or self-reported learning. Qualitative findings indicate that unplugged simulations and collaboration fostered a safe, supportive learning environment that increased engagement and confidence with AI concepts. These results highlight the importance of in-class instructional design in improving students' learning experiences without compromising rigor.

翻译：入门级人工智能（AI）课程因概念抽象、数学复杂性及学生技术背景差异显著而面临重大学习挑战。尽管主动式与合作式教学法常被推荐，但受限于大班规模和教师需投入的大量设计工作，其规模化实施存在困难。本文通过准实验案例研究，探讨大学《人工智能导论》课程课堂教学时间的重新设计。受"非插电式计算机科学"方法启发，我们对暑期课程进行重构，整合具身化非插电模拟、协作编程实验及结构化反思，使学生获得AI决策的第一人称视角。课程作业、考试与评估方式与传统讲授式课程保持一致。通过课程评价数据、期末成绩分布及课后访谈，我们分析学生参与度、学习体验与学业成果的差异。定量结果表明，尽管期末成绩及自我报告学习效果无显著差异，但重修课程学生的出勤率更高、对评估与理解匹配度的认同感更强、课程整体有效性评价更优。定性研究发现，非插电模拟与协作学习营造了安全包容的学习环境，有效提升学生对AI概念的参与度和信心。这些结果凸显了课堂教学设计在提升学习体验而不降低学术严谨性方面的重要价值。