Artificial intelligence (AI) retrieval-augmented generation (RAG) tools now enable educators to transform course materials into diverse multimedia at scale. However, it remains unclear whether such AI-generated content functions as a pedagogical scaffold or AI slop: high volume, low quality material. This innovative practice paper reports on the development, implementation, and evaluation of teacher-prompted, AI-generated supplemental materials in an English for Academic Purposes (EAP) course at a Hong Kong Community College. Using primarily Google Notebook LM, the instructor generated videos, podcasts, infographics, and individualized feedback reports from course materials and student work for 106 English as a Foreign Language learners. An explanatory sequential mixed-methods design comprising a survey, semi-structured interviews, and correlation analysis with academic scores was employed to examine students' preferences, perceptions, and learning outcomes. Findings are framed through the Technology Acceptance Model and Cognitive Load Theory. Students rated the materials highly for perceived usefulness and ease of use, and preferred assessment-linked content presented in visual and multimodal formats, particularly videos and infographics. Video preference correlated positively with academic performance; however, higher cognitive load was negatively associated with course grades, indicating that material complexity must be carefully calibrated. Notably, some lower-performing students independently adopted the materials as remedial scaffolds. The practice demonstrates that RAG tools enable scalable personalized feedback that would be less feasible through traditional methods. When aligned with student goals and cognitive principles, teacher-prompted AI generation can meaningfully enhance the EAP learning ecosystem rather than producing AI slop.

翻译：人工智能（AI）检索增强生成（RAG）工具如今使教育者能够将课程材料大规模转化为多样化的多媒体内容。然而，尚不清楚此类AI生成内容究竟是起到了教学支架作用，还是沦为"AI 垃圾"——即产量高但质量低的材料。这篇创新实践报告阐述了在香港一所社区学院的学术英语（EAP）课程中，由教师提示、AI生成的补充材料的开发、实施与评估过程。教师主要使用Google Notebook LM，为106名英语作为外语的学习者，基于课程材料和学生作业生成了视频、播客、信息图表以及个性化反馈报告。研究采用解释性序列混合方法设计，包括问卷调查、半结构化访谈以及与学业成绩的相关性分析，以考察学生的偏好、感知及学习成果。研究结果基于技术接受模型和认知负荷理论进行阐释。学生们对这些材料在感知有用性和易用性方面给予了高度评价，并偏好以视觉和多模态形式呈现的与评估相关的内容，尤其是视频和信息图表。对视频的偏好与学业表现呈正相关；然而，较高的认知负荷与课程成绩呈负相关，表明材料复杂度需精心校准。值得注意的是，部分成绩较低的学生自主地将这些材料用作补救性支架。实践表明，RAG工具能够实现传统方法难以大规模提供的个性化反馈。当与学生的目标及认知原则保持一致时，由教师提示的AI生成内容能够有意义地增强EAP学习生态系统，而非制造"AI 垃圾"。