AI Slop or AI-enhancement? Student perceptions of AI-generated media for an English for Academic Purposes course

TL;DR

This study explores how AI-generated multimedia materials, when thoughtfully integrated, can enhance English language learning by providing personalized, multimodal content that aligns with student needs and cognitive load considerations.

Contribution

It demonstrates the effective use of AI retrieval-augmented generation tools to create personalized, multimodal educational materials that improve student engagement and learning outcomes in an EAP course.

Findings

Students found AI-generated materials useful and easy to use.

Video and infographic content were particularly preferred by students.

Higher cognitive load negatively impacted course grades.

Abstract

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.