Comparing Human‐Created and NotebookLM‐Generated Podcasts for EAP Instruction: Differences in Linguistic Complexity and the Role of Prompt Design
Abstract
ABSTRACT Science popularization podcasts are valuable resources for EAP instruction because they make academic content more accessible to learners. However, producing high‐quality podcasts is time‐consuming and resource‐intensive, and existing resources may not always align with instructional goals or learners’ needs. NotebookLM, a large language model (LLM)‐based tool, may offer possibilities for generating multimedia materials more efficiently. Yet, little is known about how linguistically comparable such AI‐generated podcasts are to human‐created ones or how prompt design may shape their linguistic complexity for pedagogical use. To address this gap, this study examined the lexical and syntactic complexity of human‐created and NotebookLM‐generated podcasts derived from the same research‐article source texts. Four corpora were analyzed: a corpus of human‐created podcasts from the Nature Podcast series and three corpora of NotebookLM‐generated podcasts produced under different conditions: the default setting (no prompt) and prompts intended to approximate CEFR B1‐ and B2‐level output for EAP instruction. Fourteen indices of lexical and syntactic complexity were analyzed. The findings showed that NotebookLM‐generated podcasts were generally more lexically complex but less syntactically complex than human‐created podcasts. Prompt design also differentiated the linguistic complexity of NotebookLM‐generated output: the B1‐level prompt generally produced less complex output than the B2‐level prompt, while the default setting generated the most challenging materials. Although the prompted outputs were not fully comparable to the human‐created materials, they showed greater similarity on several linguistic features. These findings provide preliminary evidence of NotebookLM's potential for EAP materials development and highlight the importance of prompt design in shaping AI‐generated pedagogical materials.
