From Academic Text to Talk-Show: Deepening Engagement and Understanding with Google NotebookLM

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Bibliographic Details
Title: From Academic Text to Talk-Show: Deepening Engagement and Understanding with Google NotebookLM
Language: English
Authors: Marie Alina Yeo (ORCID 0000-0003-0136-2532), Benjamin Luke Moorhouse (ORCID 0000-0002-3913-5194), Yuwei Wan (ORCID 0000-0002-6377-7904)
Source: TESL-EJ. 2025 28(4).
Availability: TESL-EJ. e-mail: editor@tesl-ej.org; Web site: http://tesl-ej.org
Peer Reviewed: Y
Page Count: 14
Publication Date: 2025
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Artificial Intelligence, Research Tools, Technology Uses in Education, Computer Mediated Communication, Questioning Techniques, Second Language Learning, Language of Instruction, Bilingual Education, Epistemology, Error Patterns, Misinformation
ISSN: 1072-4303
Abstract: This paper looks at Google's NotebookLM, an AI-powered research assistant tool that can represent dense academic content in a range of output modes, like FAQs, timelines, study guides, and, most uniquely, as "Deep Dive" discussions. The discussions mimic a talk-show, where two AI-hosts unpack complex ideas from reading or audio texts, for example, a journal article or lecture, connecting them with related ideas. The talk-show format provides a relaxed, low-pressure way for learners to grasp key concepts. NotebookLM is especially helpful for those who may find academic reading overwhelming, as it transforms the reading process into a more enjoyable, multimodal experience. The paper describes and evaluates the discussion generation function in relation to principles of second language and digital learning. Overall, NotebookLM shows promise in helping learners to understand and engage with academic content more deeply and may be especially valuable for second language learners in English Medium Instruction (EMI) or bilingual education contexts which emphasize content development alongside language learning. However, the tendency of the AI-hosts to extract, embellish and augment the content may lead to misrepresentation of the original source text, raising epistemological concerns about the spread of misinformation.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1464032
Database: ERIC
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