Chapter 3. Extensive Reading and AI: This Could Be Great
Mark Brierley and Gary Ross
Mark Brierley and Gary Ross
Abstract
In the evolving landscape of the fourth industrial revolution, generative AI presents opportunities to enhance language acquisition by re-evaluating and directly supporting language learning processes. This chapter explores the intersection of Extensive Reading (ER) and generative AI (GenAI), focusing on parallels in their approaches to language learning. We also look at the potential for GenAI to create customized reading materials. ER emphasizes fluency development through reading large volumes of engaging texts at an appropriate level; this mirrors the proficiency gains of large language models (LLMs) through exposure to diverse datasets. Both approaches appear more effective than traditional, formalized study by favouring immersive input and fostering a more natural language acquisition process. As well as providing a metaphor to inform our language learning, GenAI can address the challenge of sourcing suitable ER content by tailoring texts to each learner's proficiency levels and interests. GenAI also has the potential to generate ER materials in languages that are poorly resourced. With approximately 40% of the world's languages at risk of extinction, GenAI could play a crucial role in creating ER materials to support linguistic diversity. GenAI in ER allows educators to foster dynamic, personalized learning environments, enhancing language acquisition and supporting linguistic diversity.
About the Contributors
Mark Brierley is an associate professor at Shinshu University and has taught a range of courses from low energy building to dialects of English. He has been teaching English in Japan for over twenty years. His research focuses on using technology to support Extensive Reading, particularly the use of machine learning to assess text difficulty and generate engaging materials tailored to learners. He developed the Extensive Reading Foundation placement test and serves as a board member of the Foundation and an editor of the Journal of Extensive Reading.
Gary Ross is an Associate Professor in the Faculty of Pharmacy at Kanazawa University, Japan. His research interests include artificial intelligence, online learning, extensive reading, speech recognition, and speech synthesis in language education. He also serves as lead programmer of Erai.app. Originally from Ireland, he has worked in Japan for many years, combining teaching and research with the development of practical tools to support language learning and student engagement.
Citation
Brierley, M., & Ross, G. (2025). Expensive reading and AI: This could be great. In L. Ohashi, M. Hillis, & R. Dykes (Eds.), Artificial intelligence in our language learning classrooms (pp. 58-77). Candlin & Mynard. https://doi.org/10.47908/38/3
In the evolving landscape of the fourth industrial revolution, generative AI presents opportunities to enhance language acquisition by re-evaluating and directly supporting language learning processes. This chapter explores the intersection of Extensive Reading (ER) and generative AI (GenAI), focusing on parallels in their approaches to language learning. We also look at the potential for GenAI to create customized reading materials. ER emphasizes fluency development through reading large volumes of engaging texts at an appropriate level; this mirrors the proficiency gains of large language models (LLMs) through exposure to diverse datasets. Both approaches appear more effective than traditional, formalized study by favouring immersive input and fostering a more natural language acquisition process. As well as providing a metaphor to inform our language learning, GenAI can address the challenge of sourcing suitable ER content by tailoring texts to each learner's proficiency levels and interests. GenAI also has the potential to generate ER materials in languages that are poorly resourced. With approximately 40% of the world's languages at risk of extinction, GenAI could play a crucial role in creating ER materials to support linguistic diversity. GenAI in ER allows educators to foster dynamic, personalized learning environments, enhancing language acquisition and supporting linguistic diversity.
About the Contributors
Mark Brierley is an associate professor at Shinshu University and has taught a range of courses from low energy building to dialects of English. He has been teaching English in Japan for over twenty years. His research focuses on using technology to support Extensive Reading, particularly the use of machine learning to assess text difficulty and generate engaging materials tailored to learners. He developed the Extensive Reading Foundation placement test and serves as a board member of the Foundation and an editor of the Journal of Extensive Reading.
Gary Ross is an Associate Professor in the Faculty of Pharmacy at Kanazawa University, Japan. His research interests include artificial intelligence, online learning, extensive reading, speech recognition, and speech synthesis in language education. He also serves as lead programmer of Erai.app. Originally from Ireland, he has worked in Japan for many years, combining teaching and research with the development of practical tools to support language learning and student engagement.
Citation
Brierley, M., & Ross, G. (2025). Expensive reading and AI: This could be great. In L. Ohashi, M. Hillis, & R. Dykes (Eds.), Artificial intelligence in our language learning classrooms (pp. 58-77). Candlin & Mynard. https://doi.org/10.47908/38/3
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Information About the Book
Title: Artificial Intelligence in Our Language Learning Classrooms Editors: Louise Ohashi, Mary Hillis, & Robert Dykes Read more... |