Why reading matters more than writing in AI-enhanced L2 classrooms
Willy A Renandya, 19 April 2025
Reading is essential for good writing. Experienced writers have always understood the value of reading because it helps them learn new ideas, understand different perspectives, and see how others structure their thoughts. By immersing themselves in a variety of materials, whether it’s novels, articles, essays, or research papers, they gain a deeper understanding of language, tone, and style. This exposure is a valuable source of their ideas and it also sharpens their ability to communicate effectively.
In today’s world of AI, tools such as Qwen 2.5, DeepSeek, and ChatGPT can quickly create clear and well-written text for emails, reports, essays, and even creative stories. However, while AI can generate content based on patterns from large datasets, it doesn’t truly understand meaning, context, or the purpose behind the communication.
This is where reading becomes crucial. Skilled writers use the content and linguistic knowledge they’ve gained from their wide reading to assess and improve AI-generated text. By applying what they’ve learned from exposure to different ideas, text types, writing styles, and complex topics, they can ensure that the final result is thoughtful and accurate in terms of content and well-structured and coherent in terms of language.
Thus, it becomes increasingly clear that the ability to read widely and deeply is more important than ever before.
Readers as editors and collaborators
Students who read widely can spot issues in AI writing and revise content for clarity and accuracy. As a result, they’re better able to do the following:
1. Judge the quality of AI writing: AI can produce grammatically correct text that may still be factually wrong or misleading. For instance, a tool might generate:
“The Eiffel Tower is located in Rome and was built in 1850.”
A reader with strong background knowledge and critical literacy skills will notice the errors and cross-check the facts. As Godwin-Jones (2023) notes, reading in the AI era requires learners to evaluate the accuracy and reliability of digital content, a skill that is essential for academic and real-world communication.
2. Improve AI-generated writing: AI outputs can sometimes be vague, flat, or inappropriate in tone. For example:
“To solve climate change, people just need to care more and try harder.”
A critical reader would recognize that this oversimplifies a complex issue. They might revise it to:
“Addressing climate change requires coordinated policy measures, public engagement, and technological innovation.”
Kohnke, Zou, and Zhang (2023) argue that learners must be trained to evaluate and adapt AI-generated texts to suit different genres and communicative purposes—skills best developed through reading a wide range of authentic texts.
3. Understand information deeply: AI-generated summaries are often superficial. A summary like “The story is about a boy who goes on an adventure” may ignore deeper themes such as courage, justice, or identity. Critical reading helps students interpret implied meanings and detect missing layers of interpretation. Dooly and Sadler (2020) note that reading builds the critical capacities learners need to interpret text beyond literal meanings—especially when working with machine-generated content.
4. Recognize good writing: Exposure to different text types and styles helps students develop a sense of what effective writing looks like. When students read widely, they become more aware of elements such as coherence, organization, and tone. This awareness enables them to recognize weak writing, even from AI, and revise it to meet appropriate standards. Wang and Lee (2021) found that students who read reflectively are better equipped to improve both their own writing and AI-assisted drafts.
Implications for Second Language Teaching
The growing use of AI tools in writing has important implications for second language (L2) education. Rather than focusing only on writing production, language teaching should give more attention to developing students’ reading skills, especially through extensive reading.
Extensive reading (ER) involves reading large amounts of enjoyable and understandable texts. It is one of the most effective ways to improve vocabulary, grammar awareness, reading fluency, and overall language proficiency (Renandya, 2007; Renandya & Jacobs, 2016). Learners who read extensively are also more confident users of the language and are better equipped to deal with a variety of real-world texts.
In the age of AI, these benefits take on new importance. Learners who engage in ER build a rich knowledge base and sharpen their ability to detect vague ideas, repetitive language, or logical gaps in AI-generated content. Learners need a large amount of meaningful language input to become fluent readers and confident language users; extensive reading provides exactly that.
Moreover, ER contributes to what Renandya & Jacobs (2016) refer to as robust language learning, where learners develop a deeper understanding of how language is used in different contexts. This knowledge is vital when working with AI tools that often produce generalized, decontextualized, or overly neutral texts. With strong reading skills, learners can refine AI output by drawing on their own understanding of how tone, formality, and cultural nuances affect communication.
Language teachers, therefore, should make extensive reading a central part of the curriculum. Providing access to graded readers, digital texts, and online platforms helps students find material they enjoy and understand. Teachers can also pair ER with classroom discussions, reflection tasks, and AI editing exercises to connect reading with critical thinking and writing improvement.
References
Dooly, M., & Sadler, R. (2020). Mediating language learning through AI writing assistants: The role of critical reading. ReCALL, 32(3), 313–329.
Godwin-Jones, R. (2023). Literacy in the age of AI: Teaching learners to read critically and creatively. Language Learning & Technology, 27(1), 1–10.
Kohnke, L., Zou, D., & Zhang, R. (2023). Artificial intelligence and writing in language education: Challenges and opportunities. Computer Assisted Language Learning, 36(5-6), 556–573.
Lee, J., & Griffiths, P. (2022). Human-AI collaboration in L2 writing: Developing critical engagement. TESOL Quarterly, 56(4), 1304–1321.
Renandya, W. A. (2007). The power of extensive reading. RELC Journal, 38(2), 133–149
Renandya, W. A., & Jacobs, G. (2016). Extensive reading and listening in the L2 classroom. In W. A. Renandya & H. P. Widodo (Eds.), English Language Teaching Today: Linking Theory and Practice (pp. 97–110). Springer.
Wang, Y., & Lee, S. (2021). Revising with AI: Second language students’ engagement with automated writing feedback. Journal of Second Language Writing, 52, 100809.
Webb, S., & Nation, P. (2020). How Vocabulary Is Learned. Oxford University Press.