AI in the Literacy Classroom: Three Thoughtful Uses

AI in the literacy classroom often enters the conversation through fear: students will use it to avoid reading, generate their writing, or submit work that teachers cannot verify. Amy Hutchison’s article takes a more practical route. She asks how teachers might use ChatGPT to develop the literacy skills students need when AI-generated text is already part of their information environment.

This is a practitioner article, not an empirical study. Hutchison does not test an intervention or report learning gains. She draws on established research in reading, digital literacy, background knowledge, critical evaluation, and technology integration to propose classroom activities. The activities are useful starting points, but they still need to be tried and studied with students.

Three ways to use AI during literacy instruction

The first use is building background knowledge before reading. In Hutchison’s example, students ask ChatGPT what Because of Winn-Dixie is about. They identify unfamiliar words and ideas, ask follow-up questions, and then discuss the answers as a class. The chatbot becomes an interactive starting point that can respond to each student’s questions.

I can see the value, especially when limited vocabulary or prior knowledge makes a text difficult to enter. Still, this use needs careful teacher guidance. Incorrect background information can shape how students understand everything that follows. Hutchison acknowledges that AI responses may be incomplete or inaccurate, so teachers need to preview the activity and build verification into it.

The second use is stronger because it makes evaluation part of the lesson itself. Students examine an AI-generated interpretation and ask what is vague, missing, biased, or poorly communicated. One example compares ChatGPT’s discussion of The Giving Tree with details conveyed through the illustrations. Students discover that the response misses important visual information.

Here, the weak AI response becomes a text to interrogate. As Hutchison puts it, “it is the student’s responsibility to analyze and consider the quality of the response” (p. 902). This moves classroom attention away from accepting a polished answer and toward understanding how meaning is constructed. It connects well with the expanded view of critical AI literacy I discussed previously, where an AI response is treated as one source among others and examined within a wider social and informational context.

The third use is lateral reading. Students take claims produced by ChatGPT, leave the chatbot, and check what other sources say. Hutchison illustrates this with a paragraph about differences between frogs and toads. Students compare the AI response with child-friendly websites, confirming some claims and finding relevant information the chatbot omitted.

For me, this is the most important activity in the article. Fluency and confident wording can make generated text appear complete even when it is thin or unsupported. Lateral reading interrupts that effect. It teaches students that plausibility is not evidence.

What teachers should watch carefully

Hutchison also addresses authorship and assessment. In a small demonstration, she asked ChatGPT whether a passage she had written was generated by the system. ChatGPT falsely claimed that it had produced the text. This was not a formal test of AI detection, but it neatly illustrates why asking a chatbot to identify authorship is unreliable. More recent research I reviewed on this blog reaches the same conclusion about the problems with AI detection in education.

Her alternative is to give greater attention to the writing process: brainstorming, drafts, revisions, discussion, and students’ explanations of how they used AI. That is a sound pedagogical move because it gives teachers richer evidence of learning. It also keeps responsibility with the student. AI may contribute ideas or provide a model, while the student must still make decisions, check information, and explain the final text.

The article was published in 2024 and reflects the early classroom conversation around ChatGPT. Some examples and technical descriptions will age as the systems change. A more substantial limitation is that several educational possibilities are inferred from earlier research on digital texts and literacy technologies. We cannot assume that generative AI will produce the same benefits. Its ability to generate fluent misinformation creates a different instructional problem.

Even with that caution, Hutchison gives teachers a useful principle: bring AI into literacy instruction when its presence creates more reading, questioning, comparing, and revising. If the tool completes those acts for students, the task needs to be redesigned. The teacher’s role is to make the thinking visible and keep students responsible for the meaning they produce.