You are reading something online. The information seems accurate. The voice is confident. The facts feel trustworthy. But who wrote it? A reporter? A student? An author? A chatbot?
Questioning sources and evaluating an author’s credibility is not new work. These skills, alongside synthesizing information, determining central ideas, identifying key details, and making inferences are being taught in most literacy classrooms and remain essential as technologies evolve. In fact, they have never been more important: We want students applying these reading strategies when interacting with any text, particularly as they encounter nonfiction content created by both humans and machines.
At first glance, AI-generated content appears strikingly similar to traditional informational texts. A user asks a question or gives a prompt, and the system generates a text in response. Because users can produce multiple outputs in a single chat—spanning different structures, forms, or even genres—it might feel like AI-generated content is simply another form of hybrid text, the kind many students learn to navigate in upper elementary and beyond.
But here’s the crucial difference: Traditional texts are fixed once published. They do not change. AI-generated text, on the other hand, is fluid. It reshapes itself in real time based on user inputs. This kind of dynamic text is entirely new, and it demands a new literacy. Students need the ability to deeply and critically read what AI writes.
From Extraction to Exchange
Traditional nonfiction instruction often prioritizes extraction: “What’s the author’s claim?” “What evidence is presented?” “Can you summarize the text?” Then, when students make inferences about the text, those inferences are based on the static information they have pulled. But AI tools invite a different approach, one based on back-and-forth exchanges of user inputs and AI-generated outputs. What does this mean for teaching? To support students in reading AI-generated text as an exchange rather than a static source, educators should:
- Model dynamic interactions: This means demonstrating that an AI response isn’t a finished product but a draft—a starting point, not the end. Make the human role in the exchange visible by modeling how readers can guide the text being generated through prompts, such as asking AI to debate or present a counterargument or requesting clarification or further explanation.
- Promote curiosity: Ask AI questions like, “What else could this text show me?” or “What other types of texts would help me understand better?” In this way, you’re encouraging students to use AI as a tool to explore their curiosity by asking it for another perspective, trying a different format, or requesting a deeper explanation. These kinds of moves shift students from passive recipients of text to active knowledge-builders, laying the groundwork for the agency they’ll need as readers in a world of dynamic texts.
- Teach response iteration: Give students time to practice taking one AI-generated response and asking for variations (a summary, a bulleted list, an expanded example) or have them use the same prompt in multiple AI platforms and compare the texts that are generated. This shows students the range of what AI can generate and highlights how meaning changes across forms and platforms.
The key shift for students is mindset: Reading AI-generated text is not about extracting the information that’s there, it’s about knowing what else can be made visible. With this shift, students move from treating text as fixed to seeing it as dynamic, iterative, and open-ended. Figure 1 provides a comparison that teachers can use to help students understand the differences in reading static and dynamic texts.
From Consumption to Conversation
Once students have made this mindset shift, it’s important that they learn specific moves to effectively “talk” with AI. During traditional literacy instruction, talk is a tool for thinking. Whether it’s a partner conversation, small-group discussion, or oral rehearsal before writing, educators use talk to help students develop ideas, clarify confusion, and grow their understanding.
Generative AI can serve as another kind of thinking partner. It’s not a replacement for human interaction, but it is an embedded, always-available conversation partner. Teaching students how to talk with AI, while reading AI-generated content, opens up a new dimension of literacy: a chance to practice and extend thinking while reading.
Specific dialogue moves that support deeper reading and reasoning include:
- Push Back: What’s a possible counterargument to this response?
- Translate: Explain this using simpler language or visuals.
- Compare: How does this compare to what [author/source] says?
- Reframe: What would a [one role] or [another role] say about this issue?
- Clarify: I don’t understand. Can you explain it in a different way?
These widely applicable moves help students become active readers of dynamic AI-generated texts, capable of negotiating meaning and clarifying confusion. When students use these moves, teachers can see evidence of deeper reasoning in real time, through follow-up questions, peer talk, or quick written responses. We’re not just teaching them to read, we’re teaching them to converse with text, to rehearse their own ideas, and to build confidence in their comprehension.
From Vertical to Lateral Reading
Once students understand AI-generated responses as dynamic and are practicing ways to dialogue with AI, it’s important that they learn how to read beyond the response. AI responses can feel authoritative, but students shouldn’t treat them as definitive. Students must learn how to verify claims, compare AI-generated text with credible sources, question content that differs from what they think or know, and follow lines of inquiry across multiple perspectives.
This process, known as lateral reading, is essential in today’s digital landscape. As Wineburg and McGrew (2019) found, “Expert readers evaluate credibility by leaving the site and opening new tabs. Novices stay on the page.”
Lateral reading means reading across texts, not just down one. It involves quickly stepping away from the original source to gather additional information about an author or organization, but also about facts, ideas about a topic, or claims being made. Instead of staying locked inside a single text, students learn to move across sources.
A lateral approach is about reading through different lenses to arrive at a more accurate and deeper understanding of content being consumed—it’s an active stance toward reading that resists passively accepting what’s in front of you.
When reading dynamic, machine-generated texts, this might look like students:
- Asking AI to define a concept, then cross-referencing it with academic sources.
- Reading an AI-generated summary, then checking it against a longer nonfiction article.
- Generating responses on multiple AI platforms and comparing them.
- Using AI to draft pros and cons, then researching real-world examples to support or challenge the list.
Figure 2 shows a protocol educators can teach students so that lateral reading becomes a regular part of the nonfiction reading process.
In today’s digital world, we are surrounded by an increasing volume of quickly generated content, a significant amount of which involves little to no human oversight. Some of it is inaccurate. Some of it is biased. Some of it withholds important perspectives readers should know.
This is why we must teach students to question texts, now more than ever. Not just who wrote a text, but how it was created, why it was written, and whether it’s worth trusting. Lateral reading is a habit that empowers students to engage critically with any text, especially those with unclear origins or questionable authority. When students learn to pause, open new tabs, and dig deeper, they’re not just verifying facts; they’re thinking critically about texts before accepting them.
Building Agency in an AI World
AI has done anything but make literacy skills obsolete—in fact, it has made them more powerful. When it comes to nonfiction reading, the same habits of questioning, synthesizing, and inferring long used in literacy instruction remain essential, but now they are the floor, not the ceiling. Students can go deeper with those skills while also learning to treat texts as spaces for conversation, iteration, and discovery.
Literacy today is about equipping learners with agency and a sense of purpose so they can shape their own lives and contribute to the lives of others (OECD, 2018). As Sarah Eaton (2023) notes, “We are moving into a world where text is no longer static, but collaborative and iterative.” When students learn to treat texts as dynamic, they discover their own role in shaping knowledge. They move beyond consuming information to creating meaning. Without these competencies, students risk becoming passive users of technology rather than critical thinkers and collaborators alongside it.
That is the deeper promise of reimagining literacy in an AI world: preparing not only stronger readers, but more adaptable thinkers—learners capable of clarity, curiosity, and confidence in a world where texts no longer stand still.
