With over two decades of experience as an ESL educator and a Master’s in Curriculum and Instruction, Nesreen El-Baz has dedicated her career to crafting innovative strategies for multilingual learners. In a world increasingly saturated with AI-generated content, she champions visual literacy not as an optional add-on, but as a core reading competency essential for all students. Her work bridges the gap between traditional print-based reading and the critical analysis of digital media, showing educators how to transform AI from a potential pitfall into a powerful tool for deep comprehension. This conversation explores her practical, classroom-tested approach, touching on themes like the connection between art analysis and digital skepticism, the importance of treating images as constructed texts, and how visual literacy can create more equitable and thoughtful learning environments.
In programs like Literacy Through Photography, students who struggle with traditional text often engage deeply with abstract images. How does that experience of slowing down to infer meaning from art translate to teaching students to critically analyze AI-generated content? Please share a classroom example.
That experience was foundational for me. I remember seeing students who were usually quiet and hesitant suddenly come alive when discussing an abstract photograph. They weren’t afraid of being “wrong.” They would point to a shadow or a strange angle and start building a story, making inferences based on what they saw. It was a powerful rehearsal for what we ask them to do with complex texts. This translates directly to AI. Instead of just accepting a generated image, we can present it like one of those abstract photos and ask, “What do you notice? What feels real here, and what feels slightly off?” In one lesson, a student analyzed an AI image of a forest and pointed out that the shadows were all falling in different directions. That small observation opened up a huge conversation about how AI “builds” an image versus how light works in the real world. That’s the moment they stop being passive consumers and start becoming critical readers of visual media.
A key concept is viewing images not just as “data” or output, but as “signs” that construct meaning. Why is this distinction so critical for students today, and what is the first step an educator can take to help a class see images as constructed texts?
This distinction is everything, especially now. When students see an image as “data,” they treat it as a neutral fact—something that just is. But when they see it as a collection of “signs,” they understand it’s a text that someone constructed with a purpose. It has an author, a viewpoint, and an intended effect. The first and simplest step an educator can take is to remove the caption from a compelling photograph or AI image and just ask two questions: “What did the creator want you to notice first?” and “What choices did they make to get you to look there?” Suddenly, students are talking about framing, color, and focus. They’re deconstructing the image instead of just summarizing it. That single shift in questioning moves the conversation from passive observation to active analysis, and they begin to see that every image, whether from a camera or a computer, is telling a story of its own.
When a student analyzes photographic elements like “light and shadow” or “scale and angle,” they are practicing reading strategies like making inferences or understanding perspective. Could you describe how you explicitly teach this connection, helping a student see they are using the same skills for images and text?
Making that connection explicit is where the magic happens. I use a T-chart or a simple table with two columns. On one side, we list compositional elements like “Light and Shadow,” and on the other, we list reading strategies like “Making Inferences.” When a student says, “The deep shadows in the corner of the photo make it feel mysterious,” I’ll pause and say, “That’s fantastic. You just made an inference. You used a clue in the image to understand the mood, just like you use a character’s words to infer their feelings in a story.” I’ll then write that on the chart. By physically mapping the visual analysis to the language of reading comprehension, students have this powerful “aha” moment. They realize it’s not a different skill set; it’s the same cognitive muscle. Saying, “You’re doing the same thing you do when you read a paragraph,” validates their thinking and makes the transfer of skills tangible and repeatable.
You advocate for an iterative process where students refine AI prompts from a simple request to a more complex one using compositional language. Can you walk us through how this shifts AI from a “shortcut” tool toward one that supports intentional design and deepens literacy skills?
This process is about moving students from being consumers to being creators with intention. Left to their own devices, a student might just type, “a dog in a park,” and take the first thing the AI generates. That’s the shortcut. We slow it down. First, we model how a simple prompt yields a generic image. Then we introduce compositional language. The next prompt becomes, “A photorealistic image of a dog positioned using the rule of thirds, with a warm color palette.” The image immediately becomes more thoughtful. Finally, we push for greater depth: “A photorealistic image of a dog positioned using the rule of thirds, framed by tree branches, low-angle view, strong contrast.” At each stage, the students have to explain why their changes improved the image. They aren’t just getting a picture; they’re learning how specific language choices create specific effects. This reflective process transforms AI from a magic button into a design tool that demands precision, planning, and a deep understanding of how visual signs create meaning.
Classroom talk often shifts from simple statements like “I like it” to more analytical ones like “The creator wants us to notice…” What instructional strategies have you found most effective in fostering this change, and how does it create a more thoughtful environment for all learners?
The most effective strategy is providing students with sentence stems that anchor them in analytical language. We move them away from personal opinion with frames like, “This detail suggests…,” or “At first I thought…, but now I think…” These aren’t just fill-in-the-blanks; they are structures for metacognition. They require students to point to evidence within the image and to show how their thinking has evolved. This immediately elevates the conversation. It also creates a safer, more thoughtful environment because the focus shifts from judging the image to interpreting the creator’s choices. When the talk is about evidence and intention, more students feel comfortable participating. The classroom gets quieter, not because students are disengaged, but because they are looking more closely and thinking more deeply before they speak.
For emerging readers or multilingual learners, images can lower the barrier to participation in complex discussions. How does visual literacy provide an accessible entry point for these students without lowering the cognitive demands of the lesson? Can you provide an example of this in action?
This is one of the most powerful aspects of visual literacy. For a multilingual learner who may not yet have the vocabulary to debate the nuances of an author’s tone in a dense paragraph, a complex photograph offers an immediate entry point. They can literally point to what they see. The thinking comes first, and the language follows. For example, when analyzing an image with a strong sense of perspective, I had a student new to English who couldn’t articulate “author’s viewpoint” but could stand up and physically demonstrate the low-angle view of the camera. He said, “The man is big. I am small.” He was analyzing perspective and power dynamics at a very high cognitive level, even with simple language. This doesn’t lower the demand; it scaffolds it. We honor his complex thought and then provide the academic vocabulary to label it, building a bridge from his intuitive understanding to formal analysis.
What is your forecast for visual literacy?
My forecast is that visual literacy will cease to be seen as a niche skill or an “art class” topic and will become as fundamental to core ELA instruction as phonics or textual analysis. As AI-generated media becomes indistinguishable from reality, the ability to deconstruct an image, question its source, and understand its constructed nature will be a critical survival skill for navigating information. I foresee curriculum standards evolving to explicitly include the analysis of visual and digital texts, and teacher training programs will need to integrate these practices deeply. It will no longer be an option; it will be the very definition of what it means to be a literate person in the 21st century.
