Teaching AI Literacy
Human agency at the front and center
When I started my journey into the world of AI and began building the "AI To Learn" curriculum in spring 2023, only a few experts were talking about "AI literacy"; two years later, in 2025, it has turned into a buzz word, and AI literacy programs are coming out of woodwork. Many of AI literacy frameworks are centered on technical skills and, perhaps inadvertently, make AI the central actor.
AI To Learn is built with a different vision.
Paddling into unknown waters
AI To Learn Framework
In the "AI To Learn" curriculum, AI literacy is conceived as more than knowing how to operate emerging technologies. It is a human-centered disposition toward using AI in ways that support learning, creativity, well-being, and broader human flourishing. This framework defines AI literacy as a set of interconnected capacities grounded in purpose, ethics, and agency. It emphasizes that AI should amplify human inquiry rather than replace it, and that meaningful engagement with AI begins with the learner, not the tool.
At its foundation, I conceptualize AI literacy as disposition, or durable habits of the mind and action that orient the user toward human-centered engagement with technology. This mindset values curiosity, metacognition, and the intrinsic importance of human learning. Students begin by recognizing that AI is not a shortcut to answers but a catalyst for deeper thinking. They are encouraged to question, explore, reflect, and learn with a sense of ownership. Cultivating this mindset prepares learners to navigate ambiguity, avoid overdependence on AI, and engage technology with confidence and discernment.
Building on this foundation, the second layer is a human-centered workflow. This workflow places human intention and creativity at the center of the AI engagement process. Students clarify what they want to understand or create, use AI tools to support their inquiry, and evaluate the outputs thoughtfully. This approach emphasizes intentionality and reflection: AI is used to extend the learner’s thinking and creativity, not drive it. Ethical awareness—including attention to bias, context, and integrity—is embedded naturally within the workflow rather than treated as an afterthought.
At the top of the framework are context-specific competencies. These are the practical skills needed to apply AI productively within a discipline, profession, or creative domain. Examples include crafting effective prompts, evaluating the quality and reliability of AI-generated content, and integrating AI into research, writing, design, or analysis. These skills evolve as technologies evolve, and they are effective only when grounded in the deeper layers of mindset and workflow.
Together, these three layers form a holistic model of AI literacy. They prepare learners not just to use AI tools, but to use them thoughtfully, responsibly, and in ways that expand human possibility.
Learning to Teach: My Story
I'm a humanistic anthropologist, trained in qualitative research. I teach interdisciplinary courses in social sciences and humanities, where reading, writing and talking are the primary academic activities. I'm decent at using common technology tools in my daily life - check emails, write lecture notes, watch movies, stay in touch with friends via social media - but have no training in IT, computer science, programming, etc. I am clueless about what actually happens inside my desktop computer or Android phone when I type words on my keyboard.
I went through a period of panic when I decided to look into what the hype around generative AI was all about, primarily to figure out how this new technology would affect my students' learning and how I would need to adjust my teaching strategies. Beyond the most generic marketing schpiel on OpenAI's public-facing website and some pretty pictures made with their image generation model, very little I read or saw made sense. It got worse when I got to OAI's Discord channel. I looked through the general conversation on ChatGPT, and I could understand maybe one out of 10 messages. And the posts kept coming in one after another as I watched. I went back the next day and the same thing. I went back again and this time, I scrolled down and found the chanels dedicated to AI-generated images, which unexpectedly became my entry point into the AI universe (more in DALL-E Legacy if you are curious what happened).
You may be thinking, "What does AI image generation have to do with teaching AI literacy?" Not so fast! I'm just trying to say, before people like me (and probably you, too, if you are still reading my story) to get started with AI, we need to find an entry point where we can find something to hang on to for the rest of the ride (which, I promise you, will be turbulent). I learned the quirky ways of generative AI and got started with essential skills, like prompt engineering, which I was able to transfer to other areas like writing, research, and teaching.
Those of us from non-technology background has the advantage of having started this journey recently, getting lost, and feeling confused. All these experiences become our biggest asset when the time comes to help students, many of whom will struggle, as we have, to begin their own journey. Another distinct advantage of the technologically disinclined is the fact that we know our stuff - our content knowledge and content-specific pedagogical skills. That is, I know how anthropologists write; I also know how to teach students to think like an anthropologist and write like an anthropologist. Technology piece is an assistive element in this pedagogical process, and neither I nor my students need to know everything about the technology tool. Rather, we need to learn "just enough" about it to be a savvy user in the specific context of use.
Curricular Integration: Incremental Approach
Getting started is the hardest part of integrating AI literacy in one's courses. When I decided to do this in 2023, there were few usable curricular resources available. I ended up developing one from the ground up, iterating and improving it over two academic years.
In the first academic year of implementing AI literacy curriculum, I took a modular approach: three 30-minute AI-literacy sessions in every class I taught that covered a very basic introduction to responsible AI use. In the second year, I began to experiment with a more integrated approach. Students still went through the foundational modules, but they were also asked to use AI in the stages of some graded assignments, for example, using AI feedback in an essay revision process or conducting AI-assisted data analysis. AI literacy, in other words, becomes the foundation upon which students can build their context-specific competencies to leverage AI and accomplish real-life tasks more efficiently and accurately. By the fourth semester of integration, I was able to teach a fully AI-integrated course. In the third year of implementation, all my courses are fully AI-integrated with one class session dedicated to AI literacy introduction.
If you are thinking about incorporating AI literacy training into your curriculum, I strongly recommend starting with the modular approach first. In most cases, we can accommodate three 30-minute sessions with a modest amount of syllabus/course plan revision. Try it for a semester or an academic year, and move gradually to a more integrated approach, keeping the amount of curricular work manageable.
If you are not ready to go there yet, consider making just one small move this semester: brainstorm where in your existing syllabus/course plan you can potentially introduce one guided activity in which students will encounter generative AI tool in a structured, thoughtful way. If you are interested, click the button below for more quick-start resources.