Leveraging AI for Human Flourishing
Human actors - their curiosity, creativity, and purpose - are front and center of "AI To Learn" framework.
Paddling into unknown waters - unnerving yet exhilarating
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 on a different vision.
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, AI literacy rests on a learning mindset. 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
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.
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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).
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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.
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Think of it this way: we are embarking on the journey into the unknown world, where we will encounter entities with non-human intelligence. We need to learn their customs, adjust to their strange ways of communicating, and tolerate their outlandish behavior at times. Once we get over our initial culture shock, stop being mad at AI everytime it makes a mistake, and start realizing that it takes "two to tango," then, you are off to a good start to explore this brave new world.
Those of us from non-technology background has the advantage of having started this journey recently, getting lost, and feeling confused. All these experiences are our 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.
Teaching with AI
People often seem surprised when I tell them about my AI literacy instructions. "You are teaching AI? I didn't know you are into computer science!" That statement is inaccurate in two ways. First, I don't teach "AI" but instead, "how to use AI productively and responsibly." Second, the advantage of using today's generative AI is that we can literally "'talk" to it in our own languages, instead of computer codes.
I do give a 30-minute presentation in every class I teach that covers a very basic, minimalist introduction to generative AI. Beyond that, I don't really teach "AI." What I do instead is to "teach AI literacy" as a foundation so that I can "teach with AI," that is, incorporate generative AI as key learning tools in the student learning process. AI literacy is the foundation upon which students can build their skills to leverage AI and accomplish tasks more efficiently and accurately. "Teaching AI literacy" to me means to give students a chance to develop a basic toolkit to be a smart user of AI technology. I have been incorporating AI tools more and more when I teach research and writing. For example, when I taught an interview method course, I had my students use ChatGPT to code their interview data. In another upper-division course with research paper requirement, students started their library research by using ChatGPT's "deep research" functionality. In a first-year writing course, students used ChatGPT to evaluate and comment on their drafts. That is what I mean by "teaching with AI" - deploying AI tools to enhance teaching and learning.
For those of us who have no prior technology background, the first step toward teaching AI literacy is to educate ourselves about what generative AI is, how it "generates" texts, images, graphics, videos, etc. Some of us might think we've played around with one of those chatbots enough to know what they can, and can't, do. Keep it in mind, though: AI technology has been evolving at the breakneck speed, and what was true last year - or just a couple of months ago - may no longer be true. More importantly, the current generative AI technology needs capable human users to define, contextualize, and direct the generative process. In other words, users without the basic understanding of how generative AI works or the skills to communicate what tasks to be completed and how, are likely to see more limitations than possibilities in generative AI.
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Unlike when I began my journey in 2023, there are a mind-boggling amount of free information available for beginners who would like to start learning more systematically about generative AI. In fact, there is so much information coming out all the time, it might be difficult to know even where to start. If you are in this boat, take a look at the resources I have used to get myself started and also incorporate into the introductory module of my AI literacy curriculum.