Episode 3: Rethinking AI Literacy Through Human Agency
Authors: Teddy Kim; info@ciddl.org
Episode 1: Rethinking Agency in the Age of AI: Gaining an Initial Understanding
Episode 2: Rethinking Agency in the Age of AI: Why Does Agency Matter in the Age of AI?
In earlier episodes of this series, we explored how artificial intelligence (AI) can influence student agency, particularly for students with disabilities (SWD). Also, we discussed human agency through Social Cognitive Theory (Bandura, 1989), Self-Determination Theory (Deci & Ryan, 2012), and Causal Agency Theory (Shogren & Raley, 2022). We then considered how AI-mediated environments can reshape human roles, responsibilities, and decision-making.
Building on that foundation, this episode asks a related question: What does it mean to be AI literate while remaining an active human agent?
AI literacy has often been discussed as the set of competencies people need to understand, use, evaluate, and critically engage with AI systems (Long & Magerko, 2020; Ng et al., 2021). However, recent discussions suggest that students need more than the ability to operate AI tools, write prompts, or identify AI-generated errors. They also need to make thoughtful decisions about when, why, how, and whether to use AI. In this sense, AI literacy in the age of AI is not only about tool use. It is also about human agency.
Why Tool-Based AI Literacy Is Not Enough
AI literacy is often framed in terms of practical competencies. Students are expected to understand basic AI concepts, use AI tools appropriately, evaluate AI-generated outputs, and recognize ethical concerns such as bias, misinformation, privacy, and accountability. These goals matter. However, these competencies do not fully explain whether students remain active decision-makers when AI becomes part of their learning.
Maeda and colleagues’ scoping review helps clarify this issue. They reviewed 129 studies on AI literacy and algorithmic literacy published between 2015 and 2024. Their findings showed that 68% of the studies (n = 87) discussed AI literacy as a competence, encompassing knowledge, skills, attitudes, and ethical considerations. They also found that 33% of the studies (n = 43) aimed to cultivate awareness and understanding of AI or algorithmic systems without addressing users’ broader decision-making about AI, which the authors connect to agency.
These findings point to a gap in how AI literacy is often discussed. Much of the current conversation focuses on helping students participate more “effectively” in AI-mediated environments. However, being able to participate does not necessarily mean that students are making intentional decisions about their own learning. Students might follow AI-generated suggestions because they are convenient, polished, or difficult to challenge, even when those suggestions begin to shape the direction of their thinking.
With this perspective, the agency-centered approach asks different questions. Can students decide whether AI is appropriate for this task? Can they question AI-generated outputs? Can they explain why they accepted, revised, or rejected a suggestion? Can they recognize how AI shapes their thinking? Can they choose not to use AI when it does not support their learning, values, or responsibilities?
Comprehensive AI Literacy and Human Agency
If tool-based AI literacy is not enough, what should educators emphasize instead? One answer is to place human agency at the center of AI literacy. Tadimalla and colleagues describe human agency as the empowered capacity for “intentional, critical, and responsible choice.” From this perspective, AI literacy is not only about knowing how AI works or how to operate AI tools. It is about helping students remain intentional and reflective in AI-mediated learning environments.
Recent international efforts also reflect this shift toward agency-centered AI literacy. For example, the OECD and European Union’s Empowering Learners for the Age of AI presents AI literacy as more than simple AI tool use. The framework defines AI literacy as the knowledge, skills, and attitudes learners need to engage with, create, manage, and shape AI, while critically evaluating its benefits, risks, and ethical implications. Importantly, the framework explicitly emphasizes that learners should use AI as a creative partner while maintaining human agency, divide work intentionally between humans and AI, and make informed judgments about AI for themselves and others. In this way, the OECD/EU framework shows that agency is increasingly being recognized as a central component of AI literacy, not an optional add-on.
This matters because AI should not be treated as something students and educators must automatically adopt simply because it is available, efficient, or widely used. Instead, students should learn to evaluate AI in relation to their purposes, contexts, values, and responsibilities. They should understand that AI-generated outputs are not neutral or final. AI can support learning, but it should not replace human judgment.
This concern becomes even more urgent as AI systems begin to act on behalf of users, not just generate content for them. Some AI systems can now plan, recommend, automate, and act across digital environments. This shift is often described as agentic AI. In these cases, students are not only evaluating AI-generated content. They may also be delegating part of a task, decision, or learning process to an AI system.
Nama describes this emerging challenge as agentic literacy debt: the growing gap that occurs when AI systems gain the ability to act on behalf of users before people have the knowledge, skills, or support needed to understand, supervise, and challenge those actions. Although many classroom AI tools are not fully autonomous, this concept helps educators anticipate where AI-supported learning may be heading. As AI systems become more capable of recommending, planning, and acting, students will need more than output evaluation. They will need opportunities to set boundaries, monitor AI-supported processes, and decide what authority should or should not be delegated to AI.
What This Means for Educators
For educators, agency-centered AI literacy shifts the focus of instruction. The goal is not simply to teach students how to use AI tools. The goal is to help students become thoughtful decision-makers in AI-mediated learning environments.
This can begin with a few guiding questions:
- Why am I using AI for this task?
- What barrier am I trying to reduce?
- What part of the thinking should I do myself?
- What information or context is the AI missing?
- What parts of the AI output need to be checked?
- What suggestions should I accept, revise, or reject?
- Does AI actually support my learning
These questions help students slow down their use of AI. Instead of moving directly from prompt to output to final product, students learn to pause, evaluate, and decide. Educators can also design activities that make student judgment visible. For example, students might annotate an AI-generated response by explaining which parts they trust and which parts they question. They might document how they revised an AI suggestion or reflect on why they chose to use or not to use AI for a particular task.
These practices do not position AI as the center of learning. Instead, they position students’ thinking, judgment, and responsibility at the center. This aligns with a human-centered view of AI, in which AI should augment human judgment and creativity rather than replace them.
Looking Ahead
As discussed earlier, if AI literacy focuses only on tool use, students may become efficient users without becoming thoughtful decision-makers. A human agency-centered approach offers another path. It helps students learn how to use AI when it supports learning, question AI when its outputs are limited, and use AI in ways that align with their purposes, context, and values.
In the age of AI, students need more than access to powerful tools. They need the agency to use those tools in ways that reflect their values, support their goals, and keep them in control of their own learning.
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