Inside the AI Literacy Conversation: Approaches for Pre-Tertiary Learners
This summary discusses the conversation from a Community of Interest event on Artificial Intelligence for AI literacy for tertiary learners, a collaboration by the World Bank and EdTech Hub’s AI Observatory and Action Lab, supported by FCDO.
Conversations on AI literacy are becoming timely as it is a moment when the global focus on AI in education is accelerating at an unprecedented pace, making the need for productive, practical discussions around AI skills and literacy increasingly important.
Evolution is a natural and necessary part of education systems. We can learn from previous transitions from ICT literacy to digital literacy, then to data and information literacy, all on top of the basics of reading and numeracy. Now AI literacy has taken center stage. What should we be paying attention to now and why does it matter?
The World Bank and EdTech Hub’s AI Observatory brought together three different experts to discuss their different approaches and insights from their work on this topic. Through this conversation, experts explored how schools and governments across low-middle and high-income countries are integrating AI literacy inside and outside the classroom, what content and competencies are being taught, and how education systems are measuring and improving AI-specific learning outcomes.
We’re sharing what we heard during this special Community of Interest (COI) session on November 25. 2025, and what questions remain.
Watch the webinar here
Featured speakers 🔉
- Alice Carter, Innovation Lead, EdTech Hub
- Mike Trucano, Senior Advisor, EdTech Hub AI Observatory and Action Lab and Non-Resident Fellow, Brookings Institution
- Jarvinen, Jussi-Pekka Eemeli, Head of National Projects, Eduten
- Pati Ruiz, Director of Learning Technology Research, Digital Promise
- Maria Rebeca Barron Rodriguez, Research Analyst, World Bank
Key takeaways 📝
Here are five key takeaways from the discussion about AI literacy, where it fits in the curriculum and its support for teachers and learners.
1. To safely and effectively use these AI systems and tools, we need informed users
Pati Ruiz from Digital Promise emphasized that AI literacy is the set of skills that enables people to critically understand, use, and evaluate AI systems so they can participate meaningfully in an increasingly digital world. She highlighted two core values: centering human judgment and centering justice. By doing this we can ensure decisions remain with people and acknowledge that AI systems reflect human biases.
A human-centered approach means pedagogy leads and technology follows—not the other way around. Pati highlighted that AI tools can help re-energize collaborative, inquiry-based learning, but only when educators critically assess when AI adds value and when it may distort learning goals, especially in tasks grounded in personal experience or emotional intelligence. Human-centered AI starts with learning science: designing tools that scaffold critical thinking, creativity, and collaboration rather than replacing them.
Digital Promise’s AI Literacy Framework outlines key practices, such as data privacy and security. It further dives into navigating misinformation, alongside three interconnected modes of engagement, understanding, using, and evaluating AI, and three purposes for using AI tools: interacting, creating, and problem-solving.
Eduten’s Jussi-Pekka Jarvinen built on this by explaining that AI literacy is not entirely new but an extension of existing literacies that help learners understand and shape the world around them. He added the importance of recognizing AI in everyday systems, beyond chatbots and LLMs, and understanding when AI adds value or complexity. This recognition helps learners act as critical, informed participants in an AI-shaped society.
While thinking and working with the users, Maria Barron, from the World Bank, argued AI literacy must be practical, hands-on, and grounded in real use. Learners need devices and opportunities to experiment, while also being taught both the benefits and risks of AI. Content should be engaging and culturally relevant — especially for adolescents — using formats like short videos or game-based activities. Reflection and critical thinking are essential, so students understand AI’s strengths and limits. Effective programs are codesigned with teachers and students, supporting teacher workflows and accommodating varied levels of digital literacy.
2. AI literacy is relevant at all levels of the curriculum
A key insight from Pati posited AI literacy is not a standalone subject; it is a cross-cutting competence that can be woven throughout the curriculum. She highlighted Broward County’s approach as a strong example. Instead of treating AI as an isolated topic, the district redesigned its professional learning model to help teachers curate learning experiences that embed AI literacy within existing subjects. Teachers work one-on-one with coaches to identify where concepts such as data literacy naturally align with their 3 content, particularly within STEM courses, and subsequently pilot lessons to be shared across grade-level and subject teams. This collaborative approach helps teachers determine when integrating AI literacy enriches learning and when it may not be appropriate, ensuring thoughtful alignment with pedagogy, technology, and subject-matter goals.
3. AI should support teachers—not replace their professional judgment
Teachers and learners work in tandem. A recurring theme from teachers is the hope that AI can give them ‘more time’ for one-on-one support, reduced administrative work, and better insights about learners.
As Maria put it, “When we are designing programs on AI literacy for students, it’s a nonnegotiable to also train the teachers, because who will support students when attending these concepts? It is the teacher. So, to design a comprehensive AI literacy programme at the school level, you need to focus on teachers and students.”
Alongside this hope sits a risk of false expectations about what AI can safely and realistically do today, especially around grading and feedback, where privacy and reliability concerns remain. The key message is AI must be introduced responsibly, grounded in evidence of real value rather than hype. Effective tools should surface datadriven insights while keeping teachers firmly in the driver’s seat. AI can point to possible misconceptions or learning gaps, however only teachers understand the full context of students’ lives and can make sound pedagogical decisions. Human judgment remains essential.
4. AI literacy isn’t a static checklist but a moving target shaped by rapid advances in technology
What remains constant is the need for learners to be hands-on: to use real tools, test ideas, make mistakes, and understand both the power and the limits of AI. This requires collaboration across private companies, governments, teachers, and researchers to create safe, contextualized, classroom-ready resources. Effective AI literacy blends practical experimentation with critical thinking, helping learners interrogate outputs rather than copy-paste them. Frameworks will keep evolving, although one principle stays firm: meaningful learning happens through guided, reflective, real-world use.
5. It is a shared responsibility for building safe, equitable AI literacy systems
Governments, private sector developers, researchers, and educators must coordinate to create guardrails that protect learners and ensure productive use of AI. Maria noted promising frameworks, such as the OECD’s AI literacy framework and emerging opensource content. However, she stressed two gaps: (1) AI literacy resources must be contextualized for diverse realities, especially in the Global South and (2) these regions must help shape, not just receive, the standards. Co-creation, not top-down adoption, is essential.
Questions 🙋🏾♀️
The following questions were posed by community members. We’re sharing to help stimulate further discussions and knowledge exchanges. Please note that some questions may have been edited for spelling or clarity.
Evidence-base and learning outcomes →
- I’d love to learn more about the evidence base in the ‘digital/ IT literacy’ space. What impact do digital/ IT literacy initiatives have on student learning outcomes? Under what conditions do they work?
- There’s a World Bank blog circulating this week that alerts us that AI can be a barrier to thinking and learning. How would the panel respond to that?
- Which functions of AI do teachers find particularly useful and which functions do they think they can do without or hamper the learning process?
Frameworks, standards and definitions →
- Is there any meta-analysis of AI literacy frameworks? Can AI do it?
- Is there currently a UNESCO framework for AI Literacy just like the one in Digital Literacy? Many countries might be tempted to domesticate it before developing a customized one to suit regions
- Is there a way to define AI literacy that avoids cognitive offloading and skill loss?
Systems and collaboration →
- Where might AI literacy further develop to deepen understanding, comprehension, and knowledge gain that intensifies interdisciplinary approaches, but marries government, industry, and policy simultaneously?
Resources 📚
🔎 The following resources were shared by community members and participants. These have not been reviewed by the World Bank or EdTech Hub, but are useful indicators of what conversations, evidence, and methods are being explored in the sector.
Resources from the World Bank
- Empowering Learners for the Age of AI: An AI Literacy Framework for Primary and Secondary Education (European Commission, OECD, code.org)
- People-centered AI in education: Five lessons from the Global South
Resources from EdTech Hub
Resources from Eduten
Resources from Digital Promise
- AI Literacy—Digital Promise
- 5 Essential Strategies for Responsible AI Integration in PreK-16 Education
- The AI Literacy Imperative: New Briefs to Guide AI Literacy Implementation Across Learning Environments
Other resources
- AI Leap 2025 (Estonia)
- Why AI literacy is now a core competency in education (World Economic Forum)
- Understanding AI Literacy (Stanford Teaching Commons)
- Does “AI Literacy” Actually Mean Anything? (Carl Hendrick)
- AI literacy: What it is, what it isn’t, who needs it and why it’s hard to define (The Conversation)
- Digital literacies in the era of AI; A lively discussion in South Africa
- Establishing AI Literacy before Adopting AI
- Lower Student AI Literacy Predicts Higher Propensity to Use AI on Assignments
- Exploring the effects of AI literacy in teacher learning: an empirical study
- The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review
- Context counts: Measuring how AI reflects local realities in education
- The Generation AI project: Resources on teaching AI literacy and other AI related skills.
- UNICEF initiative around accessible textbooks
- Policy and Guidance Resources from TeachAI
This is part of an ongoing series hosted by the World Bank and EdTech Hub’s AI Observatory and Action Lab. The AI Observatory is made possible by support from UK International Development. Please follow along and join the conversation on LinkedIn!
