AI LITERACY AS A CORE COMPETENCE FOR TEACHERS AND STUDENTS

INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “INTEGRATION OF MODERN LINGUISTICS WITH SCIENCE, EDUCATION, AND PRACTICE IN THE PROCESS OF GLOBALIZATION: NEW APPROACHES, OPPORTUNITIES, AND CHALLENGES”, DECEMBER 4, 2025 925 AI LITERACY AS A CORE COMPETENCE FOR TEACHERS AND STUDENTS Ruzieva Maftuna Doctoral student, Navoiy State University https://doi.org/10.5281/zenodo.17837218 Abstract. As artificial intelligence becomes more common in education, AI literacy is no longer something optional. It is turning into an essential competence that both teachers and students need. In this paper, I try to explain what AI literacy means, what its main components are, and why it should be considered a key requirement in today’s learning environments. Based on recent research, I argue that AI literacy is not only about knowing how to work with digital tools. It also requires the ability to understand how AI systems operate, to question their outputs, and to use them in an ethical and responsible way. The discussion also presents several recommendations for teacher training, curriculum development, and school-level policies so that AI literacy can grow naturally as part of modern education. Keywords: AI literacy, teacher competence, student skills, ethical AI use, critical evaluation, digital education, UNESCO framework, OECD framework. Introduction. In the last ten years, artificial intelligence has developed very quickly and has moved from being a theoretical idea to something that is used in daily life. In education, this change is especially visible. Many schools and universities are already using AI for assessment, tutoring, translation, writing support, and even some administrative processes. Students are also becoming active users of generative AI tools such as ChatGPT, Claude, and Gemini, which they use for exploring new information or completing various assignments. Because of this rapid growth, educators today face an important question: what kind of skills and knowledge do teachers and students need in a world where AI is becoming part of almost every learning activity? Most of the recent studies give a similar answer. Both teachers and learners need strong AI literacy, which includes technical understanding, critical awareness, ethical judgement, and the ability to use AI meaningfully in the learning process. Traditional digital literacy mainly focused on how to use computers or search for information online. AI literacy, however, is deeper. It requires understanding how AI tools generate answers, what their limits are, and how their use may influence students’ thinking and overall learning experience. AI literacy is still a developing concept, but many researchers agree that it includes several connected dimensions. AI literacy combines conceptual knowledge, practical skills, critical awareness, and ethical responsibility. Long and Magerko also point out that AI literacy requires knowing what AI can do, what it cannot do, and how to evaluate its outputs before accepting them. For the purpose of this article, AI literacy is understood as: “The ability to understand, evaluate, and ethically interact with AI systems in a way that supports meaningful learning and informed decision-making”. Based on this definition, AI literacy consists of the following elements: INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “INTEGRATION OF MODERN LINGUISTICS WITH SCIENCE, EDUCATION, AND PRACTICE IN THE PROCESS OF GLOBALIZATION: NEW APPROACHES, OPPORTUNITIES, AND CHALLENGES”, DECEMBER 4, 2025 926 1. Foundational knowledge: understanding basic ideas such as machine learning, training data, and the limits of AI models. 2. Practical skills: being able to use AI tools for writing, analysis, language learning, problem solving, and creativity. 3. Critical evaluation: checking AI outputs for accuracy, possible bias, hallucinations, or inconsistencies. 4. Ethical and responsible use: protecting privacy, avoiding plagiarism, and ensuring that the final work remains original. 5. Collaborative and reflective use: treating AI as a partner that helps thinking, not as something that does the whole task instead of the learner. Discussion. AI literacy is becoming a necessary competence today because artificial intelligence is already influencing the way students learn and how teachers organise their teaching practices. Research in recent years clearly shows that AI tools are no longer just optional digital resources they shape how learners think, interact with information, and approach academic tasks. For example, studies such as Touretzky et al. emphasise that even at the K–12 level, students need a basic understanding of how AI systems function, what they can do, and where their limitations lie. According to these authors, AI has become so deeply embedded in society that children should begin learning foundational concepts early, not only to use AI tools but also to understand them. AI also directly changes how learners engage with knowledge. Long and Magerko argue that AI literacy includes not only conceptual understanding but also critical judgement, because users often interact with AI in ways that influence their reasoning processes. If students do not have the ability to evaluate AI-generated information, there is a risk that they may rely too heavily on automated suggestions. This point becomes even stronger when we consider findings from Zamfirescu-Pereira et al., who show that many non-experts struggle with effective prompting and often misunderstand how language models produce their outputs. This means that without critical awareness, students may misinterpret AI responses or fail to recognise inaccuracies and biases. Several studies highlight the importance of practical skills for interacting with AI meaningfully. Ng, Leung, Chu, and Qiao describe AI literacy as a combination of knowledge, skills, and ethical responsibility. They emphasise that being able to use AI tools is not enough; learners must understand how to evaluate them and when to apply them appropriately. Their work also draws attention to the ethical side of AI use, showing that students need guidance on privacy, fairness, transparency, and responsible authorship. In many educational settings, teachers themselves are still developing their own AI literacy. Research by Lee et al. demonstrates that when teachers integrate AI literacy explicitly in their lessons, middle school students develop stronger knowledge and more positive attitudes toward AI. This shows that teachers play a crucial role in shaping how young learners understand and use AI. However, Laupichler et al. found that AI literacy among teachers in higher and adult education remains uneven, which suggests the need for more systematic training, especially as AI tools become more advanced and widely available. AI literacy is also relevant for supporting higher-level thinking and preparing students for future learning. Su, Zhong, and Ng in their review of educational approaches in the AsiaPacific region, found that AI education can improve learners’ conceptual understanding and confidence, but only when programs are well-designed and supported by clear instructional INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “INTEGRATION OF MODERN LINGUISTICS WITH SCIENCE, EDUCATION, AND PRACTICE IN THE PROCESS OF GLOBALIZATION: NEW APPROACHES, OPPORTUNITIES, AND CHALLENGES”, DECEMBER 4, 2025 927 strategies. Similarly, Kong, Cheung, and Zhang showed that university students who participated in AI literacy programs reported stronger empowerment and ethical awareness. These programs helped students not only understand how AI works but also reflect on how it affects their academic decisions and interpretation of information. The importance of AI literacy begins even earlier than formal schooling. Studies such as Su, Ng, and Chu point out that early childhood learners can also develop simple AI concepts when teachers introduce developmentally appropriate activities. Their work shows that AIrelated tasks encourage curiosity and early reasoning skills, but they also require careful planning so that children do not misunderstand AI as a “magic machine.” Another key theme in the research is the need for critical reflection. Ng, Leung, and Chu highlight that AI literacy must include the ability to think about AI outputs, recognise possible biases, and question results rather than accept them automatically. This becomes especially important when students engage with generative models, which can produce convincing but incorrect or biased outputs. The studies collectively emphasise that AI literacy is not only about operating tools but also about developing the awareness to evaluate them. Challenges and Solutions. Although AI literacy brings many benefits, the research also shows that its integration into education comes with several challenges that must be taken seriously. One of the most common problems is that students may easily become dependent on AI tools. Because generative AI can produce quick answers, some learners may stop thinking independently or avoid the effort required for analysing information. Studies such as those by Zamfirescu-Pereira et al. reveal that many users struggle even with basic prompting techniques, which means they often accept AI outputs without questioning them. This creates a risk that students may overlook errors or misunderstand how AI arrived at its responses. Another challenge concerns ethical issues. Ng, Leung, Chu, and Qiao point out that students often lack awareness of privacy, bias, fairness, and responsible authorship. AI systems can generate convincing but inaccurate information, and without proper ethical knowledge, learners may unintentionally engage in plagiarism or produce misleading work. This problem becomes even more serious when we consider that AI models are trained on large datasets that sometimes contain cultural or gender biases. As Laupichler et al. note, educators must be prepared to guide students in recognising biased outputs and understanding why they appear. There is also a practical challenge related to unequal access. Not all students have the same opportunities to work with advanced AI tools. In some schools, access to technology is limited, while others may have more resources. Research such as Su, Zhong, and Ng’s review shows that different regions face different levels of readiness for AI education, which may widen the digital divide. Without equal access, AI literacy can become another source of inequality rather than a tool for supporting learning. Another difficulty is that teachers themselves may feel unprepared or unsure about how to integrate AI literacy into their teaching. Laupichler et al. found that AI literacy levels vary significantly among educators, especially in higher and adult education. Without proper training, teachers might avoid AI tools altogether or rely on them in ways that do not contribute to meaningful learning. This lack of confidence can make it harder for students to receive proper guidance. Despite these challenges, the literature offers several practical solutions that can help schools integrate AI literacy more effectively. One important step is to include AI-related INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “INTEGRATION OF MODERN LINGUISTICS WITH SCIENCE, EDUCATION, AND PRACTICE IN THE PROCESS OF GLOBALIZATION: NEW APPROACHES, OPPORTUNITIES, AND CHALLENGES”, DECEMBER 4, 2025 928 modules in teacher education programs. Studies such as Lee et al. show that when teachers receive structured training, students develop stronger understanding and more positive attitudes toward AI. This suggests that teacher preparation should not focus only on technical aspects but also on ethical issues, evaluation strategies, and classroom applications. Another solution is to embed AI literacy across different subjects rather than teaching it as a separate topic. According to Su, Ng, and Chu, even young learners can benefit from developmentally appropriate AI activities when they are connected to broader learning goals. For older students, integrating AI into science, language learning, and social studies helps them gain experience in applying AI responsibly and meaningfully. Schools also need clear institutional policies that define acceptable AI use. Drawing on the concerns highlighted by Ng et al., these guidelines should include rules for citation, privacy protection, and distinguishing between student work and AI-generated content. Policies help both teachers and learners understand what responsible use looks like and reduce confusion around academic integrity. Encouraging reflective and transparent use of AI is another valuable approach. Research from Kong, Cheung, and Zhang shows that students benefit from activities that require them to explain how they used AI, what decisions they made, and how AI influenced their thinking. Reflection journals, AI-use logs, or peer reviews of AI-assisted drafts can help students stay aware of their learning process rather than relying passively on the tool. Finally, continuous professional development for teachers is essential. Laupichler et al. emphasize that teachers need ongoing support to keep up with rapid changes in AI technologies. Workshops, micro-credentials, and hands-on training can help educators build confidence and develop practical strategies for integrating AI into their classrooms. Overall, while AI literacy presents real challenges including dependency, ethical risks, bias, unequal access, and teacher readiness the solutions offered in the research show that these issues can be managed through thoughtful planning, clear guidelines, reflective practices, and strong teacher preparation. With the right support, schools can help both teachers and students develop AI literacy in a balanced and responsible way. Conclusion. In general, the review of recent research shows that AI literacy is no longer a secondary skill but a core competence that both teachers and students must develop in today’s educational environment. As artificial intelligence becomes part of everyday learning activities from writing support to problem-solving and even early childhood tasks teachers and learners need to understand how these systems work and how to use them responsibly. The studies discussed in this paper show that AI literacy is not only about using tools correctly. It includes conceptual knowledge, practical skills, ethical awareness, and the ability to evaluate AI outputs critically.It is also clear that AI will continue shaping how students think and how teachers plan their lessons. This means that educators must be prepared to guide students, explain risks, and create learning tasks where AI supports understanding without replacing the learner’s own effort. When teachers themselves develop strong AI literacy, they can help students use AI for deeper thinking, creativity, and meaningful learning rather than simply for convenience. At the same time, the challenges connected with AI use such as dependency, misinformation, bias, unequal access, and academic integrity show that AI literacy must be developed carefully. Schools need clear guidelines, reflective practices, and continuous teacher training to make sure that AI is used in a responsible and transparent way. Research from different contexts also suggests that AI literacy should be connected to various subjects, not taught in isolation, so that students can apply what they learn in real situations. INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE “INTEGRATION OF MODERN LINGUISTICS WITH SCIENCE, EDUCATION, AND PRACTICE IN THE PROCESS OF GLOBALIZATION: NEW APPROACHES, OPPORTUNITIES, AND CHALLENGES”, DECEMBER 4, 2025 929 Overall, AI literacy has already become a necessary part of education, and its importance will only continue to grow. If teachers and students are supported with the right knowledge, ethical principles, and practical strategies, AI can turn into a powerful tool for improving learning and building important cognitive skills. With thoughtful integration, AI literacy can help create classrooms where technology does not replace human thinking but strengthens it, preparing learners for a future where AI will be present in almost every field. FOOTNOTES 1. Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K–12: What Should Every Child Know about AI? Proceedings of the AAAI Conference on Artificial Intelligence. 2. Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and Design Considerations. Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’20). 3. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI Literacy: An Exploratory Review. Computers and Education: Artificial Intelligence. 4. Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023). Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’23). 5. Lee, I., Ali, S., Zhang, H., Dipaola, D., & Breazeal, C. (2021). Developing Middle School Students’ AI Literacy. Proceedings of the ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ’21). 6. Su, J., Zhong, Y., & Ng, D. T. K. (2022). A Meta-Review of Literature on Educational Approaches for Teaching AI at the K–12 Levels in the Asia-Pacific Region. Computers and Education: Artificial Intelligence. 7. Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial Intelligence Literacy in Higher and Adult Education: A Scoping Literature Review. Computers and Education: Artificial Intelligence. 8. Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & Qiao, M. S. (2021). AI Literacy: Definition, Teaching, Evaluation and Ethical Issues. Proceedings of the ASIS&T Annual Meeting. 9. Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2023). Evaluating an Artificial Intelligence Literacy Programme for Developing University Students’ Conceptual Understanding, Literacy, Empowerment and Ethical Awareness. Education and Technology & Society. 10. Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities. Computers and Education: Artificial Intelligence.