“A recent scientific study published by IAES Publisher reveals a startling fact. Although generative AI has penetrated classrooms worldwide, the majority of users, especially educators, lack the necessary literacy to utilize it effectively and responsibly.”
ChatGPT, Gemini, Claude, Preplexity, and various other generative AI tools are now familiar to teachers and lecturers. Many have tried them for creating exam questions and developing lesson plans. But how deeply do we truly understand this technology? How will it impact education?
Yuan et al. (2026) analyzed over 35 scientific studies from various countries and found that the application of generative AI in teacher education falls into four main areas:
- Understanding teacher and institutional perceptions of AI
- Creating teaching materials, questions, and lesson plans
- Designing and updating AI-based curricula
- Collaborative learning models between students and AI
This demonstrates that generative AI is no longer merely an experiment but has become part of real-world practice in educational institutions.
Being literate in generative AI is different from simply being able to use it.
It turns out that many definitions currently circulating are quite superficial. They only expand on the conventional concept of digital literacy without addressing the unique challenges posed by generative AI itself, such as:
- Prompt engineering is the ability to formulate appropriate instructions so that AI produces truly useful output.
- Critical evaluation of AI output; not all AI output is accurate. The ability to assess the quality and accuracy of output is a core competency.
- Complex ethical considerations, copyright issues, privacy, plagiarism, and the social impact of AI content require a much deeper understanding than simply knowing how to use it.
To address this challenge, Zainal et al. (2026) proposed a new competency framework adapted from Bloom’s Taxonomy. Bloom’s Taxonomy is a classic learning model long used in education. This framework divides generative AI literacy into three areas:
- Technical proficiency: the ability to operate and optimize GenAI tools
- Ethical responsibility: awareness of using AI fairly and transparently
- Social awareness: understanding the impact of AI on society and the world of work
These three points are then developed through five progressive cognitive stages:
understanding → applying → analyzing → evaluating → creating. Therefore, the higher the stage, the deeper a person’s ability to interact meaningfully with AI.
Access is there, but readiness is not. These two studies confirm that generative AI has entered education, but user readiness has not kept pace. The opportunity is real, but it will only be realized if institutions are serious about building true literacy, not just introducing tools. Zainal et al.’s (2026) framework and Yuan et al.’s (2026) findings offer a concrete roadmap for this opportunity. The question now is no longer “is AI appropriate for the classroom?” but “are we truly ready to use it wisely?
References:
https://ijai.iaescore.com/index.php/IJAI/article/view/28334
https://ijere.iaescore.com/index.php/IJERE/article/view/37225