AI curriculum adaptation can help teachers explore different explanations, additional activities, and changes to existing materials. In a study of teachers in Türkiye, Karataş, Eriçok, and Tanrikulu (2025) describe how educators reported using tools such as ChatGPT for these purposes. Their findings offer a picture of emerging practice, although they do not establish that the adaptations improved student learning.
Reading this paper, I kept returning to the judgement behind each change. A tool can suggest a simpler explanation or a different activity. The teacher still needs to decide whether that change preserves the intended learning and makes sense for the students in the classroom.
Adapting a curriculum involves choices about learning
Curriculum adaptation is familiar work. Teachers adjust examples, add resources, reorganize material, and find different ways to explain difficult concepts. A prescribed curriculum provides a common direction, while its classroom enactment requires decisions about particular learners and circumstances.
AI gives teachers another way to generate possible adaptations. That can be useful when an explanation is not working or when an activity needs a more relevant context. But the ease of generating alternatives can also make a change seem worthwhile before we have examined its educational purpose.
An engaging activity may address a different skill from the one the lesson was intended to develop. A simplified text may remove a qualification students need to understand. These are questions I would bring to any proposed adaptation.
This connects with my discussion of Intelligent-TPACK and AI integration, where teachers’ professional knowledge matters to the use of technology. Knowing how to request a change is only part of the work of deciding whether to use it.
What the AI curriculum adaptation study examined
Karataş and colleagues used an explanatory sequential mixed-methods design, beginning with a survey of 440 full-time teachers in Türkiye and following it with a focus group involving 10 teachers selected from that sample. Data were collected in the spring of the 2022–2023 academic year.
The survey examined teachers’ general curriculum adaptation patterns; the focus group explored experiences of using AI, particularly ChatGPT. These are related sources of evidence, but the survey findings should not be treated as measurements of AI’s effects.
The survey suggested that teachers extended curriculum content more frequently than they omitted it, with revising or replacing content falling between those practices. Adaptation patterns differed by professional experience and subject area.
In the focus group, teachers described using AI to identify material they considered repetitive or less relevant, develop additional exercises and activities, and revise explanations or examples. Their accounts included simplifying complex concepts through stories and seeking materials connected to students’ contexts.
The study relied on reported practices and experiences. It did not compare learning outcomes in lessons adapted with and without AI, establish that the proposed materials were accurate, or examine long-term effects.
The convenience sample and small focus group also limit generalization. The researchers acknowledge that challenges such as ethical concerns, technical constraints, and training needs were not comprehensively investigated.
Reviewing an adaptation before using it
The authors recommend professional development to strengthen teachers’ AI literacy and support curriculum adaptation. I see a practical starting point in making the reasons for a proposed change explicit. The following routine is my suggested adaptation of the paper’s discussion; the study did not test it.
Begin with the curriculum outcome and identify what students need to learn or demonstrate. Then describe the difficulty the current material presents. Perhaps the language obscures the concept, the example assumes unfamiliar background knowledge, or students need another opportunity to practise. A clear description gives us something against which to judge the AI suggestion.
Next, examine what changed. If AI simplifies a reading, check that it preserves essential meanings and relationships. If it generates an additional exercise, check whether the exercise develops the intended skill. If it supplies a contemporary example, verify its factual details before teaching with it.
I would be particularly careful with suggestions to omit content. Students’ lack of interest does not establish that something is dispensable. Before removing material, examine its relationship to required outcomes and to learning that comes later. The focus-group accounts make omission visible as a practice; they do not establish which omissions are educationally sound.
Cultural relevance also needs examination. A reference to a local place or tradition may make a resource familiar without adequately representing the community. My review of AI and culturally responsive teaching considers related questions about context and representation.
Finally, revisit the change after teaching. Look at students’ explanations, questions, and work to judge whether it addressed the original difficulty. Greater participation can be encouraging, while evidence of understanding helps us assess the adaptation more directly.
Within AI and teaching practice, this paper gives us a useful account of what some teachers are trying. The next question is how those adaptations hold up when we examine their content, classroom use, and contribution to learning.
Reference
Karataş, F., Eriçok, B., & Tanrikulu, L. (2025). Reshaping curriculum adaptation in the age of artificial intelligence: Mapping teachers’ AI-driven curriculum adaptation patterns. British Educational Research Journal, 51(1), 154–180. https://doi.org/10.1002/berj.4068
