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<p>Generative AI tools offer teachers opportunities to adapt curricula to meet diverse student needs. However, many educators lack the structured training and reflective frameworks necessary for effective and critical use of these tools. Numerous studies have examined the appropriate use of generative AI (GenAI) tools in facilitating differentiation, feedback, and personalized learning. However, teachers need to be trained on the “what” and “how” of integrating GenAI into curriculum adaptation to ensure a positive impact on student learning. This study evaluates the impact of training in-service teachers to use GenAI tools for curriculum adaptation. Specifically, the study investigates three research questions: (1) How does training teachers to use GenAI tools impact their knowledge in curriculum adaptation and confidence level? (2) How do teachers perceive the effectiveness, challenges, and applications of GenAI tools in curriculum adaptation? and (3) How do teachers’ experiences of using GenAI tools influence their instructional practices? Employing a mixed-method design, the study analyzed the knowledge, perceptions, and reflective practices of K-12 teachers (<i>N</i> = 51) from national and international schools in Ras Al Khaimah, United Arab Emirates (UAE). Although this sample size is appropriate for an exploratory study, the limited geographic scope may impact the generalizability of the findings. Participants underwent an 8-week training program that integrated Kolb’s model and the Technological Pedagogical Content Knowledge (TPACK) framework. Quantitative data were collected via pre- and post-tests and a 25-item survey, analyzed using paired t-tests and descriptive statistics, while qualitative data were gathered through focus group discussions and analyzed using thematic analysis. The results revealed a significant post-training improvement in teachers’ knowledge (pre-test M = 74.71 to post-test M = 93.14, Cohen’s d= − 0.869) and confidence levels. Insights from focus group discussions indicated that teachers recognized the benefits of GenAI tools in facilitating differentiated and student-centered instruction, but noted challenges in aligning AI-generated content with curriculum standards. This study contributes to the field in three key ways: (1) it integrates the TPACK framework and Kolb’s Experiential Learning Cycle to create a comprehensive model for GenAI teacher training that addresses both the necessary knowledge domains and the learning processes through which teachers develop competence, thereby advancing theoretical understanding of technology integration; (2) it focuses specifically on curriculum adaptation, a critical but understudied application of GenAI in K-12 education; and (3) it examines the impact of this training in the UAE context, where culturally and linguistically diverse classrooms require tailored approaches to AI integration. Future research should expand to other regions and school types, and employ longitudinal designs to assess the sustained impact of such training on classroom practices and student outcomes.</p>

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The impact of generative AI training on teachers’ curriculum adaptation using reflective practices

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  • 发表日期：

    2026年6月1日

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  • Areej ElSayary

摘要

Generative AI tools offer teachers opportunities to adapt curricula to meet diverse student needs. However, many educators lack the structured training and reflective frameworks necessary for effective and critical use of these tools. Numerous studies have examined the appropriate use of generative AI (GenAI) tools in facilitating differentiation, feedback, and personalized learning. However, teachers need to be trained on the “what” and “how” of integrating GenAI into curriculum adaptation to ensure a positive impact on student learning. This study evaluates the impact of training in-service teachers to use GenAI tools for curriculum adaptation. Specifically, the study investigates three research questions: (1) How does training teachers to use GenAI tools impact their knowledge in curriculum adaptation and confidence level? (2) How do teachers perceive the effectiveness, challenges, and applications of GenAI tools in curriculum adaptation? and (3) How do teachers’ experiences of using GenAI tools influence their instructional practices? Employing a mixed-method design, the study analyzed the knowledge, perceptions, and reflective practices of K-12 teachers (N = 51) from national and international schools in Ras Al Khaimah, United Arab Emirates (UAE). Although this sample size is appropriate for an exploratory study, the limited geographic scope may impact the generalizability of the findings. Participants underwent an 8-week training program that integrated Kolb’s model and the Technological Pedagogical Content Knowledge (TPACK) framework. Quantitative data were collected via pre- and post-tests and a 25-item survey, analyzed using paired t-tests and descriptive statistics, while qualitative data were gathered through focus group discussions and analyzed using thematic analysis. The results revealed a significant post-training improvement in teachers’ knowledge (pre-test M = 74.71 to post-test M = 93.14, Cohen’s d= − 0.869) and confidence levels. Insights from focus group discussions indicated that teachers recognized the benefits of GenAI tools in facilitating differentiated and student-centered instruction, but noted challenges in aligning AI-generated content with curriculum standards. This study contributes to the field in three key ways: (1) it integrates the TPACK framework and Kolb’s Experiential Learning Cycle to create a comprehensive model for GenAI teacher training that addresses both the necessary knowledge domains and the learning processes through which teachers develop competence, thereby advancing theoretical understanding of technology integration; (2) it focuses specifically on curriculum adaptation, a critical but understudied application of GenAI in K-12 education; and (3) it examines the impact of this training in the UAE context, where culturally and linguistically diverse classrooms require tailored approaches to AI integration. Future research should expand to other regions and school types, and employ longitudinal designs to assess the sustained impact of such training on classroom practices and student outcomes.