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<p>As artificial intelligence (AI) tools become increasingly integrated into educational environments, lecturers across generations are being challenged to adapt their instructional practices. This study investigates generational differences in AI-related competencies, adoption challenges, and pedagogical strategies among postgraduate lecturers enrolled in postgraduate education programs while teaching in IB schools. A total of 80 postgraduate lecturers teaching in IB schools, including 40 senior lecturers (aged 45 and above) and 40 young lecturers (aged 35 and below), participated in this mixed-method study. Qualitative data were gathered through open-ended questions embedded in a Google Form, and were thematically analyzed to identify patterns in experiences and strategies. Additionally, a structured Likert-scale survey was used to compare perceived AI competency, challenges, and implementation strategies using independent samples t-tests. The findings revealed significant differences in perceived AI competency between the two groups. Young lecturers (M = 3.48, SD = 0.43) reported significantly higher levels of AI competency compared to senior lecturers (M = 3.01, SD = 0.46), t(35.2) = 2.85, p = 0.006, suggesting that age and prior exposure to digital technologies may influence confidence and readiness in AI integration. However, no statistically significant differences were found in perceived challenges related to AI adoption, t(78) = 0.95, p = 0.343, Cohen’s d = 0.21, or teaching strategies, t(78) = 0.32, p = 0.752, Cohen’s d = 0.07. While the mean scores for each of these categories were similar, the qualitative analysis of the data collected from each of the participants revealed notable qualitative differences in how lecturers from different generations interpreted and enacted AI in their teaching practices. For instance, the younger lecturers tended to use AI in their classrooms in ways that were more experimental and learner-centered, but senior lecturers tended to be more cautious about their use of AI in the classrooms. Due to the nature of the data collection method, which collected the data from participants from one institutional context, there are some limitations to the generalizability of the findings of this research study. Nevertheless, the study provides valuable insights into how lecturers from different generations perceive and integrate AI within educational practice.</p>

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Senior and young lecturers’ AI competency, challenges, and teaching strategies in International Baccalaureate education

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  • 发表日期：

    2026年7月22日

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  • XiaoLi Zhou,
  • Yangzi Zhang,
  • Kenny S. L. Cheah,
  • Wen Fen Beh

摘要

As artificial intelligence (AI) tools become increasingly integrated into educational environments, lecturers across generations are being challenged to adapt their instructional practices. This study investigates generational differences in AI-related competencies, adoption challenges, and pedagogical strategies among postgraduate lecturers enrolled in postgraduate education programs while teaching in IB schools. A total of 80 postgraduate lecturers teaching in IB schools, including 40 senior lecturers (aged 45 and above) and 40 young lecturers (aged 35 and below), participated in this mixed-method study. Qualitative data were gathered through open-ended questions embedded in a Google Form, and were thematically analyzed to identify patterns in experiences and strategies. Additionally, a structured Likert-scale survey was used to compare perceived AI competency, challenges, and implementation strategies using independent samples t-tests. The findings revealed significant differences in perceived AI competency between the two groups. Young lecturers (M = 3.48, SD = 0.43) reported significantly higher levels of AI competency compared to senior lecturers (M = 3.01, SD = 0.46), t(35.2) = 2.85, p = 0.006, suggesting that age and prior exposure to digital technologies may influence confidence and readiness in AI integration. However, no statistically significant differences were found in perceived challenges related to AI adoption, t(78) = 0.95, p = 0.343, Cohen’s d = 0.21, or teaching strategies, t(78) = 0.32, p = 0.752, Cohen’s d = 0.07. While the mean scores for each of these categories were similar, the qualitative analysis of the data collected from each of the participants revealed notable qualitative differences in how lecturers from different generations interpreted and enacted AI in their teaching practices. For instance, the younger lecturers tended to use AI in their classrooms in ways that were more experimental and learner-centered, but senior lecturers tended to be more cautious about their use of AI in the classrooms. Due to the nature of the data collection method, which collected the data from participants from one institutional context, there are some limitations to the generalizability of the findings of this research study. Nevertheless, the study provides valuable insights into how lecturers from different generations perceive and integrate AI within educational practice.