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<p>The rapid development of artificial intelligence (AI) has had a significant impact on teachers. In response, teachers need to acquire AI-related knowledge and skills to effectively integrate AI into their teaching practices, thereby enhancing both teaching efficiency and quality. Besides professional training, teachers can further develop their AI competencies through self-learning, where self-regulated learning (SRL) plays a crucial role. However, there remains a lack of SRL assessment tools specifically for teachers learning AI. To address this issue, based on Zimmerman’s cyclical phase model of SRL and existing SRL scales, this study developed a 30-item Self-Regulated Learning Scale for Teachers’ AI Learning (AI-SRLS). This scale comprises five factors: task planning, self-monitoring, help seeking, institutional support, and self-evaluation. Two rounds of data were collected for the study, with a total of 604 teachers participating. The 248 responses collected in the first round were employed for Exploratory Factor Analysis (EFA), while the 356 responses gathered in the second round were utilized for Confirmatory Factor Analysis (CFA). The results verified the five-factor structure of the AI-SRLS and demonstrated high reliability and validity. Furthermore, a Random Forest Model (RFM) was employed to assess the significance of each factor and item, revealing that the key factors influencing teachers’ AI-SRL, in descending order, were task planning, self-evaluation, institutional support, self-monitoring, and help-seeking. AI-SRLS thus provides teachers with an effective self-assessment tool for learning AI, which can facilitate the acquisition of AI knowledge and skills.</p>

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Development and validation of the artificial intelligence self-regulated learning scale (AI-SRLS) for teachers

  • 期刊论文

  • 发表日期：

    2025年7月1日

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  • Wenjun Zhang,
  • Yimin Ning,
  • Na Yu,
  • Dengming Yao,
  • Haifeng Wu,
  • Jiheng Wu,
  • Bowen Fang

摘要

The rapid development of artificial intelligence (AI) has had a significant impact on teachers. In response, teachers need to acquire AI-related knowledge and skills to effectively integrate AI into their teaching practices, thereby enhancing both teaching efficiency and quality. Besides professional training, teachers can further develop their AI competencies through self-learning, where self-regulated learning (SRL) plays a crucial role. However, there remains a lack of SRL assessment tools specifically for teachers learning AI. To address this issue, based on Zimmerman’s cyclical phase model of SRL and existing SRL scales, this study developed a 30-item Self-Regulated Learning Scale for Teachers’ AI Learning (AI-SRLS). This scale comprises five factors: task planning, self-monitoring, help seeking, institutional support, and self-evaluation. Two rounds of data were collected for the study, with a total of 604 teachers participating. The 248 responses collected in the first round were employed for Exploratory Factor Analysis (EFA), while the 356 responses gathered in the second round were utilized for Confirmatory Factor Analysis (CFA). The results verified the five-factor structure of the AI-SRLS and demonstrated high reliability and validity. Furthermore, a Random Forest Model (RFM) was employed to assess the significance of each factor and item, revealing that the key factors influencing teachers’ AI-SRL, in descending order, were task planning, self-evaluation, institutional support, self-monitoring, and help-seeking. AI-SRLS thus provides teachers with an effective self-assessment tool for learning AI, which can facilitate the acquisition of AI knowledge and skills.