Recent research indicates the significant potential of artificial intelligence (AI) in enhancing teachers’ instructional practices in areas such as lesson planning, personalized teacher intervention and feedback, and performance assessment. To fully realize the potential of AI in teaching, it is crucial to understand how teachers innovatively apply and critically evaluate AI applications in their teaching practices. However, there is a research gap in investigating how teachers use various features of an AI-enabled system and their perceptions of AI integration in teaching to promote teachers’ effective AI integration practices. Employing an exploratory case study design, we investigated how six science teachers utilized an AI-enabled inquiry intelligent tutoring system (Inq-ITS) within their teaching and examined their perceptions of AI integration. Classroom observations and teacher interview data were collected. When using Inq-ITS functionalities, two teachers with a pedagogical orientation of teacher-guided scientific inquiry mainly engaged with its virtual tutor and teacher report summary features. Conversely, four teachers, practicing the pedagogical orientation of AI-guided scientific inquiry, relied on the AI system to guide student learning, interacting intensively with its features, particularly real-time teacher alerts and teacher inquiry practice support. Regardless of the differences in using Inq-ITS features, all teachers recognized the potential benefits of pedagogical change and encountered various challenges. This analysis also revealed that teachers exhibited distinct perceptions regarding the role of Inq-ITS integration in their teaching. Teachers who adopted a teacher-guided pedagogical orientation perceived the Inq-ITS as a supporting tool that enhanced traditional teaching methods. In contrast, those with an AI-guided pedagogical orientation viewed the Inq-ITS as akin to a teaching assistant and a pedagogical collaborator. The findings underscored the importance of enhancing teachers’ realization of the pedagogical affordance of AI in teaching through their use of AI functionalities. It is essential to consider teachers’ diverse perceptions of AI integration when promoting their integration of AI into teaching practices.

The rapid advancement of generative artificial intelligence (GenAI) technologies presents new opportunities and challenges for educators, necessitating specialized competencies distinct from those required for general AI tools. This study addresses the critical need for a validated instrument to assess teachers’ competencies in effectively and ethically integrating GenAI into educational practice. We developed and validated the Teachers’ GenAI Competencies (T-GAIC) instrument, grounded in a comprehensive review of AI competency frameworks and empirical literature. The instrument comprises five dimensions: (1) Technological proficiency of GenAI, (2) pedagogical compatibility of GenAI in teaching, (3) preparing students with effective practices of GenAI, (4) GenAI-related professional development and communication, and (5) risk and ethical awareness of GenAI in education. Content and face validity were established through expert review and think-aloud protocols with pre-service teachers. Data from 478 pre-service and in-service teachers informed psychometric validation through item discrimination analysis, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). The final 36-item instrument demonstrated strong internal consistency (Cronbach’s alpha > 0.90), robust construct validity (RMSEA = 0.078, CFI = 0.946, TLI = 0.934), and strong convergent and discriminant validity. The T-GAIC instrument offers a theoretically grounded and empirically validated tool for assessing GenAI competencies, supporting professional development, curriculum design, and educational research on AI integration in teaching and learning.

Advancements in artificial intelligence (AI) signal a significant shift toward more automated and data-driven workplaces, emphasizing the need for Human Resource Development (HRD) to prepare the workforce with adequate AI competencies for AI-empowered environments. AI policies and initiatives play a crucial role in providing the frameworks and actions that guide HRD efforts. In this study, we analyzed education and workforce policies in national AI strategies (NAISs) from 50 countries to delineate educational policy priorities, strategies, and support resources for developing an AI-competent workforce. Our analysis revealed that only 13 countries demonstrated high-level prioritization with clear objectives and comprehensive measures, primarily developed economies in Europe. We identified six categories of educational and training strategies focused on AI talent preparation and workforce reskilling. Additionally, four types of support resources were highlighted as key investments to enhance the success of these educational and training strategies. The findings have significant implications for HRD practice, particularly in designing and implementing AI-focused, workplace-oriented, and inclusive HRD curricula, programs, and policies that consider contextual and cultural factors. The results suggest the need for further HRD education research to explore emerging theories, AI workforce training models, pedagogical strategies, and the impact of contextual and cultural elements on workforce competency development.

The advancement of technologies has promoted the increasing popularity and integration of mobile technologies in science education in the past decade. These trends have led to an increased interest among scholars to understand the effects of mobile technologies in science education and whether those effects differ depending on how mobile technologies are used in learning and teaching (eg, student‐led, teacher‐led, collaborative). In this study, we performed a meta‐analysis of 34 studies that directly examined the effects of users' pedagogical role on K‐16 students' achievement in science when engaging in mobile learning (ML). The analysis of the 34 studies yielded an overall significant main effect of ML on K‐16 science learning outcomes. We applied the mixed‐effects model with moderator variables and found that users' pedagogical role significantly moderated the ML effects as a whole. Collaborative and student‐led uses had a statistically significant impact on student science learning, whereas teacher‐led use did not. Findings from this meta‐analysis are consistent with prior research, providing synthesized research‐based evidence of the effects of ML on science learning that holds implications for both mobile curriculum design and mobile technology use. Practitioner notes: What is already known about this topic Mobile technology has been increasingly adopted in science learning with great potential to support learning and teaching. Prior meta‐analysis has suggested multiple moderators in measuring the mobile learning effect. Prior empirical studies examined the effect of mobile users' pedagogical role in the specific subject domains (eg, physics) and grade levels (eg, high school). What this paper adds This meta‐analysis is among the first to examine the moderator effect of mobile users' pedagogical roles on ML in K‐16 science education. This study found that mobile technology use is associated with significant science learning outcomes across 34 studies. This study found that the effect of mobile learning was moderated by mobile users' pedagogical roles (ie, who initiated the use). Collaborative use between teachers and students tends to be the most effective. Teacher‐led use tends to be the least effective. Implications for practice This study suggests that we should consider who leads the use of mobile technology when integrating mobile learning in science education. In particular, educators should encourage collaborative and student‐led mobile use for learning and instruction.

Engineering and technology teachers have advocated using robotics and coding to cultivate students’ computational thinking and problem-solving skills at PreK-12 levels. Understanding teachers’ perceptions of how preschool children develop strategies and skills for engaging in robotics and coding activities is crucial for promoting the early use of these technologies in classrooms. This qualitative case study explored two preschool teachers’ perceptions regarding capabilities and strategies to code and interact with the socio-technical world during robotics activities among children aged 3–5. The teachers used a Blue-Bot® robot to engage children in interactive coding and shared their experiences and insights in interviews. A thematic analysis revealed that teachers observed children employing three strategies for directing the robot, physical visualization, verbalization, and multiple pathways, with age and gender differences in strategies employed. Furthermore, teachers perceived children were able to understand and apply basic coding concepts through practices, even though they were not verbally articulating them. Additionally, teachers perceived children were developing favorable relationships with peers and the robotics world during their robotics and coding activities. The findings suggested that teachers should consider the developmental stage of children aged 3–5, along with the diverse strategies they employ, when implementing robotics education in early childhood settings. Moreover, they should recognize that children’s understanding of fundamental coding concepts and their perspectives on socio-technical relationships are critical to consider when engaging children in robotics and coding activities.