Abstract National policies significantly shape resource allocation and priorities in EdTech development. This paper presents insights from six countries through a systems thinking perspective. Drawing on lessons from the COVID-19 pandemic, the authors explore how national policies evolved in response to global EdTech trends. Each country offers unique approaches as case studies. China emphasizes digital transformation and the use of artificial intelligence in teacher education. Germany focuses on enhancing student media literacy through school initiatives. Japan accelerates digitalization through cross-ministerial efforts to address educational disparities. Italy emphasizes the updating of school curricula, tertiary offerings, and digital innovation projects. Norway underscores teacher autonomy and innovative methods for digital literacy, while the United States focuses on academic recovery and addressing the challenges posed by new forms of digital divide. The case studies offer insights into global EdTech trends while cautioning against viewing the field as monolithic. The authors synthesize key strategies in national priorities and practices and provide recommendations for an ethical and effective approach to EdTech development.

Abstract This study examines the integration of digital game design as a pedagogical tool to teach and contextualize ethical frameworks in an undergraduate ethics course. A mixed-method, qualitative-focused study was conducted with 16 participants. Students were tasked with narrative-based games using Twine, a story-focused digital game creation tool. Data were collected through pre- and post-activity surveys, reflective essays, and semi-structured interviews. Thematic analysis was carried out for qualitative data. Most students opted to apply the utilitarian framework in their design. Our analysis indicates that the intervention did not significantly boost overall confidence in ethical knowledge or alter ethical decision-making approaches. The reflective process inherent in game design, however, allowed students to contextualize and, in some cases, critique the frameworks. Our findings reveal the potential of narrative game design to deepen ethical reflection, while also suggesting that a multifaceted instructional approach may be necessary to enhance students’ understanding of ethical frameworks.

Abstract Generative artificial intelligence (GenAI) has received increasing attention from language researchers and educators due to its capability to influence student language performance. Very limited previous meta-analysis studies have examined instructional factors in their analysis, while it deserves great attention. Task-based language teaching (TBLT) is a widely applied language instructional approach. Hence, this systematic review and meta-analysis study adopts TBLT to investigate how these instructional factors (e.g., task-based variables, learner variables) impact student language learning in GenAI contexts. Using the PRISMA guidelines, we collected literature and included 25 empirical studies involving 2,431 participants. The study confirmed the positive influence of GenAI on language learning and identified three moderators—student AI literacy, task planning, and task assessors—that potentially moderate its effectiveness. This finding likely relates to the GenAI tools employed; specifically, ChatGPT—not primarily designed for language learning—was used most frequently. This general-purpose GenAI may require students to understand how AI works, how to plan their tasks, and the necessity of involving teachers as assessors. We suggest teachers should get more involved in language learning with GenAI, and more language learning-specific GenAI should be developed. These suggestions help institutions and teachers to plan their instructions in the AI era.

Abstract Teachers' beliefs about data use directly influence their decisions to use it. This study aimed to investigate the beliefs of K-6 teachers regarding Data-Based Decision Making (DBDM) and how their self-efficacy and anxiety influence their perceptions of using DBDM. Using Self-Efficacy theory as its theoretical framework, the research followed an explanatory sequential mixed-methods design with a survey and one-on-one interviews with teachers to help understand the quantitative results. Results showed that teachers' perceptions of efficacy and anxiety when navigating educational technology platforms to access and utilize data should not be grouped, as they are distinct. Four main themes emerged from the data analysis. These themes were Data Dashboard Navigation, Data, Connecting Data to Practice, and Frustration. The first three themes were factors that can potentially impact teachers' efficacy and anxiety with DBDM and provided insight into K-6 teachers' data use practices, beliefs, affective states, and barriers to using data in the classroom. The final theme, Frustration, found that teachers' efficacy, anxiety, and perceptions may be influenced by their frustration with different DBDM tasks.