Contextualised mobile assisted language learning (MALL) has been known for its potential in language learning rooted in social constructivism theories. However, a consistent approach to constituents of contextualised MALL, in addition to a quantitative tool to evaluate it, is missing in reported case studies. The present research, drawing upon literature context definitions, presents a research framework for analysing, designing and evaluating contextualised MALL. Real world and real life context variables were analysed and the potential influence of the country (target and non-target) and the mobile activities language learning orientation (generic/dedicated) on contextualised MALL was suggested. Empirical data collected from 53 L2 Chinese students in Taiwan and Israel, encompassing 296 types of MALL activities performed by students, was used to develop the contextualised MALL model as well as an index for measuring real world and real life context learning. This measuring index was subsequently established in a combined top-down and bottom-up process, using pre-defined context literature augmented with students’ stories. At the end of the research procedure a quantitatively operative evaluation tool was developed. Real world was measured by the amount of content activity in relation to the place, typical or non-typical objects of the place and typical situations at the place. Real life was measured by the degree to which other tools assisted in other core activities whose purpose was not learning. The paper presents the research framework, the developed model and index with examples illustrating their application. They are offered for the research and practice community to use.

Teachers play a crucial role in promoting self-regulated learning (SRL) and problem solving (PS) in classrooms, yet their knowledge in these domains remains understudied and challenging to evaluate. While traditional assessment tools like surveys exist, there is a need for diversification, particularly through situational judgment tests (SJT). This study leverages human-AI collaboration to develop an SJT tool for evaluating teachers’ SRL-PS knowledge. Human interaction with generative artificial intelligence (Gen-AI) enables overcoming difficulties and mutually complementing each other. The purpose of this paper is to present an initial attempt to develop an SJT tool for evaluating teachers’ SRL-PS knowledge using the assistance of ChatGPT and to examine how the researcher-ChatGPT interaction contributed to the development of this tool. Through systematic interaction between researchers and ChatGPT, we developed a comprehensive tool comprising 15 difficulty categories and 20 scenarios. The research demonstrates how scenarios created by researchers can be effectively complemented by ChatGPT-generated content. A synthesized map of difficulty categories and scenarios from both researchers and ChatGPT revealed patterns of over- and under-representation across categories, and identified opportunities for synthesized new difficulty categories that could facilitate new scenarios. The study advances SRL knowledge evaluation tools through SJT methodology while demonstrating the benefits of human-Gen-AI collaborative interactions.

Self-regulated learning (SRL) is a critical competency for learners in increasingly technology-enhanced educational environments, yet little is known about how SRL is fostered within video-based interventions in K-12 settings. While existing reviews and meta-analyses focus on the effectiveness of SRL interventions, this study aims to address current gaps by specifically examining the implementation processes, instructional tools, and the role of video. Addressing this, the present study conducted a systematic literature review of peer-reviewed K-12 intervention studies published since 2010, guided by PRISMA standards and other methodological frameworks in the field of SRL. 30 quantitative or mixed-methods studies focusing on K-12 SRL interventions were selected from Web of Science and ERIC, with the requirement that video served as an instructional component rather than a research tool. These studies were then systematically coded by eight researchers for SRL strategies, instructional methods, video roles, and pedagogical settings. Findings show that most video interventions targeted multiple SRL strategies across different phases of the SRL cycle, offering a comprehensive approach to fostering regulation. However, while cognitive and metacognitive strategies were frequently addressed, motivational and resource-management strategies were seldom included within explicit instruction, which focused primarily on cognitive and metacognitive training. Video played multiple pedagogical roles, including delivering disciplinary content, teaching SRL strategies, or combining both. A thematic analysis identified four pedagogical settings that characterized successful interventions: Teacher-guided, Active, Social, and Knowledge-based (TASK) learning. These settings appear to mitigate common challenges of video-based learning, such as cognitive load and learner passivity. The review contributes a novel synthesis of SRL-video integration and proposes TASK learning as a framework for designing SRL interventions.