Those involved in education see great importance in promoting self-regulated learning (SRL) and problem-solving (PS) among learners. Teachers are a major factor in promoting SRL-PS in the classroom, but very little is known about whether and how they promote it in their classroom, therefore a tool is needed to assess teachers in this domain. Furthermore, the situational judgment test (SJT) is a potential tool to diagnose teachers’ knowledge. However, such a tool is currently lacking in the SRL-PS domain. Human interaction with generative artificial intelligence (GenAI) 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 assessing teachers’ SRL-PS knowledge using the assistance of ChatGPT. This humane-machine interaction led to the formulation of 15 difficulties categories and 20 scenarios that form the basis of the SJT tool for SRL-PS. It was found that scenarios created by the researchers can be complemented by those generated by ChatGPT. In some instances, the scenarios from both sources are quite similar, while in others, those formulated by ChatGPT either expand upon or present an alternative perspective on the difficulty. Moreover, ChatGPT has proposed new scenarios in certain cases. A Significant product of the study is a map that enables the analysis of the pool of scenarios and the identification of over- or underrepresented difficulties categories.

Self-regulated learning (SRL) is required for learners of all ages and disciplines. There is a need for a variety of assessment tools to assess the professional knowledge of teachers in SRL and self-regulated teaching (SRT). Situational judgment tests (SJT) can serve as a measurable assessment tool for evaluating teachers' SRL and SRT knowledge. This research constitutes the second stage in the development of such an SJT tool dealing with the formulation of responses to scenarios created in the previous stage. Additionally, the introduction of GenAI-assisted evaluation poses the need to explore the still understudied human-GenAI interaction during assessment tools development. This study used thematic analysis for exploring the responses generated by GenAI (ChatGPT) and researchers for the previously formulated scenarios to examine the contribution of both agents in the tool development process. A main theme – standardization and patterning (GenAI) versus flexibility and openness (researchers) was found. The study highlights the potential of combining GenAI and researchers in developing assessment tools and the contribution of each.

As education shifts toward student-centered learning, evaluating pre-service teachers' ability to foster self-regulated learning (SRL) is essential. This study examines lesson plans as performance-based assessment tools for evaluating pre-service teachers' competence in designing SRL-supportive instruction in online courses. Through qualitative analysis of 37 lesson plans created by 73 pre-service teachers, the research applies a three-dimensional analytical framework focusing on SRL strategy implementation, technology integration, and pedagogical competence. Findings reveal significant variability in how teachers structure SRL, scaffold autonomy development, and incorporate instructional technologies. High-quality lesson plans explicitly embedded metacognitive scaffolds and included structured support for student autonomy, while weaker plans provided inadequate SRL elements and lacked clear instructional rationales. Technology integration varied, with some lesson plans leveraging digital tools to enhance reflection and feedback, while others used technology in a passive, content-delivery manner. These findings reinforce the validity of lesson plans as assessment tools, offering teacher educators a structured alternative to self-reports in evaluating pre-service teachers' instructional reasoning and readiness to promote SRL in diverse learning environments.

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.