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.

Generative AI (GenAI) could be beneficial for instructional designers, expediting the design of educational mobile apps. One needed app, currently absent, is a digital diary for second and foreign-language learners to record their outside-classroom activities. Given the research and learning merits of keeping such diaries, a diary-research app (Langy) was previously developed to measure learners’ real-life (RL) and real-world (RW) language learning. This study aims to provide design guidelines to transform this app into a learning app based on the interaction with ChatGPT during this design process and examine the human-machine interaction. The prompt submitted to ChatGPT4 described the diary-research app and asked for suggestions on how to transform it into a diary-learning app. ChatGPT produced 14 suggestions, upon which the researchers deliberated. The mutually created design suggestions emerging from this reciprocal interaction provide a blueprint for the new diary-learning app, adding a new purpose of promoting RL and RW learning (in addition to measuring) and relevant features such as goal settings, gaming rewards, and reflective prompts. Three processes were revealed during the complementary human-machine interaction: 1. ChatGPT’s offered generic suggestions, which the researchers adapted to more specific app requirements. 2. The researchers linked between the ChatGPT’s suggestions and added theoretical grounds. 3. The researchers, in some cases, altered or completely negated the ChatGPT’s suggestions. This study contributes to the research field of language-learning diaries. Moreover, it contributes to our understanding of how GenAI can be used as an instructional designer to assist in the design of educational mobile apps.

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.