This study introduces and examines the Transactive Memory System with GenAI (TMS-GenAI) measurement tool, an instrument designed to capture transactive memory processes in human–AI partnership. Drawing from Transactive Memory System theory, Extended Mind theory, and Cognitive Self-Esteem, the tool encompasses six theoretically grounded dimensions: Ability to Think, Ability to Remember, Specialization, Coordination, Credibility, and Generative AI Offloading. Using exploratory factor analysis across culturally distinct samples (Turkiye, N=437; United States, N=476), we evaluated structural consistency and cultural divergence of these dimensions. Results indicate strong cross-cultural stability for self-evaluative and offloading constructs (Ability To Think, Ability to Remember, and Generative AI Offloading), alongside culturally specific structuring of teamwork-related dimensions. As the first phase of a multi-stage development process, this study provides foundational evidence for the TMS-GenAI measurement tool. Future research employing confirmatory factor analysis, measurement invariance testing, and item refinement will enable its progression into a validated scale. The findings offer practical insights for assessing human–AI partnership across educational, professional, and organizational contexts.

This work aims to better analyze student engagement in socio-technical platforms by investigating whether the language students produce in online discussions is an indication of their cognitive engagement in collaborative activities. Primarily, this study evaluates whether a combination of linguistic features related to lexical sophistication and cohesion can capture students' cognitive engagement levels in an online course. We downloaded and annotated posts from the online platform for an undergraduate information sciences and technology course to create the human-coded dataset. Then, we assessed the lexical sophistication and cohesion of human-annotated posts and used lexical sophistication and cohesion indices in multivariate analysis of variance (MANOVA). A subsequent analysis using discriminant function analysis (DFA) suggested that the discriminant functions obtained from the human-annotated posts indicate a distinction between cognitive engagement categories. While the DFA model developed using cohesion indices shows a clear separation between cognitive engagement categories, the model built on lexical sophistication indices provides a partial separation. Study results suggest a promising approach for the application of linguistic features to support the categorization of discourse based on cognitive engagement.

Previous research indicates that task difficulty (i.e., students' judgments on a task's complexity) impacts their task performance. However, whether students' perceived task difficulty changes depending on the technology they use when completing tasks is still under investigation. The present study aims to address this gap in the literature. One hundred twenty-three students completed the study procedures. Students were randomly assigned to one of four groups (one control group and three experimental groups). Students were not allowed to use any technology in the control group. In contrast, those in experimental groups were permitted to use one of the following tools: e-textbook, Google, and ChatGPT. Students in each group completed three tasks with different complexities in the same order. The data was analyzed using repeated-measures ANOVA. The study revealed a significant interaction effect between groups and task difficulty perceptions at three levels. In all groups, perceived difficulty increased as the task complexity increased, but the change in students' perceived task difficulty across three tasks was impacted by the tool used when completing the tasks.

We examine the major challenges AI experts encounter when deploying AI solutions. We conduct in-depth interviews with AI experts to elicit the frequent challenges they face. Our preliminary results highlight several factors that contribute to the challenging nature of the field, namely data scarcity, uncertainty, ill-structured problems, user behavior, and the application domain. This work indicates important areas that can be targeted during the curriculum design and development process.

Apologizing or praising has various effects on people's motivation levels. One way to employ emotions in computerized environments is to present humanized messages like apologetic statements. In this study, a game offering apologetic statements for a group of subjects was used to understand the effect of apologetic statements in computerized environment. Findings have shown that the apologetic feedbacks made the subjects feel more respected, more comfortable, and more sensitive to their feelings. These findings confirm the legitimacy of the claim that computers' offering apologetic statements to the users can realize the real user-centered design.