Advances in generative artificial intelligence (GenAI) have the potential to transform learning. As research increasingly calls for transforming the human-GenAI relationship from mere tool use to a collaborative partnership, there remains a notable gap in exploring an important factor influencing this relationship: trust in GenAI, being crucial amidst growing concerns about disuse, misuse, and abuse of GenAI in learner-GenAI interaction primarily via language. This study seeks to address the gap by investigating university students’ trust in GenAI, its related factors of reliance and resistance, and boundary conditions in using GenAI for language learning. The research utilized an explanatory sequential mixed-methods design. In Study 1, a proposed conceptual model within the stimulus-organism-response framework was quantitatively tested through structural equation modeling using a survey. Data were collected from 682 university students who used GenAI in language learning. Study 2 further confirmed the results through 40 qualitative interviews and refined the conceptual model by offering a deeper understanding of the relationships. The findings indicate that trust in GenAI is a significant factor in university students’ use of GenAI for language learning, with its influence on behavioral intention to use mediated through reliance and resistance. Moreover, forms of the five factors are identified: trust in GenAI, reliance, resistance, perceived risk, and actual use. Additionally, two boundary conditions are identified: perceived risk between trust in GenAI and reliance/resistance, and self-directed learning between behavioral intention to use and actual use. These findings provide theoretical, research, and practical implications regarding the appropriate use of GenAI in education.

Emerging technologies (ETs), such as AI and reality techniques, are reshaping educational measurement. However, existing studies remain dispersed and are rarely synthesized in ways that clarify how ETs participate in the evidentiary work of educational measurement. Guided by PRISMA 2020, we systematically reviewed 933 empirical studies published between 2016 and 2025 in formal educational settings. We coded studies by (a) grain size (micro, meso, macro), (b) Constructing Measures Theory building blocks (construct map, item design, outcome space, measurement model), and (c) ET category. Results showed a strong concentration at the micro level (88.88%) and in outcome space and measurement model work (86.80% combined), indicating that ET-enabled innovation has focused primarily on transforming performances into indicators and modeling those indicators for interpretation and decision-making. Learning analytics and educational data mining, machine learning and deep learning, and automated scoring and feedback systems were the dominant ET clusters. These findings point to an uneven development of ET-enabled educational measurement. Included studies also indicating recurring concerns about transparency, fairness, and governance are linked to the field’s main areas of ET-enabled concentration. We therefore argue for closer alignment among construct claims, evidence, modeling, and intended use, and offer implications for developers, researchers, and education practitioners.

This study examines the concentration of doctoral training and faculty production within global higher education systems, revealing a highly imbalanced structure dominated by a small group of top-tier universities. By analyzing data from 6,205 universities or institutions across 104 countries, a longitudinal study spanning 1995–2022 was conducted. The study demonstrates that top-tier universities, especially in the U.S. and U.K., disproportionately produce academic faculty. Findings show that this concentration is most pronounced in STEM fields, particularly in the Natural Sciences, Engineering, and Medicine & Health, while faculty production in the Humanities remains relatively dispersed. Moreover, U.S. universities emerge as critical factors in global doctoral education: graduates from top-tier American universities are over-represented in faculty positions at non-U.S. universities, reinforcing international academic dependency and institutional hierarchies. The study also uncovers significant gender disparities, with male doctorate holders generally facing more concentrated career trajectories than their female counterparts, indicating entrenched systemic biases in academic hiring and promotion. In the short term, the introduction of global university rankings initially widened these disparities, as evidenced by increasing pre-ranking and decreasing post-ranking Gini coefficients; however, the magnitude of this effect appears to have moderated over time. Overall, these results underscore the persistence of monopolistic tendencies in doctoral training and faculty recruitment, and they call for targeted policy interventions to promote greater diversity, equitable recruitment practices, and a more inclusive global academic landscape.