Teachers' trust in artificial intelligence (AI) in education depends on how they balance its perceived benefits and risks. Yet global discussions about scaling AI in education rely on fragmented evidence, as most studies of teachers' perceptions focus on single countries or small samples. This lack of representative cross-national evidence limits both theory building and policy development. At the same time, large language models (LLMs) are increasingly used in research, policy, and teachers' professional workflows, despite limited validation in education. To address these gaps, we conduct a large-scale audit of LLM alignment with teachers' perceptions of AI by combining representative international survey data with systematic model evaluation. Using OECD TALIS data from 55 countries and territories, we measure cross-national variation in teachers' perceived benefits and risks of AI. We then benchmark responses from eight state-of-the-art LLMs across four providers under both general and country-specific prompting, comparing higher- and lower-reasoning models. Results reveal substantial cross-national variation in teacher perceptions that is not reliably reflected in LLM outputs. Models compress country differences, overestimate both benefits and risks, and show limited gains from identity prompting or enhanced reasoning. This misalignment matters because LLM-generated guidance and professional discourse increasingly shape how teachers learn about and discuss AI, potentially influencing trust and future adoption decisions. Our findings caution against treating LLM outputs as substitutes for direct engagement with teachers when informing global AI-in-education initiatives. At the same time, some models (e.g., Gemini 3 Fast) partially capture cross-national ranking patterns, suggesting a complementary role in hypothesis generation and exploratory comparative analysis.

In this position paper, we argue that baseball-and sports more broadly-provide a unique and under-explored opportunity for researchers to study human-robot interaction (HRI) in real-world settings. Using the rise of robot umpires in baseball as a primary example, we examine emerging themes such as power dynamics among players and umpires, labor implications, and technical challenges. We emphasize the affordances and benefits of studying sports within HRI, including the integration of interdisciplinary perspectives, the large-scale deployment of robots, and the examination of their role in deeply rooted cultural practices.

Procrastination has been linked to lower academic performance and sociodemographic achievement gaps in a variety of educational contexts, posing challenges to student success and educational equity. While prior research acknowledges that learning environments play a crucial role in shaping student procrastination alongside personal traits, there is a lack of solid empirical evidence on the connection between specific variations in learning environments and academic procrastination. This study provides a large-scale evaluation of the relationship between course and assignment characteristics and student procrastination behavior using a sample of 33,514 students across 3,169 courses at a US university. Using fixed effects linear regression models, we find that students tend to procrastinate less in courses with larger enrollment, non-introductory content, and well-structured deadlines. Procrastination is also lower for assignments with spaced-out deadlines, weekend deadlines, and a quiz or discussion post format. However, these patterns do not apply equally across all student groups. Male, ethnic minority, and first-generation college students exhibit higher levels of procrastination than their peers, especially for courses and assignments with specific characteristics. We suggest two instructional design strategies to help manage procrastination across student populations: (1) allowing more time before the first assignment deadline, and (2) ensuring adequate spacing between deadlines. This study provides large-scale evidence of the complex relationship between learning environment design, student characteristics, and procrastination.

Planning is a self-regulated learning strategy and widely used behavior change technique that can help learners achieve academic goals (e.g., pass an exam, apply to college, or complete an online course). Numerous studies have tested the effects of planning interventions, but few have examined the content of learners’ plans and how it relates to their academic outcomes. Building on a large-scale intervention study, we conducted a qualitative content analysis of 650 learner plans sampled from 15 massive open online courses (MOOCs). We identified a number of planning tactics, compared their prevalence, and examined which ones significantly predict course progress and completion using regression analyses. We found that learners whose plans specify a time of day (e.g., morning, afternoon, night) are significantly more likely to complete a MOOC, but only 25% of the learners in our sample used this tactic. The high degree of variation in the effectiveness of planning tactics may contribute to mixed intervention findings in scale-up studies. Models of plan effectiveness can be used to provide feedback on the quality of learners’ plans and encourage them to use effective tactics to achieve their learning goals.

Several emotional theories that inform the design of Virtual Learning Environments (VLEs) categorize affect as either positive or negative. However, the relationship between affect and learning appears to be more complex than that. Despite several empirical investigations in the last fifteen years, including a few that have attempted to complexify the role of affect in students’ learning in VLE, there has not been an attempt to synthesize the evidence across them. To bridge this gap, we conducted a systematic review of empirical studies that examined the relationship between student outcomes and the affect that arises during their interaction with a VLE. Our synthesis of results across thirty-nine papers suggests that except engagement, all of the commonly studied affective states (confusion, frustration, and boredom) have mixed relationships with outcomes. We further explored the differences in student demographics and study context to explain the variation in the results. Some of our key findings include poorer learning outcomes arising for confusion in classrooms (versus lab studies), differences in brief versus prolonged confusion and resolved versus persistent confusion, more positive (versus null) results for engagement in learning games, and more significant results for rarer affective states like frustration with automated affect detectors (versus student self-reports). We conclude that more careful attention must be paid to contextual differences in affect's role in student learning. We discuss the implication of this review for VLE design and research.