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The growing prevalence of AI chatbots in everyday life has prompted educators to explore their potential applications in promoting student success, including support for classroom engagement and communication. This exploratory study emerged from semester-long observations of class participation apprehensions in an introductory educational psychology course, examining how chatbots might scaffold students toward active and independent classroom contribution. Four students experiencing situational participation anxiety voluntarily participated in a pilot intervention using AI chatbots as virtual peer partners. Following comprehensive training in AI use and prompt design given to the entire class, participants employed systematic consultation frameworks for managing classroom discourse trepidations. Data collection involved regular instructor meetings documenting student experiences, challenges, and developmental trajectories through qualitative analysis emphasizing contextual interpretation. While students reported general satisfaction with chatbot integration, implementation revealed three critical complexities: temporal misalignment between AI consultation and real-time discussion dynamics; feedback inflation creating disconnects between AI reassurance and classroom reception; and unintended progression from supportive scaffolding toward technological dependency. Individual outcomes varied, with some students developing independence while others increased reliance on external validation. AI-assisted participation interventions demonstrate both promise and limitations, requiring careful consideration of classroom dynamics. Effective implementation necessitates rehearsal-based rather than validation-focused applications, emphasizing human mentorship and community-centered approaches that preserve educational autonomy while leveraging technological scaffolding strategically.

Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily represented in training data and embed strong semantic cues, raising the possibility that apparent causal competence may reflect semantic pattern recombination rather than structure-sensitive causal reasoning. Drawing on human developmental theories of causal induction, this perspective argues that genuine causal understanding requires robustness to novelty and reliance on conditional structure rather than semantic familiarity. To illustrate the testability of this claim, the paper includes a pilot demonstration using synthetic causal micro-worlds. Identical numerical evidence was presented to a LLM under two conditions: semantically meaningful variable labels and non-semantic coded labels. Across paired cases, the model reliably selected the correct causal structure when labels were meaningful, but frequently misidentified or exhibited instability in causal model selection under coded labels, despite producing locally coherent explanations. These divergences emerged most clearly in diagnostically ambiguous settings requiring suppression of misleading marginal associations. The results align with the claim that semantic scaffolding can support and stabilize apparent causal competence in LLMs without implying structure-sensitive reasoning.

In our increasingly technology-dependent society, the importance of promoting digital literacy (e.g., computational thinking, coding, and programming) has become a critical focus in the field of childhood education. While young children these days are routinely and extensively exposed to digital devices and tools, the efficacy of the methods for fostering digital skills in the early childhood classroom has not always been closely considered. This is particularly true in settings where early childhood educators are not digital experts. Currently, most of the efforts in standard early childhood settings, taught by teachers who are not digital experts, appear to revolve around “unplugged” activities that do not directly involve digital tools or devices, and it is not entirely clear how well these “unplugged” lessons promote the corresponding skills in “plugged” settings, such as coding and programming. This article discusses how, through further research, we may be able to devise an effective method for seamlessly building digital literacy among young children, transcending the “unplugged vs. plugged” barriers effortlessly.

This theoretical paper introduces the Verbal-Cognitive Scaffold (VCS) Model, a cognitively inclusive framework which proposes the cognitive architectures underlying computational thinking (CT). Moving beyond monolithic theories of cognition (e.g., executive-function and metacognitive control models), the VCS Model posits inner speech (InSp) as the predominant cognitive pathway supporting CT operations in neurotypical populations. Synthesizing interdisciplinary scholarship across cognitive science, computational theory, neurodiversity research, and others, this framework articulates distinct mechanisms through which InSp supports CT. The model specifies four primary pathways linking InSp to CT components: verbal working memory supporting decomposition, symbolic representation facilitating pattern recognition and abstraction, sequential processing enabling algorithmic thinking, and dialogic self-questioning enhancing debugging processes. Crucially, the model posits these verbally mediated pathways as modal rather than universal. Although non-verbal architectures are acknowledged as possible alternative routes, their precise mechanisms remain underspecified in the existing literature and, therefore, are not the focus of the current theoretical exploration. Given this context, this manuscript focuses on the well-documented verbal support provided by InSp. The VCS Model's theoretical contributions include the following: (1) specification of nuanced cognitive support systems where distinct InSp functions selectively enable particular CT operations; (2) generation of empirically testable predictions regarding aptitude-pathway interactions in computational training and performance; and (3) compatibility with future empirical efforts to inquire into neurodivergent strategies that may diverge from verbal architectures, while acknowledging that these alternatives remain underexplored. Individual variations in InSp phenomenology are theorized to predict distinctive patterns of CT engagement. This comprehensive framework, thus, elaborates and extends existing verbal mediation theories by specifying how InSp supports and enables CT, while laying the groundwork for possible future inquiry into alternative, non-verbal cognitive pathways.

Universities across the U.S. have increasingly emphasized internationalization, leading to rising numbers of international students attending U.S. institutions of higher education. However, these students tend to gravitate toward larger research-intensive universities with many other institutions seeing no increase in international student enrollments. Little is known concerning how to attract international students to regional institutions lacking name recognition. To address the above and promote internationalization through increasing the presence of students from abroad, an academic department at a regional public U.S. college used needs analysis to develop a pilot program for Japanese university students (N = 13). The program involved a synthesis of English as a Second Language instruction, social justice as a content area, and service learning, in a two-week credit-bearing summer session course. A post-participation survey revealed highly positive reactions, particularly in terms of working with local community members, and broad agreement that the program had been life-altering. The implications for international student program development at regional institutions are discussed.

Artificial intelligence (AI), once a phenomenon primarily in the world of science fiction, has evolved rapidly in recent years, steadily infiltrating into our daily lives. ChatGPT, a freely accessible AI-powered large language model designed to generate human-like text responses to users, has been utilized in several areas, such as the healthcare industry, to facilitate interactive dissemination of information and decision-making. Academic advising has been essential in promoting success among university students, particularly those from disadvantaged backgrounds. Unfortunately, however, student advising has been marred with problems, with the availability and accessibility of adequate advising being among the hurdles. The current study explores how AI-powered tools like ChatGPT might serve to make academic advising more accessible, efficient, or effective. The authors compiled a list of questions frequently asked by current and prospective students in a teacher education bachelor’s degree program in the United States. Then, the questions were typed into the free version of ChatGPT, and the answers generated were explored and evaluated for their content and delivery. ChatGPT generated surprisingly high-quality answers, written in an authoritative yet supportive tone, and it was particularly adept at addressing general and open-ended career-related questions, such as career outlook, in a clear, comprehensive, and supportive manner using plain language. We argue that AI-powered tools, such as ChatGPT, may complement but not necessarily replace human academic advisers and that these tools may very well serve to promote educational equity by empowering individuals from a wide range of backgrounds with the means to initiate effective methods of seeking academic advice.

This theoretical article examines how belongingness, defined as the sense that one’s participation is legitimate and valued, interacts with cultural value orientations to help explain persistent disparities in U.S. technology engagement, including emerging technologies, across racial and ethnic groups. While structural barriers (e.g., racism, poverty, linguistic bias, etc.) remain essential to understanding such inequity, we argue that engagement patterns in technology also reflect how different cultural communities may define and experience belongingness in relation to digital domains. Drawing on Triandis and Gelfand’s framework, and focusing specifically on educational contexts, we propose the Belongingness through Cultural Value Alignment (BCVA) model, whereby belongingness serves as a catalyst between cultural value orientations and technology engagement, with vertical collectivism deriving belongingness primarily through structured skill development and validation while horizontal collectivism focusing instead on belonging based on community integration. When technological environments value practices that are consistent with vertical collectivist norms, individuals from horizontal collectivist cultures may experience cultural misalignment not from disinterest in technology or exclusionary efforts but, instead, because dominant engagement modes conflict with their familiar frameworks for fostering a sense of belonging. By examining how cultural value orientations mediate the sense of belonging in contexts involving modern technologies, the proposed perspective offers a novel framework for understanding why access alone may have proven insufficient to address technological participation gaps, and suggests directions for creating technology spaces where individuals from a wider range of communities can experience the authentic sense of belonging.

Background. The current Coronavirus pandemic has been linked to a dramatic increase in anti-Asian American and Pacific Islander (AAPI) hate incidents in the United States. At the time of writing, there does not appear to be any published empirical research examining the mechanisms underlying Asiaphobia during the current pandemic. Based on the stereotype content model, we investigated the idea that ambivalent attitudes toward AAPIs, marked primarily with envy, may be contributing to anti-AAPI xenophobia. Methods. Study 1 (N = 140) explored, through a survey, the link between envious stereotypes toward AAPIs and Asiaphobia. Study 2 (N = 167), utilizing autobiographical recall tasks, experimentally induced the affect of envy in order to establish causality between feelings of envy toward AAPIs and Asiaphobia. Results. In Study 1, envious stereotypes toward AAPIs were found to be predictive of Asiaphobia and, in Study 2, the inducement of envy led to heightened levels of Asiaphobia. Conclusions. The current research provides support for the proposition that, consistent with the stereotype content model, stereotypes and attitudes toward AAPIs marked with ambivalent and envious views, consisting of a mix of perceived competence and lack of “human warmth,” may be fueling Asiaphobia. Implications for potential applications and future research are discussed.

Meningioma accounts for ~25% of all primary intracranial neoplasms and the incidence increases with age. Prvios population-based studies demonstrated that the annual incidence of intracranial meningiomas was 1.2-3.1/100,000 population. In particular, the incidence of this disease among the elderly is high. Recently, increased life expectancy and greater use of diagnostic radiological imaging led to an increased incidence in the diagnosis of intracranial meningiomas, both symptomatic and asymptomatic, in the elderly. Thus, neurosurgeons may be increasingly confronted with the management of intracranial meningiomas in the elderly. In practice, it is often difficult for physicians to determine whether traditional surgical resection is the optimal management strategy for intracranial meningiomas in the elderly. However, reported clinical studies about the outcome of surgical resection of intracranial meningiomas in the elderly are limited. Increased risk of mortality and morbidity associated with surgical treatment for intracranial meningiomas in the elderly compared with younger patients have been controversial. In the present study, the clinical features of intracranial meningiomas in 70 consecutive intracranial meningioma patients that underwent surgical treatment at the affiliated hospital of University of Occupational and Environmental Health between 2007 and 2013 were assessed. In addition, patient selection and surgical management of intracranial meningioma in elderly patients was discussed. Preoperative factors, including symptoms, tumor location, tumor size, Karnofsky Performance Scale (KPS) score and American Society of Anesthesiology (ASA) score, and postoperative factors, including pathological diagnosis, tumor proliferation index (Ki-67), resection rate (Simpson grade), length of hospital stay and discharge destination were retrospectively analyzed in patients aged ≥75 years (n=16; elderly group) and <75 years (n=54; younger group). Outcomes were assessed 6 months after surgery. Multivariate logistic regression revealed that tumor resection rate (Simpson grade III-V) was an important predictor of surgical complications (odds ratio, 5.662; 95% confidence interval, 1.323-24.236; P=0.0194). Perioperative morbidity was not correlated with age (>75 years), tumor location, tumor size, KPS score or ASA score. Thus, the present study indicated that age is not associated with surgical outcome in elderly meningioma patients. Regardless of patient age, the decision to perform surgical resection should be made on an individual basis wherein tumor characteristics and the general health of the patient are considered.

Background: The COVID-19 pandemic presented unique and unprecedented challenges for international students, those studying at institutions of higher education outside of their home countries, due to their distinct circumstances and vulnerabilities. This literature review examines the multifaceted mental health burdens they experienced and highlights the need for targeted support and interventions. Methods: A rigorous search across three databases (i.e., PubMed, PsycINFO, and ERIC) yielded 50 empirical studies for inclusion in this literature review. A six-phase thematic analysis framework was employed to identify and synthesize key themes. Results: Seven prominent themes emerged: (1) academic and professional disruptions; (2) challenges navigating international student status; (3) social isolation and loneliness; (4) difficulties with living arrangements; (5) financial and food insecurity; (6) health concerns for self and loved ones; and (7) experiences of discrimination and xenophobia. Conclusions: This review highlights a range of tolls that mental health consequences took on international students, and it suggests the need for targeted interventions and support services to address these challenges. It also identifies critical research gaps, such as the need for longitudinal studies and comparative analyses with domestic students. The implications for inclusive policies and supportive environments to promote international students’ well-being are discussed.