Search Results (28)

This mixed-methods study examines how specific design features (SDF) in e-picture books influence phonological awareness (PA) and print knowledge (PK) among Chinese preschoolers. Ninety 5-year-old children were assigned to three groups: an e-picture-book group, a paper-book group, and a regular-preschool-program control group. All participants completed pretests of PA and PK, followed by a four-week reading intervention and post-tests. The quantitative results showed that the e-picture-book group significantly outperformed the control group in terms of PA, whereas no statistically significant differences were found between the e-picture book and paper-book groups or across groups regarding PK. Qualitative analyses of observations and semi-structured interviews further revealed two key mechanisms through which design features, such as pronunciation support, playback functions, and interactive hotspots facilitated children’s sensitivity to sound structures. In contrast, features intended to support PK (e.g., highlighted print) showed limited effectiveness, partly due to insufficient salience and the visual complexity of Chinese characters. These findings suggest that e-picture books may support phonological development in Chinese preschoolers, particularly through auditory and interactive design features, although no significant advantage was observed compared to narrated paper-book reading. However, their impact on print knowledge appears to depend on the alignment between design features and the characteristics of the Chinese writing system. This study highlights the importance of optimizing print-referencing features and incorporating instructional support to enhance early literacy outcomes. Full article

Orthographic depth varies across alphabetic writing systems and plays a central role in spelling acquisition. In immersion education, a second language (L2) is used as a language of instruction for part of the curriculum, such that learners are primarily confronted with its writing system during the initial stages of literacy development. This early exposure may shape the spelling strategies subsequently deployed in the first language (L1), which also corresponds to the dominant language of the surrounding community. This article provides a structured review of key mechanisms involved in spelling acquisition, orthographic depth, and cross-linguistic influence in bilingual and immersion contexts. On this basis, it proposes a conceptual and predictive framework specifying how the orthographic depth of the instructional language modulates spelling strategies and spelling error profiles in L1. Focusing on French-speaking pupils enrolled in immersion programmes with L2s characterised by either predominantly phonemic or opaque orthographies, the framework integrates strategy-based models of orthographic development. The model distinguishes phonological, lexical, and morphographic components of orthographic knowledge and predicts that immersion in phonemic-dominant orthographies favours phonographic dominance and regularisation patterns, whereas immersion in opaque orthographies promotes greater reliance on lexical–orthographic strategies, resulting in distinct and systematic spelling error profiles in French. Full article

In HPC systems and hyper-scale data centers, the adoption of high-performance NVMe SSDs and high-speed networks has shifted storage bottlenecks to the network stack. Under high-concurrency workloads, frequent interrupt processing exhausts CPU resources while protocol-level control–data dependencies in the NVMe over TCP write path introduce additional serialization penalties. Existing optimizations either require specialized hardware, dedicate CPU cores to user-space polling, or apply semantically blind batching that delays time-sensitive control messages. We present SENS, a Semantic-aware NVMe over TCP Scheduler embedded within the NVMe over TCP driver of the Linux kernel. SENS combines two mechanisms: (1) PDU vectorization, which aggregates discrete Protocol Data Units into memory vectors before network transmission, amortizing per-I/O system call overhead and reducing soft-interrupt frequency; and (2) instruction-aware dispatch, which detects control PDUs such as R2T and triggers an early flush of the aggregation window, mitigating the serialization penalty on the write path. A prototype evaluation with physical NVMe SSDs and 100 GbE networks shows that SENS saturates the SSD throughput ceiling using 4–5 CPU cores, halving the host-side core budget compared to the native TCP driver. With a RAMDisk backend that removes storage-media constraints, SENS sustains up to 2.5× higher concurrent IOPS. These results show that exposing storage-protocol semantics to the batching layer improves the scalability of NVMe over TCP without additional hardware. Full article

Background/Objectives: Children develop the ability to construct meaning from language long before they learn to read. These foundational language processes support learning across academic contexts and form the basis for later reading and writing. However, many students, particularly those with Developmental Language Disorder (DLD), enter school with weaknesses in oral academic language that limit their ability to understand and express increasingly complex classroom discourse. Despite the central role of narrative and expository language in early instruction, explicit and systematic intervention targeting both discourse genres remains uncommon in the early grades. The purpose of this randomized controlled trial was to examine the immediate and maintained effects of a structured, small-group oral language intervention targeting both narrative and expository discourse for early elementary-age students with and without DLD. Methods: Participants (N = 80) were kindergarten through second-grade students identified as having DLD or significantly weak narrative language performance and were randomly assigned to an intervention condition or business-as-usual control. Narrative outcomes were collected for all participants at pretest, posttest, and a two-month follow-up, and expository outcomes were collected at posttest. Results: Results indicated statistically significant intervention effects for narrative language at posttest, with gains maintained at follow-up. Treatment effects were not moderated by language status, and subgroup analyses demonstrated large effects for students with DLD. Regression analyses indicated a non-significant intervention effect on expository outcomes. Conclusions: Findings provide experimental evidence that explicit, contextualized narrative and expository language instruction delivered in brief small-group sessions can produce meaningful and durable improvements in narrative language for young children, including those with DLD. Full article

The rapid adoption of generative artificial intelligence tools such as ChatGPT in educational settings has generated both enthusiasm and concern regarding their influence on student learning. While several studies report improvements in efficiency, confidence, and perceived understanding, evidence for durable conceptual learning and knowledge transfer remains mixed. This article examines these tensions through the concept of fluency illusion, a cognitive phenomenon in which information that is easy to process is mistakenly judged as being well understood. Using a narrative conceptual review approach, this study synthesizes findings from 41 publications identified through searches of Google Scholar, Scopus, Web of Science, and ERIC covering the period from 2022 to early 2026. The reviewed literature includes 28 empirical studies, nine conceptual or theoretical analyses, and four review articles addressing the use of ChatGPT in educational contexts. Across domains such as writing and language learning, STEM problem solving, feedback and tutoring, and assessment, the literature shows a recurring pattern in which fluent AI-generated responses increase learners’ confidence without consistently improving deeper conceptual understanding. Drawing on research from cognitive psychology and metacognition, this review proposes an integrative conceptual account of how fluent AI output may shape learners’ judgments of understanding and influence their engagement with learning tasks. The paper concludes by discussing implications for instructional design, assessment practices, and metacognitive scaffolding, and outlines directions for future research aimed at empirically examining the proposed framework and identifying strategies to reduce fluency-driven misjudgments while preserving the potential benefits of generative AI in education. Full article

Higher education continues to encounter the challenge of redesigning writing pedagogy beyond the rapid adoption of emerging technologies. This challenge is particularly evident in English writing courses, which play a role in developing students’ writing and research skills in universities across the United Arab Emirates (UAE). While generative artificial intelligence (GenAI) tools offer practical affordances for writing instruction, their growing use has also raised concerns about academic integrity, authenticity, and critical engagement. Although early discourse has focused on the risks and potential of GenAI, there remains a clear dearth of frameworks to guide instructors in designing meaningful and engaging writing assignments. This paper introduces Ai-RACE, an adaptable pedagogical framework for designing purposeful and innovative writing tasks. Grounded in classroom-based insights, principles of writing pedagogy, constructivist and multimodal learning theories, Ai-RACE conceptualises assignment design around five interconnected components: AI integration, Relevance, Authenticity, the 4Cs, and Engagement. Employing a design-focused qualitative approach, the study uses instructional practices and student reflections to examine the implementation of Ai-RACE in writing contexts. Although situated within a specific institutional context, the study offers transferable guidelines for designing writing assignments across international higher education settings. By positioning Ai-RACE as a design heuristic, the study demonstrates its potential in supporting engagement, critical thinking, writing skills and ethical use of AI, and highlights the importance of rethinking writing pedagogy and the professional development in AI- influenced contexts. Full article

Composing in early childhood classrooms offers a critical opportunity to strengthen children’s language skills, yet many teachers feel underprepared to provide this instruction. This study examines whether an AI-enhanced digital platform (L4C) can serve as a sustainable, community-based professional development model that bridges theory and practice. Twenty-nine teachers in the southeastern United States engaged with L4C, a professional learning model designed to integrate principles from the Science of Literacy, Learning, and Instruction into a cohesive platform that links teachers’ content and pedagogical knowledge-building with lesson planning and reflective practice. Data sources included surveys, pre- and post-lesson plans, and AI usage logs from the lesson planning tool. Findings showed that teachers initially reported significant barriers to composing instruction and sought professional learning responsive to their classroom needs. After using L4C, teachers demonstrated notable growth in their knowledge of language components and the quality of their composing lesson designs. Teachers evaluated the platform positively, particularly valuing the linked videos and scripted lesson tools for making theoretical concepts actionable. These findings suggest that AI-driven platforms like L4C can advance teacher learning in practical, individualized, and contextually relevant ways, offering a promising pathway for professional development in early literacy instruction. Full article

Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. Full article

Integrating literacy-focused curricula in preschool settings may help support children’s literacy learning. In this study, we explored the use of literacy-focused curricula and how it was associated with preschool children’s literacy gains (i.e., print and letter knowledge, phonological awareness, language and comprehension, and emergent writing) relative to non-literacy-focused curricula. We estimated multilevel structural equation models using data from an intervention study that included a sample of 571 children nested within 98 preschool classrooms. Because early disparities in emergent literacy are associated with later reading and writing difficulties, we examined how such associations might be moderated by child risk status, receipt of emergent literacy intervention, and program settings. We found that literacy-focused curricula were not often used by teachers in preschool classrooms, but teachers’ use of such curricula was positively associated with children’s phonological awareness gains. Risk status did not moderate the association between use of literacy-focused curricula and children’s emergent writing gains. Additionally, emergent literacy intervention and program settings did not moderate the associations. However, we found that teachers’ use of literacy-focused curricula was positively associated with print and letter knowledge, phonological awareness, and language and comprehension for children identified as at risk for later reading difficulties compared to those who were not at risk. As such, our findings suggest that integrating or supplementing existing classroom instruction with literacy-focused curricula could yield meaningful benefits for children identified as at risk for later reading difficulties. Full article

Generative artificial intelligence (GenAI) is reshaping science, technology, engineering, and mathematics (STEM) education by offering new strategies to address persistent challenges in equity, access, and instructional capacity—particularly within Hispanic-Serving Institutions (HSIs). This review documents a faculty-led, interdisciplinary initiative at the University of La Verne (ULV), an HSI in Southern California, to explore GenAI’s integration across biology, chemistry, mathematics, and physics. Adopting an exploratory qualitative design, this study synthesizes faculty-authored vignettes with peer-reviewed literature to examine how GenAI is being piloted as a scaffold for inclusive pedagogy. Across disciplines, faculty-reported benefits such as simplifying complex content, enhancing multilingual comprehension, and expanding access to early-stage research and technical writing. At the same time, limitations—including factual inaccuracies, algorithmic bias, and student over-reliance—underscore the importance of embedding critical AI literacy and ethical reflection into instruction. The findings highlight equity-driven strategies that position GenAI as a complement, not a substitute, for disciplinary expertise and culturally responsive pedagogy. By documenting diverse, practice-based applications, this review provides a flexible framework for integrating GenAI ethically and inclusively into undergraduate STEM instruction. The insights extend beyond HSIs, offering actionable pathways for other minority-serving and resource-constrained institutions. Full article

This study examined the literacy outcomes of 368 deaf elementary students in the United States, focusing on reading and writing performance and their connections with demographic and language variables. Standardized assessment data were analyzed from students in grades 3–6. Results indicated wide variability in reading and writing performance, from scores at a standard deviation above the mean to more than 3 below the mean. There were demonstrated disparities in mean literacy outcomes based on disability status. A strong positive correlation was found between reading and writing scores, suggesting interconnected development of literacy skills. Notably, writing outcomes were consistently higher than reading across analyses. Performance trajectories differed by grade, with literacy gaps widening over time. Gender, race, and hearing level explained 2–3% of the variance in literacy outcomes, while language proficiency (in ASL and/or spoken English) and phonological knowledge (fingerspelled and/or spoken) predicated 55–63% of the models. These findings highlight the need for early accessible language exposure and responsive literacy instruction aligned with deaf learners’ language strengths. Full article

This paper is a case study of supporting students in developing their Generative Artificial Intelligence (GAI) literacy as well as guiding them to use it ethically, appropriately, and responsibly in their studies. As part of the study, a law coursework assignment was designed utilising a four-step Problem, AI, Interaction, Reflection (PAIR) framework that included a problem-solving task that required the students to use GAI tools. The students were asked to use one or two GAI tools of their choice early in their assessment preparation to research and were given a set questionnaire to reflect on their experience. They were instructed to apply Gibbs’ or Rolfe’s reflective cycles to write about their experience in the reflective part of the assessment. This study found that a GAI-enabled assessment reinforced students’ understanding of the importance of academic integrity, enhanced their research skills, and helped them understand complex legal issues and terminologies. It also found that the students did not rely on GAI outputs but evaluated and critiqued them for their accuracy and depth referring to primary and secondary legal sources—a process that enhanced their critical thinking and problem-solving skills. Full article

This article examines Xinzeng Gegu yaolun 新增格古要論 (Expanded Essential Criteria of Antiquities), a connoisseurship manual compiled in 1460 by the mid-Ming official Wang Zuo 王佐. Drawing upon Cao Zhao’s 曹昭 early Ming Gegu yaolun 格古要論 (Essential Criteria of Antiquities), Wang reconfigured a manual focused on authentication and appreciation into a text structured by Confucian values and political ethics. He added ritual-oriented entries in chapters four and ten through thirteen, such as “An Examination of Song Dynasty Attire and Rank Titles” 宋制服裝入銜考 and “An Inquiry into Gold and Silver Insignia” 佩金銀牌考, reinforcing Confucian ideology through commentary on ritual institutions and the inclusion of imperial edicts and commemorative inscriptions. He also reorganized the placement of guqin 古琴, calligraphy and painting, while redefining evaluative standards to integrate material objects into moral instruction and bureaucratic discipline. In doing so, Wang reinforced a shared community of scholar–officials, using ritual hierarchy, loyalist writings, and gift exchange to respond to the uncertainty of a fractured political order. This article argues that through a non-canonical text like Xinzeng Gegu yaolun, mid-Ming scholar–officials extended Confucian discourse into antiquities, transforming antiquarian writing into a visible enactment of ethical values and collective identity. Full article

Background/Objectives: Artificial intelligence (AI) is transforming nursing, with generative AI (GenAI) tools such as ChatGPT offering opportunities to enhance education through personalized learning pathways. This study aimed to explore nursing students’ use of generative artificial intelligence (GenAI) and their perceptions of its use in nursing education, including its advantages, disadvantages, and perceived support needs. Methods: This study employed an online survey. The participants were 99 undergraduate nursing students in New York City. Data was collected online through self-report measures using semi-structured, open-ended questions. The data was analyzed using content analysis. Results: Most participants (92%) used GenAI tools to access accurate information, clarify nursing concepts, and support clinical tasks such as diagnoses and health assessments, as well as schoolwork, grammar checks, and health promotion. They valued GenAI as a quick, accessible resource that simplified complex information and supported learning through definitions, practice questions, and writing improvements. However, the participants noted drawbacks, such as subscription costs, over-reliance, information overload, and accuracy issues, leading to trust concerns. The participants suggested financial support, early guidance, and instructional modules to better integrate AI into nursing education. Conclusions: The results indicate that GenAI positively impacts nursing education and highlight the need for guidelines on critical evaluation. To integrate GenAI effectively, educators should consider introductory sessions, support programs, and a GenAI-friendly environment, promoting responsible AI use and preparing students for its application in nursing education. Full article

Early writing performance strongly predicts long-term literacy performance. It follows that early underachievement in writing is highly correlated with early underachievement in reading. One strategy teachers and students can use to approach writing in the kindergarten classroom is invented spelling. Invented spelling is children’s spontaneous or self-directed attempts to represent words in print by matching sounds to known letters or phonics patterns. A quasi-experimental study was used to evaluate the impact of invented spelling on foundational literacy skills and writing motivation in 63 kindergarten students at a rural school in the Mid-South. The research questions focused on the impact of invented spelling instruction on a variety of literacy outcomes, including foundational skills, spelling, and motivation. The results indicate the significant main effects of invented spelling instruction on students’ invented spelling (p < 0.001), conventional spelling (p < 0.001), complex vocabulary use (p < 0.001, writing motivation (p = 0.040), and writing achievement (p < 0.001). Other outcomes as well as implications and future directions are reported. The invented spelling intervention encouraged low-stake risk taking when writing and removed barriers to writing entry. Allowing time and space for invented spellings means students can focus on communicating their ideas in print without being hindered by the expectation to conform to conventional spellings. Full article