Computers and Education Artificial Intelligence最新文献

{"title":"Stretching AI's reach: Assessing an AI-driven feedback system for extended academic writing","authors":"Jim Lo ,&nbsp;Christy Wong ,&nbsp;Agnes Ng ,&nbsp;Pinna Wong ,&nbsp;Denise Cheung ,&nbsp;Pauli Lai","doi":"10.1016/j.caeai.2025.100511","DOIUrl":"10.1016/j.caeai.2025.100511","url":null,"abstract":"<div><div>Advances in large language models (LLMs) enable timely and scalable writing evaluation. Previous research has shown that LLM-driven conversational systems, such as ChatGPT, can provide feedback on short essays. However, it is unclear whether AI can effectively evaluate more demanding genres. This study investigates a custom-built writing feedback system developed at a Hong Kong university that uses OpenAI's GPT-4 Turbo (0125-preview) to provide rubric-based feedback on a 1500-word academic report. Guided by a detailed, rubric-aligned prompt, the system generated 333 feedback items from 37 undergraduates, which were analysed for accuracy, tone, and inclusion of examples. The analysis showed that most feedback was accurate and addressed both strengths and weaknesses, but over half lacked concrete examples. Often recycling phrases from rubric descriptors, the feedback was largely generic and occasionally inaccurate. Interview data from six students revealed that the AI feedback was valued for its coverage, efficiency, and constructive tone, yet its generic nature undermined its usefulness. Despite these limitations, students expressed interest in receiving both AI and teacher feedback for the efficiency and coverage that AI offers, alongside the specificity and relevance of teacher input. These findings suggest that employing a well-crafted prompt on an AI model with a large context window does not necessarily guarantee substantive feedback. Therefore, educators using AI-driven feedback systems should thoroughly assess these systems' capacity to handle extended academic writing. Future research could explore ways to refine prompts and system design for long-form writing assignments.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100511"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145718922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How high-school pressure and autonomy support are linked to dual AI learning pathways: A cross-contextual SEM analysis","authors":"Shen Qinjie ,&nbsp;Wynn Arunrugstichai","doi":"10.1016/j.caeai.2026.100621","DOIUrl":"10.1016/j.caeai.2026.100621","url":null,"abstract":"<div><div>Generative AI tools are rapidly becoming embedded in students' everyday learning, yet students' AI use varies from constructive support to compulsive dependence. Drawing on Self-Determination Theory and the Hook model, this study proposes a dual-pathway framework examining how retrospective reports of high-school learning climate relate to university students’ GenAI-use patterns and perceived AI-supported learning quality through motivational and behavioral relations. Using cross-sectional survey data from university students with different high-school backgrounds and current university contexts (overall N = 508; China-HS group n = 305 and non-China-HS group n = 203; China-Uni n = 193 and Thailand-Uni n = 315), we tested the model using structural equation modeling and multi-group analyses. Results were consistent with a pressure-compulsive pathway: high-school pressure was associated with higher controlled motivation, which was in turn associated with higher compulsive AI use, lower well-being, and lower perceived AI-supported learning quality. In parallel, results supported an autonomy-adaptive pathway: high-school autonomy support and current university autonomy support were associated with higher autonomous motivation and planned trigger readiness, which were associated with more adaptive AI use, higher perceived academic performance and well-being, and higher perceived AI-supported learning quality. The model explained substantial variance in key endogenous outcomes. Scalar-level measurement invariance supported meaningful cross-group comparisons, and multi-group analyses suggested that the overall pattern of associations was broadly similar across the sampled high-school and university groupings, with differences concentrated in specific links. Overall, the findings suggest that motivational climate may be an important correlate of whether GenAI is incorporated into learning in more adaptive versus more reliance-oriented ways within the China, Thailand-linked contexts examined in this study.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100621"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From knowledge gaps to learning opportunities: Leveraging student questions and dual use of generative AI to support student learning at scale","authors":"Stanislav Pozdniakov ,&nbsp;Jonathan Brazil ,&nbsp;Oleksandra Poquet ,&nbsp;Stephan Krusche ,&nbsp;Santiago Berrezueta-Guzman ,&nbsp;Shazia Sadiq ,&nbsp;Hassan Khosravi","doi":"10.1016/j.caeai.2025.100509","DOIUrl":"10.1016/j.caeai.2025.100509","url":null,"abstract":"<div><div>University courses with hundreds of students have become common, particularly during early years of university studies. The sheer scale of these courses limits traditional instruction, shifting it towards a one-to-many mode of delivery. This shift reduces student–instructor interaction and tailored instructor feedback which are crucial for student success. Automated feedback systems allow scaling feedback, but they often reduce instructor contributions to student learning. This paper investigates how emerging technologies can support, rather than replace, instructors in tailoring their teaching and feedback to identify and correct student knowledge gaps at scale. To address this challenge, the paper introduces a novel technological solution: the Knowledge Gaps to Mastery (KG2M) approach. KG2M combines discussion forum data with course-specific content and leverages large language models (LLMs) and Retrieval-Augmented Generation (RAG) for the dual purpose of identifying prevalent class-level knowledge gaps and transforming them into targeted learning activities and formative assessments. The approach was deployed across three computer science courses with a combined enrollment of 1,355 students and evaluated through semi-structured interviews with five instructors. Results indicate that instructors found the tool intuitive and pedagogically valuable, particularly for surfacing knowledge gaps and generating actionable teaching insights. The paper reports on the tool, the evaluation, and the current limitations of the approach that emerged during instructor evaluation.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100509"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM sentiment quantification reveals selective alignment with human course-evaluation raters","authors":"Joyce W. Lacy ,&nbsp;Chi Nnoka ,&nbsp;Zachary Jock ,&nbsp;Cathleen Morreale","doi":"10.1016/j.caeai.2026.100545","DOIUrl":"10.1016/j.caeai.2026.100545","url":null,"abstract":"<div><div>Student course evaluations contain rich qualitative feedback in the form of comments written in response to open-ended questions. However, this qualitative data, which may be more nuanced and detailed than quantitative ratings, is often unexamined in both administrative and research settings due to the labor-intensive nature of manual analysis. We investigate whether large language models (LLMs), including BERT, RoBERTa, and OpenAI model variants, can accurately replicate human judgments of sentiment in these comments. We compare masked and generative language models, using both naïve and fine-tuned approaches, to analyze a curated dataset of 1000 de-identified course evaluation responses. Results show that some artificial intelligence (AI) models can approach inter-rater reliability with humans remarkably well and quickly with limited tuning or training data provided. However, performance varied and not all models were able to produce a reliable sentiment analysis, even after training. This has implications for future avenues of qualitative data analysis within course evaluations as well as the large repositories of course evaluations available at institutions of higher education. Importantly, consideration should be taken when selecting an AI model as this decision has ramifications for the reliability and validity of the generated output.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100545"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models in education: a systematic review of empirical applications, benefits, and challenges","authors":"Yuhong Shi,&nbsp;Kun Yu,&nbsp;Yifei Dong,&nbsp;Fang Chen","doi":"10.1016/j.caeai.2025.100529","DOIUrl":"10.1016/j.caeai.2025.100529","url":null,"abstract":"<div><div>The rapid advancement of Large Language Models (LLMs), particularly following the release of ChatGPT in November 2022, has significantly transformed educational methodologies. This systematic review aims to synthesize empirical studies published between November 2022 and March 2025, examining the implementation and effectiveness of LLMs in educational settings. 88 empirical studies identified key applications, benefits, and challenges associated with LLM integration in education. Our findings reveal that LLMs are utilized across various educational contexts in six primary applications, with Intelligent Tutoring Systems being particularly prominent. The benefits include improved academic performance, increased student engagement, enhanced accessibility, optimized resource utilization, and strengthened cognitive and skill development. However, challenges such as student over-reliance on AI, technical reliability issues, assessment fairness, and privacy concerns were identified. This review provides educators, researchers, and policymakers with evidence-based insights and practical guidance for effective LLM integration, contributing to the ongoing transformation of teaching and learning in the era of Generative Artificial Intelligence (GenAI) technology.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100529"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effectiveness of an AI-integrated VR oral training application in reducing public speaking anxiety and interview anxiety","authors":"Peiwen Huang ,&nbsp;Yanling Hwang ,&nbsp;Jui Ling Hsu ,&nbsp;Chien Fand Peng ,&nbsp;Cheng Han Tsai ,&nbsp;Chih Yao Wang","doi":"10.1016/j.caeai.2025.100514","DOIUrl":"10.1016/j.caeai.2025.100514","url":null,"abstract":"<div><div>Despite the growing importance of English oral communication skills, traditional language learning approaches show limited effectiveness in simultaneously addressing psychological barriers and speaking proficiency among college students. While previous studies have explored anxiety reduction or speaking enhancement separately, a significant gap exists in research examining integrated approaches that tackle Public Speaking Anxiety (PSA), Interview Anxiety, and English-speaking proficiency improvement simultaneously. This study investigated whether an AI-integrated VR oral training application could effectively address these interconnected challenges. A quasi-experimental design was employed with 20 English major students from a mid-central university in Taiwan. Participants completed five training sessions using Meta Quest 2 headsets and an AI-integrated VR oral training application providing tailored feedback on pronunciation, grammar, and fluency based on IELTS standards. Pre- and post-intervention assessments utilized validated instruments including the Personal Report of Public Speaking Anxiety (PRPSA) and Measure of Anxiety in Selection Interviews (MASI), alongside comprehensive speaking proficiency measures. Results demonstrated significant improvements in English speaking proficiency, including increased sentence length and word count, with grammatical errors and incomplete sentences decreasing markedly (p &lt; .001). Concurrently, significant reductions in both PRPSA and MASI scores (p &lt; .05) were observed, though lexical diversity showed slight decline. VR-related motion-sickness symptoms were mildly alleviated, and participants' perceived control increased significantly (p &lt; .05), while interest and attention levels remained stable. These findings suggest that AI-integrated VR oral training applications can effectively enhance English speaking proficiency while simultaneously reducing anxiety levels and improving self-efficacy among English learners. The study addresses a critical research gap by demonstrating the potential of integrated technological approaches to tackle multiple barriers to effective English oral communication, offering promising implications for language education and anxiety management in academic contexts.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100514"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in K-12 education: An umbrella review","authors":"Ruiping Huang ,&nbsp;Yue Yin ,&nbsp;Na Zhou ,&nbsp;Fei Lang","doi":"10.1016/j.caeai.2025.100519","DOIUrl":"10.1016/j.caeai.2025.100519","url":null,"abstract":"<div><div>This umbrella review examines the current state of artificial intelligence in education (AIEd) in K-12 by synthesizing 102 systematic reviews. We described their characteristics, built a framework to summarize the review foci, synthesized the major findings, and examined their quality. The main findings are as follows: (a) AIEd Review Framework: We developed a framework that maps the territory of AIEd to understand the research topics distributed and interconnected. (b) Innovation: AI has been applied to support teachers’ instruction, personalize learning, enhance engagement and collaboration, automate assessment and feedback, and manage educational content, reflecting its multifaceted potential in education. (c) Synthesized Knowledge: The reviews reveal how AI is developed, applied, and evaluated in education, with major challenges including technological limitations, pedagogical hurdles, ethical risks, and systemic barriers. (d) AI Education and Literacy: More reviews are needed on AI education and literacy. Compared with the rapid advancement of AI applications, preparing teachers and students to understand and ethically use AI has received much less attention. (e) AIEd Theories: More reviews are needed for theoretical development for AIEd. Most studies apply existing frameworks descriptively rather than empirically testing or extending them. (f) Quality Assessment: We developed a rubric to assess systematic reviews and found gaps in data management, extraction, analysis, and transparency. Overall, this umbrella review maps the territory of AIEd, identifies underexplored areas and future directions. The AIEd Review Framework can be used to guide future reviews in AIEd. The quality assessment rubric can be used for future umbrella reviews in general.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100519"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling generative Al's influence on students' perceived decision capability: A cognitive load and decision augmentation approach","authors":"Jaskirat Singh Rai ,&nbsp;Sakshi Kathuria ,&nbsp;Harpominder Kaur ,&nbsp;Amanpreet Singh ,&nbsp;Maher N. Itani","doi":"10.1016/j.caeai.2026.100596","DOIUrl":"10.1016/j.caeai.2026.100596","url":null,"abstract":"<div><div>There is a resonant association between the implementation of Generative Artificial Intelligence (GenAI) and students’ ability to identify, analyse, and select the best options among alternatives. Accordingly, this study examined the impact of GenAI adoption on business students' perception of their decision-making capability through the lenses of decision augmentation theory (DAT) and cognitive load theory (CLT). A survey was administered to 350 university students studying business in the United Arab Emirates. Using structural equation modelling, the study examined key constructs, including cognitive load reduction, information quality, and decision confidence as mediators, and GenAI trust as a moderator. The results revealed that GenAI significantly improves business students' perceived decision capability by improving confidence in decision-making and information quality. Although GenAI effectively reduces cognitive load, this reduction is negatively associated with students' perceived decision capability. Mediation analysis confirmed that decision confidence and information quality significantly mediate the relationship between GenAI adoption and perceived decision capability. Additionally, moderation analysis revealed that high trust in GenAI weakens the positive effects of GenAI on cognitive load reduction and information quality. These findings emphasise the importance of balanced GenAI integration, critical user engagement, and responsible trust development. The study provides valuable insights for students, educators, academic institutions, and industries seeking to use GenAI tools for enhanced learning and decision-making, and identifies limitations and directions for future research on GenAI-enabled education.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100596"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Face value: How avatar identity shapes epistemic trust in AI-mediated learning” [Computers and Education: Artificial Intelligence 10 (2026) 100610]","authors":"Zach Anthis,&nbsp;Avgousta Kyriakidou-Zacharoudiou","doi":"10.1016/j.caeai.2026.100619","DOIUrl":"10.1016/j.caeai.2026.100619","url":null,"abstract":"","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100619"},"PeriodicalIF":23.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148565826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering university teachers in higher education: A generative AI-responsive competency framework","authors":"Daner Sun ,&nbsp;Shen Ba ,&nbsp;Yingying Cha ,&nbsp;Jiahui Yu ,&nbsp;Feng-Kuang Chiang ,&nbsp;Hai Min Dai ,&nbsp;Cher-Ping Lim","doi":"10.1016/j.caeai.2026.100542","DOIUrl":"10.1016/j.caeai.2026.100542","url":null,"abstract":"<div><div>The integration of generative artificial intelligence (GenAI) into higher education necessitates a reconceptualization of teacher competencies, moving beyond technical proficiency to encompass pedagogical strategies for fostering critical, ethical, and developmentally appropriate student-AI collaboration. Existing competency frameworks, however, exhibit notable limitations in equipping university teachers with actionable guidance for designing GenAI-mediated learning experiences that cultivate their students’ higher-order thinking and subject knowledge. In response, this paper develops and proposes a GenAI-responsive competency framework for university teachers to supplement existing frameworks and address areas that are not sufficiently covered or elaborated. Developed through a systematic analysis of digital and AI-related competency frameworks, the proposed model is grounded in constructivist learning theory, sociological perspectives, and student-centered pedagogy. Its theoretical and practical robustness was further refined through iterative expert review and consultation. The resulting framework comprises four core dimensions: <em>GenAI Literacy</em>, <em>Curriculum/Learning Design</em>, <em>Teaching and Learning</em>, and <em>Assessment</em>. Each dimension is articulated through a dual perspective: teachers’ own proficiency and their capacity to foster students’ critical engagement with GenAI. Competency progression is structured across three developmental levels: <em>Basic</em>, <em>Intermediate</em>, and <em>Advanced</em>, representing a continuum from technical awareness to guided application, and ultimately to critical and creative integration. The proposed framework supports teachers’ ongoing professional growth and enhances their ability to facilitate student autonomy, ethical reasoning, and collaborative engagement with GenAI. It provides a structured yet flexible tool for self-assessment, instructional design, and targeted professional development in higher education, thereby advancing the discourse on effective and responsible human-AI collaboration.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"10 ","pages":"Article 100542"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}