Computers and Education Open最新文献

Student engagement with ChatGPT for educational tasks: Effects of inoculation training on verification intentions and behavior 学生参与ChatGPT教育任务：接种训练对验证意图和行为的影响

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2026-01-19 DOI: 10.1016/j.caeo.2026.100335

Chi B. Vu, James J. Cummings, Daniel Y. Park

Generative AI tools, especially ChatGPT, have become prevalent in education. Despite numerous benefits and convenience, potential risks such as hallucinations, misinformation, and limitations in advanced problem-solving underscore the need for inoculation training to safeguard students from these threats. International EFL students might be particularly vulnerable to the risks associated with ChatGPT due to their high demand for language and academic assistance, which can be provided through this tool. This study investigated whether an inoculation message would enhance students’ intentions to verify information provided by ChatGPT and encourage them to do so. The study employed a 2 (student status: domestic vs. international EFL) x 2 (inoculation status: inoculated vs. non-inoculated) x 2 (time: pre-test vs. post-test) mixed factorial design. This two-part study revealed that a generic inoculation message containing forewarnings prompted students to exhibit a higher level of caution when interacting with ChatGPT, as evidenced by their verification of ChatGPT-generated answers in the academic-source-summary task. Students who received inoculation training were more likely to verify the academic-source-summary task compared to those who did not receive the message. The inoculation intervention did not have a significant impact on their intentions to verify information provided by ChatGPT. The findings from this research provide educators, curriculum developers, and administrators with insights into improving and better delivering training programs tailored to different groups of students, helping them safely utilize generative AI tools.

生成式人工智能工具，尤其是ChatGPT，已经在教育领域变得普遍。尽管有很多好处和便利，但潜在的风险，如幻觉、错误信息和高级问题解决的局限性，强调了接种培训的必要性，以保护学生免受这些威胁。国际英语学生可能特别容易受到ChatGPT相关风险的影响，因为他们对语言和学术援助的需求很高，这些都可以通过这个工具提供。本研究调查了接种信息是否会增强学生验证ChatGPT提供的信息的意愿并鼓励他们这样做。该研究采用了2（学生状态：国内与国际EFL） x2（接种状态：接种与未接种）x2（时间：测试前与测试后）混合因子设计。这项由两部分组成的研究表明，包含预警的通用接种信息促使学生在与ChatGPT互动时表现出更高的谨慎程度，这一点可以通过他们在学术来源摘要任务中验证ChatGPT生成的答案来证明。与没有接受接种训练的学生相比，接受接种训练的学生更有可能验证学术来源总结任务。接种干预对他们核实ChatGPT提供的信息的意图没有显著影响。这项研究的结果为教育工作者、课程开发人员和管理人员提供了改进和更好地提供针对不同学生群体的培训计划的见解，帮助他们安全地使用生成式人工智能工具。

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引用次数: 0

Learning personalization in block-based programming languages using clustering and static code analysis 使用聚类和静态代码分析学习基于块的编程语言中的个性化

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2026-01-14 DOI: 10.1016/j.caeo.2026.100333

Tatiana Person , Juan Antonio Caballero-Hernández , Cristóbal Romero , Iván Ruiz-Rube , Juan Manuel Dodero

Personalized learning in programming education aims at adapting instructional strategies to the diverse needs of students, particularly novice programmers. However, traditional approaches often fail to accommodate individual learning styles or emphasize clean coding practices. This study proposes a data-driven method to personalize programming education by analyzing large-scale datasets from block-based Visual Programming Language (VPL) projects created by novice programmers. Using static code analysis and machine learning, the approach examines the coverage of programming concepts and code quality metrics to identify patterns in student performance, enabling adaptive learning pathways. This approach is implemented in BlocklyMining, a tool that integrates static code analysis, quality assessment through SQALE, and machine-learning-based clustering. The study applies K-Means and Hierarchical Agglomerative Clustering to 215,244 MIT App Inventor projects, evaluating cluster quality using the Silhouette Coefficient (SC) and Davies–Bouldin Index (DBI). Results show that the clustering algorithms effectively group projects, thereby facilitating the generation of personalized learning recommendations tailored to novice programmers’ skill levels.

编程教育中的个性化学习旨在使教学策略适应学生，特别是新手程序员的不同需求。然而，传统的方法往往不能适应个人的学习风格或强调干净的编码实践。本研究提出了一种数据驱动的方法，通过分析由新手程序员创建的基于块的可视化编程语言（VPL）项目的大规模数据集来个性化编程教育。使用静态代码分析和机器学习，该方法检查编程概念和代码质量度量的覆盖范围，以识别学生表现中的模式，从而实现自适应学习途径。这种方法在BlocklyMining中实现，BlocklyMining是一种集成了静态代码分析、通过SQALE进行质量评估和基于机器学习的聚类的工具。该研究将K-Means和分层聚类应用于215,244个MIT App Inventor项目，使用剪影系数（SC）和戴维斯-布尔丁指数（DBI）评估聚类质量。结果表明，聚类算法有效地对项目进行了分组，从而便于根据新手程序员的技能水平生成个性化的学习建议。

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引用次数: 0

An experimental study exploring human–AI complementarity in early social-emotional learning 一项探索人类在早期社交情绪学习中的互补性的实验研究

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2026-01-08 DOI: 10.1016/j.caeo.2026.100331

Doris Kristina Raave , Tyler Colasante , Eric Roldan Roa , Juan Carlos Ramos Martinez , Hongtao Li , Sayan Mukherjee , Tina Malti

Many children are not receiving crucial social-emotional learning (SEL) support due to systemic constraints, such as high educator workload and training burdens. Pedagogical conversational agents (PCAs)—generative AI-powered virtual characters trained to converse with students like a teacher—offer promising solutions that could extend to SEL. However, the current affective-computing capacities of PCAs may be limiting, as SEL facilitation requires deeper emotional and relational attunement than cognition-heavy subjects (e.g., literacy, numeracy). The emotional and relational skills of human educators likely confer benefits beyond those of PCAs in SEL instruction. Despite this potential complementarity, no studies have experimentally assessed the relative performance of PCAs and educators in SEL contexts. It remains unclear if and how PCAs can help educators close the SEL gap. In this study, we used a controlled comparison to reveal complementary strengths, comparing the performance of a PCA to that of human educators when independently facilitating story-based SEL activities with a static AI child. We used a static AI child to help ensure that any performance differences could be attributed to facilitator type. The PCA (n = 18 simulations) and educators (n = 18) each delivered three SEL activities (N = 108 observations). Expert raters, blind to facilitator type, coded dialogue excerpts for evidence-based SEL support techniques and indicators of pedagogical quality. A mixed ANOVA and t-tests revealed significant differences. The PCA showed strengths in basic relational and instructional domains, maintaining respectful tone and routinely providing procedural scaffolding. Educators showed strengths in deeper SEL instruction, guiding reflection and promoting social-emotional knowledge. We discuss implications for SEL through the lens of human–AI complementarity.

许多儿童没有得到重要的社会情感学习（SEL）的支持，由于系统的限制，如高教育者的工作量和培训负担。教学会话代理（PCAs）是一种由人工智能生成的虚拟角色，经过训练可以像老师一样与学生交谈，它提供了有前途的解决方案，可以扩展到SEL。然而，目前pca的情感计算能力可能是有限的，因为SEL促进比认知重的科目（如识字、算术）需要更深的情感和关系协调。在SEL教学中，人类教育者的情感和关系技能可能会带来比pca更大的好处。尽管存在这种潜在的互补性，但还没有研究通过实验评估在SEL背景下PCAs和教育者的相对表现。目前还不清楚是否以及如何帮助教育工作者缩小SEL差距。在本研究中，我们使用对照比较来揭示互补优势，比较PCA与人类教育者在与静态AI儿童独立促进基于故事的SEL活动时的表现。我们使用一个静态的AI子组件来帮助确保任何性能差异都可以归因于促进者类型。PCA （n = 18次模拟）和教育者（n = 18次）分别提供了3次SEL活动（n = 108次观察）。专家评分者，无视促进者类型，基于证据的SEL支持技术和教学质量指标的编码对话摘录。混合方差分析和t检验显示显著差异。PCA在基本的关系和教学领域表现出优势，保持尊重的语气，并定期提供程序脚手架。教育工作者在更深层次的SEL教学、引导反思和促进社会情感知识方面表现出优势。我们通过人类与人工智能的互补性来讨论对SEL的影响。

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引用次数: 0

Design and validation of a questionnaire on teachers' uses of generative artificial intelligence 教师使用生成式人工智能问卷的设计与验证

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2026-01-08 DOI: 10.1016/j.caeo.2026.100332

Héctor Pérez-Montesdeoca , Daniel Rodríguez-Rodríguez , David Stendardi , Aitana Fernández-Sogorb

The use of generative artificial intelligence (GAI) in secondary education is transforming teaching practices and students' learning experiences. This study presents the design and validation of a questionnaire to assess the various educational uses of GAI by secondary school teachers. An initial pool of items was developed based on a comprehensive review of recent literature and validated through expert judgment. The preliminary version of the instrument was administered to a sample of 486 secondary school teachers in Spain. Confirmatory factor analyses supported a six-dimension structure: teacher management, creation of teaching materials, student assessment, student empowerment, attention to diversity, and student motivation. The questionnaire demonstrated high internal consistency and adequate convergent and discriminant validity. The results show that teachers use GAI both to optimize their professional performance and to enrich students' learning experiences. Practical implications of the instrument for teacher training, institutional decision-making, and future research directions are discussed.

在中学教育中使用生成式人工智能（GAI）正在改变教学实践和学生的学习体验。本研究提出了一份问卷的设计和验证，以评估中学教师对GAI的各种教育用途。最初的项目池是基于对最近文献的全面审查和通过专家判断进行验证而开发的。该工具的初步版本对西班牙的486名中学教师进行了抽样调查。验证性因素分析支持六维结构：教师管理、教材创作、学生评估、学生授权、对多样性的关注和学生动机。问卷具有较高的内部一致性，具有足够的收敛效度和判别效度。结果表明，教师使用GAI既可以优化他们的专业表现，也可以丰富学生的学习体验。讨论了该工具对教师培训、制度决策和未来研究方向的实际意义。

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引用次数: 0

Harnessing artificial intelligence for preservice teachers’ development: A scoping review of applications, benefits, and challenges 利用人工智能促进职前教师发展：应用、利益和挑战的范围审查

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2026-01-07 DOI: 10.1016/j.caeo.2026.100330

Liu Ziying , Hu Yongchun , Zhang Qiaoping

The integration of artificial intelligence (AI) into teacher education is profoundly reshaping the professional development of preservice teachers. This scoping review examines the application of AI in this context, aiming to delineate its characteristics, benefits, and challenges. Following the PRISMA-ScR guidelines, this study conducted a systematic search of three academic databases (Web of Science, ScienceDirect, and EBSCOhost) for empirical research published between 2020 and 2025. After screening an initial pool of literature, 55 studies were included for analysis. The findings reveal a diverse landscape of AI applications, technologies, and subject distributions in preservice teacher development. The analysis indicates that AI can significantly enhance various competencies, including instructional design, subject-specific instruction, practical teaching skills, evaluation efficiency, reflective practice, critical thinking, technology integration, and pedagogical innovation. However, several challenges were identified, encompassing technical limitations, the risk of over-reliance, ethical and social concerns, and barriers to implementation. This review advocates for a proactive approach to leveraging AI’s innovative potential while reducing its associated risks. Future research should focus on the continual evaluation and adaptation of AI tools to ensure they effectively support and advance the professional development of preservice teachers.

人工智能（AI）与教师教育的融合正在深刻地重塑职前教师的专业发展。这篇范围审查审查了人工智能在这一背景下的应用，旨在描述其特征、好处和挑战。根据PRISMA-ScR指南，本研究系统检索了三个学术数据库（Web of Science、ScienceDirect和EBSCOhost），检索了2020 - 2025年间发表的实证研究。在筛选了最初的文献池后，纳入了55项研究进行分析。研究结果揭示了职前教师发展中人工智能应用、技术和学科分布的多样化前景。分析表明，人工智能可以显著提高教学设计、学科教学、实践教学技能、评估效率、反思实践、批判性思维、技术整合和教学创新等各项能力。但是，确定了若干挑战，包括技术限制、过度依赖的风险、伦理和社会问题以及执行方面的障碍。本报告主张采取积极主动的方法，利用人工智能的创新潜力，同时降低相关风险。未来的研究应侧重于对人工智能工具的持续评估和适应，以确保它们有效地支持和促进职前教师的专业发展。

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引用次数: 0

Longitudinal insights into AI in education: Usage, ethics, and policy development in higher education 人工智能在教育中的纵向洞察：高等教育中的使用、伦理和政策发展

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2026-01-01 DOI: 10.1016/j.caeo.2025.100329

Luke Parker , A. Jane Loper , Christopher W. Carter , Josh Hayes , Alice Karakas

Since the launch of ChatGPT in November of 2022, its role in education has been a focal point of debate, with discussions centering on whether it will revolutionize learning, prove to be a temporary trend, or mark a significant technological shift. While initial studies have explored artificial intelligence's (AI) impact on specific classes and institutions, there has been a severe lack of longitudinal research within teacher education programs. This study addresses this gap by analyzing four semesters of data on the evolving use and ethical perceptions of generative AI among students in a teacher education program at a university in the Midwestern United States. Surveys conducted with over 300 students over this time reveal the growing use of GenAI in their educational practices. The findings indicate significant growth in the use of AI for assessment preparation and studying, with usage rates rising from 57 % to 83 % and 44 % to 76 %, respectively. The study also examined the ethical considerations surrounding AI use, identifying ongoing uncertainty and a statistically significant difference in ethical perceptions between AI users and non-users. Additionally, the research highlighted how students perceived the integration of AI into teaching and its impact on academic performance. Findings emphasize the need for teacher education programs to incorporate AI training and address ethical concerns comprehensively and formally.

自2022年11月推出ChatGPT以来，它在教育中的作用一直是争论的焦点，讨论的焦点是它是否会彻底改变学习，证明是一个暂时的趋势，还是标志着重大的技术转变。虽然最初的研究探索了人工智能（AI）对特定班级和机构的影响，但在教师教育项目中严重缺乏纵向研究。本研究通过分析美国中西部一所大学教师教育项目中学生对生成式人工智能不断发展的使用和伦理观念的四个学期的数据，解决了这一差距。在此期间对300多名学生进行的调查显示，在他们的教育实践中越来越多地使用GenAI。研究结果表明，人工智能在评估准备和学习中的使用显著增长，使用率分别从57%上升到83%和44%上升到76%。该研究还调查了围绕人工智能使用的道德考虑因素，确定了人工智能用户和非用户之间持续的不确定性和统计上显著的道德观念差异。此外，该研究还强调了学生如何看待将人工智能融入教学及其对学习成绩的影响。研究结果强调，教师教育项目需要纳入人工智能培训，并全面、正式地解决道德问题。

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引用次数: 0

Sustaining innovation: Teacher perspectives on content and language integrated learning (CLIL) and digital tool integration in Albanian online education 持续创新：阿尔巴尼亚在线教育中教师对内容和语言整合学习（CLIL）以及数字工具整合的看法

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-29 DOI: 10.1016/j.caeo.2025.100328

Gerda Sula , Merita Hoxha

This study explores the perceptions and experiences of secondary school teachers in Albania who implemented Content and Language Integrated Learning (CLIL) combined with digital tools, particularly EdPuzzle, in teaching natural sciences subjects during and after the COVID-19 pandemic (online and face to face). The research focuses on understanding the long-term impact of these methodologies on teaching practices and their potential for continued use in an ever-changing educational landscape. A phenomenological approach was employed to gather in-depth insights through semi-structured interviews, capturing thirty language and natural sciences teachers' reflections on the benefits, challenges, and future possibilities of integrating CLIL with video-based learning tools. Using phenomenological approach, the findings reveal that the teachers observed significant improvements in student engagement, comprehension, and autonomy through the use of CLIL and EdPuzzle. However, they also highlighted challenges related to technological access and the need for ongoing professional development. The study concludes that while the innovative teaching methods fostered during the pandemic have the potential for sustainable use, their success hinges on continued support and adaptation to the evolving educational context in Albania. Recommendations include the integration of CLIL into broader curriculum design, the provision of resources and training for teachers, and further research into the scalability of these practices across different educational settings.

本研究探讨了阿尔巴尼亚中学教师在2019冠状病毒病大流行期间和之后（在线和面对面）在自然科学课程教学中实施内容和语言综合学习（CLIL）并结合数字工具（特别是EdPuzzle）的看法和经验。研究的重点是了解这些方法对教学实践的长期影响，以及它们在不断变化的教育环境中继续使用的潜力。采用现象学方法通过半结构化访谈收集深入见解，捕捉30位语言和自然科学教师对将CLIL与基于视频的学习工具相结合的好处、挑战和未来可能性的反思。使用现象学方法，研究结果显示，教师观察到通过使用CLIL和EdPuzzle，学生的参与度、理解力和自主性有了显著的提高。然而，他们也强调了与技术获取和持续专业发展的需要有关的挑战。该研究的结论是，虽然在大流行病期间培养的创新教学方法具有可持续使用的潜力，但其成功与否取决于对阿尔巴尼亚不断变化的教育环境的持续支持和适应。建议包括将CLIL整合到更广泛的课程设计中，为教师提供资源和培训，以及进一步研究这些实践在不同教育环境中的可扩展性。

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引用次数: 0

Motivation to shape the future of education with Artificial Intelligence: An international comparison between Switzerland and China 用人工智能塑造未来教育的动力：瑞士与中国的国际比较

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-16 DOI: 10.1016/j.caeo.2025.100327

Judit Martínez-Moreno , Xinyan Zhou , Dominik Petko , Thomas K.F. Chiu

As digital technologies and Artificial Intelligence (AI) continue to reshape educational environments, understanding the motivations of student teachers is critical to developing effective teacher education programmes. This study cross-culturally validates the (D) FIT-Choice (Digital Factors Influencing Teaching Choice) scale and examines motivational differences among 416 student teachers in Switzerland and China. The results offer partial validation of the (D)FIT-Choice scale, with several higher-order factors demonstrating acceptable to strong internal consistency, but limitations in model fit for some factors, indicating the need for further refinement to ensure cross-cultural applicability. When comparing the motivations of Swiss and Chinese student teachers, Swiss student teachers reported stronger social utility values and intrinsic motivations than their Chinese counterparts, suggesting a focus on personal fulfilment, social contribution and aligning career choice with personal identity, possibly shaped by Switzerland's individualistic culture and civic engagement. In contrast, Chinese student teachers showed higher perceived digital teaching competence and greater enthusiasm for integrating AI into education, differences that may reflect national educational strategies and technological infrastructures, such as China's long-standing investment in educational digitalisation and the presence of AI-focused education initiatives. These results reveal different levels of willingness to shape the future of education with AI, highlighting how cultural and systemic factors influence the motivation of student teachers. In general, this study highlights how sociocultural, economic and technological contexts shape the motivations of future teachers, emphasising the importance of adapting teacher education to local contexts to prepare future teachers to actively participate in the digital transformation of education.

随着数字技术和人工智能（AI）不断重塑教育环境，了解实习教师的动机对于制定有效的教师教育计划至关重要。本研究跨文化验证了(D) FIT-Choice（数字化因素影响教学选择）量表，并考察了416名瑞士和中国见习教师的动机差异。结果对(D) fit - choice量表进行了部分验证，有几个高阶因子表现出较强的内部一致性，但对某些因素的模型拟合存在局限性，表明需要进一步改进以确保跨文化适用性。在比较瑞士和中国学生教师的动机时，瑞士学生教师比中国学生教师表现出更强的社会效用价值观和内在动机，这表明他们更注重个人成就感、社会贡献，并将职业选择与个人身份相结合，这可能是受瑞士个人主义文化和公民参与的影响。相比之下，中国学生教师表现出更高的数字教学能力和更大的将人工智能融入教育的热情，这些差异可能反映了国家教育战略和技术基础设施，例如中国对教育数字化的长期投资和以人工智能为重点的教育计划的存在。这些结果揭示了用人工智能塑造未来教育的不同程度的意愿，突出了文化和系统因素如何影响见习教师的动机。总体而言，本研究强调了社会文化、经济和技术环境如何塑造未来教师的动机，强调了使教师教育适应当地环境的重要性，以使未来教师为积极参与教育的数字化转型做好准备。

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引用次数: 0

Learning metabolism with virtual reality to optimize biochemistry education in the Spanish-speaking region 利用虚拟现实学习代谢优化西语区生物化学教学

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-13 DOI: 10.1016/j.caeo.2025.100326

Rosa C. Lopez-Sanchez , Ana G. Rodriguez-Mendoza , Juan P. Nigenda-Alvarez , Lizette S. Hernandez-Cardenas , Luis M. Villela-Martinez , Jose A. Hernandez-Hernandez

Learning science is undoubtedly a challenge, considering that new generations have plenty external stimuli to overcome. Students are surrounded by digital experiences such as video games, animation and videos, etc., therefore, their way of learning has evolved to be more attached to visual and dynamic resources rather than to plain texts. Hence, the objective of this study was to design, implement, and evaluate a virtual reality environment as a microteaching mechanism for human metabolism, including the electron transport chain and oxidative phosphorylation. This was a pilot study used to evaluate the learning experience of a platform designed at our institution. The study included the design of Virtual Reality experience, the organization of activities and instructional design, and the evaluation of the knowledge acquired, as well as the students' perception of its usefulness. Nine groups of students from our institution's health entry programs, from different academic periods, were taught the electron transport chain and oxidative phosphorylation using Virtual Reality by different instructors. Their learning through Virtual Reality was compared to what they would have learned through 2D activities. The results showed that the level of knowledge increased significantly associated with the use of Virtual Reality compared to their traditional system (p<0.00001). A high percentage of students (>90%) found it beneficial to use Virtual Reality. The implementation of a Virtual Reality strategy for learning complex topics in metabolism provides a learning facilitator that students enjoy, promoting engagement, as it allows them to be transported to virtual scenarios in the actual "digital world."

学习科学无疑是一个挑战，因为新一代有很多外部刺激需要克服。学生们被电子游戏、动画、视频等数字体验所包围，因此，他们的学习方式已经演变成更多地依赖于视觉和动态资源，而不是单纯的文本。因此，本研究的目的是设计、实现和评估虚拟现实环境作为人体代谢的微教学机制，包括电子传递链和氧化磷酸化。这是一项试点研究，用于评估我们机构设计的平台的学习经验。研究内容包括虚拟现实体验的设计、活动的组织和教学设计、所学知识的评估以及学生对其有用性的感知。来自我校健康入门项目的九组学生，来自不同的学术时期，由不同的讲师使用虚拟现实技术教授电子传递链和氧化磷酸化。他们通过虚拟现实学习的内容与通过2D活动学习的内容进行了比较。结果表明，与传统系统相比，使用虚拟现实的知识水平显著提高（p<0.00001）。很高比例的学生（>90%）认为使用虚拟现实是有益的。虚拟现实策略的实施为学习新陈代谢中的复杂主题提供了一个学生喜欢的学习促进者，促进了参与，因为它允许他们被传送到实际“数字世界”中的虚拟场景中。

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引用次数: 0

Ethical conditions for university students’ adoption of large language models in exam preparation contexts 大学生在备考环境中采用大型语言模型的伦理条件

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-13 DOI: 10.1016/j.caeo.2025.100323

Antonio Pérez-Portabella , Mario Arias-Oliva , Graciela Padilla-Castillo , Jorge de Andrés-Sánchez

Large Language Models (LLMs), have become a disruptive force in higher education, assisting students in tasks such as summarizing content, clarifying concepts, and preparing for exams. Their rapid adoption has generated both enthusiasm and ethical concern, particularly regarding fairness and responsible use in assessment contexts. This study examines whether university students’ ethical perceptions regarding the use of LLMs for exam preparation influence their intention to use them (IU), and whether this intention, in turn, affects their actual use (USE). Data were collected from 151 undergraduate students enrolled in Spanish universities. Ethical perceptions were measured using the Multidimensional Ethics Scale (MES), which captures three normative perspectives: moral equity (ME), consequentialism (CO), and deontology (DE). The first research objective (RO1) was to determine whether a minimum threshold in each ethical dimension is required for students to form the intention to use LLMs. The second (RO2) assessed whether higher ethical evaluations lead to greater acceptance and use. Necessary Condition Analysis revealed that all three ethical dimensions are necessary conditions for IU (ME: d = 0.338; CO: d = 0.274; DE: d = 0.207; all p < 0.001). Partial Least Squares-Structural Equation Modeling showed that only CO (β = 0.350, p < 0.001) and DE (β = 0.329, p < 0.001) exert statistically significant and sufficient effects on IU, while ME does not (β = 0.108, p = 0.414). Finally, IU is both a necessary (d = 0.325, p < 0.001) and sufficient predictor of USE (β = 0.905, p < 0.001). The findings highlight that ethical reasoning is central to the responsible adoption of LLMs in exam preparation, offering practical guidance for educators and institutions promoting academic integrity.

大型语言模型（llm）已经成为高等教育中的一股颠覆性力量，它帮助学生完成总结内容、澄清概念和准备考试等任务。它们的迅速采用既引起了人们的热情，也引起了伦理上的关注，特别是在评估环境中公平和负责任的使用方面。本研究考察了大学生关于使用法学硕士备考的道德观念是否会影响他们使用法学硕士的意图（IU），以及这种意图是否反过来影响他们的实际使用（use）。数据来自西班牙大学的151名本科生。使用多维伦理量表（MES）测量伦理观念，该量表包含三个规范视角：道德公平（ME）、结果主义（CO）和义务论（DE）。第一个研究目标（RO1）是确定学生是否需要每个伦理维度的最低阈值才能形成使用法学硕士的意图。第二个（RO2）评估更高的伦理评价是否会导致更大的接受和使用。必要条件分析显示，所有三个伦理维度都是IU的必要条件（ME: d = 0.338; CO: d = 0.274; DE: d = 0.207；所有p <； 0.001）。偏最小二乘-结构方程模型显示，只有CO （β = 0.350, p < 0.001）和DE （β = 0.329, p < 0.001）对IU有统计学意义上的充分影响，而ME没有（β = 0.108, p = 0.414）。最后，IU既是USE的必要预测因子（d = 0.325, p < 0.001），也是充分预测因子（β = 0.905, p < 0.001）。研究结果强调，道德推理对于法学硕士在考试准备中负责任的采用至关重要，为教育工作者和机构促进学术诚信提供了实践指导。

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引用次数: 0