Internet and Higher Education最新文献_第2页

Can theory-driven learning analytics dashboard enhance human-AI collaboration in writing learning? Insights from an empirical experiment 理论驱动的学习分析仪表板能否增强人类与人工智能在写作学习中的协作？来自经验实验的见解

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-09-20 DOI: 10.1016/j.iheduc.2025.101054

Angxuan Chen , Jingjing Lian , Xinran Kuang , Jiyou Jia

The integration of Generative AI (GenAI) into education has raised concerns about over-reliance and superficial learning, particularly in writing tasks in higher education. This study explores whether a theory-driven learning analytics dashboard (LAD) can enhance human-AI collaboration in the academic writing task by improving writing knowledge gains, fostering self-regulated learning (SRL) skills and shaping different human-AI dialogue characteristics. Grounded in Zimmerman's SRL framework, the LAD provided real-time feedback on learners' goal-setting, writing processes and reflection, while monitoring the quality of learner-AI interactions. A quasi-experiment was conducted involving 52 postgraduate students in a human-AI collaborative writing task. The students were divided into an experimental group (EG) that used the LAD and a control group (CG) that did not. Pre- and post- knowledge tests, questionnaires measuring SRL and cognitive load, and students' dialogue data with GenAI were collected and analyzed. Results showed that the EG achieved significantly higher writing knowledge gains and improved SRL skills, particularly in self-efficacy and cognitive strategies. However, the EG also reported increased test anxiety and cognitive load, possibly due to heightened metacognitive awareness. Epistemic Network Analysis revealed that the EG engaged in more reflective, evaluative interactions with GenAI, while the CG focused on more transactional and information-seeking exchanges. These findings contribute to the growing body of literature on the educational use of GenAI and highlight the importance of designing interventions that complement GenAI tools, ensuring that technology enhances rather than undermines the learning process.

将生成式人工智能（GenAI）整合到教育中，引起了人们对过度依赖和肤浅学习的担忧，特别是在高等教育的写作任务中。本研究探讨了理论驱动的学习分析仪表板（LAD）是否可以通过提高写作知识获取、培养自我调节学习（SRL）技能和塑造不同的人类与人工智能对话特征，来增强学术写作任务中的人类与人工智能协作。基于Zimmerman的SRL框架，LAD对学习者的目标设定、写作过程和反思提供实时反馈，同时监控学习者与人工智能互动的质量。我们对52名研究生进行了一项准实验，让他们参与人类与人工智能的协作写作任务。学生被分为实验组（EG）使用LAD和对照组（CG）不使用。收集和分析了知识前后测试、测量SRL和认知负荷的问卷以及学生与GenAI的对话数据。结果表明，EG显著提高了学生的写作知识增益，提高了学生的SRL技能，尤其是在自我效能感和认知策略方面。然而，EG也报告了考试焦虑和认知负荷的增加，可能是由于元认知意识的增强。认知网络分析显示，EG与GenAI进行更多的反思性、评价性互动，而CG则侧重于更多的交易性和信息寻求性交流。这些发现促进了越来越多关于GenAI在教育中的应用的文献，并强调了设计干预措施以补充GenAI工具的重要性，确保技术增强而不是破坏学习过程。

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引用次数: 0

Students' preferences for online learning formats – Results of a latent class analysis 学生对在线学习形式的偏好——潜在类别分析的结果

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-09-17 DOI: 10.1016/j.iheduc.2025.101053

Sabine Fischer, Karina Fisch, Caroline Hobelsberger, Simone Jung

Considering students' preferences for learning formats is an important issue for institutions in higher education regarding high competition due to demographic changes. Using data from a student survey at Deggendorf Institute of Technology (DIT) in Germany we conduct a latent class analysis to identify heterogenous preferences for learning formats. Our results show that the student population can best be described in four different segments that we classify as traditional learners, blended learners, synchronous online learners and hybrid learners. We offer a conceptual proposition that hypothesizes how the four classes might emerge naturally out of differing needs for personal interaction, temporal and spatial flexibility. We derive potential directions for lecturers and administrators who aim at catering to students' preferences.

考虑学生对学习形式的偏好是高等教育机构面临的一个重要问题，因为人口结构的变化带来了激烈的竞争。使用来自德国德根多夫理工学院（DIT）学生调查的数据，我们进行了潜在类别分析，以确定对学习格式的异质偏好。我们的研究结果表明，学生群体可以分为四个不同的部分，我们将其分类为传统学习者、混合式学习者、同步在线学习者和混合式学习者。我们提出了一个概念性命题，假设这四个类别如何从个人互动、时间和空间灵活性的不同需求中自然出现。我们为旨在迎合学生喜好的讲师和管理人员提供了潜在的方向。

引用次数: 0

Exploring undergraduate students' perceptions of AI vs. human scoring and feedback 探索本科生对人工智能与人类评分和反馈的看法

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-09-04 DOI: 10.1016/j.iheduc.2025.101052

Mackenzie L. Thomas, Seyma N. Yildirim-Erbasli, Shruthi Hariharan

The use of artificial intelligence (AI) in educational assessment offers scalable solutions to traditional grading challenges, yet concerns about reliability, fairness, and acceptance remain, particularly in subjective domains like writing. This study examines undergraduate students' perceptions of AI-generated scoring and feedback compared to human evaluators. Participants reviewed scores and feedback provided by either AI or a human and completed a survey measuring their perceptions before and after disclosure of the source. Analyses revealed that students often struggled to accurately identify the evaluator. Additionally, while perceptions of AI scoring and feedback were generally moderate, exposure to AI significantly reduced students' confidence in AI scoring. The source of the grading and identification accuracy significantly influenced students' perceptions. Human grading was associated with more positive perceptions, while incorrect identification—when not combined with human grading—also led to more positive perceptions. However, the interaction of human grading and incorrect identification resulted in more negative perceptions. Factors such as comfort with technology, familiarity with AI, and frequency of AI use were significant predictors of students' attitudes toward AI. These findings enhance our understanding of student attitudes toward AI in educational assessment and emphasize the importance of thoughtful implementation to support acceptance in educational contexts.

在教育评估中使用人工智能（AI）为传统的评分挑战提供了可扩展的解决方案，但对可靠性、公平性和接受度的担忧仍然存在，特别是在写作等主观领域。本研究考察了与人类评估者相比，本科生对人工智能生成的评分和反馈的看法。参与者回顾了人工智能或人类提供的分数和反馈，并完成了一项调查，测量了他们在消息来源披露前后的看法。分析显示，学生们经常难以准确地识别评估者。此外，虽然对人工智能评分和反馈的看法总体上是中等的，但接触人工智能大大降低了学生对人工智能评分的信心。评分来源和识别准确性显著影响学生的认知。人为评分与更积极的看法有关，而错误的识别-当不与人为评分结合时-也会导致更积极的看法。然而，人为评分和错误识别的相互作用导致了更多的负面看法。对技术的舒适度、对人工智能的熟悉程度以及使用人工智能的频率等因素是学生对人工智能态度的重要预测因素。这些发现增强了我们对学生在教育评估中对人工智能的态度的理解，并强调了在教育环境中支持接受的深思熟虑的实施的重要性。

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引用次数: 0

The impact of learning supports in digital game-based learning on learners with different levels of prior knowledge 基于数字游戏的学习支持对不同先验知识水平学习者的影响

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-08-19 DOI: 10.1016/j.iheduc.2025.101044

Zhihui Cai , Caiyan Liu , Yajiao Yang , Bo Li

With rapid advancements in digital technology and the booming gaming industry, digital game-based learning (DGBL) has become a crucial research area in education. Effective learning support in educational games could yield positive outcomes, while poorly designed learning support may hinder learner's flow and autonomy. Prior research has often focused on evaluating its effectiveness but overlooked its optimization based on design characteristics. This study conducted two experiments to explore how learning support design features impact learning outcomes by considering learners' characteristics. Study 1 examined the effects of learning support timing (pre-support vs. post-support) on learners with varying prior knowledge. Results revealed interactive effects between learning support timing and learners' prior knowledge on learning achievement, intrinsic cognitive load, and intrinsic motivation. Behaviorally, pre-support benefited novices while post-support benefited experts by improving learning performance and reducing frustration. Study 2 investigated the effects of the mode of support delivery (learner-controlled vs. system-controlled) on learners with different levels of prior knowledge. Findings indicated an interactive effect between the mode of support delivery and learners' prior knowledge on learning achievement. Behaviorally, system-controlled support helped novices by guiding their exploration, while experts benefited from learner-controlled support due to their competence in regulating their learning. Future research is encouraged to explore cognitive neural mechanisms to provide deeper insights into it.

随着数字技术的飞速发展和游戏产业的蓬勃发展，数字游戏学习（digital game-based learning， DGBL）已成为教育领域的一个重要研究领域。教育游戏中有效的学习支持能够产生积极的结果，而设计不良的学习支持则会阻碍学习者的心流和自主性。以往的研究往往侧重于评估其有效性，而忽视了基于设计特征的优化。本研究通过两个实验来探讨学习支持设计特征在考虑学习者特征的情况下对学习效果的影响。研究1考察了学习支持时间（支持前与支持后）对不同先验知识学习者的影响。结果表明，学习支持时间和学习者的先验知识对学习成就、内在认知负荷和内在动机有交互作用。行为上，支持前有利于新手，而支持后有利于专家，通过提高学习成绩和减少挫折感。研究2考察了学习者控制与系统控制两种支持传递方式对不同先验知识水平学习者的影响。研究结果表明，支持提供方式与学习者的先验知识对学习成绩有交互作用。在行为上，系统控制的支持通过引导新手的探索来帮助他们，而专家则受益于学习者控制的支持，因为他们具有调节学习的能力。鼓励未来的研究探索认知神经机制，以提供更深入的见解。

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引用次数: 0

Learning from an asynchronous video lecture: Note-taking helps, smartphone sounds harm 从异步视频讲座中学习：记笔记有用，智能手机听起来有害

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-08-12 DOI: 10.1016/j.iheduc.2025.101043

Markus H. Hefter

Research evidence reveals that note-taking is an effective strategy to learn from video lectures, but also that smartphone noises harm learning. Both these important findings originate from on-site settings, such as in the classroom or lab, however. We therefore decided to address still-open questions about note-taking’s potential beneficial effects and smartphone noises’ potential detrimental effects on learning in an authentic asynchronous online scenario. The present online field experiment involved 196 undergraduate psychology students (144 female; M_age = 23.44 years) who learned from a prerecorded 12-min video lecture on the topic “Learning strategies.” Following a 2 × 2-factorial design, participants did or did not receive a note-taking request before the video lecture (Factor A: note-taking request) and did or did not experience smartphone noises during the video lecture (Factor B: smartphone noises). Participants were asked whether they had actually taken notes. Note-takers (n = 67) reported stronger learning engagement and scored higher on the posttest than the non-note-takers (n = 129). Learning engagement mediated the note-taking effect on learning outcomes. The smartphone noises reduced the learners’ compliance to fulfill the request to take notes. Furthermore, the smartphone noises were detrimental to the learning outcomes of non-note-takers. By contrast, note-takers with and without smartphone noises performed equally well in the posttest. Overall, these findings provide ecologically valid empirical support of two pieces of practical advice for students, namely: how beneficial it is to take notes and how detrimental it is to become distracted by smartphone noises when learning from an asynchronous video lecture.

研究证据表明，记笔记是学习视频课程的有效策略，但智能手机的噪音也会损害学习。然而，这两个重要的发现都来自于现场环境，比如教室或实验室。因此，我们决定在真实的异步在线场景中解决关于记笔记的潜在有益影响和智能手机噪音对学习的潜在有害影响的悬而未决的问题。目前的在线现场实验涉及196名心理学本科生（144名女性；法师= 23.44岁），他们从预先录制的12分钟视频讲座中学习，主题是“学习策略”。根据2 × 2因子设计，参与者在视频讲座之前是否收到笔记要求（因素a：笔记要求），以及在视频讲座期间是否经历智能手机噪音（因素B：智能手机噪音）。参与者被问及他们是否真的做了笔记。记录者（n = 67）报告了更强的学习投入，并且在后测中得分高于不记录者（n = 129）。学习投入在笔记对学习成果的影响中起中介作用。智能手机的噪音降低了学习者完成笔记要求的依从性。此外，智能手机的噪音对不记笔记的人的学习效果有害。相比之下，在有和没有智能手机噪音的情况下，记笔记的人在后测中的表现一样好。总的来说，这些发现为学生提供了两项实用建议的生态学上有效的经验支持，即：在学习异步视频课程时，记笔记是多么有益，而被智能手机的噪音分散注意力是多么有害。

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引用次数: 0

Gamified self-regulated learning improves EFL Reading comprehension, motivation, self-regulation skills and process patterns: Quasi-experiment with process mining 游戏化自我调节学习提高英语阅读理解、动机、自我调节技能和过程模式：过程挖掘的准实验

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-07-31 DOI: 10.1016/j.iheduc.2025.101042

Gulipari Maimaiti , Khe Foon Hew

This study investigates the effects of a flipped gamified self-regulated learning (SRL) approach in an English as a Foreign Language (EFL) classroom for first-year undergraduates. Using a quasi-experiment with process mining approach, 177 students were divided into two groups: a gamified experimental group (N = 91) and a non-gamified control group (N = 86). The findings revealed that the gamified SRL approach significantly improved English reading comprehension and motivation, as evidenced by enhanced intrinsic value and consistently higher voluntary task completion. Moreover, SRL skills were enhanced, as shown by survey results and high engagement with SRL strategies observed in the trace data. Process mining analyses using First-Order Markov Models further demonstrated that gamification fostered more dynamic SRL behaviors, including greater engagement with scaffolds and a stronger focus on evaluative learning strategies. These results highlight the potential of the gamified SRL approach to enhance students' SRL skills, motivation, and improve learning outcomes. This study offers actionable insights for designing effective interventions that integrate SRL scaffolds with motivational elements to support students' success in flipped learning environments.

本研究探讨了翻转游戏化自我调节学习（SRL）方法在本科一年级英语课堂教学中的效果。采用过程挖掘的准实验方法，将177名学生分为两组：游戏化实验组（N = 91）和非游戏化对照组（N = 86）。研究结果显示，游戏化阅读教学法显著提高了学生的英语阅读理解能力和阅读动机，其表现为内在价值的增强和自愿任务完成率的持续提高。此外，从调查结果和跟踪数据中观察到的对SRL策略的高度参与可以看出，SRL技能得到了提高。使用一阶马尔可夫模型的过程挖掘分析进一步表明，游戏化促进了更动态的SRL行为，包括更多地参与脚手架和更关注评估学习策略。这些结果突出了游戏化SRL方法在提高学生SRL技能、动机和改善学习成果方面的潜力。本研究为设计有效的干预措施提供了可行的见解，这些干预措施将SRL框架与动机元素结合起来，以支持学生在翻转学习环境中取得成功。

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引用次数: 0

The generation mechanism of teachers' anxiety toward digital human instructors in higher education: A mixed-methods perspective 高等教育中教师对数字化人类讲师焦虑的产生机制：混合方法视角

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-07-31 DOI: 10.1016/j.iheduc.2025.101041

Hao Dong , Yanxun Zhu , Jianglong Shen , Shengze Xue

Digital Human Instructors (DHIs) are revolutionizing higher education by providing personalized and interactive learning experiences. However, the adoption of DHIs also brings about complex challenges related to teacher anxiety, particularly concerning professional security, ethics, and the broader implications of technology in education. Addressing the barriers to DHI adoption is crucial for safeguarding both educational quality and teacher well-being. This study aims to investigate these barriers by developing a theoretical model that integrates elements from Social Ecological Systems Theory, considering both micro-level individual factors (such as neuroticism and personal innovativeness) and meso-level social factors (such as negative word-of-mouth). Data were collected from 500 university teachers and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). The PLS-SEM results revealed that neuroticism and negative word-of-mouth (WOM) have a significant positive impact on technology anxiety, while personal innovativeness has no significant direct effect. Moreover, perceived invasiveness plays a key mediating role in the relationships between neuroticism and technology anxiety, as well as between negative WOM and technology anxiety, whereas perceived authenticity does not exhibit a significant mediating effect. The fsQCA findings further revealed that technology anxiety does not stem from a single causal pathway. Instead, four configurations that include the presence and absence of certain conditions can lead to this desirable outcome. These findings not only contribute to the academic discourse on DHI adoption but also provide practical insights for educational managers seeking to mitigate teacher anxiety and enhance user protection in this dynamic educational environment.

数字人类讲师（DHIs）通过提供个性化和互动的学习体验，正在彻底改变高等教育。然而，DHIs的采用也带来了与教师焦虑相关的复杂挑战，特别是在职业安全、道德和技术在教育中的更广泛影响方面。消除采用个人健康指数的障碍对于保障教育质量和教师福祉至关重要。本研究旨在整合社会生态系统理论的要素，结合微观层面的个体因素（如神经质和个人创新）和中观层面的社会因素（如负面的口碑），建立一个理论模型来研究这些障碍。采用偏最小二乘结构方程模型（PLS-SEM）和模糊集定性比较分析法（fsQCA）对500名高校教师的数据进行分析。PLS-SEM结果显示，神经质和负面口碑对技术焦虑有显著的正向影响，而个人创新对技术焦虑没有显著的直接影响。此外，感知侵入性在神经质与技术焦虑、负向WOM与技术焦虑之间的关系中起关键中介作用，而感知真实性在技术焦虑与负向WOM之间的中介作用不显著。fsQCA的研究结果进一步表明，技术焦虑并非源于单一的因果途径。相反，包括存在和不存在某些条件的四种配置可以导致这种理想的结果。这些发现不仅有助于DHI采用的学术论述，而且为教育管理者在这个动态的教育环境中寻求减轻教师焦虑和加强用户保护提供了实践见解。

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引用次数: 0

What they provide and how: An intervention study on pre-service teachers' GenAI-assisted writing feedback 提供什么及如何提供：职前教师genai辅助写作反馈的干预研究

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-07-23 DOI: 10.1016/j.iheduc.2025.101040

Siyu Zhu , Jialin Li , Yuan Yao , Yi Guan , Xinhua Zhu

Providing effective writing feedback to students could promote students' writing development. However, offering high-quality feedback remains a significant challenge for pre-service teachers (PSTs). Recent advancements in GenAI technology may offer solutions to this issue. The study examines the influence of a short-term teaching intervention on the feedback levels and feedback types of PSTs' ChatGPT-assisted feedback on students' written compositions, utilizing an explanatory sequential mixed-method design with 30 PSTs. The quantitative results revealed significant improvements in both feedback levels (i.e., higher-level feedback issues including ideas and elaboration and style) and feedback types (e.g., explanations and general suggestions). Additionally, the findings highlighted specific strategies employed by PSTs when considering levels and types in combination. Subsequent interviews identified the underlying influential factors of these improvements, namely the improvements in ChatGPT usage skills (i.e., prompt engineering and source use) and a deeper understanding of the feedback process (i.e., introspection). By demonstrating how short-term teaching interventions can leverage PST's ability to use GenAI tools to provide writing feedback, this research advances the theoretical understanding of human-AI collaboration in the context of writing and provides pedagogical insights for teacher training programs.

为学生提供有效的写作反馈可以促进学生的写作发展。然而，提供高质量的反馈对职前教师（pst）来说仍然是一个重大挑战。基因人工智能技术的最新进展可能为这一问题提供解决方案。本研究采用解释性顺序混合方法设计，考察了短期教学干预对教师在chatgpt辅助下对学生作文反馈的水平和反馈类型的影响。定量结果显示，在反馈水平（即，更高层次的反馈问题，包括想法、阐述和风格）和反馈类型（例如，解释和一般建议）方面都有重大改进。此外，研究结果强调了PSTs在综合考虑水平和类型时采用的具体策略。随后的访谈确定了这些改进的潜在影响因素，即ChatGPT使用技能的改进（即，即时工程和源使用）和对反馈过程的更深入理解（即，内省）。通过展示短期教学干预如何利用PST使用GenAI工具提供写作反馈的能力，本研究推进了对写作背景下人类与人工智能协作的理论理解，并为教师培训计划提供了教学见解。

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引用次数: 0

The affordances of Artificial Intelligence (AI) and ethical considerations across the instruction cycle: A systematic review of AI in online higher education 人工智能（AI）的功能和整个教学周期的伦理考虑：在线高等教育中人工智能的系统回顾

IF 6.4 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-07-19 DOI: 10.1016/j.iheduc.2025.101039

Min Zhuang , Siyu Long, Florence Martin, Daniela Castellanos-Reyes

As Artificial Intelligence (AI) advances, discussions regarding its potential in education have attracted significant attention. This systematic review synthesizes AI affordances in online higher education, particularly identifying various ways AI is used throughout the online instruction cycle. We analyzed fifty-five studies focusing on publication trends, research methodology and quality, AI affordances during the design, facilitation, assessment and evaluation stages, and ethical considerations in this context. The findings revealed the applications of AI-empowered systems and Machine Learning (ML) models in various tasks such as establishing learning objectives in design, supporting cognition in facilitation, automatic grading in assessment, and measuring instruction quality in evaluation. We also discussed the trends regarding the limited incorporation of theoretical frameworks, a dominance of quantitative methods, a focus of big data, and a tendency towards personalization and adaptation. The ethical considerations were categorized for each research phase - data collection, analysis, and interpretation and usage, with an emphasis on validation approaches for AI generated outputs. These findings have implications on effectively integrating AI technologies by highlighting possibilities and competencies required for practitioners and inferring potential opportunities for researchers.

随着人工智能（AI）的发展，有关其在教育领域潜力的讨论引起了人们的极大关注。这篇系统综述综合了人工智能在在线高等教育中的应用，特别是确定了人工智能在整个在线教学周期中使用的各种方式。我们分析了55项研究，重点关注出版趋势、研究方法和质量、人工智能在设计、促进、评估和评估阶段的启示，以及在此背景下的伦理考虑。研究结果揭示了人工智能系统和机器学习（ML）模型在各种任务中的应用，例如在设计中建立学习目标，在促进中支持认知，在评估中自动评分，以及在评估中衡量教学质量。我们还讨论了理论框架的有限结合、定量方法的主导地位、大数据的关注以及个性化和适应的趋势。对每个研究阶段的伦理考虑进行了分类-数据收集，分析，解释和使用，重点是人工智能生成输出的验证方法。这些发现通过突出从业者所需的可能性和能力以及推断研究人员的潜在机会，对有效整合人工智能技术具有重要意义。

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引用次数: 0

Empowering international PhD students: Generative AI, Ubuntu, and the decolonisation of academic communication 授权国际博士生：生成人工智能，Ubuntu和学术交流的非殖民化

IF 6.8 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Internet and Higher Education

Pub Date : 2025-07-18 DOI: 10.1016/j.iheduc.2025.101038

Lynette Pretorius , Huy-Hoang Huynh , Anak Agung Ayu Redi Pudyanti , Ziqi Li , Abdul Qawi Noori , Zhiheng Zhou

Generative AI (GenAI) provides distinct affordances that contribute meaningfully to learning. However, there are also several challenges, most notably ethical considerations. While much of the current research on GenAI in academia focuses on technical capabilities or ethical concerns, few studies have examined how GenAI can be leveraged to promote equity and inclusivity, particularly in academic communication. In this study, we adopt the theoretical lens of Ubuntu, a Southern African philosophy that emphasises interconnectedness and community, to reconceptualise GenAI as a democratising force. Five international PhD students in Education and their PhD supervisor (all multilingual users of English) explored their experiences of using GenAI. We used a collaborative autoethnographic approach, incorporating reflective prompting, individual writing, group sharing and refining, and group-based writing. We demonstrate that GenAI reshaped power dynamics and challenged academic hierarchies by providing real-time language support and improving academic writing clarity. This empowered us to participate in academic discourse, disrupting traditional gatekeeping mechanisms. By automating routine tasks, GenAI also shifted our academic focus from technical skills to intellectual contributions, fostering inclusivity and equity. This study highlights the potential of GenAI in terms of knowledge creation and academic discourse, particularly for doctoral scholars navigating linguistic and cultural barriers. Academia is predominantly Anglophone-centric, marginalising non-English-speaking scholars by imposing additional barriers to participation and recognition, perpetuating inequities in knowledge creation and dissemination. We argue that embracing GenAI can help to decolonise these academic practices, thereby promoting epistemic justice and creating a more equitable scholarly environment for all.

生成式人工智能（GenAI）提供了对学习有意义的独特启示。然而，也有一些挑战，最明显的是道德方面的考虑。虽然目前学术界对基因人工智能的研究大多集中在技术能力或伦理问题上，但很少有研究探讨如何利用基因人工智能促进公平和包容性，特别是在学术交流中。在这项研究中，我们采用了乌班图的理论视角，这是一种强调相互联系和社区的南部非洲哲学，将GenAI重新定义为一种民主化力量。5名来自教育部的国际博士生和他们的博士生导师（都是多语种的英语使用者）探讨了他们使用GenAI的经验。我们采用了一种协作式的自我民族志方法，结合了反思提示、个人写作、群体分享和提炼以及基于群体的写作。我们证明，GenAI通过提供实时语言支持和提高学术写作清晰度，重塑了权力动态，挑战了学术等级。这使我们能够参与学术讨论，打破传统的把关机制。通过自动化日常任务，GenAI还将我们的学术重点从技术技能转移到智力贡献上，促进了包容性和公平性。这项研究强调了GenAI在知识创造和学术话语方面的潜力，特别是对于克服语言和文化障碍的博士学者。学术界主要以英语为中心，通过对参与和认可施加额外的障碍，使非英语学者边缘化，使知识创造和传播方面的不平等永续存在。我们认为，接受GenAI可以帮助这些学术实践去殖民化，从而促进认知正义，并为所有人创造一个更公平的学术环境。

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引用次数: 0