Computers and Education Open最新文献_第3页

Special Issue: Datafied by default: Examining the intersect between children's digital rights and education 特刊：默认数据化：审视儿童数字权利与教育之间的交集

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2024.100237

Tiffani Apps , Karley Beckman , Rebecca Ng

引用次数: 0

Does ChatGPT-enhanced collaborative learning foster critical thinking in education? A Bloom’s Taxonomy perspective chatgpt增强的协作学习在教育中培养批判性思维吗？Bloom的分类法透视图

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100316

Abdhy Aulia Adnans , Yithro Serang , Ixora Javanisa Eunike , Andri Dayarana K. Silalahi

This study examines how ChatGPT-supported collaborative learning influences critical thinking in health education using Bloom’s Taxonomy. Purposive sampling was used to collect data from 665 Indonesian health students through an online survey. Partial Least Square – Structural Equation Modelling (PLS-SEM) assessed the direct effects of cognitive processes on critical thinking. Necessary Condition Analysis (NCA) identified essential cognitive conditions, while fuzzy sets qualitative comparative analysis (fsQCA) explored different cognitive pathways leading to high or low critical thinking. Collaborative learning significantly enhances understanding, applying, and remembering. Understanding has the strongest effect on critical thinking, while applying and remembering have moderate effects. These findings suggest that deep comprehension drives analytical reasoning, whereas applying and remembering serve complementary roles. NCA confirms that understanding and applying are necessary for fostering critical thinking, while remembering plays a supporting role. fsQCA results indicate that students who combine deep understanding with memory retention exhibit strong critical thinking. In contrast, students who rely solely on remembering without comprehension or application struggle to develop higher-order reasoning. This study reveals that ChatGPT does not inherently enhance critical thinking but must be integrated into structured collaborative learning. Effective AI-assisted education requires active discussion, application, and critical evaluation of AI-generated insights. These findings offer a framework for optimizing AI-driven health education to support both knowledge acquisition and analytical reasoning in clinical decision-making.

本研究使用Bloom分类法考察了chatgpt支持的协作学习如何影响健康教育中的批判性思维。采用有目的抽样方法，对665名印尼卫生专业学生进行在线调查。偏最小二乘-结构方程模型（PLS-SEM）评估了认知过程对批判性思维的直接影响。必要条件分析（NCA）确定了必要的认知条件，而模糊集定性比较分析（fsQCA）探索了导致高批判性思维和低批判性思维的不同认知途径。协作学习可以显著提高理解、应用和记忆能力。理解对批判性思维的影响最大，而运用和记忆的影响一般。这些发现表明，深度理解驱动分析推理，而应用和记忆则起到互补的作用。NCA证实理解和应用对于培养批判性思维是必要的，而记忆起着辅助作用。fsQCA结果表明，将深刻理解与记忆保持相结合的学生表现出较强的批判性思维。相比之下，仅仅依靠记忆而不理解或应用的学生很难发展高阶推理。这项研究表明，ChatGPT本身并不能增强批判性思维，但必须将其整合到结构化的协作学习中。有效的人工智能辅助教育需要对人工智能产生的见解进行积极的讨论、应用和批判性评估。这些发现为优化人工智能驱动的健康教育提供了一个框架，以支持临床决策中的知识获取和分析推理。

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引用次数: 0

Generative AI chatbots in higher education: Student experiences and perceived ethical challenges 高等教育中的生成式AI聊天机器人：学生体验和感知到的伦理挑战

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100311

Neda Hadinejad , Katarina Sperling , Cormac McGrath

The integration of generative artificial intelligence (GAI) chatbots in higher education has introduced both opportunities and challenges for students’ academic practices. To date, most qualitative research has examined the implications of this phenomenon for teaching practices and the experiences of teachers. This explorative study investigates how students use GAI chatbots in their studies and how they evaluate AI generated text, particularly in relation to academic integrity, reliability, and ethical norms. Using a qualitative research approach, we conducted semi-structured interviews with higher education students to a) examine students’ experiences of using GAI chatbots in their academic studies, and b) to explore how students perceive the ethical impact of using chatbots on their studies. The findings reveal that while students use GAI chatbots to support writing, idea generation, and language improvement, they also face challenges related to plagiarism concerns, the reliability of AI-generated content, and the lack of clear institutional guidelines on responsible AI use. Additionally, the study highlights how students develop individual strategies to navigate these challenges, including seeking validation from educators and critically assessing AI-generated outputs. The findings emphasize the need for clearer academic policies and ethical frameworks to support students in making informed and responsible decisions about the use of GAI chatbots in higher education.

生成式人工智能（GAI）聊天机器人在高等教育中的整合为学生的学术实践带来了机遇和挑战。迄今为止，大多数定性研究都考察了这一现象对教学实践和教师经验的影响。这项探索性研究调查了学生如何在学习中使用人工智能聊天机器人，以及他们如何评估人工智能生成的文本，特别是在学术诚信、可靠性和道德规范方面。采用定性研究方法，我们对高等教育学生进行了半结构化访谈，以a)检查学生在学术研究中使用GAI聊天机器人的经历，以及b)探索学生如何看待使用聊天机器人对他们学习的道德影响。研究结果显示，虽然学生们使用人工智能聊天机器人来支持写作、创意产生和语言提高，但他们也面临着与剽窃问题、人工智能生成内容的可靠性以及缺乏明确的负责任的人工智能使用制度指南相关的挑战。此外，该研究还强调了学生如何制定个人策略来应对这些挑战，包括寻求教育工作者的认可和批判性地评估人工智能生成的输出。研究结果强调，需要制定更清晰的学术政策和道德框架，以支持学生在高等教育中使用GAI聊天机器人时做出明智和负责任的决定。

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引用次数: 0

Conceptualizing pre-service teachers' readiness for AI integration into teaching practices: An intelligent-TPACK approach 概念化职前教师将人工智能融入教学实践的准备：一种智能tpack方法

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-29 DOI: 10.1016/j.caeo.2025.100320

José Reyes-Rojas , Brayan Díaz , Camila Ruz-Reveco , Angela Castro , David Reyes-González

Pre-service teachers can play a crucial role in integrating AI-based tools into the new educational landscape. However, there is a need to validate specialized instruments, apply current conceptualizations such as intelligent-TPACK, and address ethical issues, as pre-service teachers are often overlooked in the development of tools for AI integration. To address these gaps, we adapted a previously existing instrument designed for in-service teachers to measure pre-service teachers’ integration of AI within their training context. We conducted a quantitative cross-sectional survey with a total of 366 pre-service teachers to evaluate the adapted intelligent-TPACK instrument and examine participants' demographic characteristics related to the framework dimensions. Data analysis included a Confirmatory Factor Analysis to assess the factor model of the adapted instrument, followed by correlations to compare participant variables such as gender, type of university, and stage in the training program with the Intelligent-TPACK model factors. To investigate the differences among groups, the nonparametric ANCOVA test (Quade test) was utilized, enabling the control of covariates like age and academic progress level to ensure comparability across the dimensions of the Intelligent-TPACK model. Findings reveal a high fit of the Intelligent-TPACK model for pre-service teachers (CFI=0.997; TLI=0.997). The data also shows statistically significant effects related to academic progress level and type of institution, while factors -gender, geographic location, and type of major- did not demonstrate noteworthy differences. These results highlight key areas for future curriculum development and support for pre-service teachers in integrating AI education.

职前教师可以在将基于人工智能的工具整合到新的教育环境中发挥关键作用。然而，有必要验证专门的工具，应用当前的概念，如智能tpack，并解决道德问题，因为在人工智能集成工具的开发中，职前教师经常被忽视。为了解决这些差距，我们采用了先前为在职教师设计的工具，以衡量职前教师在培训环境中整合人工智能的情况。我们对366名职前教师进行了一项定量横断面调查，以评估适应性智能tpack工具，并检查参与者与框架维度相关的人口统计学特征。数据分析包括验证性因素分析，以评估适应工具的因素模型，然后将参与者变量（如性别、大学类型和培训计划阶段）与Intelligent-TPACK模型因素进行相关性比较。为了研究组间差异，采用非参数ANCOVA检验（Quade检验），控制协变量，如年龄和学业进步水平，以确保智能- tpack模型各维度的可比性。结果显示，智能- tpack模型对职前教师具有较高的拟合性（CFI=0.997； TLI=0.997）。数据还显示，在统计上，学业进步水平和机构类型对学生的影响显著，而性别、地理位置和专业类型等因素并没有显示出显著的差异。这些结果突出了未来课程开发的关键领域，并支持职前教师整合人工智能教育。

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引用次数: 0

The perceived importance of active learning techniques in online STEM courses 主动学习技术在在线STEM课程中的重要性

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-19 DOI: 10.1016/j.caeo.2025.100309

Montgomery Van Wart , Mirand McIntyre , Jing Zhang , Pamela Medina , Anna Ni , Lewis Njualem

It is widely acknowledged that active learning strategies increase engagement and long-term retention, while reducing attrition and frustration of students with less academic preparation and self-efficacy. Promoting active learning methods in STEM has been a long-term project in higher education. This study examines the perceptions of active learning techniques in online STEM education, leveraging a large, diverse sample (N = 727) across four STEM fields. The post-pandemic context of the study offers unique insights into how students and faculty perceive the effectiveness of various active learning methods in a rapidly changing educational environment. For eight of the nine methods studied, more than half of students and faculty found each active learning strategy to be helpful for online learning achievement. On average, both students and faculty found active learning methods to be modestly more important in online courses than face-to-face courses. A novel finding that was striking was that by a wide margin, both students and faculty perceived requiring activities more helpful than offering them on an optional basis. This implies that active learning methods become a meaningful portion of the course grade. However, faculty and students disagree on how heavily such activities should contribute to course grades. On average, students believe about half of their grade (52%) should comprise active learning activities, whereas faculty report that 32% of grades in their courses come from formative active learning assessments. The implications of activity-based STEM learning in online courses are discussed.

人们普遍认为，主动学习策略可以提高参与度和长期记忆力，同时减少学业准备不足和自我效能低下的学生的流失和挫折感。在STEM中推广主动学习方法一直是高等教育的一个长期课题。本研究考察了在线STEM教育中主动学习技术的看法，利用了四个STEM领域的大量不同样本（N = 727）。该研究的大流行后背景为学生和教师如何在快速变化的教育环境中感知各种主动学习方法的有效性提供了独特的见解。对于所研究的九种方法中的八种，超过一半的学生和教师发现每种主动学习策略都有助于在线学习成绩。平均而言，学生和教师都认为主动学习方法在在线课程中比面对面课程更重要。一项引人注目的新发现是，在很大程度上，学生和教师都认为必修活动比提供可选活动更有帮助。这意味着主动学习方法成为课程成绩中有意义的一部分。然而，教师和学生对这些活动对课程成绩的影响有多大存在分歧。平均而言，学生认为大约一半的成绩（52%）应该包括主动学习活动，而教师报告说，他们课程中32%的成绩来自形成性主动学习评估。讨论了在线课程中基于活动的STEM学习的含义。

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引用次数: 0

AI-driven gamified speech training for primary students: framework and evaluation 基于ai的小学生游戏化语音训练：框架与评价

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-17 DOI: 10.1016/j.caeo.2025.100312

Xing Sun , Zi-Xiang Xu , Ling-Chen Meng , Ding-Nan Shi

Traditional public speaking education often suffers from limited learner engagement, delayed formative feedback, and a lack of interactive and adaptive training environments. This study proposes an AI-driven gamified speech learning framework (AI-GSLF), which combines real-time feedback technologies with motivational game design principles to address these issues. Based on this framework, a serious game—Strongest Speech Streamer—was developed using the Godot engine. The system integrates automatic speech recognition, sentiment analysis, and a novel speech rate detection algorithm to provide immediate feedback, helping learners adjust pacing, reduce anxiety, and enhance fluency during practice. A true experimental design was employed, involving 57 primary school students randomly assigned to either the experimental group using the gamified system or a control group following traditional methods over one month. Quantitative results showed that the experimental group demonstrated statistically significant improvements in motivation, confidence, speech accuracy, and delivery fluency. To our knowledge, few prior studies have integrated real-time AI feedback with systematic gamification for primary-level formal speech training. Findings support the potential of AI-GSLF as an effective, scalable approach to enhancing student performance and engagement in public speaking education.

传统的公共演讲教育往往存在学习者参与度有限、形成性反馈滞后、缺乏互动性和适应性训练环境等问题。本研究提出了一个人工智能驱动的游戏化语音学习框架（AI-GSLF），它将实时反馈技术与动机游戏设计原则相结合，以解决这些问题。基于这个框架，我们使用Godot引擎开发了一款严肃的游戏——《最强的语音流》。该系统集成了自动语音识别、情感分析和一种新颖的语音率检测算法，提供即时反馈，帮助学习者在练习中调整节奏，减少焦虑，提高流利度。采用真正的实验设计，在一个月的时间里，57名小学生被随机分配到使用游戏化系统的实验组和使用传统方法的对照组。定量结果显示，实验组在动机、信心、语言准确性和表达流畅性方面表现出统计学上显著的改善。据我们所知，之前很少有研究将实时人工智能反馈与系统游戏化相结合，用于初级水平的正式语音训练。研究结果支持AI-GSLF作为一种有效的、可扩展的方法来提高学生在公共演讲教育中的表现和参与度的潜力。

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引用次数: 0

Room for collaboration: Analyzing group learning in spatial digital learning environments 协作空间：分析空间数字学习环境中的小组学习

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-17 DOI: 10.1016/j.caeo.2025.100308

Michael Montag , Heinrich Söbke , Mario Wolf , Florian Wehking , Eckhard Kraft

Collaboration within learning activities, i.e., solving tasks together, is one of the effective elements for designing learning activities. Collaboration is also seen as conducive to learning in spatial digital learning environments (SDLE), such as virtual reality environments. However, less is known about the detailed design of collaboration, such as learning requirements and specific interaction between learners. Thus, this evaluation study examines the groupwise exploration of SDLEs using the example of a 360°-based digital representation of a waterworks. The study combines ecological authenticity with a multidimensional assessment approach, examining motivation, emotions, cognitive load, and social presence in authentic group learning contexts. We used standardized instruments to collect data regarding motivation and emotion as learning requirements, social presence and cognitive load. A pre- and post-test supplemented the data, as did semi-structured interviews. T-tests show that learning in groups can be more stressful for learners but leads to a more positive affect overall (Collaborative Load–Motivation Trade-off Principle). Further, we found that pairs achieve better learning outcomes than triads. A side finding revealed that learning in face-to-face settings appears to be more effective than online learning. The study contributes to an informed instructional design of learning in SDLEs and thus might advance this valuable learning technology.

学习活动中的协作，即共同解决任务，是设计学习活动的有效要素之一。协作也被视为有助于在空间数字学习环境（SDLE）中学习，如虚拟现实环境。然而，对协作的详细设计知之甚少，例如学习需求和学习者之间的具体交互。因此，本评估研究以自来水厂的360°数字表示为例，考察了SDLEs的分组探索。本研究将生态真实性与多维评估方法相结合，考察了真实群体学习情境中的动机、情绪、认知负荷和社会存在。我们使用标准化的工具来收集动机和情绪作为学习要求、社会存在和认知负荷的数据。前测和后测补充了数据，半结构化访谈也是如此。t检验表明，小组学习对学习者来说压力更大，但总体上产生了更积极的影响（协作负载-动机权衡原则）。此外，我们发现结对学习比三合学习取得更好的学习效果。另一项研究发现，面对面学习似乎比在线学习更有效。该研究有助于在特殊语言学习环境中进行明智的教学设计，从而可能推进这一有价值的学习技术。

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引用次数: 0

Intelligent‑TPACK in practice: design and evidence from a three‑week teacher preparation module 智能TPACK在实践中：设计和证据从一个为期三周的教师准备模块

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-08 DOI: 10.1016/j.caeo.2025.100306

Tugce Aldemir , Selcuk Kilinc , Ali Bicer , Patricia Grant , Trina Davis , Noelle Wall Sweany

Rapid advances in generative AI sharpen the need for teachers to develop pedagogical and ethical capacities for AI‑integrated instruction. While Technological Pedagogical Content Knowledge (TPACK) provides a valuable framework for technology integration, it does not fully capture AI’s unique complexities. This study presents an integrated i‑TPACK approach that extends Intelligent‑TPACK by adding AI‑as‑content (i‑CK) and AI‑for‑professional development (i‑PD) and by threading a five‑stage AI‑literacy progression (Know→ Use→ Evaluate→ Ethics→ Create) within each domain, treating ethics as distributed and iterative. We designed and examined a three-week professional development module for preservice teachers using a convergent mixed-methods design. Pre–post surveys (n = 25 matched pairs) with a six‑subscale Integrated i‑TPACK instrument showed statistically significant gains across all domains (Wilcoxon, Holm‑adjusted; medium‑to‑large effects). Qualitative analyses of lesson artifacts, decision logs, reflections, and micro-teaching documented instances of layered ethical decision-making (privacy/data governance, bias/fairness, transparency/provenance/accountability), progression along the AI literacy stages, and discipline-aligned pedagogical designs. Embedding an ethical decision‑making checkpoint across performance‑based activities made ethics visible in teacher work and coincided with more explicit safeguards and verification steps in lesson artifacts and micro‑teaching within the module. By detailing this empirically grounded model, our study offers theoretical and practical insights for teacher educators seeking to cultivate principled GenAI-supported instruction.

生成式人工智能的快速发展使教师更加需要培养与人工智能相结合的教学和伦理能力。虽然技术教学内容知识（TPACK）为技术集成提供了一个有价值的框架，但它并没有完全捕捉到人工智能独特的复杂性。本研究提出了一种集成的i - TPACK方法，通过在每个领域内添加AI - as - content （i - CK）和AI - for - professional development (i - PD)，并将AI - literacy的五阶段进展（Know→Use→Evaluate→Ethics→Create），将伦理视为分布式和迭代的，从而扩展了Intelligent - TPACK。我们采用融合混合方法设计，为职前教师设计并检验了一个为期三周的专业发展模块。使用六亚量表集成i - TPACK仪器进行的前后调查（n = 25对配对）显示，在所有领域（Wilcoxon， Holm调整；中大型效应）都有统计学上的显著收益。对课程人工制品、决策日志、反思和微观教学的定性分析记录了分层道德决策（隐私/数据治理、偏见/公平、透明度/来源/问责制）、人工智能素养阶段的进展以及与学科一致的教学设计的实例。在基于绩效的活动中嵌入道德决策检查点，使道德在教师工作中可见，并与模块内的课程工件和微教学中更明确的保障和验证步骤相吻合。通过详细介绍这个基于经验的模型，我们的研究为寻求培养有原则的基因人工智能支持教学的教师教育工作者提供了理论和实践见解。

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引用次数: 0

Intelligent-TPACK in teacher education: Examining preservice elementary teachers’ emerging views about AI classroom use 教师教育中的Intelligent-TPACK：职前小学教师关于人工智能课堂使用的新观点研究

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-08 DOI: 10.1016/j.caeo.2025.100307

Jeffrey Radloff , Ibrahim H. Yeter , Thomas K.F. Chiu

As artificial intelligence (AI) is increasingly implemented in educational contexts, elementary teacher preparation programs must equip preservice teachers (PSTs) with knowledge and skills related to AI. AI presents novel challenges for teachers while holding transformative potential for teaching and learning. Grounded in IntelligentTPACK, this study examines the perceptions of elementary (i.e., PK-6) PSTs regarding AI and its perceived classroom applications. Participants include 49 PSTs at a northeastern US teaching college enrolled in science methods and critical media literacy courses that explicitly and reflectively introduce AI applications and their uses. Data were collected through researcher-developed pre- and post-surveys, as well as open-ended Intelligent-TPACK reflections. Data were analyzed using thematic coding, with Intelligent-TPACK serving as the lens. Our analyses revealed that PSTs held mixed views and varied perceptions of AI's uses, as well as some uncertainty. Yet, most recognized the potential of AI for supporting differentiated learning, brainstorming, and the generation of teaching materials (I-PK). Trained as PK-6 ‘generalists,’ few PSTs expressed specific disciplinary connections (I-CK). Only half described concerns about AI biases and overreliance (Ethics), and the majority discussed AI as a tool (ITK). As such, PSTs demonstrated emerging Intelligent-TPACK, with a need for more attention to fostering content-specific uses and AI ethics. Findings support similar literature while providing novel PST perspectives, and as such, reveal discrete entry points for further Intelligent-TPACK consideration and research. Results further inform IntelligentTPACK explorations and underscore the role of teacher education in shaping PSTs’ ethical and effective use of AI in their future classrooms.

随着人工智能（AI）越来越多地应用于教育领域，小学教师培训项目必须为职前教师（pst）提供与人工智能相关的知识和技能。人工智能给教师带来了新的挑战，同时也为教学和学习带来了变革潜力。本研究以IntelligentTPACK为基础，考察了小学（即PK-6年级）学生对人工智能及其课堂应用的看法。参与者包括美国东北部一所教学学院的49名pst，他们参加了科学方法和批判性媒体素养课程，这些课程明确地、反思性地介绍了人工智能应用及其用途。数据收集通过研究人员开发的前后调查，以及开放式智能- tpack反思。数据分析采用主题编码，以Intelligent-TPACK为镜头。我们的分析显示，pst对AI的用途持有不同的观点和不同的看法，以及一些不确定性。然而，大多数人都认识到人工智能在支持差异化学习、头脑风暴和教材生成（I-PK）方面的潜力。作为PK-6的“通才”，很少有pst表现出特定的学科联系（I-CK）。只有一半的人表达了对人工智能偏见和过度依赖的担忧（伦理），大多数人认为人工智能是一种工具（ITK）。因此，pst展示了新兴的智能tpack，需要更多地关注促进特定内容的使用和人工智能伦理。研究结果支持了类似的文献，同时提供了新颖的PST视角，因此，为进一步的Intelligent-TPACK考虑和研究揭示了离散的切入点。结果进一步为IntelligentTPACK的探索提供了信息，并强调了教师教育在塑造pst在未来课堂中道德和有效地使用人工智能方面的作用。

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引用次数: 0

Relationship between pre-service teachers’ perceived competencies, affective dispositions, and readiness to use artificial intelligence: A study informed by the intelligent-TPACK 职前教师感知能力、情感倾向和使用人工智能的准备程度之间的关系：一项由智能tpack提供信息的研究

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-08 DOI: 10.1016/j.caeo.2025.100305

Ayesha Sadaf , Daniel Maxwell , Can Küplüce , Heiko Holz

As artificial intelligence (AI) continues to reshape educational contexts, understanding pre-service teachers’ (PSTs) readiness to integrate AI into their teaching is increasingly critical. This study explored the relationship among PSTs’ perceived AI competencies, affective dispositions, and intentions to use AI in teaching. Eighty PSTs from a southeastern U.S. university completed an online survey assessing their technological, pedagogical, content, and ethical knowledge related to AI, as well as affective dispositions such as interest, self-efficacy, attitudes, and behavioral intentions. Findings revealed that PSTs reported low-to-moderate AI competencies, with the highest confidence in TPACK and the lowest in technological knowledge and AI ethics.

Affective responses were mixed, with moderate interest and perceived relevance of AI, but low self-efficacy and intention to use. Correlational analyses showed strong relationships between AI competencies and positive affective dispositions, particularly self-efficacy and interest, which significantly predicted intention to use AI. Regression analyses further showed that ethical awareness and integrated pedagogical-technological knowledge predicted perceptions of teacher preparation, while ethics also reinforced technological content knowledge. This study highlights the interconnected roles of competency, affect, and ethics in shaping PSTs’ readiness for AI integration in teaching.

随着人工智能（AI）继续重塑教育环境，了解职前教师（pst）是否愿意将人工智能整合到他们的教学中变得越来越重要。本研究探讨了教师感知的人工智能能力、情感倾向和在教学中使用人工智能的意图之间的关系。来自美国东南部一所大学的80名pst完成了一项在线调查，评估了他们与人工智能相关的技术、教学、内容和伦理知识，以及兴趣、自我效能、态度和行为意图等情感倾向。调查结果显示，pst报告了低至中等的人工智能能力，对TPACK的信心最高，对技术知识和人工智能伦理的信心最低。情感反应是混合的，对人工智能的兴趣和感知相关性中等，但自我效能和使用意图较低。相关分析显示，人工智能能力与积极情感倾向之间存在很强的关系，尤其是自我效能感和兴趣，它们显著地预测了使用人工智能的意愿。回归分析进一步表明，伦理意识和综合教学技术知识预测教师准备的感知，而伦理也增强了技术内容知识。本研究强调了能力、情感和道德在塑造教师将人工智能融入教学的准备方面的相互关联的作用。

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引用次数: 0