教师人机协同教育素养

随着人工智能助力教育变革时代的到来，思想政治教育作为立德树人的关键一环，也迎来了新机遇新挑战。在此背景下，人工智能赋能思想政治教育，实现“人机协同”育人模式成为了思政教育育人实践的新模式，但技术的快速迭代与教师数字素养不足、数智技术适配难题以及专业建设人才短缺等问题，导致教育实践中的理想与现实存在偏差、学生注意力分散与学习技能不足等方面给数智时代思政教育“人机协同”育人带来了新挑战。为进一步提升思政教育“人机协同”育人效果，既需要提升教师数字素养，加强教师数字技能与人工智能技术的融合，推动思想政治教育的数字化转型，也应通过完善政策，加强数字技术与思政教育的深度融合，推动思想政治教育的精准化、智能化发展，实现教育强国建设目标。

随着人工智能技术在教育领域的不断推进，人工智能协同辅助教学被视为一种可能带来教育变革的创新工具。通过个性化学习支持、智能反馈和教学管理的自动化，人工智能有潜力提升教育质量、优化学习体验。然而，在人工智能应用的过程中，我们也面临着技术、社会层面等多重挑战。本文深入探讨了人工智能协同辅助教学带来的主要挑战，并分析了其对教师、学生和教育体系的深远影响。通过对这些问题的剖析，本文提出了应对策略，并展望了未来的发展方向。

生成式人工智能与基础教育教学的深度融合，正在重构知识生产模式、学习交互方式与教学评价体系，推动基础教育向“技术赋能–人机协同–生态重构”的智能化阶段演进。在此背景下，本文聚焦生成式人工智能在基础教育教学场景中的实践应用，系统梳理其在个性化学习支持、智能化教学辅助、动态化学情诊断等领域的创新实践，揭示技术对教学效率提升、教育公平促进的赋能效应。与此同时，文章深刻反思技术应用过程中衍生的伦理风险，强调以“人机协同”为核心，通过政策规范引导、构建教育伦理约束机制等路径，推动生成式人工智能与基础教育教学的良性融合，为教育数字化转型提供理论反思与实践策略参考。

随着人工智能技术与教育的深度融合，教师智能素养已成为推动教育数字化转型的核心命题。针对当前教师智能素养培育中存在的理论认知与实践应用非同步、培训供给同质化以及技术伦理缺位等问题，本研究引入AI-TPACK理论，在深入剖析智能时代教师角色转型需求与素养内涵演进的基础上，系统构建了包含智能技术认知、智能教学能力、智能内容整合与智能伦理素养的双圈层协同框架。据此，研究进一步提出分层级研修体系构建、基于实证课例的实践迭代以及评价保障机制完善的系统化培养路径，并结合小学数学教学实证案例，剖析了AI-TPACK从理论架构向教学行为转化的具体机制，为建设高素质专业化创新型教师队伍提供学理支撑与实践参照。

本研究聚焦于《决策理论与方法》课程教学中存在的“经验依赖、数据缺位、评价失衡”等决策困境，提出融合人机协同与循证决策理念的教学改革路径。基于“数据启发”范式，构建了教师认知系统与机器智能系统协同运行的循证教学决策模型，设计了覆盖教学计划、课堂互动与学习评价三阶段的“三阶循证”教学模式，实现从经验导向向证据支撑的教学决策转型。教学实践表明，该模式能够有效提升学生的决策认知能力、科研创新能力与反思能力，强化了课程的实践导向与方法论价值。研究实现了决策科学与教学实践、人工智能与教育智慧、数据证据与教育人文的三重融合，为研究生课程教学改革与高层次人才培养提供了新思路与实践范式。

智能时代的易变性、不确定性、复杂性和模糊性给传统的教学法带来了挑战，为了进一步探索智能时代教学法的有效路径，本研究从教学设计的角度，针对智能教学法的背景、追求宗旨、研究重心、思维模式、框架支撑、活动平台六大方面构建了面向智能时代的教学设计框架，搭建了智能教学法的理论模型，促进人机协同智能与学生智慧化的实现。

随着科学技术的快速发展，人类已经进入了智能时代，人工智能的普及和发展冲击着各个领域，智能技术和教育的融合势不可挡，以“人机协同”为特点的教育会改变目前的课堂形式、教学方式等方面，而中学教师作为基础教育改革的主要动力也面临着以单向的知识传授者、可靠的信息所有者、忠实的教学评价者为主的教师角色危机，因此，本文基于梅罗维茨的媒介场景理论下的“新媒介–新场景–新行为”理论框架，分析造成中学教师角色危机主要成因表现在媒介技术的更新、教育场景的变迁、教育行为主体的转换，中学教师应对角色危机必须重新重塑自己的角色定位，转危为机，成为打造课程知识的建构者、人机协同教育的助推者、学生成长的陪伴者的角色形象。

人工智能时代的到来，对人文学科的价值与教学范式提出了严峻挑战与历史性机遇。当知识获取与基础文本分析能力被AI极大赋能后，高校人文学科教育的核心使命必须从“知识传授”转向“思维能力发展”。文章首先剖析了AI时代对人文学科思维特质(批判性思维、价值判断、共情与创造)的凸显，进而反思了传统讲授式教学在促进学生思维发展上的局限性。在此基础上，本文系统构建了以“AI为协作者，而非替代者”为核心理念的四种教学方式：批判性对话式教学、生成–甄别式教学、虚实融合的具身化教学以及基于项目的跨学科协同教学，并阐述了新型教学模式实施面临的现实障碍与应对策略。最后，本文探讨了教师角色向“思维教练”“价值导航员”与“跨学科联结者”的转型，以期为人工智能时代人文教育的高质量发展提供理论参考与实践范式。

教学研究是高校教师探索教学的本质和规律所必须掌握的一项技能，也是高校教师实现个人专业成长和提升教育教学质量的重要手段。随着人工智能时代的到来，教学研究被赋予了新的意义和内涵。人工智能背景下高校教师教研能力的提升是促进教师专业化发展的有效途径。本文分析了人工智能赋能教师教研工作的必要性，剖析了教师教研能力存在的现状和困境，并从加强顶层设计、健全教研能力培训保障体系、加强数智化教学平台建设和完善教师信息化教学评价体系四个方面介绍了人工智能背景下高校教师教研能力的提升策略，并针对性讨论了AI赋能教研过程中可能面临的风险、挑战和伦理问题。

人工智能正驱动教育系统性变革。文章基于建构主义、社会文化理论等教育学心理学框架，结合实证数据与政策分析，揭示AI引发教学模式转型、评价体系多元化、师生关系重构及教育公平双重效应等结构性变革。针对技术伦理、数字鸿沟等挑战，提出分层治理策略与教师AI素养发展模型，并前瞻性预测神经适应性学习、教育元宇宙等核心趋势。构建“人类尊严优先”的智能教育生态需政策、技术、伦理协同推进。

在人工智能技术推动高等教育从数字化向数智化转型的背景下，高校教师AI课堂教学能力成为重构教学模式、提升育人质量的核心要素。本文以TPACK概念为基础，参考国内外部分大学AI教学经验，探讨了高校教师AI课堂教学能力的关键要素，并指出高校教师AI教学能力发展现状中存在的一些认识误区、技术匹配度不够高以及缺乏对相关道德风险防范意识等问题，提出了更新观念、精准培训、情境支持和政策激励等方面的优化建议，建构“认知–能力–实践–保障”四位一体的能力发展模型，为高校开展AI + 教育教学、培养高质量教师队伍提供一定的理论依据及实践启示。

在高等教育普及化与数字化转型的双重语境下，如何化解“规模化培养”与“个性化发展”之间的结构性张力，已成为亟待回应的时代命题。生成式人工智能(GenAI)凭借其生成性、交互性与情境感知力，为突破这一工业化教育遗留的困境提供了新的技术杠杆。本文基于技术哲学与教育学视阈，解析GenAI赋能个性化教育的内在机理：即通过重塑“师–机–生”三元主体关系、构建数据驱动的自适应学习闭环以及促成科教融汇的知识生产新形态。然而，技术的深度嵌入亦引发了师生主体性弱化、算法偏见及数据伦理等衍生风险。鉴于此，研究提出应超越单纯的技术工具论，通过构建智慧教育生态、重塑师生数智素养及完善伦理规制体系，探寻技术逻辑与育人规律的辩证统一，以期为高等教育的高质量发展提供理论参照。

随着人工智能技术与教育的深度融合，教育领域正经历着全方位的格局与流程重塑，这一变革催变了师范生原有的知识结构与教学模式，为师范生能力培养带来了新的发展机遇和路径。在原有的整合技术的学科教学知识(TPACK)的基础之上，以“技术”作为梳理与探索其发展的逻辑主线，注入“人工智能技术”这一活跃的时代要素，搭建整合人工智能技术的学科教学知识理论框架(AI-TPACK)。基于该理论框架，进一步剖析人工智能时代师范生能力培养面临的AI-TPACK培养理念缺失、培养模式单一、协同培养缺位的现实困境。并以个人行为层面的明确，为师范生能力培养提供动力支点；以体质机制层面的创新，为师范生能力培养提供方向引领；以人机协同层面的拓展，为师范生能力培养提供技术支撑，有效助推高质量师范教育体系的全面建设与落实。

随着人工智能生成内容(AIGC: Artificial Intelligence Generated Content)技术的快速发展，国际中文教育正迎来数字化转型的新机遇。本文基于笔者参加“国际中文教师AIGC专项技能提升”专题工作坊的学习经历，结合工作坊中文案生成、图像处理、视频合成、数字人制作等实操内容，从教育资源开发、教师角色转型、学生学习体验、行业生态建设等维度，系统探讨AIGC技术在国际中文教育中的应用价值与现实挑战，并提出相应的发展建议，以期为推动中文教育智能化、个性化、高效化提供参考。

本文研究了AI时代对语言素养的动态需求，拓展传统语言能力评价体系的解释边界；同时探索生成式AI与语言教育的深度融合路径，考察语言教育主体在三元互动框架中的角色定位。本文构建出语言能力维度图谱，涵盖工具性、人文性、批判性三重维度，提出人机协同语言教育框架。

生成式人工智能正在深刻变革职业院校的教师角色。当前，职业院校教师生成式AI素养的评价研究成为了教育技术领域的热点问题。基于此，本文提出了一种双轴评价模型，其横向的“认知–技能–思维–伦理”素养轴从“知”到“行”再到“思”与“德”衡量教师生成式AI素养的内在结构与能力类型，纵向的“微观–中观–宏观”层级轴由“内”到“外”衡量教师生成式AI素养的成长阶段与发展路径。本文构建的“认知–技能–思维–伦理”与“微观–中观–宏观”双轴结构，从能力维度与发展纵深两个方向刻画了职业院校教师的生成式人工智能素养。同时，本论文从不同维度与层级出发提出了职业院校教师生成式AI素养的提升策略。这些成果将为推动职业院校关键办学能力的智能升级提供了理论支撑与实践路径。

随着人工智能在教育领域的深入应用，高校教师AI素养的提升成为教育信息化的重要议题。本文旨在探究高校教师AI素养的现状及其影响因素。研究基于260份问卷调查数据，采用描述性统计、差异性比较和多元回归分析方法进行统计分析。结果发现，高校教师AI素养整体处于中上水平，并且在性别、学科、职称等主要人口学特征上无显著差异。人口学变量对教师AI素养影响不显著，而AI使用频率则有显著正向作用。研究结论认为，培训普及和政策支持缩小了教师间的群体差异，建议进一步加强AI实践机会和持续性培训，同时关注组织支持及个人动机等非人口学因素对AI素养的促进作用。

本研究剖析了人工智能教育时代对师范生群体提出的人工智能素养要求，旨在深入探讨师范生在人工智能时代面临的素养发展困境，以期提出相应的培养发展建议。本研究通过对相关文献的系统梳理，明确了师范生人工智能素养的内涵及构成要素，厘清了当前师范生人工智能素养发展面临的诸多困难与挑战，并针对当前亟待解决的现实问题提出了切实可行的应对策略，建立了积极的实践路径，为师范生的人工智能素养发展提供了理论依据与实践指导，对推动教师教育与人工智能的融合发展具有重要意义。

本研究旨在基于AI-TPACK理论构建“AI-TPACK——数字素养动态整合模型”，探索高校英语教师数字素养现状、影响因素及其分层提升路径。通过混合研究设计对高校英语教师数字素养现状进行了实证分析，结果表明，在AI-TPACK各维度中，技术知识(AI-TK)与整合技术的教学法知识(AI-TPK)得分较低，反映出数字工具应用及其与教学策略整合存在不足；整体AI-TPACK整合能力对教师数字素养的预测效应显著(β = 0.45, p < 0.001)；高、低AI-TPACK教师在教学设计和技术应用上存在显著差异。据此本文提出了“三阶发展路径”——短期以技术赋能为主、中期侧重教学创新、长期则着力于内容深化。研究结果为优化高校英语教师培训模式与政策制定提供理论与实践依据。

随着教育数字化转型推进及“AI + 教育”政策落地，中学英语教师AI焦虑问题凸显。本文梳理国内外研究发现，国内多聚焦教师AI素养、数字焦虑，缺乏对该群体的针对性探讨；国外侧重一般或职前教师，亦未充分覆盖中学英语教师。在此基础上，从政策落地、素养成长、课堂协同、个性化教学、区域均衡五维度，分析缓解其AI焦虑的赋能路径。同时指出当前研究跨文化、跨学段不足，未来需关注教师在“新课标实施”与“技术融合”双重挑战下的焦虑，以推动AI与中学英语教学深度融合。

人工智能(AI)正深刻重塑英语教学测评的形态，同时也对英语教师测评素养提出了全新要求。本文系统阐释了AI时代英语教师测评素养的新内涵，主要包括AI技术应用能力、数据驱动决策能力和智能伦理审视能力，并深入剖析了其在技术适应、数据素养、理念更新与伦理风险等方面面临的挑战。为此，本文提出构建融合型分层培训体系、推动测评理念深层转型、深化人机协同测评实践、完善制度保障与激励机制和强化数据伦理与人文关怀五方面策略，旨在为构建“AI增强型测评素养”框架提供理论与实践参考，从而推动英语教育测评体系的发展，最终实现技术赋能下“以评促学”的高质量英语教学。

人工智能技术革命与少子化人口结构变迁，正在对师范教育系统构成前所未有的双重外部冲击。本文旨在超越表层的“挑战与机遇”论述，深入剖析这两股力量如何相互交织，从需求规模、能力结构、培养范式、组织形态四个维度解构其对师范教育系统的深层影响机制。研究指出，双重冲击的本质是驱动系统从“规模扩张”的工业化范式向“精益卓越”的生态化范式转型。在此基础上，提出“系统性重构”的应对核心逻辑，并构建一个包含“精准供给–素养重构–流程再造”三位一体的转型路径框架，旨在为师范教育在变局中主动塑造未来提供理论参照与实践指引。

本研究综述了人工智能在第二语言教学中的应用及其对教师角色的影响。通过文献总结，发现人工智能在个性化学习和自动化评估方面具有优势，不仅增强了教师的传统角色，还拓展了其作为学习引导者、技术运用者和情感支持者的新角色，它们在生成式人工智能背景下被赋予了全新的内涵与实践要求。但其应用也存在局限性，本研究因此提出了针对性策略并强调协同合作是提升教学效果的关键。最后，对人工智能与第二语言教学的关系发展进行了展望，指出教师与人工智能的结合能为学习者创造更优质的语言学习环境。

新时代，教师要牢固树立终身学习理念，认真践行教育家精神，做“四有”好老师。人工智能时代高校教师专业发展存在角色之困、专业发展之差、赋能效果之疑等问题，其背后原因包括理念之惑、规划之滞以及培训之隙。为此，文章认为国家、学校与教师自身应协同合作，打破固有观念，关注教师专业发展诉求并为其提供有力保障，最终形成多元主体参与的人工智能高效赋能教师专业发展新生态，共同助力教育数字化、信息化、智慧化转型。

智慧教育对教师角色产生深刻影响，教学系统和教育对象的演变促使传统教师角色摆脱桎梏得到解放。数字化教育和数字原住民敦促教师从管理者到合作者、监督者到服务者、教学者到导学者转变，实现教育理想中的教师角色。通过背景梳理、角色转变剥析、教学实践循证、支撑角色转变的核心能力探讨，揭示智慧教育为教师提供的全新机遇与挑战，为教育领域的决策者、研究者和从业者提供深入的理解。

本文研究探讨了生成式人工智能(人工智能)对高校教师胜任力的影响。通过文献综述和实证研究，分析了生成式人工智能在高等教育中的应用现状及其对教师角色和职责的潜在影响。研究构建了高校教师胜任力量表，并提出相关假设。采用问卷调查和数据分析方法，验证了生成式人工智能对教师专业知识、教学能力、科研能力、技术应用能力和职业发展能力的影响。研究结果表明，生成式人工智能对高校教师胜任力既带来机遇也构成挑战。基于研究结果，提出了高校教师应对生成式人工智能影响的策略建议，为高等教育机构制定相关政策提供了参考。

生成式人工智能通过其在提供个人协助(例如ChatGPT、DALL·E)、天气预报、面部识别等技术中的广泛应用而日益成为当代生活的一部分。生成式人工智能技术是指基于生成对抗网络、大型预训练模型等人工智能的技术方法，通过对已有数据的学习和识别，以适当的泛化能力生成相关内容的技术。现阶段对教育潜在影响进行了广泛的研究诸如ChatGPT之类的生成式人工智能(AI)在教育领域的应用，但很少有人对教育工作者认为教学质量评估应如何因生成式人工智能而发生改变进行大规模研究。进入21世纪以来，教师课堂教学评估模式面临着理念创新和技术升级的双重机遇和挑战。教学与课堂评估的关系发生了深度转变，课堂评估从教学的附属和辅助手段逐步转变为教育教学的中心环节；因此，为了提高基于生成式人工智能教师课堂教学质量评估的准确性和性能，本文分析了人工智能在课堂教育评估中的应用，确认了智能技术对课堂教育的促进作用。在此基础上，提出了提高教师课堂教学质量评估的若干策略。研究结果为人工智能在教师课堂教学质量评估中的应用提供了很好的参考。

生成式人工智能在教育领域的应用为课堂教学带来新的机遇。其通过动态资源生成、自然语言交互与跨模态能力，为课堂变革注入新动能。高中信息技术课堂教学面临教学方式趋同化、主体地位弱化、评价难度升级及伦理争议等挑战。本研究以高中信息技术课堂为对象，系统分析生成式人工智能赋能教学的挑战，构建技术赋能与教育价值协同发展的理论框架，并提出针对性应对策略。研究指出，未来需探索生成式人工智能与信息伦理教育融合、工具普适性应用及师生情感联结重塑，推动课堂教学向高质量和信息化发展。

2023年7月，日本文部科学省发布《初等中等教育阶段使用生成式人工智能的暂定指南》，为生成式人工智能在中小学教育中的应用指明了行动方向。随后，为验证生成式人工智能在中小学教育现场的应用成效，文部科学省依据《暂定指南》设立生成式人工智能试点学校，希冀通过“试点–推广”的形式探索生成式人工智能教育应用的可能性，进一步加速学校教育数字化转型。

生成式人工智能能够对用户输入数据进行处理，总结内在规律，自动生成新内容，给高校辅导员工作带来很多机遇，如根据学习者特点设计“个人画像”，推出个性化学习和培养模式。但同时也存在一定的风险和挑战，如意识形态滑坡、数据偏见和安全、教育缺乏情感和温度等问题。本文立足于高校辅导员工作，探索生成式人工智能给高校思政教育带来的机遇、挑战以及应对策略，促进生成式人工智能更好地为高校辅导员工作赋能。

生成式人工智能(AIGC)的快速发展为环境设计专业深化产教融合提供了新动能。文章围绕AIGC如何赋能环境设计专业产教融合展开研究，探讨其在知识生成、能力培养、协同机制与教育治理等层面的逻辑机理，分析教学内容、平台系统、教师能力与制度机制的实践路径，并进一步提出实现协同育人高质量发展的未来方向。研究指出，AIGC不仅是设计辅助工具，更是支撑教育与产业深度耦合的基础设施，对推动应用型本科教育高质量发展、服务国家现代化战略具有重要意义。

生成式人工智能的迅速发展正引发计算机专业教育的范式重构。本文以计算机学科核心基础课数据结构为研究对象，深入剖析了该课程“高抽象、强逻辑、重实践”的教学特点及其长期存在的“学生理解困难、教师因材施教难、理论与实践脱节”等问题。在此基础上，系统构建了一个生成式AI深度融入数据结构课程教学的“三维四阶”融合模式框架，涵盖教学内容、教学过程与教学评价三个维度，以及从工具辅助到思维重构的四个进阶阶段。本文结合二叉树旋转、图算法优化等典型案例，详细阐述了基于生成式AI的启发式教学设计、个性化学习路径生成及智能实践环境构建等具体实践方案。同时，本文还讨论了融合过程中衍生的学术诚信边界模糊、模型“幻觉”误导认知、数据隐私与算法偏见、以及学生批判性思维与元认知能力弱化四大伦理挑战。最终，提出了以“人机协同、智能增强”为核心，涵盖“教学范式重塑、师生数字素养提升、制度与技术保障”的综合性应对策略。本研究旨在为人工智能时代计算机专业核心课程的教学改革提供系统的理论参考、可操作的实践路径与系统性的伦理审视。

人工智能正在快速改变人类的教育方式与学习方式。智慧教室作为教育信息化的研究成果，具有云网端一体化、教学内容优化呈现等基本特征。项目式学习以培养学生的核心素养能力为根本目标，促进自主学习能力和问题解决能力的发展，能有效应对高校学生创造性解决问题能力不足的挑战。智慧教室支持下项目式教学模式由课前自主学习和检测反馈；课中自主学习、确定项目、制定计划、协作探究、形成成果、交流展示和总结评价；课后及时补救等环节构成，在教学实施时应注意以下几点：教师应扮演学生学习的促进者和引导者角色；充分利用智慧教学平台，提升学生的活动效率；同时推进“师–生–机”三元协同，促进学生全面发展。

教师专业发展是教师教育研究永恒的主题。人工智能时代，以大数据、云计算、人工智能为代表的新兴技术改变传统教学方式、促进知识容量倍增、引起教师角色转变，面对这些教育行业外在形态的“变”，本文将结合教育本质的“不变”和教师立德树人的根本任务，重新思考教师在人工智能时代的发展问题。技术不能改变教育本质，无法取代教师本职，但较之于当下先进的技术优势，教师需要不断更新自我核心素养，发展其信息意识、信息技术应用能力、创新能力以及合作能力，通过移动化网络提升知识涵养，运用人机协作助推智能化教学技能，借助远程团队协作提升科研水平，探索人工智能时代的教师专业发展之路。

在信息技术和人工智能快速发展的时代背景下，AI双师教学模式成为提高教学效率，促进个性化与深层次学习的必然选择。这一新的教学模式在革新线上线下混合式教学形态的同时，其技术复杂性与应用情境多元性也衍生出一系列潜在风险。只有有效规避风险，采取有效路径，才能显著提升线上线下混合式教学的效果和效率，推动高等教育向更加智能化、个性化、人本化的方向发展，最终实现更高水平的育人目标。

随着第三次科技革命的到来，人工智能如雨后春笋般出现，而人工智能和教育的结合对教育领域产生了深刻的影响。同时，国际中文课堂教学在人工智能时代背景下也呈现出了新的特点，教学角色转变、教学环境多元化、教学效率提高、学习资源丰富多样、学习方式和教学内容都发生了转变等。但是人工智能是把双刃剑，它在促进国际中文教学的同时也为国际中文课堂教学带来新的挑战，具体表现在师生可能成为机器和数据的奴隶、加剧了知识异化的风险、交际水平弱化、向师生提出更高的要求、教育数据繁多复杂等。在此问题的基础上，文章也提出了人工智能时代背景下提高国际中文课堂教学有效性的策略和方法。本文探讨了“人工智能”时代背景下课堂教学有效性的问题，通过对目前及未来可能存在的问题进行讨论，提出相应的对策，为国际中文教师提高课堂有效性提供思路和理论参考。

在“智能+”时代的背景下，教育信息化和教学改革不断推进，学术交叉融合的趋势也日益明显。虚拟教研室的兴起已成为新型基层教学组织的一种创新探索。本文探讨了“人工智能+”(“AI+”)虚拟教研室的建设背景，在师资队伍建设、跨学科的教学资源建设、教学评价与管理机制构建，以及在线的互动交流平台建设等方向进行深入地分析与探讨。最后，本文依托课堂派、虚拟实验平台以及知识图谱等工具，分析了“AI+智能制造”、“AI+启蒙教育”虚拟教研室的建设成效。

Artificial Intelligence (AI) literacy has come to the spotlight, empowering individuals to adeptly navigate the modern digitalised world. However, studies on teacher AI literacy in the English as a Foreign Language (EFL) context remain limited. This study aims to identify intraindividual differences in AI literacy and examine its associations with age and years of teaching experience among 782 English teachers. Given the absence of a reliable instrument to measure teacher AI literacy, we first constructed and validated a scale encompassing five sub‐scales: AI Knowledge, AI Use, AI Assessment, AI Design, and AI Ethics. Subsequently, latent profile analysis (LPA) was conducted using Mplus 7.4, with the results revealing four distinct profiles: Poor AI literacy (C1: 12.1%), Moderate AI literacy (C2: 45.5%), Good AI literacy (C3: 28.4%), and Excellent AI literacy (C4: 14.1%). Multinomial logistic regression analyses indicated significant associations between teacher AI literacy and both age and years of teaching experience. Additionally, 32 respondents participated in semi‐structured interviews. The qualitative data analysed with MAXQDA 2022 triangulated the quantitative results and offered deeper insights into teachers’ perceptions of their AI literacy. This study provides both theoretical and practical implications for understanding teacher AI literacy in the Chinese EFL context.

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Abstract The transformative potential of generative artificial intelligence (GenAI) in language education highlights the importance of AI literacy among teachers to ensure its ethical and effective integration into teaching practices. Although studies have examined the application of AI in language education, there is a lack of comprehensive reviews focusing on the interaction between language teachers and ChatGPT, a GenAI tool, particularly in fostering human–AI collaboration within educational contexts. This review addresses this gap by synthesising findings from empirical studies. The Scopus database was used as the primary source for this review. A total of 19 journal articles, published between 2023 and 2024, were identified. The review first analyses the research participants and research methods of the selected studies. Key themes are organised into five dimensions: AI foundations and applications, AI ethics, a human-centred mindset, AI pedagogy and AI for professional development, which are derived from the framework proposed by Miao and Cukurova (2024. AI competency Framework for teachers. Paris, France: UNESCO). This review adopts their framework as an analytical lens for evaluating both the opportunities and challenges associated with integrating ChatGPT into language education. The findings highlight the importance of a balanced approach to AI integration to safeguard educational integrity. By offering actionable insights for teachers, curriculum designers and policymakers, this review presents a roadmap for the responsible adoption of AI in language education, ensuring that teachers remain central to the learning process.

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Introduction. The rapid development of artificial intelligence (AI) technologies and their integration into the educational system necessitate that teachers become proficient in applying AI tools in their professional activities. Currently, the advancement of artificial intelligence literacy (AI literacy) is a focal point internationally, with educational programmes aimed at enhancing AI literacy for both students and teachers being implemented. However, despite a general interest in the topic of artificial intelligence, the term “AI literacy” is rarely used in Russian scientific publications. Therefore, there is a clear need to understand the concept of “literacy in the field of artificial intelligence” from a new perspective. In this context, the professional activities of teachers and the identification of their content become the aim of this study. Methodology and research methods. The primary research methods employed included the analysis of scientific, pedagogical, and methodological literature, as well as the synthesis, systematisation, and generalisation of facts and concepts. Additionally, content analysis was conducted on the materials sourced from leading quotation and analytical databases. Results and scientific novelty. In this paper, AI literacy is defined as a set of knowledge, skills, and abilities in the field of artificial intelligence that enables individuals to understand the fundamental principles of AI technologies and interact with them effectively while addressing both professional and personal tasks. Additionally, it allows individuals to critically evaluate the ethical risks and societal consequences associated with the use of these technologies. The authors have developed and presented a component structure of teachers’ AI literacy, which comprises five interrelated components: cognitive, activity-based, reflexive, personal, and ethical. The authors have elucidated the content of each component and proposed potential directions for the professional development of teachers in this domain.Practical significance. The materials presented in this paper can serve as a foundation for enhancing the professional development system for teachers in the context of digital transformation in education.

Conducting teacher AI literacy assessments is a key task in implementing the digital transformation of education. In 2024, UNESCO released the AI Competency Framework for Teachers, providing systematic guidance for developing AI literacy for teachers worldwide. This article first systematically introduces the framework's five aspects and three progression levels. Based on this framework, it designs and develops a teacher AI literacy assessment system, aiming to provide tools to support the diagnosis, training, and personalized development of teachers' AI competences.

This article critically examines global research on teacher AI literacy, highlighting significant gaps in theory, assessment, and pedagogical integration. Focusing on language education, particularly Chinese as a Foreign Language, it argues for a shift beyond technical skills toward context-aware, ethically grounded, and pedagogically meaningful frameworks that enable a profound educational transformation. 

In the evolving landscape of educational technology, artificial intelligence (AI) is reshaping how educators understand and influence student behavior. This study examines the impact of student-perceived teacher AI literacy on students’ innovative behavior through the theoretical lens of educational psychology and behavior analysis, guided by the conservation of resources theory. Data were collected using stratified random sampling from multiple universities. The study investigated the mediating role of students’ positive emotion and the moderating effect of organizational support in the relationship between student-perceived teacher AI literacy and student innovation. Findings reveal that student-perceived teacher AI literacy significantly enhances students’ innovative behavior and positive emotion. Students’ positive emotion serves as a key mediating variable, while organizational support amplifies the positive effects of student-perceived teacher AI literacy on student outcomes. This research demonstrates that educators’ AI competencies can foster emotionally and behaviorally engaged learning environments, leading to improved student innovation. It provides practical implications for integrating AI into pedagogical strategies and contributes to the growing field of AI-driven educational psychology.

Artificial intelligence has significantly transformed educational practices across disciplines. This study investigated the cognitive–behavioral mechanisms underpinning mathematics teachers’ engagement with AI teaching tools through an extended technology acceptance model. Utilizing structural equation modeling with data from 500 mathematics educators, we delineated psychological pathways connecting perceptual variables to technology engagement and pedagogical outcomes. Results revealed that perceived usefulness functioned as the primary determinant of AI engagement, while perceived ease of use operated exclusively through sequential mediational pathways, challenging conventional technology acceptance paradigms. Domain-specific factors, such as teacher AI literacy and mathematics teaching beliefs, emerged as significant mediators that conditioned technology-related behavioral responses. The mediators in this study illustrated differential attitudinal mechanisms through which perceptual variables transformed into engagement behaviors. These findings extended technology acceptance theories in educational contexts by demonstrating how domain-specific cognitive structures modulated perception–behavior relationships in professional technology adoption in mathematics education.

Teacher education increasingly requires educators to engage with generative AI technologies, yet critical and reflective engagement opportunities remain scarce. While AI is often framed as a tool for automation, its broader pedagogical and ethical implications receive less attention. To address this gap, we implemented a critical co-discovery approach within an online AI in Education (AIEd) course to enhance educators’ AI literacy. This illustrative case study examines which AI literacy components can be developed through critical co-discovery and how this approach fosters educators’ reflective, critical, and participatory engagement with AI. Findings revealed that through co-discovery activities, educators co-constructed an understanding of AI concepts, ethical considerations, and context-specific applications. The study highlights the need for prolonged engagement with AI literacy by integrating it into teacher education program to ensure educators can critically navigate and assert their agency in AI’s complex role in education.

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As most practitioners (including teachers) do not know how AI functions and cannot make full use of AI in education, there is an urgent need to investigate teachers’ intentions to learn AI and related determinants so as to promote their AI learning. This study collected survey data from a total of 318 K-12 teachers from sixteen provinces or municipalities in China. A two-step structural equation modeling approach was performed to analyze the data. Our findings show that K-12 teachers’ perceptions of the use of AI for social good and self-efficacy in learning AI are two direct determinants of behavioral intentions to learn AI, while awareness of AI ethics and AI literacy are two indirect ones. AI literacy has a direct impact on perceptions of the use of AI for social good, self-efficacy in learning AI and awareness of AI ethics and has an indirect impact on behavioral intentions to learn AI. This study represents one of the earliest attempts to empirically examine the power of AI literacy and explore the determinants of behavioral intentions to learn AI among K-12 teachers. Our findings can theoretically and practically contribute to the virgin field of K-12 teachers’ AI learning.

The application of artificial intelligence in education prompts an evolution in the professional competencies required of teachers. Current discussions on teacher AI literacy are predominantly situated within the context of general education, failing to capture the unique characteristics of vocational education, such as industry-education integration and school-enterprise collaboration. Consequently, a specific framework for vocational college teachers is absent, and existing research has not addressed this need. This study, grounded in empowerment theory, constructs an AI literacy framework for vocational college teachers. It elaborates on the competency dimensions related to human-computer collaboration, including the use of AI to understand industry demands, design instructional scenarios, and align curriculum with workplace requirements. The research further analyzes the practical constraints on literacy enhancement from the perspectives of policy environments, institutional support mechanisms, and teacher cognition, proposing corresponding developmental pathways. This study aims to provide a theoretical reference and practical guidance for the professional development of educators in the vocational sector.

With the recent advancements in generative artificial intelligence (AI) technology, the need for AI utilization in school education has increased, leading to active research on teacher AI literacy (competency). This study aims to develop self-assessment items for teacher AI literacy, recognizing the importance of developing and evaluating teachers' AI literacy in the context of school education where AI-integrated education is expected to become widespread. Using a research methodology that involved a meta-analysis of domestic and international literature on teacher digital literacy and digital competency frameworks, common core constructs were extracted. These constructs were integrated into Eisner’s (2002) conceptual framework of teachers' three types of professional knowledge, forming a novel AI literacy assessment framework. The resulting self-assessment items comprise 36 questions across nine domains: understanding the basic principles of AI, foundational knowledge of data science, educational applications of AI, practical use of AI tools, foundational programming skills, problem-solving capabilities, understanding AI ethics, analyzing social impacts, and responsible AI usage. To verify the content and structural validity of the items, a participatory design approach was employed, applying the developed AI literacy assessment items to in-service teachers. Feedback was collected through focus group interviews (FGI) with research participants, and the results were used to refine the final self-assessment items for teacher AI literacy. The self-assessment scale developed in this study serves as a practical tool for teachers to evaluate and enhance their educational competencies in alignment with the technological environment of the AI era.

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The rapid integration of artificial intelligence (AI) in education requires teachers to develop AI competencies while preparing students for a society influenced by AI. This study evaluates the impact of an online teacher training program on German in-service teachers’ AI literacy, usage behaviors, and attitudes toward AI. A pre-post design study was conducted with teachers (N = 436 for attitude assessment, among whom N L  = 291 teachers for AI literacy) participating in the course. The program combined synchronous and asynchronous learning formats, including webinars, self-paced modules, and practical projects. The participants exhibited notable improvements across all domains: AI literacy scores increased significantly, and all attitude items regarding AI usage and integration demonstrated significant positive changes. Teachers reported increased confidence in AI integration. Structured teacher training programs effectively enhance AI literacy and foster positive attitudes toward AI in education.

The latest developments in artificial intelligence (AI) have paved the way for new AI-integrated pedagogical skills and competencies in language teaching. AI literacy and AI competency are related terms; however, they differ in educational contexts. AI literacy refers to the skills to understand and evaluate AI technologies, as well as to engage with them. In contrast, AI competency extends beyond conceptual proficiency and is related to the practical application of AI knowledge in educational domains. Considering the significance of effective and ethical AI use in language teaching, this study focused on the role of AI literacy training in teacher education programs and aimed to investigate pre-service EFL teachers’ perceptions of their AI literacy and competency as well as their experiences regarding AI literacy training in EFL teacher education programs. A small-scale exploratory approach was adopted in this qualitative study, which was conducted at the English Language Teaching Department at a state university in Türkiye. The participants were 15 pre-service EFL teachers. Semi-structured interviews were utilized to gather qualitative data, which was analyzed using thematic analysis. The study results revealed that pre-service EFL teachers’ perceptions of AI use in language teaching were positive; however, they were unconfident about their AI knowledge and skills. Also, the study enlightened a lack of exposure to AI education, including AI knowledge, practices, and ethical issues in EFL teacher education programs. Based on pre-service EFL teachers’ needs and expectations, the results of this study may provide beneficial insights for teacher educators, practitioners, and policymakers.

Preparing new teachers for the reality of artificial intelligence in education (AIEd) has become a pressing issue. This study was conducted in a Canadian teacher education program that offers a course on digital technologies incorporating a module on AIEd. This paper addresses two research questions: 1) What were teacher candidates’ (TCs’) experiences with the module on AIEd? and 2) What were TCs’ views on the use of AI by themselves and their students? The study employed an explanatory mixed methods design, combining quantitative and qualitative data gathered via a survey administered to TCs directly following their module completion. Participants were two cohorts of TCs (108 TCs in 2024 and 104 TCs in 2025). Findings show TCs’ satisfaction with the module as they highlighted three major benefits: offering useful teaching resources; more acceptance to explore the technology and embrace it critically; and promoting AI literacy. TCs expressed an inclination to use AI as teachers. However, they expressed negative views toward their students’ use of AI. Additionally, most TCs demonstrated developing levels of critical AI literacy, especially among the most recent cohort. This research offers insights into promoting TCs’ AI literacy and presents implications for teacher education research, practice, and policy.

Generative AI tools such as ChatGPT are reshaping educational practice, yet first-year teacher-education students often lack the prompt-engineering skills and confidence required to use them responsibly. This pilot study examined whether a concise three-session clinic on prompt engineering could simultaneously boost AI literacy and reduce technology anxiety in prospective teachers. Forty-five freshmen in a Peruvian teacher-education program completed validated Spanish versions of a 12-item AI-literacy scale and a 12-item technology-anxiety scale one week before and after the intervention; normality-checked pre–post differences were analysed with paired-samples t-tests, Cohen’s d, and Pearson correlations. AI literacy rose by 0.70 ± 0.46 points (t (44) = −6.10, p < 0.001, d = 0.91), while technology anxiety fell by 0.58 ± 0.52 points (t (44) = −3.82, p = 0.001, d = 0.56); individual gains were inversely correlated (r = −0.46, p = 0.002). These findings suggest that integrating micro-level prompt-engineering clinics in the first semester can help future teachers engage critically and comfortably with generative AI and guide curriculum designers in updating teacher-training programs.

Teacher education programs face the challenge of integrating multiple standards into their licensure courses with limited instructional time. To address this, we need innovative approaches to effectively integrate topics like computational thinking (CT) and Artificial Intelligence (AI) literacy, which are increasingly included in teacher standards. We developed a core educational technology course for teachers from diverse fields-deaf education, math, music, early childhood, and more-to introduce these concepts. Using ''scaffolded critique rubrics,'' teachers evaluated tools and resources from platforms like Code.org or PBS Kids, assessing their suitability for their students. The rubrics helped teachers critique computing tools for accessibility issues, differentiate AI biases from automation ethics, and engage deeply with CT concepts. By connecting these topics to their student needs, teachers reflected on how to adapt resources for their classrooms. This report outlines the course development and its impact on teacher engagement

This study examined the association between teacher education students’ artificial intelligence (AI) literacy and their digital competence during educational internships. Grounded in the AI-TPACK framework, the study explored to what extent the three dimensions of AI literacy, AI Knowledge (AIK), AI Affectivity (AIA), and AI Thinking (AIT) affect five dimensions of digital competence: Digital Technology Proficiency (DTP), Digital Teaching Competence (DTC), Digital Learning and Innovation (DLI), Digital Values (DV), and Digital Personality Traits (DPT). Based on the survey data collected from 304 teacher education students, the findings revealed that AIA, reflecting positive attitudes and confidence toward AI, was the strongest predictor across all dimensions of digital competence. In contrast, AIK, representing theoretical knowledge about AI, showed no significant predictive effect, while AIT significantly predicted only DTP, DTC, and DLI. These findings suggest that teacher education programs must emphasize the affective and cognitive aspects of AI literacy and practical experiences to cultivate comprehensive digital competence among future educators effectively.

No abstract available

Objectives The purpose of this study is to explore the relationship between pre-service early childhood teachers' play expertise, AI literacy, and AI-enabled play support competence. Methods The subjects of the study were 187 pre-service early childhood teachers studying in young children-related majors at universities located in the Seoul metropolitan area. The measurement tools were play expertise, AI literacy, and AI-enabled play support competence, and descriptive statistics, Pearson correlation analysis, and stepwise multiple regression analysis were conducted using SPSS 27.0. Results The results of the study showed that, first, pre-service early childhood teachers' play expertise is significantly and positively related to their AI literacy and AI-enabled play support competence. Second, AI literacy of pre-service early childhood teachers is a static predictor of play expertise, and among the subfactors of AI literacy, AI play and teaching support is the most influential static predictor, and AI understanding is also a static predictor. Third, understanding of AI-enabled play, a subfactor of AI-enabled play support competence, was found to be a static predictor of pre-service early childhood teachers' play expertise. Conclusions Therefore, this study helped teacher educators in higher education institutions understand the importance of play expertise for preparing pre-service early childhood teachers to perform the educationally meaningful AI education with young children, and proposed the development of a pre-service teacher education program that can foster AI literacy and AI-enabled play support competence to enhance pre-service early childhood teacher's play expertise.

Artificial Intelligence (AI) is rapidly transforming education, demanding that educators possess a multidimensional literacy extending beyond technical skills to include affective, behavioural, cognitive, and ethical competencies. This study systematically investigates these four dimensions of AI literacy among 265 pre-service teachers (124 STEM, 141 non-STEM) at Institut Pendidikan Guru Kampus Pendidikan Teknik (IPGKPT) using the validated AI Literacy Questionnaire (AILQ) grounded in the ABCE framework. Results reveal that Ethical Learning (EL) and Affective Learning (AL) scored highest, indicating strong ethical awareness and motivation, while Cognitive Learning (CL) lagged, highlighting a persistent gap in foundational understanding. Notably, independent samples t-tests showed no significant differences between STEM and non-STEM groups in AL and BL, but a moderate advantage for STEM participants in CL and EL. These findings challenge the notion that AI literacy is exclusive to technical fields, underscoring the need for equitable, cross-disciplinary integration of AI literacy within teacher education. Building on previous research, the study identifies a disconnect between awareness and application, particularly in cognitive and behavioural domains. It recommends embedding hands-on, ethics-anchored, and discipline-inclusive AI training into teacher education curricula, aligned with the values of Education 5.0. The study further advocates for institutionalising a comprehensive AI literacy framework as a national competency model, ensuring that future educators are not only AI-literate but also equipped to lead ethical, inclusive, and transformative AI integration in Malaysian classrooms and beyond. This research offers actionable recommendations for curriculum reform and policy, aiming to empower educators with agency, adaptability, and ethical judgment in post-pandemic learning environments.

The paper presents research and preliminary findings aimed at improving curricula so that digital competencies are aligned with the required Artificial Intelligence (AI) literacy. The research was conducted at the Faculty of Technical Sciences in Čačak, University of Kragujevac (Serbia). The participants in the research were future computer science teachers and IT engineering students. The research tool for self-evaluation of AI literacy was a questionnaire based on the Serbian version of the AILS (Artificial Intelligence Literacy Scale), while digital competencies, based on the DigComp framework, were determined by objective testing. The research took into account the socioeconomic status of the students, demographic characteristics, and English language proficiency. Preliminary results indicated the persistence of significant relationships between certain digital competencies (such as programming, digital signal processing, and creative thinking) and all four constructs of AI literacy. The research findings highlight the impact of AI literacy on data analysis performance and problem solving.

ABSTRACT This self-study highlights the collaborative journey of two elementary teacher educators from a rural institution as they navigated the integration of Generative AI (GenAI) into teaching and research. To grow their AI literacy, they engaged in reflective practices, collaborative discussions, and hands-on implementation of GenAI technologies. Through this process, they documented their experiences with AI-driven chatbots, offering insights into their learnings and concerns. Their reflections underscored the importance of understanding GenAI’s role in education, the challenges of integrating GenAI tools effectively, and the potential of GenAI in teaching and learning. The educators concluded that while GenAI can augment human capabilities, it cannot replace human expertise due to its limitations. By capturing their journey, this study contributed to the growing discourse on AI literacy among teacher educators, emphasizing the need for upskilling and ongoing professional development in the face of rapidly evolving technologies.

Generation-Artificial Intelligence (Gen-AI) is widely used in education and has been shown to improve students’ mathematical abilities. However, dependency on Gen-AI may negatively impact these abilities and should be approached with caution. This study uses Structural Equation Modeling (SEM) to determine the relationship between AI literacy, AI trust, AI dependency, and 21st-century skills in preservice mathematics teachers. This research utilizes a self-designed questionnaire with 469 preservice mathematics teachers as respondents. SPSS and AMOS software were used for data analysis. The findings reveal that both AI trust and AI literacy significantly influence preservice mathematics teachers’ dependency on Gen-AI. Furthermore, this dependency on Gen-AI among preservice mathematics teachers has a significant negative effect on their problem-solving ability, critical thinking, creative thinking, collaboration skills, communication skills, and self-confidence. This research provides new information to governments, schools, and teachers that caution should be exercised when attempting to enhance AI literacy and trust in AI among preservice mathematics teachers.

In the realm of education, the integration of AI literacy into computer science teaching is becoming increasingly crucial (Walsh et al., 2023; Voulgari et al., 2022; Velander et al., 2023). Teachers play a pivotal role in bridging the gap between research and practical knowledge transfer of AI-related skills, necessitating a solid foundation in AI-related technological, pedagogical, and content knowledge (TPACK) (Velander et al., 2023). As AI systems permeate various aspects of society, including education, teachers must adapt and develop competencies in AI to effectively impart these skills to students (Kreinsen & Schulz, 2023). The incorporation of AI ethics into the curriculum requires teachers to navigate complex issues such as biases related to race, gender, and social class, challenging both computer science and humanities educators to step out of their comfort zones and collaborate to provide high-quality instruction (Walsh et al., 2023). By leveraging their expertise in different domains and receiving support from research teams, teachers can create engaging learning experiences that prepare students for the ethical and technical challenges posed by AI systems (Walsh et al., 2023).This article aimed to study the AI literacy level of teacher students major in digital technology who study at Nakhon Sawan Rajabhat University in Thailand. There were 98 students responded the AI literacy questionnaire which contained of 4 factors (Knowledge and Use of AI 24 questions, Creation of AI 3 questions, AI Self-Efficacy 6 questions, and AI Self-Competency 7 questions). The results showed that, 1) there were no statistically significant differences in gender among Knowledge and Use, AI Self-Efficacy, and AI Self-Competency while has statistically highly significant as P < 0.05 in Creation of AI factor, 2) there were no statistically significant different in level of study and use of time used of computer among AI literacy factors, and 3) there was relationship between AI literacy factors with statistically highly significant as P < 0.01.

The development of artificial intelligence is rapidly transforming education systems worldwide, including those in the Czech Republic. This paper assesses the level of AI literacy among teachers and its integration into the Czech education system. The aim is to determine how well teachers are equipped with knowledge, skills, and understanding of AI, and how effectively they can integrate AI and AI awareness into their teaching. The methodology entails gathering data via a questionnaire survey distributed to teachers across various educational levels. The survey comprises questions that concentrate on AI literacy aspects, such as basic AI principles, the capacity to apply AI to teaching, discussing ethical issues related to AI use in the school environment, and critical thinking abilities. The questionnaire analyses the current state and challenges teachers face when integrating AI into teaching and learning practices. The results will be evaluated to identify areas where teachers' AI literacy needs strengthening and to propose strategies and recommendations for improving teacher training programmes and support. These strategies comprise teacher training, the provision of resources and support for integrating AI into the classroom, and reflection on the ethical and societal aspects of AI. It is equally important to invest in the long-term development of AI literacy among teachers as a fundamental step towards effectively harnessing the potential of AI in education and preparing students for the digital future. This article presents strategies and recommendations for the further development of AI literacy among teachers in the Czech Republic. The objective is to enhance their capacity and facilitate the more effective utilisation of AI in education.

The integration of artificial intelligence (AI) in education highlights the growing need for AI literacy among K–12 teachers, particularly to enable effective human–machine cooperation. This study investigates Saudi K–12 educators’ AI literacy competencies across four key dimensions: awareness, ethics, evaluation, and use. Using a survey of 426 teachers and analyzing the data through descriptive statistics and structural equation modeling (SEM), this study found high overall literacy levels, with ethics scoring the highest and use slightly lower, indicating a modest gap between knowledge and application. The SEM results indicated that awareness significantly influenced ethics, evaluation, and use, positioning it as a foundational competency. Ethics also strongly predicted both evaluation and use, while evaluation contributed positively to use. These findings underscore AI literacy skills’ interconnected nature and point to the importance of integrating ethical reasoning and critical evaluation into teacher training. This study provides evidence-based guidance for educational policymakers and leaders in designing professional development programs that prepare teachers for effective and responsible AI integration in K–12 education.

While the transformative potential of artificial intelligence (AI) in education is widely recognized, the rapid evolution of these technologies necessitates a corresponding evolution in teacher education. This research sought to investigate the impact of a targeted training program on pre-service physics teachers’ AI literacy levels and their subsequent attitudes and intentions toward AI adoption in their future teaching.A pre-post-test control group quasi-experimental study was implemented among physics teacher education students. A 5 weeks long out-of-curriculum intervention was designed and implemented that combined theoretical grounding with practical, problem-based learning activities, with a focus on the use of various AI tools.There was a significant upswing in AI literacy performance post-intervention, showcasing that the training was effective in facilitating participants’ understanding and application of AI in educational contexts. Additionally, perceived usefulness of AI was found to be a partial mediator in the link between literacy scores and behavioral intention to embed generative solutions into potential teaching.The study concludes that incorporating comprehensive AI literacy programs into teacher education curricula is essential for fostering a technologically adept and pedagogically innovatively minded teaching workforce. Further research is needed to explore the long-term effects of AI literacy training on teacher practice and student learning outcomes.

This study investigates the relationships among Al literacy, teacher efficacy, and professional identity among English teachers in Korea's EFL context. As Al increasingly integrates into educational environments, it is crucial to understand how these factors interplay in shaping teachers' professional identities. The findings suggest that fostering Al literacy can significantly enhance teachers' sense of professionalism and efficacy, thereby reinforcing their unique roles in the Al era. However, the initial study's limited sample size necessitates ongoing research with a larger cohort to validate these results. Future studies should aim to refine research tools, expand sample sizes, and explore Al literacy development strategies tailored to different career stages and generations. Such efforts will contribute to a more comprehensive understanding of Al's impact on education and offer practical insights for teacher development in an Al-driven world.

This article highlights the significance of AI Literacy for promoting sustainable teacher education in an AI-driven world. Given the rapid progress of AI, a crucial aspect of organisational development for teacher education institutions involves fostering AI Literacy among teaching staff, and enabling them to use and teach AI ethically and responsibly. We underscore the necessity for teacher education institutions to create opportunities for developing AI Literacy as a fundamental goal for sustainable development. Further, we explore recommendations for sustainable organisational and professional development as well as future research directions.

Artificial Intelligence (AI) and its associated applications are ubiquitous in today's world, making it imperative that students and their teachers understand how it works and the ramifications arising from its usage. In this study, we investigate the experiences of seven teachers following their implementation of modules from the MIT RAICA (Responsible AI for Computational Action) curriculum. Through semi-structured interviews, we investigated their instructional strategies as they engaged with the AI curriculum in their classroom, how their teaching and learning beliefs about AI evolved with the curriculum as well as how those beliefs impacted their implementation of the curriculum. Our analysis suggests that the AI modules not only expanded our teachers' knowledge in the field, but also prompted them to recognize its daily applications and their ethical and societal implications, so that they could better engage with the content they deliver to students. Teachers were able to leverage their own interdisciplinary backgrounds to creatively introduce foundational AI topics to students to maximize engagement and playful learning. Our teachers advocated their need for better external support when navigating technological resources, additional time for preparation given the novelty of the curriculum, more flexibility within curriculum timelines, and additional accommodations for students of determination. Our findings provide valuable insights for enhancing future iterations of AI literacy curricula and teacher professional development (PD) resources.

Purpose This study aims to investigate how preservice teachers’ stages of concern, beliefs, confidence and interest in AI literacy education evolve as they deepen their understanding of AI concepts and AI literacy education. Design/methodology/approach AI literacy lessons were integrated into a technology integration course for preservice teachers, and the impacts of the lessons were evaluated through a mixed-methods study. The Concerns-Based Adoption Model was employed as the analytical framework to explore participants’ specific concerns related to AI. Findings Findings revealed that participants initially lacked AI knowledge and awareness. However, targeted AI literacy education enhanced preservice teachers’ awareness and confidence in teaching AI. While acknowledging AI’s educational benefits, participants expressed ongoing concerns after AI literacy lessons, such as fears of teacher displacement and the potential adverse effects of incorporating generative AI on students’ critical learning skills development. Originality/value Despite the importance of providing preservice teachers with AI literacy skills and knowledge, research in this domain remains scarce. This study fills this gap by enhancing the AI-related knowledge and skills of future educators, while also identifying their specific concerns regarding the integration of AI into their future classrooms. The findings of this study offer valuable insights and guidelines for teacher educators to incorporate AI literacy education into teacher training programs.

Objectives This study aimed to analyze the current status of AI competency among nursing students in a teacher training program and propose practical strategies to effectively integrate AI-based education into the curriculum. Methods A survey and focus group interviews were conducted with nursing students at a university in South Korea. The survey examined AI concept understanding, human-AI relationship perception, application competency, and AI ethics awareness. Focus group interviews explored curriculum design, instruction, and assessment improvements for AI integration. Results Students highly recognized the need for AI and digital technology education within the teacher training curriculum. They emphasized the importance of project- and practice-based learning, AI simulation tools, blended learning, and team-based problem-solving. Additionally, they expressed the need to integrate AI education into existing courses, apply performance-based assessments, and include AI competency evaluation in teaching practicums. Conclusions To enhance AI competency, expanding practice-based learning, systematically integrating AI content, and establishing performance-focused assessments are essential. This study presents specific improvement directions and highlights the need for broader follow-up research to build a sustainable, AI-integrated teacher education model.

The rapid evolution of cloud-native technologies demands agile pedagogical approaches to cultivate engineering competencies. However, current education struggles with cognitive overload caused by tool abstractions, outdated curricula, and ineffective skill transfer. To bridge these gaps, this paper introduces AI-Augmented Pedagogy (AIAP), an innovative teacher-AI collaborative approach integrating outcome-based education (OBE) with AI engines. Its core contribution is a teacher-driven optimization loop which empowers teachers to leverage AI engines to: (1) align curriculum with industry demands in real time; (2) generate authentic scenario-based experiments contextualizing theoretical knowledge; (3) structure adaptive visual knowledge graphs that decompose technical complexity; and (4) identify multidimensional skill gaps using AI-powered assessments. Validation in cloud-native courses demonstrated AIAP's tangible efficacy. Teachers dynamically aligned curricula with innovations in the cloud-native ecosystem and cultivated students’ essential abilities to design, deploy, diagnose, and optimize, resulting in a significant improvement in high-score rates. This establishes a reusable paradigm where teachers, guided by AI, convert technical abstractions into diagnosable scenarios, enabling effective skill transfer in evolving technical domains.

Generative artificial intelligence (AI) tools are rapidly transforming mathematics education by enabling automated problem generation, dynamic visualizations, and adaptive learning experiences. This study presents an empirical study on incorporating MATH41 into a 15-week pre-service teacher preparation course for mathematics majors. Twenty-four participants learned to create parameterized math problems, generate vector graphics. Following guided training, each participant designed and micro-taught a 20-minute lesson incorporating MATH41-generated resources. Reflection journals and final lesson plans were analyzed thematically, while 20 participants completed a self-assessment survey on lesson design competency. Results reveal that the automated problem generation capabilities motivated pre-service teachers to explore a broader range of instructional strategies, including personalized tasks and diverse problem variants. Reflection data indicate that while integrating AI tools can significantly boost confidence and creativity in lesson planning, careful pedagogical alignment remains essential to avoid superficial learning. Participants underscored the importance of maintaining teacher oversight—especially when adapting AI-generated problems for particular learner needs. Additionally, their post-course self-assessments showed high confidence in digital tool integration, yet they acknowledged that anticipating student misconceptions requires further field experience. Overall, this study contributes to understanding how teacher education programs can enhance lesson design competencies via structured AI tool integration. It also highlights the critical role of reflective practice in ensuring that automated content creation fosters deeper instructional effectiveness rather than uncritical AI dependency.

AI holds substantial and profound implications for teaching and learning, as well as for the enhancement of teachers' roles and capabilities. The integration of AI in educational contexts demonstrates considerable potential to enable innovative approaches to instruction, learning, and administration, while also improving learning experiences and facilitating teachers' professional responsibilities. As AI continues to transform modes of production, daily life, and educational practices, it is steering human society into a new developmental phase (Zhu & He, 2012).This research employs the Delphi method and integrates the "AI Competency Framework for Teachers" (AI CFT; Miao, 2024) with the Teacher AI Competence Self-efficacy (TAICS) scale, initially explored by Thomas et al. (2024) in their study on teacher competency self-efficacy. Grounded in Thomas’s scale and Bandura’s self-efficacy theory, the present study aims to develop a scale for assessing Teacher AI Competence Self-efficacy (TAICS) among university faculty. The scale design process will incorporate the perspectives of educators and account for the specific contextual factors of AI-mediated teaching and learning within higher education institutions in China’s border ethnic regions.

A new domain Media and Artificial Intelligence Literacy (MAIL) will be assessed in PISA 2029, and will involve four dimensions: engaging with AI, creating with AI, managing AI and designing AI. While this assessment primarily targets secondary education, it will also evaluate and enhance teachers’ preparedness for AI integration across educational levels. We believe that pre-service teacher education should integrate substantial AI literacy initiatives, as graduates will take on positions as educators in either secondary or primary school. In this conceptual paper, we propose that teacher preparation programmes at higher education institutions should be prepared and oriented towards the AI Competency Framework for Teachers. In this article, we propose the framework established by UNESCO in 2024. The AI Competency Framework for Teachers (AI CFT) includes fifteen skills and dispositions across five strands: human-centred mindset, AI ethics, technical foundations, pedagogy, and professional learning. Our proposal advocates for embedding an AI literacy curriculum within subject-specific courses and teaching methods courses while also offering hands-on AI integration experiences based on the demonstrable connection between the MAIL areas and AI CFT categories. By helping teacher-candidates rise from Acquire to Deepen—and, where possible, to Create in each strand, the programme is already preparing them to design the learning experiences that PISA 2029 will assess. This study is a foundational stage in developing a nationally validated scale to assess pre-service teachers’ readiness for AI-enabled classrooms in Australia.

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The integration of generative artificial intelligence (AI) into educational assessment has shown potential in addressing inefficiencies in traditional evaluation methods, particularly in time-constrained STEM classrooms. This study proposes a hybrid framework that synergizes teacher expertise with generative AI to streamline the evaluation of verbal skills-a critical yet underexplored competency in STEM education. By focusing on collaborative dialogue, problemsolving explanations, and conceptual reasoning, this research aims to develop a system that enhances assessment efficiency while preserving the nuanced judgment of educators. Verbal skill evaluation in STEM contexts-such as assessing students’ ability to articulate hypotheses, defend solutions, or collaborate in technical discussions-remains labor-intensive and subjective. Teachers spend significant time analyzing spoken or written responses, often sacrificing opportunities for personalized instruction. While generative AI model proficiency in language processing standalone using in education raises concerns: (1) lack of contextual awareness in STEM-specific discourse, (2) potential biases in automated scoring, and (3) displacement of teachers’ formative feedback roles. In this research using a mixed-methods approach was employed across three phases: Framework Development, Pilot Testing, Scalability. By bridging the divide between automation and human judgment, this hybrid framework demonstrates that generative AI need not replace teachers but can instead amplify their capacity to nurture critical verbal skills in STEM. Future work explored by adaptive AI tutoring systems that leverage this model to provide real-time dialogue support during student presentations or group discussions.

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Teachers, as educational personnel, are expected to enhance their competence in mastering information technology, which can later be utilized in classroom learning activities. However, in reality, teachers' knowledge and skills in mastering technology to support teaching and learning activities are still limited. Based on a survey conducted, teachers at SD Indriyasana, Baleendah, are eager to gain knowledge and skills in using open-source applications, such as Chat GPT, Gemini AI, and MajickPen AI, to support their tasks, both in teaching and administrative duties. Therefore, training and mentoring are needed in learning the use of these open-source applications to support teachers' activities. The aim of this training is to introduce and train teachers at SD Indriyasana, Baleendah, in using open-source applications based on artificial intelligence (AI), such as Chat GPT, Gemini AI, and MajickPen AI, to support teaching and administrative tasks. The method used involves training, direct practice by teachers, guided by speakers and instructors using AI-based open-source applications, namely ChatGPT, Gemini AI, and MajickPen. The implementation method includes the following steps: 1) user needs analysis, 2) literature study, 3) training module development, 4) module testing, 5) training implementation, 6) evaluation. The training results indicate that teachers acquired knowledge and practical experience in line with their needs, enabling them to use AI-based applications (Chat GPT, Gemini AI, and MajickPen AI) to support learning and administrative activities. It can be said that the training went well, as shown by the questionnaire results: 1) alignment of the training program with objectives (94%), 2) alignment of the program with partners' needs (71%), 3) adequacy of program implementation time (65%), 4) team’s ability to execute the program (88%), and 5) program sustainability (77%). This training activity is beneficial for both teachers and students because it can make learning more interesting and enjoyable for students. By utilizing AI-based applications, teachers can generate ideas for learning activities, create quizzes, or provide relevant and up-to-date teaching materials, thereby facilitating class preparation and easing teachers' administrative tasks.

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With the rapid development of Artificial Intelligence (AI) technology, the traditional role of teachers in vocational colleges and universities is undergoing profound changes. Combining the theory of educational technology integration and the theory of personalized learning, this study systematically explores the transformation of teachers' roles, including from knowledge transmitters to learning facilitators, from teaching evaluators to data analysts, from curriculum designers to technology integrators, and from classroom managers to learning community builders. Through literature analysis and case study methodology, the key competency requirements of teachers in these new roles are analyzed in depth and specific competency enhancement strategies are identified. These strategies include systematic theoretical learning, technology application practice, reflective improvement, and peer communication and sharing, aiming to help teachers effectively improve their teaching quality and professional competence while coping with the challenges of AI technology. The results of the study show that through the implementation of rational strategies, teachers are not only better able to adapt to the new technological environment, but also play a greater role in the sustainable development of vocational education.

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This study aimed to develop and validate the Mathematics Teacher Competency Diagnostic Tool (MTCDT) to systemati-cally assess mathematics teachers' professional competencies. In response to recent competency-based teacher development policies, five key domains were identified through an analysis of teacher training programs from 2019 to 2024: (1) mathematical content knowledge, (2) lesson design and implementation, (3) AI and digital integration, (4) learning guidance and coaching, and (5) reflection and improvement. A two-round Delphi survey was conducted with 13 experts in mathematics education and teacher training. All items demonstrated strong validity (CVI ≥ .90, CVR ≥ .80). The MTCDT offers a structured framework for diagnosing elementary to high school mathematics teachers' competencies and provides a foundation for designing modular, needs-based professional development programs.

This The rapid integration of digital technologies in education has transformed informal learning and teacher competency development, particularly in regions like Southern Pakistan, where access to resources is often limited. Leveraging digital platforms enhances English language acquisition and teachers’ functional skills, fostering 21st-century learning environments. Despite the growing use of Information and Communication Technologies (ICT) in education, limited research explores the combined impact of AI usage, digital competencies, and informal digital learning on teachers’ functional skills in Islamic schools in Southern Pakistan, particularly within informal learning contexts. This has important implications for improving instructional quality not only general education setting. This study aims to investigate the role of digital technologies in enhancing teachers’ functional competency, identify preferred digital platforms and activities for informal learning, and examine the influence of individual differences on engagement in informal digital learning among Islamic school teachers in Southern Pakistan. The findings may also inform the digital training need modernizing education delivery. Adopting a quantitative cross-sectional survey design, data were collected from 450 Islamic school teachers using purposive sampling based on established inclusion criteria. Data were analyzed using SPSS 22, with multiple linear regression assessing the influence of AI usage, digital competencies, and informal digital learning on teachers’ functional skills. Results revealed that informal digital learning had the strongest effect (β = 0.41, p < 0.001), followed by digital competencies (β = 0.35, p < 0.001) and AI usage (β = 0.28, p < 0.01), explaining 47% of the variance (R² = 0.47). AI’s smaller effect size suggests its underutilization in teaching practices. These findings highlight the need for enhanced teacher training in AI integration and digital competencies to maximize informal learning benefits, which can extend to professional development program in education as well, promoting competent Islamic teachers in Southern Pakistan.

The Teacher–AI–Student Triadic Model is proposed as a conceptual framework for advancing sustainable and human-centred artificial intelligence (AI) integration in vocational education. Responding to the growing demand for work-ready and sustainability-oriented graduates, the model reconceptualises the roles of teachers, students, and AI systems within AI-enhanced learning environments. Developed through a conceptual research methodology, the model draws on an in-depth literature review and synthesises constructivist learning theory, social learning theory, and competency-based education. It emphasises dynamic interactions among teachers, AI, and students as central to fostering adaptive learning, ethical reasoning, and sustainable thinking. Within the triadic structure, teachers function as ethical mentors and learning architects, students as self-regulated and adaptive learners, and AI as an intelligent collaborator supporting personalised learning and formative assessment. The model illustrates potential applications in vocational contexts, including green technology simulations, virtual laboratories, AI-supported assessment, and sustainability-oriented entrepreneurship training. By embedding the Sustainable Development Goals as a core pedagogical capability rather than a peripheral objective, the model offers a vocationally specific, ethically grounded framework aligned with the transition toward Society 5.0. This study contributes a structured foundation for future empirical research, policy development, and responsible AI implementation in vocational education.

This study develops an artificial intelligence (AI) education competency enhancement program for pre-service childcare teachers and examines its effects on technology acceptance intention and digital literacy. Participants included 36 pre-service childcare teachers from universities in the Daegu and Gyeongbuk regions. The 10-session program covered understanding AI, applying AI, and exploring AI values. Data were analyzed using SPSS Win 26.0, and independent t-tests and multivariate analysis of covariance (MANCOVA) were conducted. The analysis revealed that the program utilizing humanoid robots significantly improved participants’ technology acceptance intention and digital literacy. These results highlight the program’s potential for equipping pre-service childcare teachers with essential AI and digital competencies. This study contributes foundational insights for designing effective AI competency programs and offers implications for integrating AI and digital technologies into teacher education.

This paper explores teachers' readiness and competency in using AI in the classroom. Across different regions of the world, there are varied trends in how teachers adopt and implement emerging educational technologies. In technologically advanced education systems, robust professional development programs, supportive leadership, and widespread digital infrastructure facilitate greater teacher readiness and competency. Tools such as AI-powered adaptive learning platforms require teachers to rethink assessment, while virtual reality necessitates new approaches to immersive learning experiences. Some of the challenges hindering teachers' readiness includes Limited AI-Specific Training Opportunities, Technological Infrastructure Gaps in Schools, resistance to technological change and the solutions includes strengthen technical support in schools, foster leadership that champions AI integration, develop ethical guidelines and policy use of AI, address teachers' mindset and build technology self-efficacy. The growing reliance on artificial intelligence has significantly influenced the field of education, reshaping teaching, learning and assessment practices. Teachers’ readiness involves not only technical knowledge but also a positive mindset.  The future of AI in the classroom depends not just on technological advancement but on collective commitment to teacher empowerment and ethical innovation.  Therefore, this study recommends that teacher education programs must integrate AI literacy, professional development must be continuous and contextualized, and school leadership must cultivate a culture that supports innovation.

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Objectives The purpose of this study is to assess the level of AI and digital competencies among vocational high school teachers and to analyze their educational needs in order to propose effective support strategies for enhancing teacher competencies. Methods A survey was conducted with 62 vocational high school teachers who participated in the 2024 Big Data and AI Education Training Program. To assess AI and digital competency levels and educational needs, descriptive statistics, Borich Needs Assessment, and the Locus for Focus model were used. Results The results revealed that vocational high school teachers perceived the importance of all AI and digital competency items to be higher than their current competency levels, indicating a strong awareness of the significance of these technologies despite limited actual proficiency. Among the competencies, the highest educational need was identified in the area of ethics and security, followed by professional development, assessment and reflection, and teaching and learning competencies. Conclusions The findings highlight the need for policy-level support and the development of systematic training programs to enhance AI and digital competencies among vocational high school teachers. Priority should be given to strengthening competencies related to data anonymization and secure management processes, establishing ethical standards through targeted training, providing AI-based personalized learning feedback, and improving the effective integration of digital technologies into teaching practices.

This study investigates the effects of generative artificial intelligence (GenAI) tools on the professional competency development of higher education teachers. A mixed-methods design was employed, combining a quasi-experimental approach with semi-structured interviews. A total of 186 university teachers from Uzbekistan participated in the study. Results indicated that GenAI integration significantly enhanced teachers' Technological Pedagogical Content Knowledge (TPACK) scores (p < .001), with notable improvements in digital competencies and self-efficacy levels. Qualitative data revealed that GenAI tools facilitated lesson planning, personalized instruction, and assessment optimization. However, concerns regarding ethical issues, academic integrity, and over-reliance on AI were also identified. The study proposes a GenAI-TPACK pedagogical model and provides practical recommendations for teacher preparation programs in higher education contexts.

The present study explored the role of teacher attitudes towards artificial intelligence as a mediator on the relationship between ICT competency and innovative instructional practices. Utilizing a cross-sectional survey design, a proposed conceptual model was examined. Data were obtained from 593 pre-service teachers enrolled in three teacher education programs in Egypt. Structural equation modeling (SEM) was employed to analyze the structural relationships among the variables. The findings are crucial for pre-service teacher education, offering insights for curriculum designers and policymakers aiming to enhance teacher preparedness for future classrooms, because attitudes towards AI, ICT competencies, and innovative instructional practices can be cultivated through learning experiences. Prior to initiating their professions, professional learning activities may be designed to pre-service teachers, emphasizing ICT competency and attitudes towards AI to indirectly enhance their innovative practices.

The pedagogical promise of Competency-Based Education (CBE) has been historically undermined by profound challenges of scalability, creating an implementation gap between its theoretical merits and practical application. This paper proposes a testable mechanism model wherein Artificial Intelligence (AI) enables the scaling of CBE through three interconnected pathways—diagnostic tracking, adaptive supply, and teacher orchestration—formalized within a distributed cognition framework. To operationalize this model, this paper introduces novel constructs including the "Adaptive-Autonomy Curve" for systematically cultivating self-regulated learning in personalized environments, and a "Situated Performance-Based Assessment Pipeline" for authentic, scalable evaluation of complex skills. The primary contributions of this work are fourfold: first, it provides a rigorous conceptual taxonomy that delineates CBE from adjacent paradigms such as mastery learning and personalized learning; second, it advances a set of falsifiable propositions to guide future empirical research; third, it formalizes the human-AI pedagogical relationship with operational design principles; and fourth, it presents an integrated governance and interoperability protocol for the responsible and effective implementation of AI in competency-based systems.

The rapid development of information technology has put forward higher requirements for teachers, and the traditional training model is difficult to meet the demand. The article constructs a teacher digital competency framework based on the ASTD model, realizes the division of teachers' professional competence, and explains the professional core connotation of teacher digital competency in detail. A personalized resource recommendation model for teachers is constructed using artificial intelligence technology, which provides accurate recommendations for teachers through candidate resource extraction and learning resource screening. At the same time, with the help of Google Cloud Services digital tools, the design of teachers' digital teaching and research activities was accomplished, and communication and cooperation with users in the virtual community was promoted. The combination of the two is integrated into the development of teachers' professional skills to enhance their teaching competence. The mean values of accuracy, applicability, timeliness, personalization, and diversity of learning resource recommendations under artificial intelligence technology ranged from 4.123 to 4.544, with good recommendation performance. The Google Cloud Services platform can promote teaching and research exchange activities among teachers. The use of artificial intelligence and digital tools makes teachers improve their professional skills in knowledge base, instructional design, teaching and research between 24.04% and 91.00%, and with their intervention, teacher competency shows significant improvement.

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ABSTRACT Artificial Intelligence (AI) is increasingly embedded in daily life, transforming how we process information, analyse data, and communicate. As AI enables new forms of interaction, educators need to assess its implications for teaching and learning. This study examines how AI tools, specifically ChatGPT, were integrated into preservice teacher education to support learning activities and competency development. Using a mixed-methods approach, a survey was used to assess the impact of AI-supported learning on preservice teachers’ knowledge, skills, and attitudes. Additionally, follow-up interviews provided deeper insights into their experiences and perceptions of AI’s role in education. By analysing both overall patterns and personal experiences, this study explores the opportunities and challenges of integrating AI into teacher preparation.

Creativity is an essential competency for teachers in the 21st century, yet it remains underrepresented in many teacher education programs. This study explores how teaching creativity can be systematically integrated into BA-level teacher training applying AI-driven tools to enhance learning outcomes. Drawing on the Hierarchical Pyramid, AI TPACK, and UNESCO AI Competency Frameworks, the study investigates teacher-students’ and faculty perceptions of creativity and AI integration in three Azerbaijani pedagogical universities. A qualitative methodology, including semi-structured interviews with 15 teacher-students and 6 faculty members, revealed significant gaps in structured creativity training and AI literacy. Findings highlight the complementary potential of the three frameworks in fostering creativity and professional development. To address these gaps, this study proposes a structured process for integrating creativity teaching and AI tools into teacher education, providing practical strategies aligned with global competency standards. Implications for curriculum design, professional development, and further research are discussed.

Abstract: The increasing presence of artificial intelligence (AI) in education presents both opportunities and challenges for K–12 teacher education. As educators prepare students for an AI-driven world, their readiness to integrate generative AI (GenAI) tools into classrooms becomes crucial. Building on the emerging AI-TPACK framework, this paper extends its theoretical scope to the generative era by introducing three novel dimensions including prompt literacy, ethical AI engagement, and teacher-AI-student partnerships, and examining their implications for teacher readiness and professional learning. Guided by three questions on readiness, competency development, and institutional response, the paper synthesizes current research on GenAI integration and proposes a Strategic Roadmap that operationalizes this extended AI-TPACK model through curriculum design, faculty development, and ethically grounded, equity-focused preparation. Together, the extended AI-TPACK model and the Strategic Roadmap position teacher readiness for GenAI as a paradigm shift in educational thought and practice.

This paper explores how to use ChatGPT-4 as an assistant for personalized teaching in the poetry writing class of Class 1 of the third-year university majoring in Chinese language and literature, to realize the human-computer collaboration between ChatGPT and the teacher, and conduct personalized teaching to provide students with personalized teaching. professional guidance and timely feedback. Traditional poetry writing teaching is difficult to meet the personalized needs of large-scale classes, and the introduction of artificial intelligence (AI) assistants can effectively solve this challenge. Therefore, this paper discusses how to use the advice of ChatGPT -4 and the professional guidance of teachers to improve the efficiency and quality of students' poetry homework correction, achieve personalized teaching guidance for students, and thus promote the improvement of poetry writing skills.

With the rapid development and application of artificial intelligence technology, education is ushering in unprecedented changes. Through data analysis and intelligent algorithms, AI can achieve an efficient allocation of educational resources and optimization of the teaching process, while humanmachine collaboration, as a new educational model, emphasizes the collaboration between human intelligence and machine intelligence, opening up new paths for educational equity and innovation. In this study, we firstly sort out the current status quo related to human-machine collaboration and AI-assisted education; secondly, we analyze the connotation of human-machine collaboration and explore three types of human-machine collaborative teaching forms and related examples in virtual space, physical space, and hybrid space; thirdly, we illustrate the four types of application scenarios of human-machine collaboration in instructional design and teacher-student interaction, which are mainly the application and practice of AI in educational decision-making, intelligent tutoring, automatic assessment, and classroom evaluation scenarios; finally, it analyzes the future development trend of AI+education fusion, and at the same time points out the challenges of teachers' role and responsibility transformation, technology adaptability, and ethical risks, based on which it proposes the development path of future AI + education fusion.

This work explored the area of human-machine collaboration in actual educational contexts based on the analysis of the ways of interaction between the teacher and the artificial intelligence (AI) devices and how the interactions affect teaching process, student experience, and the circumstances facilitating or inhibiting AI adoption. The quantitative descriptive-analytical type of research was used to gather data using a stratified sample of 312 teachers in primary, secondary, and tertiary institutions by administering a validated Likert-scale questionnaire. Descriptive statistics showed that the sample was diverse and experienced AI (56% of which were weekly users). The outcomes of Objective 1 revealed high consistency that AI facilitated instructional decision-making, lessened the work load, and increased accuracy and permitted instructors to maintain autonomy (mean = 3.85). The regression analysis revealed that human-machine collaboration was a significant predictor of the teaching and learning outcomes that explained half of the variance (50% 0.71 = 0.000). The results of ANOVA showed that there were significant differences in the perceived AI-based challenges between the groups of teaching experience (F = 6.27, p = 0.000), indicating that the adoption of AI depends on the background and exposure of the profession. All in all, the conclusions point to the fact that AI is an effective instructional companion but needs constant institutional assistance, professional growth, and access to technology equal opportunities to be successfully incorporated into the teaching process.

This study aims at the current problems such as the disconnection and insufficient adaptability between digital teaching resources and the needs of teachers and students, and constructs a teaching resource optimization model from the perspective of human-machine collaboration, in order to improve resource allocation, enhance the accuracy of resources and personalized teaching. By analyzing the demands of teachers and students, a resource optimization model centered on the trinity collaboration of "teacher - GenAI - student" is proposed. Teaching resources are scientifically divided, and a four-step dynamic process of "data collection - demand transformation - resource generation - application iteration" is designed. Taking the "Obstacle Avoidance Design of Intelligent Logistics Robots" project of the Python course in higher vocational education as a case for practical verification, the results show that students' mastery rate of sensor principles, code debugging efficiency, and ability of cross-scenario knowledge transfer have all improved. This model has achieved the transformation of teaching resources from supply-driven to demand-led by precisely matching the needs of teachers and students and dynamically optimizing the supply of resources, providing theoretical support and practical paths for the construction of the human-machine collaborative education ecosystem.

With the latest progress and extensive application of AI in recent years, the educational sector has never experienced such a huge transformation. As a new teaching mode based on artificial intelligence technology and teacher’s subject knowledge that has been applied to College English classroom, human-machine collaboration teaching model has been applied to English classroom. This study is based on theory of symbiosis to establish and practice a “teacher-student-machine” human-machine teaching model applicable to college English teaching. The experiment continued for 12 weeks, pre-test and post-test were carried out to compare students’ academic performance and deep interviews of one teacher and nine students from experimental group were made to collect their opinions and comments about this teaching model. The above results are found to illustrate that this type of model can not only effectively enhance students’ English grade, but also stimulate teachers’ professional development and role mutation, and initially construct teachers, students and intelligent machine’s mutual-beneficial and symbiosis teaching ecosystem.

In the intelligent age, the integration of artificial intelligence (AI) into education has sparked profound transformations, challenging traditional pedagogical paradigms. This study addresses the critical research question: How can human teachers and machines synergize their distinct strengths to optimize educational outcomes in the AI era? Through a mixed-methods approach combining literature analysis, case studies, and comparative frameworks, we systematically evaluate the complementary roles of humans and machines across cognitive, emotional, and skill-based dimensions. Our findings reveal that human-machine symbiosis — not substitution — is essential for fostering personalized, equitable, and innovative education. By redefining teacher roles (e.g., from “lecturer” to “facilitator”) and repositioning machines as collaborative partners, this research proposes a dynamic interaction model that enhances pedagogical efficiency and student development. The study contributes novel theoretical insights to human-AI collaboration in education and offers actionable strategies for policymakers and educators to navigate the evolving educational landscape.

Human-machine hybrid enhanced intelligence, as a cutting-edge technology in the advancement of artificial intelligence, can provide dynamic support for innovative developments in intelligent education characterized by human-machine collaboration. Addressing the prevalent issue in current university programming courses where learning feedback relies excessively on either the teacher or machine, it is of great theoretical and practical significance to explore the tripartite composite subject hybrid enhanced teaching model. Based on the principles of human-machine hybrid-enhanced intelligence, Robert Gagne’s instructional process, and perspectives on learning feedback, a programming teaching model of "teacher-machine-student" hybrid enhancement is proposed. This model was applied to a C language course at a certain university, involving 141 students from three classes. The aim is to provide insights into the application of artificial intelligence technologies in programming education. The results demonstrate that the proposed model accurately identifies issues in programming teaching and promptly delivers personalized learning feedback, thereby enhancing teaching effectiveness.

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In the context of the digital intelligence era, artificial intelligence (AI) is driving a transformation in the education system from the traditional "teacher-student" binary classroom model to a "teacher-student-machine" triadic collaborative mode. Based on educational ecology and human-machine collaboration theories, this paper analyzes how AI reshapes the roles of teachers, students, and machines. Taking a public management course as an example, it demonstrates the practical pathways for reconstructing the teaching process in a "teacher-student-machine triadic collaborative educational ecosystem.

Against the backdrop of generative artificial intelligence driving the digital transformation of education, high school Chinese oral teaching faces long-term challenges of insufficient interactive depth and limited personalized guidance. This article primarily addresses how human-machine collaboration can reconstruct the teacher-student interaction mode in high school Chinese language classrooms to improve the quality and effectiveness of oral teaching. The study focuses on the reconstruction of the tripartite interactive relationship between "teacher student Artificial Intelligence (AI)" as the central theoretical thread, and systematically sorts out the changing characteristics of classroom interaction in terms of subject role division, interactive structure form, and teaching scene extension under the background of intelligent technology empowerment. The key technologies represented by speech recognition and education models have changed the interactive mechanism of oral teaching by providing instant feedback, generating contextualized content, and supporting multidimensional dialogue. Although notable progress has been achieved in practical applications, this study also points out that there are still practical challenges such as insufficient technological adaptability, lagging teacher professional development, and the urban-rural "digital divide". Therefore, in the future, efforts need to be made to develop subject specific intelligent agents, establish a normalized teacher collaborative development mechanism, and establish scientific ethical and evaluation standards to promote the integration of human-computer collaboration from "demonstrative applications" to "normalized and deep" classroom ecology, ultimately serving the comprehensive improvement of students' language construction and application core literacy.

This paper focuses on the transformation of the project-based learning (PBL) teaching model driven by artificial intelligence (AI), and explores the two-way reconstruction path of teacher roles and student abilities. Combining metacognitive theory to analyze the pain points of traditional PBL, this paper systematically sorts out the functional reconstruction path of AI in the dimensions of teaching design, process monitoring, and evaluation feedback. Then, starting from the social role theory, this paper deeply analyzes the transformation of teacher identity and the reconstruction of student abilities in the AI-PBL fusion scenario. AI not only reshapes the logic of cultivating students’ abilities but also prompts teachers to achieve deep changes in their roles at the cognitive, relational, and ethical levels. Human-machine collaboration should not replace teachers’ emotional values and educational judgments, but should become a key support for optimizing the educational ecology and realizing personalized education.

The rapid advancement of generative artificial intelligence is profoundly reshaping the pedagogical landscape of higher education, transforming the relationship between human teachers and intelligent machines from traditional "instrumental application" to "symbiotic collaboration." This paper introduces the concept of "AI Co-conspiring," which describes the deep synergy and bidirectional empowerment formed between teachers and AI under the guidance of teaching objectives. Under the "AI Co-conspiring" framework, the role of teachers faces a multifaceted transformation from "knowledge transmitter" to "learning ecosystem designer," "cognitive guide," and "value leader." Based on the concept of human-machine co-intelligence, this study systematically elaborates on the connotation of new teaching wisdom, reveals its dual developmental trajectory of "transformation of knowledge into wisdom" and "transformation of technology into wisdom," and constructs a three-stage progressive model of teaching wisdom generation encompassing "knowledge co-creation—thinking co-shaping—cognitive self-emergence." Further, the practical forms of new teaching wisdom are analyzed across four dimensions: teaching design wisdom, classroom interaction wisdom, evaluation feedback wisdom, and ethical judgment wisdom. Building on this foundation, the study proposes a multi-tiered teacher competency growth pathway centered on the TPAiK framework, a teaching support system oriented toward structural embedding, and an ethical framework guided by value-sensitive design as developmental pathways. The research demonstrates that "AI Co-conspiring" does not signify the dissolution of teacher subjectivity but rather presents an opportunity for the leapfrogging of teaching wisdom, ultimately aiming to construct a new higher education ecosystem characterized by human-machine collaboration and symbiotic prosperity.

The development of Artificial Intelligence (AI) has had a profound impact on all aspects of human society. The digital transformation and upgrading of artificial intelligence have promoted profound changes in the education system. As the leader of school education activities, teachers should actively play their leading role in the era of artificial intelligence. Based on the background of the era of artificial intelligence, starting from the dilemma faced by teachers’ professional development in reality, the paper analyzes the path of teachers’ leading role in the era of artificial intelligence, and proposes to adhere to man-machine collaboration and strengthen the cultivation of artificial intelligence literacy, strengthen teachers’ subjectivity and show humanistic care, realize the role transformation and establish the educational concept of lifelong learning.

PRINTEPS is currently being developed as a total intelligent application, which has sub systems for knowledge-based reasoning, speech dialogue, image sensing, motion planning, and machine learning, in order to support end users on easily developing intelligent applications for human-machine collaboration. In this paper, a lesson application for collaborative teaching among a robot, laptop PC, sensor, teachers and students was developed with PRINTEPS. The implementation lesson was performed in a science class for six grade elementary students.

This study explores teacher professional development in the AI era by conducting an in-depth analysis of global educational policy frameworks, resource allocation, teaching competencies, and ethical considerations. Its key findings highlight five critical challenges faced by educators:1)The pressure to update knowledge and integrate interdisciplinary insights;2)The shift from traditional teaching methods to innovative approaches;3)The need to balance role definition with career advancement;4)The handling of ethical dilemmas and academic integrity issues;5)he ensuring of data privacy and security compliance.To tackle these challenges, future development strategies should focus on three dimensions: role evolution, human-machine collaboration, and continuous learning. Theoretically, this research delineates the scope of challenges in AI-enhanced teacher development, identifies their interconnections, and clarifies the components and relationships of future career trajectories. These findings lay a theoretical foundation and provide a research framework for exploring the interaction mechanisms between AI and teachers in education.From a practical standpoint, teachers should proactively embrace the pressures of knowledge renewal and interdisciplinary integration. They need to enhance their teaching competencies, adapt their instructional methods, clarify their professional roles, cultivate a mindset for continuous learning, and prioritize educational ethics and data privacy security. For policymakers, it is essential to formulate reasonable educational policy frameworks based on national educational realities, provide sufficient resource support, establish robust teacher training systems, and facilitate the role evolution and human-machine collaboration of educators. This will enable better adaptation to AI-driven educational environments and promote high-quality development in the education sector.​

This paper explores the paradigm reconstruction of interpreting pedagogy driven by generative AI technology. With the breakthroughs of AI technologies such as ChatGPT in natural language processing, traditional interpreting education faces dual challenges of technological substitution and pedagogical transformation. Based on Kuhn’s paradigm theory, the study analyzes the limitations of three traditional interpreting teaching paradigms, language-centric, knowledge-based, and skill-acquisition-oriented, and proposes a novel “teacher-AI-learner” triadic collaborative paradigm. Through reconstructing teaching subjects, environments, and curriculum systems, the integration of real-time translation tools and intelligent terminology databases facilitates the transition from static skill training to dynamic human-machine collaboration. The research simultaneously highlights challenges in technological ethics and curriculum design transformation pressures, emphasizing the necessity to balance technological empowerment with humanistic education.

This paper explores the empowerment mechanisms and practical pathways for foreign language teacher professional development in the digital-intelligence era. With the rapid advancement of artificial intelligence (AI) technology, the field of foreign language education is undergoing profound transformations, presenting new demands on the professional competencies of foreign language teachers. The study examines how digital-intelligence technologies facilitate teachers’ professional growth through cognitive, affective, and behavioral empowerment mechanisms, analyzed from both "internal" and "external" dimensions. The endogenous-driven pathway emphasizes the transformation of teachers' professional cognition and the advancement of their competencies, including the integration of technology, human-machine collaboration, and educational creativity. The exogenous-collaborative pathway focuses on policy support, resource optimization, and the establishment of systematic training systems to build a new digital-intelligence education ecosystem. This research not only enriches the theoretical framework of teacher professional development but also provides practical guidance for foreign language teachers to enhance their capabilities in the digital-intelligence era, contributing significantly to the innovative development of foreign language education in China.

Artificial Intelligence is profoundly reshaping the cognitive patterns and behavioral models of human society, presenting unprecedented challenges and opportunities for traditional moral education. From the theoretical perspective of paradigm shift, this paper systematically analyzes the threefold deconstruction of moral education by AI in terms of educational authority, educational fields, and value systems, while exploring the innovative possibilities AI brings to moral education in methodology, content systems, and ecosystem development. The research indicates that moral education in the intelligent era needs to construct a new paradigm centered on "human-machine collaboration," guided by the three principles of value anchoring, critical integration, and life care, to cultivate responsible creators and change-makers equipped with digital literacy and humanistic spirit. Finally, the article proposes concrete implementation pathways from four dimensions: top-level design, curriculum development, teacher training, and environmental construction.

The integration of generative artificial intelligence (AI) into college ideological and political education helps enhance the precision of value guidance and the penetration of communication, expand the boundaries of educational interaction, and alleviate the structural problem of resource distribution inequality. However, its involvement also brings deep risks such as the weakening of emotional warmth, the blurring of value stances, and the restructuring of educational relationships. It is necessary to be vigilant about the tension between technological rationality and ideological rationality. Colleges should promote the coordinated coexistence of technology integration and value orientation by building an emotional mechanism of human-machine collaboration, establishing a multi-level content generation review system, and reshaping the teacher’s leading role.

With the digital transformation of education, artificial intelligence (AI) is driving systematic changes in the teaching philosophy, content structure, and practical approaches of military theory courses in higher education. Leveraging technologies such as data profiling, virtual simulation, and knowledge graphs, the teaching model has transformed from one-way knowledge delivery to immersive experiences and contextualized cognition. The knowledge system has undergone modular restructuring. Through practices like battlefield simulations and war-gaming, both the course appeal and the effectiveness of national defense education have been significantly enhanced. However, challenges remain, including insufficient AI literacy among instructors, the potential erosion of teacher-student agency by algorithmic power, and data privacy risks. Therefore, there is an urgent need to improve teacher training and human-machine collaboration mechanisms, and to establish robust data classification management and ethical review frameworks. This aims to unify technological empowerment with value guidance, thereby fostering a new ecosystem for military theory courses in higher education that upholds fundamental principles while encouraging innovation.

To explore the construction of the ideal paradigm of colleges and universities with artificial intelligence technology in the process of higher personnel training. Using the literature research method, this paper expounds from three aspects: origin, positioning and path. Origin: Artificial intelligence is the reality of technological development in the cultivation of higher talents in the new era, and it is a realistic demand in the cultivation of higher talents, which requires political and ideological empowerment. Positioning: Determining the role of artificial intelligence as an "education assistant", and then clarifying it as a "quasi-subject object", is the basic positioning for the introduction of higher talents at this stage. Paths: 1) Build a teacher-student interconnection model supported by "human-machine collaboration"; 2) Build a smart learning and smart teaching model supported by an "algorithm database"; 3) Establish a "people-oriented" assessment and purpose-led mechanism principle, Deeply improve the quality of the cultivation of higher education personnel in our country's high-level colleges and universities, and promote the final formation of its training pattern.

The rapid development of Artificial Intelligence (AI) is transforming traditional educational models, gradually shifting from 'human-to-human' to 'human-machine collaboration' (HMC). Human-Machine Collaboration (HMC) is gradually becoming an important paradigm in the field of education, especially in foreign language teaching. This paper explores the application of human-machine collaboration in foreign language teaching, focusing on its theoretical foundations, practical cases, advantages, and challenges. Based on Cognitive Load Theory, Constructivist Learning Theory, and Personalized Learning Theory, this paper analyzes how human-machine collaboration can optimize teaching efficiency and enhance personalized learning experiences. Through case studies such as AI language platforms and real-time translation technologies, this paper demonstrates the potential of human-machine collaboration in providing immediate feedback, customizing learning paths, and fostering intercultural communication competence. However, the paper also points out the challenges posed by over-reliance on technology, the weakening of critical thinking, and issues related to data privacy. Finally, this paper looks forward to future development directions, emphasizing the transformation of teachers' roles and the necessity of a balanced combination of artificial intelligence and human wisdom to promote comprehensive innovation in foreign language teaching.

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This work is an approach that brings together Learning Analytics and Ontologies for a data classification that promotes improvements and behavioral changes for students and teachers on e-Learning platforms. Combining training courses, dashboards, user's evaluations, and based on Design Science Research (DSR) methodology, artifacts were created. One of the most important artifacts of our work is the Sapes tool that aims to improve students’ perceptions of their learning path and to promote a better teacher overview to follow their students' progress. The results showed high approval by the participating students and teachers, who perceived the Sapes tool as a good facilitator of the teaching-learning process, with possibilities for self-monitoring, dynamization of the learning sequence and better interactivity with colleagues, highlighted as absent in standard e-Learning courses. In addition, the application changed the behavior of users towards the content provided by the teacher, with students performing self-management and self-regulation that were not commonly performed previously.

Despite the potential of learning analytics (LA) to support teachers’ everyday practice, its adoption has not been fully embraced due to the limited involvement of teachers as co-designers of LA systems and interventions. This is the focus of the study described in this paper. Following a design-based research (DBR) approach and guided by concepts from the socio-cultural perspective and human-computer interaction (HCI), we design, test, and evaluate a teacher-facing LA dashboard, the Canvas Discussion Analytics Dashboard (CADA), in real educational settings. The goal of this dashboard is to support teachers’ roles in online environments through insights into students’ participation and discourse patterns. We evaluate CADA through 10 in-depth interviews with university teachers to examine their experiences using CADA in seven blended undergraduate and graduate courses over a one-year period. The findings suggest that engaging teachers throughout the analytics tool design process and giving them control/agency over LA tools can favour their adoption in practice. Additionally, the alignment of dashboard metrics with relevant theoretical constructs allows teachers to monitor the learning designs and make course design changes on the fly. The teachers in this study emphasise the need for LA dashboards to provide actionable insights by moving beyond what things are towards how things should be. This study has several contributions. First, we make an artefact contribution (e.g. CADA), an LA dashboard to support teachers with insights into students’ online discussions. Second, by leveraging theory, and working with the teachers to develop and implement a dashboard in authentic teaching environments, we make an empirical, theoretical and methodological contribution to the field of learning analytics and technology enhanced learning. We synthesise these through practical design and implementation considerations for researchers, dashboard developers, and higher education institutions.

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In online learning, teachers need constant feedback about their students’ progress and regulation needs. Learning Analytics Dashboards for process-oriented feedback can be a valuable tool for this purpose. However, few such dashboards have been proposed in literature, and most of them lack empirical validation or grounding in learning theories. We present a teacher-facing dashboard for process-oriented feedback in online learning, co-designed and evaluated through an iterative design process involving teachers and visualization experts. We also reflect on our design process by discussing the challenges, pitfalls, and successful strategies for building this type of dashboard.

Abstract To address the challenge of overwhelming data inherent in classroom lesson videos, this study proposed a visual learning analytics (VLA) approach to video-based teacher professional development (TPD). Using a two-year experimental design, 46 secondary mathematics teachers were divided randomly into a treatment group (N = 24) and a control group (N = 22) to learn about and integrate academically productive talk into their teaching. The treatment teachers participated in a VLA-supported TPD program, while the control teachers participated in conventional knowledge-based workshops. Results show that teachers in the treatment group had more positive beliefs and higher self-efficacy in the post-test and delayed-post-test, while the control group improved, but not significantly, in their beliefs about the usefulness of classroom talk. In addition, although the control group made a significant improvement in their self-efficacy in guiding classroom talk in the post-test, this improvement was not sustained to the delayed post-test. Moreover, the coding of classroom teaching behaviour revealed that teachers in the treatment group relative to the control group significantly increased their use of academically productive talk in the post-test lessons to encourage the students' elaboration, reasoning, and thinking with others in the classroom. The results suggest that, while attending knowledge-based workshops had, to some degree, positive effects on the control teachers' beliefs and self-efficacy, these effects were not sustainable over time. In contrast, the use of visual learning analytics to support the treatment group's reflection on the classroom data not only had significant and sustained effects on the teachers' beliefs and self-efficacy but also significantly influenced their actual classroom teaching behaviour. Implications for designing VLA to support teacher learning and professional development are discussed.

This study focuses on learning analytics from the perspective of elementary and secondary classroom teachers (grades one to nine). The aim is to explore teachers’ perceptions about the use of learning analytics, the challenges and opportunities associated with the tools, and the future of the analytics. The research is based on qualitative data: open-ended responses from 144 teachers. Analysis was conducted using qualitative content analysis and latent class analysis. The results highlight the prominent role of simple drill and practice applications with dashboards and differing teacher perceptions of analytics. Learning analytics helps teachers better understand class learning activities, focus their attention and attain professional development goals. Challenges discussed include the need for new skill development, increased workloads and pedagogical limitations of the current technology. The study shows the necessity for more versatile learning analytic tools with diverse pedagogical practices. It also demonstrates that teachers’ skills must be enhanced to use analytics for pedagogically sound actions.

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Discussion amongst the learning analytics community is for the need to link learning analytics with learning design. However, learning design is a complex space, and not all approaches to design for learning are the same. With this proposed integrated practice comes unique challenges for the contemporary educator. In this paper, we focus on the underlying processes of teacher inquiry when connecting design for learning with learning analytics. We propose that valuable connection between learning analytics and design for learning is only realised through the mediation of effective teacher inquiry processes. Hence, we aim to better understand these processes in order to identify teachers’ developmental needs. To this end, we propose a working model and examine learners (as educators) inquiry process in a professional learning workshop. We conclude with implications for the proposed model and professional learning.

Collaborative problem solving (CPS) has emerged as a crucial 21st century competence that benefits students’ studies, future careers, and general well-being, prevailing across disciplines and learning approaches. Given the complex and dynamic nature of CPS, teacher-facing learning analytics dashboards (LADs) have increasingly been adopted to support teachers’ CPS assessments by analysing and visualising various dimensions of students’ CPS. However, there is limited research investigating K-12 teachers’ integration of LADs for CPS assessments in authentic classrooms. In this study, a LAD was implemented to assist K-12 teachers in assessing students’ CPS skills in an educational game. Based on the person-environment fit theory, this study aimed to (1) examine the extent to which teachers’ environmental and personal factors influence LAD usage intention and behaviour and (2) identify personal factors mediating the relationships between environmental factors and LAD usage intention and behaviour. Survey data of 300 in-service teachers from ten Chinese K-12 schools were collected and analysed using partial least squares structural equation modelling (PLS-SEM). Results indicated that our proposed model showed strong in-sample explanatory power and out-of-sample predictive capability. Additionally, subjective norms affected technological pedagogical content knowledge (TPACK) and self-efficacy, while school support affected technostress and self-efficacy. Moreover, subjective norms, technostress, and self-efficacy predicted behavioural intention, while school support, TPACK, and behavioural intention predicted actual behaviour. As for mediation effects, school support indirectly affected behavioural intention through self-efficacy, while subjective norms indirectly affected behavioural intention through self-efficacy and affected actual behaviour through TPACK. This study makes theoretical, methodological, and practical contributions to technology integration in general and LAD implementation in particular.

As a further step towards maturity, the field of learning analytics (LA) is working on the definition of frameworks that structure the legal and ethical issues that scholars and practitioners must take into account when planning and applying LA solutions to their learning contexts. However, current efforts in this direction tend to be focused on institutional higher education approaches. This paper reflects on the need to extend these ethical frameworks to cover other approaches to LA; more concretely, small-scale classroom-oriented approaches that aim to support teachers in their practice. This reflection is based on three studies where we applied our teacher-led learning analytics approach in higher education and primary school contexts. We describe the ethical issues that emerged in these learning scenarios, and discuss them according to three dimensions: the overall learning analytics approach, the particular solution to learning analytics adopted, and the educational contexts where the analytics are applied. We see this effort as a first step towards the wider objective of providing a more comprehensive and adapted ethical framework to learning analytics that is able to address the needs of different learning analytics approaches and educational contexts.

No abstract available

Teacher dashboards can help secondary school teachers manage online learning activities and inform instructional decisions by visualising information about class learning. However, when designing teacher dashboards, it is not trivial to choose which information to display, because not all of the vast amount of information retrieved from digital learning environments is useful for teaching. Information elicited from formative assessment (FA), though, is a strong predictor for student performance and can be a useful data source for effective teacher dashboards. Especially in the secondary education context, FA and feedback on FA, have been extensively studied and shown to positively affect student learning outcomes. Moreover, secondary teachers struggle to make sense of the information displayed in dashboards and decide on pedagogical actions, such as providing feedback to students.To facilitate the provision of feedback for secondary school teachers via a teacher dashboard, this study identifies requirements for designing a Learning Analytics Cockpit (LA Cockpit), that is, (1) a teacher dashboard that provides teachers with visualisations of results from formative assessment (FA) and (2) a feedback system that supports teachers in providing feedback to students.This study was conducted in the context of STEM classes and is based on semi‐structured co‐design interviews with German secondary school teachers. In these interviews, we first explored challenges teachers encountered in monitoring students' learning and providing feedback. Second, in the ideation phase, teachers were asked to define features an LA Cockpit for FA should have. Finally, in the evaluation phase, we provided teachers with a design template for an LA Cockpit, the LAC_Template, which was built upon our previous work and feedback theory, and asked them to evaluate and improve it. Further design requirements were derived based on the evaluation of the LAC_Template and teachers' suggestions for improvement.We derived 16 requirements for designing an LA Cockpit for FA in secondary schools. Findings from the interviews indicated that the feedback system of an LA Cockpit should address teachers' time limitations in giving students individualised feedback. It should therefore be designed to minimise the steps required to deliver feedback. To reduce workload, teachers requested an automated reminder to send feedback, but with the ability to adjust feedback to the learning context. Such a semi‐automated feedback system can help teachers support students individually but also underline the importance of actively involving teachers in the feedback loop and giving them control when using such technologies in secondary school practice. A challenge for future teacher dashboard designs could be to find a balance between technology and teacher control that utilises the strengths of both in a beneficial combination.

This paper presents the development of a dashboard designed specifically for teachers in English as a Foreign Language (EFL) writing education. Leveraging LLMs, the dashboard facilitates the analysis of student interactions with an essay writing system, which integrates ChatGPT for real-time feedback. The dashboard aids teachers in monitoring student behavior, identifying noneducational interaction with ChatGPT, and aligning instructional strategies with learning objectives. By combining insights from NLP and Human-Computer Interaction (HCI), this study demonstrates how a human-centered approach can enhance the effectiveness of teacher dashboards, particularly in ChatGPT-integrated learning.

ABSTRACT This paper proposes a conceptual framework enabling the development and adoption of descriptive, diagnostic, predictive and recommendatory data analytics in teacher professional learning by harnessing some of the affordances of digital technologies to convert data into actionable insights. The paper argues for a technology-enhanced approach that uses data to support teachers in selecting appropriate professional development (PD) options to improve their professional practice. The ultimate goal is to lay the foundations for a robust and adaptable data analytics framework that could offer tailored PD recommendations based on the developmental trajectories of individual teachers. The paper analyses data-supported personalised professional learning as meaning-making and the appropriation of cultural artefacts within the ‘mobile complex’ - consisting of structures, agency, and the dynamic interplay between cultural and technological tools and practices. This study undertakes a comprehensive literature review to identify key concepts, gaps, and theoretical insights, informing the development of a data analytics framework. The resultant framework integrates personalisation, teacher agency and autonomy, contextual relevance, and ethical safeguards into PD process, aiming to foster a responsive, collaborative, and context-aware data-supported PD.

人工智能持续发展，最近在互联网大火的ChatGPT引起了社会各界不同领域的广泛关注。本文从“教”与“学”的视角出发，在介绍ChatGPT的发展历史及学习路径的基础上，从教师教学、学生个性化学习、教学评估等三个方面对国内外文献进行了梳理，概括了国内外学者在ChatGPT技术影响下对未来教育前景发展的态度和看法。

随着数字化转型的深入发展，人机协同学习逐渐成为未来学习新样态，相关研究成为领域热点。基于解释的知识建构模型从阐释学的角度出发，将知识建构视为学习者在持续自我解释与交互解释中逐步构建知识体系的动态过程，这一观点与学习者在人机协同环境中进行互动协作的实践紧密契合。ICAP框架将学习者的学习参与行为划分为被动、主动、建构和交互四种类型，为深入理解知识建构过程中的认知参与提供了坚实的理论基础。本研究在上述理论的支撑下，构建了人机协同下知识建构的认知参与分析模型，并确定了7个可观察的显性指标。通过将该模型应用于本科课程实践，使用内容分析法对学习者的协同对话数据进行编码，探究了人机协同知识建构中的认知参与情况。研究结果表明：1) 人机协同知识建构中的认知参与水平主要为建设性及互动式；2) 采用提示语可以显著提升学习中的认知参与水平；3) 系统化的平台培训有助于学习者快速熟悉平台，进而更好地发挥平台在知识建构和认知参与方面的潜力；4) ChatGPT的生成性对话有助于提高学生在学习中的认知参与程度，增加学习投入。基于以上结论，本研究进一步提出未来研究展望。

在加快推进“数字强国”“中国教育现代化”建设等政策背景下，中学教师数字素养关乎着我国教育现代化进程和教育强国战略的实施，对于贯彻现代教育教学理念，提升教学效率等教育实践也具有十分重要的意义。当前我国对于初中教师数字素养的研究尚未形成系统的框架标准和提升策略。基于此，本文采用德尔菲法，进行3轮专家咨询，构建了包含数字化意识、数字技术知识与技能、数字化应用、数字社会责任和专业发展等5个一级维度，13个二级指标的初中教师数字素养评价指标体系。以湖北省黄冈市W中学初中老师为调查对象，发放问卷198份，对初中教师数字素养发展现状进行调查研究，研究发现W中学教师数字素养总体刚过合格水平，各个维度素养水平差异较大，建议通过提高数字化思维，教学赋能发展，拓宽智慧教育边界和构建数字化思维体系，为人工智能时代下初中教师数字素养的发展提供参考。

为深入了解国内人工智能与教师教育研究的现状与动态，以CNKI数据库中近6年来收录的218篇相关文献为研究对象，采用文献计量法和运用CiteSpace软件，系统掌握研究现状、研究热点及研究趋势。研究结果显示，教师职业影响、教师信息和智能素养、师范教育、教师专业发展是当前研究热点；未来将开展教师职业发展的数字化转型、人机协同的师范教育实践研究、人工智能伦理和教育价值的思考研究。

As artificial intelligence (AI) reshapes industries and society, there is an increasing demand for early AI education to ensure future generations are equipped with the skills and critical thinking required to navigate an AI-driven world. Day of AI Australia is part of an international initiative designed to equip educators and students in upper primary and early secondary school (ages 10 to 16) with foundational AI literacy. The Australian program provides a series of lessons that introduces students to core AI concepts such as machine learning, natural language processing, and AI ethics. Insights from post-lesson feedback data from over 60 participating school teachers highlights the efficacy of the curriculum, the challenges faced, and the potential for scaling AI education in Australian classrooms. We provide practical recommendations for integrating AI literacy into national and global curricula, with the goal of preparing students for the AI-driven future.

Artificial intelligence (AI) has rapidly pervaded and reshaped almost all walks of life, but efforts to promote AI literacy in K-12 schools remain limited. There is a knowledge gap in how to prepare teachers to teach AI literacy in inclusive classrooms and how teacher-led classroom implementations can impact students. This paper reports a comparison study to investigate the effectiveness of an AI literacy curriculum when taught by classroom teachers. The experimental group included 89 middle school students who learned an AI literacy curriculum during regular school hours. The comparison group consisted of 69 students who did not learn the curriculum. Both groups completed the same pre and post-test. The results show that students in the experimental group developed a deeper understanding of AI concepts and more positive attitudes toward AI and its impact on future careers after the curriculum than those in the comparison group. This shows that the teacher-led classroom implementation successfully equipped students with a conceptual understanding of AI. Students achieved significant gains in recognizing how AI is relevant to their lives and felt empowered to thrive in the age of AI. Overall this study confirms the potential of preparing K-12 classroom teachers to offer AI education in classrooms in order to reach learners of diverse backgrounds and broaden participation in AI literacy education among young learners.

Abstract This review introduces the AI CFT and examines its implications for language teacher education. It highlights the challenges faced by teachers and researchers and explores solutions to enhance language teachers’ competencies in the context of digital transformation in education.

The integration of artificial intelligence (AI) tools into professional development is reshaping the landscape for language teachers. Despite the growing presence of AI in language education, limited reviews have examined their specific impact on language teachers’ professional growth. This review addresses this gap by analysing empirical research, with a focus on ChatGPT. Using key search terms such as “ChatGPT language teaching” and “ChatGPT language teacher”, relevant studies were identified from the Scopus database and thematically analysed. Guided by Miao and Cukurova’s (2024) AI Competency Framework for Teachers, the review explores how language teachers engage with ChatGPT-assisted strategies for their professional development across three progression levels: Acquire, Deepen and Create. Findings reveal ChatGPT’s multifaceted role as a transformative tool for professional development. It enables language teachers to integrate technology into instructions, enhance teaching practices and engage in reflective knowledge reconstruction while fostering collaborative learning and co-creation. Furthermore, it supports professional adaptability by promoting AI literacy and equipping teachers to prepare students with the critical skills needed to navigate AI-enhanced educational environments. This research underscores the potential of human-AI collaboration to transform professional learning frameworks while fostering lifelong learning and adaptability in language education. It offers valuable insights into integrating AI into teacher development to encourage collective growth and advance professional practices.

Over the past two decades, STEM education (Science, Technology, Engineering, and Mathematics) has undergone significant transformations, especially in the field of pre-service teacher training. This systematic review analyzes prominent international STEM teacher education models from 2005 to 2025, aiming to identify effective pedagogical structures, interdisciplinary integration strategies, and competency assessment frameworks. Using the PRISMA protocol, 42 peer-reviewed articles from North America, Europe, Asia, and Australia were selected and analyzed. The models examined include UTeach (USA), NIE STEM (Singapore), STEM Studio (Australia), TPACK–PBL (South Korea), and STEM-TP (Europe). Findings highlight common features such as early teaching practice, inquiry-based learning, technology integration, and competency-based assessment. The article proposes policy implications and strategic recommendations for Vietnam, including the development of a 5C-AI competency framework and the establishment of STEM labs in teacher training institutions.

With the development of Machine Learning, Visual Question Answering, Automatic speech recognition and other technologies, artificial intelligence technology is driving social change from all aspects. In the field of education, the development of AI has prompted the transformation of teacher roles and teaching forms. Since the 21st century, the research of educational technology has entered a period of explosion, and the academic circle has researched Human-machine collaboration empowering education which achieved rich results. To promote the further integration of Human-machine collaboration and education, this paper uses literature analysis and CiteSpace to summarize the development of Human-machine collaboration empowering education from three aspects: author distribution, talent highland, and Keyword network. Then the future trend is predicted by using the time graph of keywords.

Artificial Intelligence (AI) has emerged as a powerful tool in language learning, offering learners personalized, self-paced practice through applications like chatbots, intelligent tutoring systems, and adaptive platforms. These technologies enhance grammar, vocabulary, and pronunciation skills while promoting learner autonomy and engagement. However, as reliance on AI tools increases, concerns arise regarding their impact on essential peer and teacher interactions. Human communication is vital for developing communicative competence, cultural awareness, and emotional expression—dimensions that AI currently cannot fully replicate. This paper reviews recent research to examine whether AI in language education functions as a supplement to or a replacement for social interaction. The findings indicate that while AI enhances technical aspects of language acquisition, it cannot substitute the richness of human exchange. The study recommends hybrid learning models that combine AI-driven feedback with structured peer collaboration and teacher facilitation to support both language proficiency and social engagement

With the rapid development of artificial intelligence technology, the application of personalized learning systems in vocational education has gained increasing attention. However, existing systems often handle only single-modal data and lack real-time monitoring and adaptive adjustments based on emotional states. To address these issues, this study proposes a personalized learning system based on human-machine collaboration, integrating multimodal data fusion and emotion-driven mechanisms, specifically designed for vocational education. Experimental validation shows that the proposed system significantly improves student learning outcomes and teacher satisfaction, particularly in the areas of complex interdisciplinary knowledge integration and personalized learning path generation. This study provides new insights into the design of personalized learning systems in vocational education and lays the foundation for the application of human-machine collaboration and multimodal data fusion technologies in the educational field.