人工智能时代课程观

在数字赋能背景下，人工智能课程思政与教学创新的融合研究日益受到关注。本文通过探讨基于数字赋能的人工智能课程思政与教学创新的融合模式，旨在探索有效的专业建设路径。研究分析当前人工智能教育中课程思政的重要性，强调其在培养学生人文素养与社会责任方面的关键作用。本文为相关教学改革与实践提供了理论支持和实践指导，旨在为培养具备扎实专业知识与高尚道德品质的人工智能专业人才贡献新的思路与方法。

随着人工智能课程在中小学的广泛推进，课程建设中存在的问题日益显露。本研究从学生的能力角度（应用知识、技能和价值观及态度方面）出发，提出课程目标模糊、教学模式尚需创新、师资培养体系尚未建立、课程难以评价等现实困境，基于能力本位教育从中小学人工智能教育的内容、方法、师资等方面探讨实施策略。建议政府、学校和教师分别从如下几方面进行改进：研制完整的基础教育人工智能课程标准，明确课程目标和内容范围，提供政策支持和资金保障；改善评价方式，开展人工智能与其他学科的课程融合，完善教师激励机制；提高自身专业水平等。

本文基于《义务教育信息科技课程标准》2022版中提到在真实的生活情境中，利用人工智能课程，提升中学生的计算思维能力。在信息科技课堂中实施了以“人脸识别”为例人工智能课程，通过知识迁移、获取支撑材料独立学习和小组合作学习，分析出人脸识别的过程；通过观察实验结果、分析大量数据信息中所体现的数据、总结得出论断，感知提炼和类比的特征序列为数字模型等一系列教学活动，让学生充分参与，不断提升计算思维。

《机器视觉与人工智能》是自动化专业一门多学科交叉的专业课程，知识覆盖面广。针对高校专业课程传统“填鸭式”教学现状，为加快新工科体系建设，采用成果导向教育（OBE）理念进行课程教学改革探索。以能力输出为导向设计新课程目标，立足落实培养目标进行课程教学设计，注重教学过程多维度考核，通过教学反馈闭环建立持续改进机制，以期望从目标到教学的反向设计，从教学到评价的正向实施，形成以学生为中心、以能力产出为精髓的持续改进的新课程体系。

随着经济和科技的快速发展，人工智能技术已经在社会多个领域得到广泛应用，处在教育信息化时代，利用人工智能技术构建智慧课堂成为教育改革研究的热点话题。本文结合当下人工智能技术与现代化信息技术，构建了高职电子专业智慧课堂教学设计模型，丰富了职业教育有关智慧课堂的理论与实践，为职业学校的智慧课堂研究提供了一定的借鉴。

随着人工智能的发展及新冠疫情的影响，线上线下混合式教学在高等教育中逐渐普及。但在教学实践中，学生在学习过程中主要存在以下问题：一是对专业课程背后的文化内涵理解不足，未能深刻认识课程价值；二是学习心态未随教学模式转变，依然以被动接受为主；三是在线学习环节中自我约束力较弱，学习效率不高；四是课程知识点理解与消化存在困难；五是面对丰富的数字化资源，学生有效信息筛选能力薄弱。针对上述问题，本文提出在混合式教学改革中应着重推进“课程思政”与人工智能的深度融合：一方面，将思想政治教育元素与课程内容相结合，激发学生的学习动力与专业认同感；另一方面，利用人工智能技术优化线上线下协同机制，提供个性化学习支持与数字化资源整合。未来研究可进一步探索如何通过人工智能赋能课程思政，推动教学内容与方法的全面改革，从而实现育人目标。

随着数智化时代的深入发展，信息技术为各行各业带来了革命性的变革，教育教学也朝着信息化方向迅猛发展。其中人工智能技术快速发展并在众多领域中扮演着日益重要的角色，同时，它也逐渐成为教育改革创新的关键驱动力。高校作为培养高素质人才的摇篮，应始终积极适应时代变化对教学改革的需要，采用与时俱进的心态迎接人工智能技术在教学中的挑战和机遇，不断优化课程体系，提升教学质量。本文将在此背景下，聚焦高校软件工程专业的课程教学实践，探讨人工智能技术在教学改革中的实际应用，为高校软件工程专业的学生提供具有互动性、智能化、个性化的教学体验，提升人才培养质量。

为适应大模型领域技术的飞速发展，夯实人工智能专业学生知识基础，实现理论与技术有目标的综合培养。本文结合国内外大模型的技术变革趋势，分析了人工智能专业的自然语言处理课程教学过程中人才培养的目标理念、课程大纲内容设计与实践环节等多方面展现出的重要问题。以本校人工智能系自然语言处理及实践课程教学为例，观察总结并分析实践课程教学过程中的问题。在OBE理念下，通过新旧模型的技术特点、设计方案和应用效果对比分析，凝练技术发展路线，激发学生实践创新意识，同时鼓励学生分享交流设计理念。在完成课程实践内容的过程中达成应用型人才的培养目标，是人工智能专业方向教学改革的有效探索。

本文聚焦小学人工智能通识课程的内容体系构建，旨在探索符合学生认知规律的教学框架。课程设计强调对人工智能基本思想与社会影响的启蒙理解，弱化技术细节，突出思维培养。内容遵循从感知到认知的逻辑脉络，系统涵盖人工智能概念、感知机制、决策原理、典型应用及安全伦理五大模块。通过这一结构完整、层次分明的体系，力图培养学生的计算思维、人机协同意识及负责任的科技使用观，为其适应智能社会奠定基础，为小学人工智能教育的科学化实施提供参考路径。

随着人工智能技术在各个领域的深入应用，该专业的人才培养和课程教学愈发显得至关重要。将课程思政融入教学过程，是一种有效的教育策略，能够显著提升学生的整体素质和价值观。本文探讨了在人工智能专业教育中如何贯彻课程思政，通过思政教育与专业知识教育相互关联融合的知识点，提出相应的解决方案与实例。通过将课程思政有效融入教学，旨在增强人工智能领域的教育质量，塑造学生的社会责任感与职业道德，从而促进人工智能教育的可持续发展。

随着人工智能技术的快速发展，其在教育领域的应用逐渐深化，为专业课程教学与评价改革提供了新路径。本文以动漫专业核心课程《插画基础》为例，探讨人工智能技术如何赋能课程教学与评价体系的创新实践。通过构建分层教学、项目引导、多元技术的教学模式，结合人工智能工具（如Midjourney）优化创作流程，实现理论与实践的高效融合。同时，设计过程性与标准化结合的评价体系，利用人工智能技术量化创作过程，提升评价的客观性与科学性。实践表明，该模式显著提高了课堂教学效率、学生作品质量及评价合理性，为数字创意产业人才培养提供了可借鉴的改革方案。

针对高职人工智能通识课“通专脱节、应用难落地”等痛点，本文提出“通专融合”的课程重构框架。研究以岗位技能迁移为导向，创设“易懂引入—交互体验—低代码实操—专业拓展”四阶递进教学模式，并构建校企协同的动态资源更新闭环。经实践表明，该方案显著消解了跨界认知壁垒。本研究为高职AI教育提供了兼具普适性与适配性的可复制路径。

CDIO工程教育模式以“构思—设计—实现—运作”为主线，将产品、过程或系统的全生命周期引入教学过程，在培养工程思维与复杂问题解决能力方面优势显著。为破解教学痛点、兼顾学生基础差异、满足课程建设“两性一度”核心要求，《人工智能导论》课程引入CDIO工程教育模式，构建“项目贯穿、四阶递进”的教学改革方案。以“火灾图像识别”项目为载体，将课程知识分解到构思、设计、实现、运作四个阶段，学生以小组形式完成需求分析、方案设计、模型训练与部署评价全过程。通过课程教学实践，分析“项目任务链设计—四阶段实施—多元评价—反馈改进”闭环路径的具体实施过程，探索课程思政融入与智能教学平台应用方法，推动学生实现从“被动接受”向“主动实践”的转变，学生学习效果评价证实了该模式的应用优势。

人工智能与实体经济深度融合加速各行各业岗位智能化升级，数字素养与智能工具运用能力已然成为技能人才的必备核心素养。中等职业教育承担一线技能人才培育任务，开设人工智能通识课，是推进职教数字化转型、对接产业升级、提升育人质量的关键路径。现阶段国内中职 AI 通识教育存在课程体系杂乱、教学资源零散、实训成本高昂、教学与评价模式僵化、师资数字化水平不足等问题，课程落地困难。本文结合中职重实操、贴合岗位的培养特点与学生学情，搭建融合教学、虚拟实训、学情监控、多元评价、教研交流的一体化 AI 通识课程平台，规划平台架构与应用模式，建立长效保障与优化方案，以期化解中职 AI 教学难题，为职教数字化建设提供理论参考与实践范式。

职业本科教育正处于内涵建设的关键时期,当前专业课程体系建设面临学科逻辑与实 践逻辑的结构性背离、能力标准与课程内容断层、人工智能应用表层化等现实困境 。本文以“能力图谱”为逻辑起点,构建了“能力图谱锚定目标—模块化架构实现组合— 行动教学促成生成—评价反馈驱动迭代”的课程体系设计框架。人工智能正从三个维 度重塑该框架,即能力图谱从“人为设定”走向“数据驱动”,课程实施从“知识讲授”走 向“人机协同”,能力评价从“结果验证”走向“过程追踪”。上述转变共同推动职业本科 课程体系从静态的“能力图谱”升级为动态的“智能生态”,实现课程体系设计底层逻辑 的根本性变革。

针对“商业模式”课程中问题分析易停留于框架套用、经营证据核查不足和学习过程难以识别等问题，文章结合课堂观察和学生结课报告，以体验式学习为理论基础，将问题识别、证据核查与判断修正等循证要求融入课程教学，重构“AI辅助设疑—现场调查取证—课堂聚焦诊断—课后方案修正”的教学流程，其中，AI用于拓展问题线索、整理信息和生成备选方案，学生则通过现场观察、访谈和公开资料对生成内容逐项核查。在此基础上，文章从教学内容、教学模式和课程考核三方面提出改革措施，为生成式AI环境下商科课程的循证能力培养提供可操作的教学设计参考。

人工智能作为重要驱动力量，正催生商科高等教育加快与人工智能融合进程。人工智能在促进商业领域全链条变革的同时，要求商科人才须具备数据驱动决策能力、复合型知识与技能、创新与跨界融合能力、人文关怀素养、批判性思维与持续学习能力。为此，须从将教育观念逐渐向以学生为中心转变、拓展教学手段、创新教育评价方法等多维度加速商科高等教育转型。转型过程中，须重新定位商科高等教育培养目标，培养能够驾驭智能商业模式的精通人工智能的商业领袖，积极优化和创新课程体系，革新教学方法与手段，加强教师队伍建设，深化实践教育与产学研合作。

人工智能技术的深度渗透正重构法律职业业态，法学教育面临跨学科转型的迫切需求。本文以“技术变革—人才需求—教育适配”为逻辑主线，系统探讨人工智能对法学教育人才培养的核心影响，明确新时代法学人才应具备的跨学科知识体系、技术与人文并重的能力素养及思政融合的价值导向，剖析当前人才培养在理念、资源、评价、人文教育等方面的困境，提出更新培养理念、重构课程体系、创新教学模式、完善评价监管、强化师资建设的适应性优化路径，为培养“技术素养+法律专长+伦理意识”的复合型法律人才提供理论支撑与实践指引。

在人工智能迅速发展并深刻影响教育领域的背景下，新文科作为推动高等教育强国建设的重要部署，正不断重塑教师角色。在这一进程中，教师作为新文科建设的中坚力量与直接实践者，其教学能力对教育强国建设成效至为重要。基于此，本文提出了文科专业教学中存在的主要问题及解决思路，从教学理念、教学内容、教学模式、教学评价四个维度，构建了新文科背景下教师角色的重塑路径，旨在不断提高新文科教育的时代性、科学性与创造性，为培育顺应时代发展的新文科教师队伍提供理论支持与实践指导。

在全球汽车产业迈向“新四化”的深刻变革中，人工智能已成为驱动技术创新的核心引擎。传统《人工智能导论》课程，因其理论化强、与产业应用脱节，难以满足汽车类专业复合型人才培养的迫切需求。文中研究如何构建面向汽车类专业的《人工智能导论》课程。首先，深入剖析了当前课程面临的挑战与汽车产业的特定需求，提出了“以汽车场景为牵引、以技术应用为核心、以能力培养为导向”的课程重构理念。其次，构建以课程目标的重新定位、模块化教学内容体系设计、理实一体化教学模式的创新，以及多元化过程性评价体系的构建。通过教学实践，证实了该课程体系在人才培养、提升实践能力与创新思维方面的显著效果。最后，总结课程面临的挑战，并对课程建设提出建议。本研究为汽车类专业的课程改革提供了可资借鉴的范式。

在人工智能大模型快速发展的时代背景下，教育智能化转型已是必然之势。本文聚焦学生认知发展困境，基于认知负荷理论、建构主义等学习理论，设计了AI赋能的智能知识图谱构建路径，并创新性地提出适配的教学模式与应用策略。该研究为多学科复杂课程的智能化教学提供了可借鉴的路径与模式，在助力人工智能与教育教学的深度融合方面具有重要参考意义。

智能时代,人工智能深刻改变知识生产、信息传播和价值塑造方式,既为思想政治理论课(以下简称"思政课")改革创新带来机遇,也提出挑战。面对智能技术赋能与风险并存的时代境遇,建设性与批判性相统一为增强思政引领力提供了重要遵循。基于此,思政课应在内容体系建设中回应思想观念变迁,强化政治引领、价值塑造、思想指导和精神凝聚功能;在教学实践中推动智能技术与思政教育融合,统一建设性引导与批判性反思,引导学生正确认识智能技术的社会影响,汇聚协同育人合力,为实现中华民族伟大复兴凝聚精神力量。

随着生成式人工智能技术的迅猛发展与普及，高等教育正面临前所未有的挑战与机遇。作为传统人文学科核心的英语文学教学，其以文本细读、批判性思维和情感共鸣为基石的教学模式受到显著冲击。本文旨在深入剖析人工智能技术为英语文学教学带来的具体挑战与潜在机遇。在此基础上，论文提出一套系统的教学改革策略，首先是构建“AI 辅助型”教学内容体系，其次是采用“人机协同”的混合式教学模式，最后是革新评价机制，强调过程性、创造性与辩护性评估。最终目标是培养能够驾驭而非依赖人工智能，具备深度批判性思维、创造性表达与深厚人文素养的新时代英语文学人才。

思想政治教育是落实立德树人根本任务的关键课程。数智化时代下的智慧课堂在焕新教学形态、优化学习模式、革新评价机制上展现出显著优势，能有效提升思想政治理论课的教学质量。当前高校思想政治理论课教学仍存在高校网络意识形态引领能力、教师技术适应能力、学生学习主体能力三方面的问题。对此，高校思想政治理论课必须跟上时代步伐，适应当代青年的新变化与新特点，借助数智技术建设思想政治课智慧课堂，从构建思想政治话语体系、提升教师信息素养、培养学生理性精神方面入手，充分发挥智慧课堂的作用，以强化课程的思想深度、理论广度和创新力度，达到立德树人的目的。

人工智能对教育教学形成有利影响的同时，师生对人工智能的依赖性也突显出来。现阶段存在教师对人工智能的认知不够深入、课程与人工智能契合度低、学生过分依赖人工智能、创新意识不强等问题，时代发展更需要的是“人的创造能力”，了解师生对人工智能的认知和应用，结合课程特点，依托概率论与数理统计课程开展“2233”教学改革模式研究。

生成式人工智能的兴起为《网络信息编辑》课程带来了革命性的变革。通过将生成式人工智能技术融入教学,该课程能够为学生提供前沿的编辑工具和技术视野,培养他们的创新能力和适应性。生成式人工智能不仅提升了内容生成的效率和创意性,还促进了新媒体信息的个性化和交互式发展,使学生能够掌握前沿的编辑工具和技术,培养创新思维和适应新媒体行业的能力。本文旨在推动智能化教学在网络信息编辑课程教学中的深度应用。

生成式人工智能以其内容生成的高效性、交互体验的沉浸性与资源供给的丰富性，为“大思政课”教学创新提供了技术支撑，推动思政教育向精准化、个性化方向发展。但技术赋能的背后潜藏着多重人文风险，包括价值引领的去中心化危机、师生主体的主体性消解、教育过程的情感联结弱化以及文化传承的同质化困境。为实现技术应用与人文价值的辩证统一，需立足“大思政课”立德树人的核心目标，从价值锚定、主体重塑、情感赋能、文化铸魂四个维度构建价值守护体系，让生成式人工智能真正服务于思政教育的本质需求，培育具有坚定理想信念、深厚人文素养与强烈社会责任感的时代新人。

生成式人工智能的快速发展正在重塑经济学研究的知识生产方式，也对经济学研究生培养提出了新的要求。随着数字经济深入发展，经济学研究对象日益呈现数据化、平台化和智能化特征，传统以理论讲授、方法训练和论文写作为主的培养模式面临调整。生成式人工智能嵌入经济学研究生培养，遵循数据驱动、算法协同与智能融合的技术逻辑，但也可能带来主体依赖和学术规范弱化等风险。为此，应从课程体系、科研训练、导学机制和评价治理等方面推进改革，构建人机协同的经济学研究生培养新范式。

生成式人工智能凭借自主生成内容、突破资源边界的技术优势，为高校社会保障课程思政建设打开了新的生长空间。本文结合当前社会保障课程思政建设中普遍存在的思政资源整合不足、学生参与度偏低、评价体系模糊、实践教学脱离现实等痛点问题，探讨生成式人工智能如何从资源整合、场景搭建、评价优化、实践创新四个维度赋能社会保障课程思政，实现专业知识传授与价值引领的深度融合，为新时代高校社会保障类课程思政改革提供可参考的实践方向。

生成式人工智能（Generative Artificial Intelligence）技术正在深刻影响着国际中文教育领域的研究和实践，为中文写作数字化平台的构建提供了新契机。在此背景下，该技术通过构建动态、自适应的学习环境，为破解传统教学模式下的资源瓶颈与效率难题提供了全新路径。其核心价值体现在，其能够依据学习者的语言水平与兴趣偏好，即时生成高度个性化的阅读材料，从而丰富教学资源的广度。教师也因此得以从备课工作中释放精力，转向更为精细的教学设计与策略优化。对于学生而言，生成式人工智能的应用，能为其提供解析文本难点、梳理篇章结构，并引导学习者进行深度思辨。

周青秀，张珂心，隋薪竹 吉林外国语大学，吉林长春 摘要：生成式人工智能作为自然语言处理与深度学习的重要成果，正深刻变革教育领域的发展形态。本文立足教育数字化转型战略导向，系统剖析生成式人工智能系统梳理其在教育教学中带来的核心机遇， 深入剖析技术应用过程中面临的多重挑战，最终为教育领域合理利用该技术、实现高质量发展提供思路。研究旨在为生成式人工智能与教育的深度融合提供理论参考与实践指引，助力教育数字化转型与高质量育人目标协同实现。

生成式人工智能的发展与应用正在改变着教与学的方式，且传统的作业设计难以满足学生个性化的学习需求。研究构建了一个包含作业设计、作业实施、作业评价以及反馈的生成式人工智能支持下的作业设计模型，并应用于小学数学作业设计与实施的教学实践。通过作业设计质量评价、学生问卷调查，以及教师访谈分析了生成式人工智能辅助设计的作业质量和师生体验。结果表明：生成式人工智能支持的作业设计在多维度展现显著优势，作业的适切性较强且能够有效地激发学生的学习兴趣，生成式人工智能的运用能够提升教师的专业技能。

随着教育场景的不断拓展，人工智能可为教学带来多种变化。在动画设计教学中，学生的创作思路和个性风格常常会受到表达能力的限制，传统教学中教师对创作细节的引导难以充分满足学生多元化的学习需求。基于此，本文探讨了“借助灵犀分析角色”“利用文心一格整理构图”“结合豆包训练”“结合Gemini收集故事”以上四项策略，旨在为教师探索生成式人工智能时代的教学策略提供有益思考。

生成式人工智能的加速发展引发了大学生自主学习方式的变革，在带来降低自主学习门槛、提高自主学习效率、优化自主学习方式以及拓展学习思维边界等新机遇的同时，也造成大学生自主学习能力结构性缺失、虚假入门现象、加剧认知阶层分化等新危机。基于此，本文从学生本身和外部环境影响双重视角探讨行之有效的生成式人工智能在自主学习中的规范性使用路径，包括明确需求、找准时机；学会提问、内化成果；建立外部支持机制。现阶段，生成式人工智能在大学生群体中应用广泛，尽管在利用过程中存在浅层化应用倾向，部分学生仍表示生成式人工智能对其自主学习起到了实质性的辅助作用，也普遍表达了对以高校为代表的外部力量提供相应支持的期待与诉求。

生成式人工智能（GenAI）的快速发展正在重新界定高等教育的基本准则，给工程学教育领域带来创新性的变革机遇。《工程材料学》作为材料类工科专业的核心基础课程，其传统教学模式长期存在知识传授与工程实践脱节、个性化教学难以实现、创新能力培养不足等局限。本文基于混合教学理念和工程教育认证理念，系统梳理了《工程材料学》传统教学模式的痛点问题，深入分析了当前 GenAI 初步应用阶段混合教学模式的特征与新挑战，构建了面向未来的“GenAI深度融合+学习者中心”教学生态模型。

在生成式人工智能快速发展的背景下,高校计算机基础课程的传统教学模式面临内容更新滞后、教学方式单一和个性化支持不足等问题。本文围绕生成式人工智能赋能教学的现实需求,系统分析其在高校计算机基础课程中的应用价值与内在逻辑,从教学目标、教学内容、教学过程以及教学评价与学习支持机制等方面,对课程教学模式重构路径进行了深入探讨。研究认为,生成式人工智能能够有效推动课程由知识传授导向向能力培养导向转变,由统一教学向人机协同与个性化教学转变,并通过数据驱动实现教学质量的持续改进。相关研究可为高校计算机基础课程教学改革与数字化转型提供有益参考。

生成式人工智能正在重塑外语阅读教学，但当前人机协作多停留在浅层信息交互，未能触及批判性思维的内 核。本文立足批判性思维视角，提出人机互启与思辨共生理念，探析生成式人工智能赋能高中英语阅读教学的路径。 研究借鉴“诊断—挑战—重构”模式，设计了情境激活、观点碰撞、批判性审视和思辨重构四阶段教学框架。以人 教版高中英语必修三戏剧文本 The Million Pound Bank Note 为例，本文展示了该框架的教学设计策略，并分析了各阶 段成效。研究发现，生成式人工智能在语言信息处理和多元视角提供方面优势显著，但对价值立场的研判、文化意 蕴的深度阐释以及自我反思的激发，仍需教师发挥不可替代的引导作用。教师从知识传授者转向思辨引导者，是实 现思辨共生的关键。

在人工智能技术深度渗透经济社会各领域的背景下，广西高校大学生的人工智能素养已成为支撑区域数字经济发展与产业智能化升级的关键能力。然而，当前广西高校的人工智能教育存在“重技术轻素养”“重技能轻伦理”“区域适配性弱”等问题，难以满足“AI+产业”融合对复合型人才的需求。本文基于“素养导向、分层分类、产教协同”的理念，结合广西重点产业（如智能制造、智慧农业、跨境数字服务）对人才能力的需求，提出涵盖“知识-能力-伦理-应用”的人工智能素养培育体系框架，从目标定位、课程结构、实践路径、保障机制等方面系统阐述构建策略，并通过典型案例验证其可行性，旨在为广西高校培养兼具技术能力与人文责任的AI人才提供理论与实践参考。

摘  要：针对生成式人工智能(AIGC)技术对学前教育师资培养的新挑战，本研究以陕西学前师范学院2022级本科毕业生484人为研究对象(实验组242人、对照组242人)，依托《毕业论文(设计)写作指导》课程开展16学时AIGC嵌入式教学准实验。采用准实验法与问卷调查法进行实证分析。结果表明，AIGC嵌入式教学可极显著提升学生人工智能素养总分；学生AIGC工具选择呈现“马太效应”，豆包与DeepSeek成为首选工具；学生素养发展呈现四大课程模块分层问题，整体呈重术轻道的结构性失衡。据此，本研究提出构建通识、专业、实操的三维嵌入式培养体系，推动毕业生从技术赋能向伦理自觉与职业保教能力全面转化。 基金项目：西安市科学技术局软科学项目（2026YBRK00036）；陕西学前师范学院重点教改课题（25JG003Z）；陕西学前师范学院教改课题（25JG01CJ）；陕西学前师范学院引进人才（博士）科研启动项目（2024DS13）

在人工智能时代,将铸牢中华民族共同体意识有效融入高校通识课程体系,是落实"立德树人"根本任务的重要课题。本文聚焦于覆盖面广、实践性强的人工智能素养课程,针对传统教育模式中存在的被动灌输问题,提出以"研究性学习"为核心的教学改革路径。研究构建了"教学研究法"与"学术研究法"两条主要实践路径,通过引导学生扮演"师者"与"学者"角色,在微课设计与课题研究等任务中,综合运用AI技术主动探索与阐释中华民族共同体的文化逻辑与发展成就。实践证明,该模式能有效实现知识建构、能力培养与价值引领的深度融合,为在理工类通识课程中系统化、沉浸式地铸牢中华民族共同体意识提供了可资借鉴的方案。

针对高职人工智能通识教育中内容更新滞后、教学模式单一、评价方式固化三大突出问题，本文以建构主义学习理论、人机协同教育理论与动态能力理论的整合框架为依托，构建了“技术赋能—资源适配—阶梯教学—智能评价”四位一体的课程体系。依托《人工智能基础与应用》课程，开发了“基础认知—工具应用—综合创新”三层模块化教学内容，设计了BOPPPS模型与AIGC技术深度融合的阶梯式教学路径，并建立了“知识—能力—素养”三维智能评价模型。经过一个学期的教学实践，数据显示学生课堂参与度达到92.7%，实践任务完成率为86.5%，91.2%的学生能够独立完成AIGC基础应用任务。本文为高职院校人工智能通识教育改革提供了一种可复制、可推广的实践范式。

随着生成式人工智能技术的快速发展，传统信息技术课程面临着内容陈旧、教学方法单一、与实际应用脱节等问题。本研究以某高职院校为例，探索构建融合生成式人工智能技术的创新型信息技术课程体系。通过文献调研、课程体系设计、教学实践验证等方法，构建了涵盖自然语言处理、图像生成、智能推荐系统等内容的新型课程体系，采用项目驱动教学、案例教学、小组合作学习等多样化教学方法，并通过对比实验验证了教学效果。研究结果表明，融入生成式人工智能技术的课程体系显著提升了学生的学习兴趣、实践能力和创新思维，为高职信息技术课程改革提供了有益参考。

随着人工智能技术快速发展与媒体融合持续深化，“网络与新媒体”专业课程体系应对行业转变显吃力。现有研究多指出课程内容滞后、学科壁垒、实践教学薄弱、产教融合不足等问题。立足人工智能的时代背景，本文围绕“网络与新媒体”专业跨学科课程体系建设展开探讨。本文先梳理分析现有研究文献，再构建以“破壁”与“融合”为核心理念的课程体系建设框架。该框架强调知识维度实现“横向破壁”，整合人文社科、数据科学、智能技术三大知识模块；路径层面实现教学方法、师资队伍与实践平台的融合，运用项目驱动教学、双师型团队建设及产教协同生态共建，系统提升人才培养质量。研究目标为高校“网络与新媒体” 专业课程改革提供理论指导与实践策略，同时培养符合智能媒体时代需求的复合型、创新型人才。

本文在人工智能技术推动汽车产业变革的背景下，主要研究车辆工程应用型人才研究生课程体系建设问题。研究首先剖析了人工智能与车辆工程融合的现状及其对复合型人才的迫切需求，论证了课程体系改革的必要性。按照应用型人才的核心能力要求及跨学科教育理念，提出了“人工智能+”特色化课程设计准则，创建起涵盖基础理论、专业核心以及实践改进三个模块的课程架构，并细致谋划了有关的教学资源构建，师资培育以及品质评定等执行担保措施，旨在为培养适应智能网联汽车产业发展的高素质应用型人才提供理论参考和实践方案。

科学技术的高速发展，对我国人工智能人才培养提出了更高的要求，通过加强对农村留守儿童的人工智能教育，有助于推进我国人工智能知识普及和人才培养。本文从人工智能意识、知识、能力和社会责任四个维度出发，实地走访调查农村留守儿童人工智能教育现状，深入剖析教育过程中存在的问题及其形成的原因。并以智能欠驱动足球机器人为载体，通过建立高校与农村学校资源共享合作机制、设计阶梯型人工智能课程教学内容和构建赛教融合教学模式三个方面对农村留守儿童人工智能教育进行改革。[1]最后从丰富教学资源、完善课程体系及践行实践教学三方面对课程体系完善提出相关建议，助力推动我国人工智能后备人才的有效培育。

祁静，廖夏云，于迪，谢文佩，毛彦，李海 广西中医药大学公共卫生与管理学院，广西南宁 摘要：人工智能（AI）技术正逐渐重塑食品行业与高等教育格局，传统食品化学课程存在内容更新滞后、教学模式单一、教学实践与产业脱节、评价体系片面等突出问题。本文以食品质量与安全专业核心课程《食品化学》为研究对象，立足AI赋能教育时代背景，剖析现行课程体系与智能产业需求的不匹配问题，从课程内容重构、教学模式革新、实践体系优化、评价体系完善、师资队伍建设5个维度，构建“AI+食品化学”一体化课程体系。通过引入智能检测、虚拟仿真等技术，实现理论教学具象化、实验教学智能化、人才培养精准化，旨在培养兼具扎实化学功底与AI应用能力的复合型食品专业人才，为新工科背景下食品类基础课程改革提供参考范式。

英语教育在被人工智能逐渐渗透的背景下，学科英语研究生对人工智能的认识和掌握程度对于中小学英语教学质量的提高有着重要影响。本文基于技术赋能与科技伦理教育的核心逻辑，结合学科英语的专业特性，界定人工智能素养的概念，剖析学科英语研究生的人工智能素养的现状特征与现实诉求，探索学科英语专业课程体系、实践平台、伦理规范和支持体系的实施路径。该研究为学科英语研究生教育适应数字化转型、培养复合型创新人才提供理论参考。

摘要：生成式人工智能的快速发展对素养评估提出新挑战。现有研究多依赖自陈式量表，且缺乏跨文化验证。基于此，本研究采用CiteSpace工具与文献计量方法，对2021-2025年间中英文文献进行可视化与定性分析。结果表明：研究理论基础从“信息素养”向“AI素养”演进；热点聚焦于“学评融合”“人机协同”等主题；现有量表虽形成“认知-技能-情感-伦理”四维结构，但存在跨文化等值性验证不足、评估缺乏标准化等问题。本研究提出“认知-技能-情感-伦理-评估-跨文化”六维度量表，为跨文化场景下的量表设计提供理论支撑。

当前人工智能与初中信息科技课程融合中存在内容整合适切性不足、教学资源配置不合理、师生技能水平不匹配等问题，严重制约核心素养培养目标实现。通过分析现状问题，提出构建智能化教学实施策略和完善辅助教学评价策略两项核心举措，在教学实施方面强调设计智能化活动、开发适应性内容、建立协作式环境，在评价完善方面，注重构建多维度评估体系、建立个性化监测机制、形成智能化反馈模式，为初中信息科技教育改革提供实践路径。

在数字化背景下，探索人工智能驱动的高职心理健康教育课程改革路径。基于建构主义学习理论，构建“知识内化—技能固化—场景迁移”的课程设计框架，课程设计涵盖认知建构、技能训练、情境应用三大模块，通过智能推送、虚拟仿真、个性化学习路径等手段，实现教学资源的精准匹配与教学过程的动态优化；整合虚拟现实、智能测评、生物反馈、大数据分析等技术，重构课程内容与教学方法。构建“过程性—结果性—增值性”三维评价体系，全面评估课程效果。通过人工智能赋能课程设计、教学实施与效果评估，增强了教学的针对性与实效性，为培养高素质技术技能人才的心理健康素养提供了有力支撑。

资源与环境专业型硕士、博士研究生作为面向地学相关行业应用的高层次人才,其大数据与人工智能素养直接关系到国家在生态文明建设、资源安全保障、环境污染防治等领域的战略实施。因此,本文首先分析了当前专业型研究生在数智素养培育上面临的课程体系滞后、跨学科师资不足、实践平台缺失等挑战;进而从"课程体系重塑、教学方法创新、实践平台构建、导师团队建设、评价机制改革"五个维度,系统性地提出了一个"五位一体"的素养培育路径;最后,构建了一个以"项目为驱动、数据为要素、平台为支撑"的融合培育模式,以期为相关院校的资源与环境专业学位研究生培养改革提供理论参考与实践指南。

生成式人工智能正深刻重塑软件工程的方法体系与能力结构，传统研究生培养模式已难以适应AI赋能的软件开发生命周期新范式，当前教育中存在知识体系滞后、工具链脱节和能力结构失衡等突出问题。为此，本文提出“知识—能力—素养”三位一体的培养模型，并构建由基础层、核心层和顶石层组成的“三层融合”课程体系，实现研究生软件工程教育的系统性 AI 化重构。该体系形成了面向智能时代的软件工程人才培养框架，为卓越软件工程师的培养提供了可实施、可推广的整体方案。

【摘要】：文章分析了人工智能通识课的现状，针对地方应用型高校现状，进行了人工智能通识课的教学设计，强调了 AI+X 的教学内容和教学方法的改革策略，指出开设人工智能通识课最终目的，是让非计算机专业学生也能掌握 AI 工具，能运用 AI 技术解决实际问题，培养“AI 技术 + 专业 +AI 伦理”三维复合型人才，适应未来职场需求。同时，文章讨论了改革面临的挑战，并提出了加强师资培训、教学资源投入和跨学科融合等建议，以期提升教学质量，培养具备人工智能素养的复合型人才。

新兴的人工智能素养超越现有的信息素养、数字素养和媒介素养，是一种理解人工智能概念与原理，并在实际生活中运用人工智能技术和工具解决问题，同时基于伦理价值观参与地区社会发展的能力。目前各阶段教育的课程设置也顺应人工智能技术发展的潮流，在教学过程中推动各种数字媒介的应用和以体验为中心的融合教育。因此，总结艺术教育中的人工智能素养，即人工智能体验扩展能力、创意融合媒体应用能力和批判视觉文化能力，同时探讨学生在艺术教育中培养人工智能素养面临的任务及挑战是亟待完成的重要任务。

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In response to the need to equip Hong Kong secondary students with Artificial Intelligence (AI) literacy, a joint effort was made by university professors from engineering and education and secondary school teachers to co-design an innovative AI curriculum. This paper investigated the experience of teachers in co-designing the curriculum. A total of 17 semi-structured interviews were conducted with 8 secondary school teachers involved in the curriculum design. Using the grounded theory approach, the teachers' perspectives and experiences during the co-design processes were unpacked. First, the teachers were motivated by the perceived need of students to join in the co-design of such AI curriculum. Second, the teachers appreciated the professor-led knowledge input that addresses their gaps in technical knowledge. Third, the teachers contributed their pedagogical knowledge and classroom experiences to guide the design of the curriculum so that it may be contextualized to the school environment and students' needs. The outcomes of the co-design were a usable curriculum and teachers' growth, which was translated to students' AI literacy and confidence in learning about AI. This paper contributes to the current educational effort in preparing students for an AI pervasive society.

This research was conducted with the aim of presenting an artificial intelligence curriculum model in primary education within the Iran. The current study was applied in terms of its objective and utilized a mixed qualitative approach (classical grounded theory and the Delphi method). The study population comprised curriculum specialists in virtual education and Information and Communication Technology (ICT) nationwide during the 2024–2025 academic year. Purposive sampling was employed until theoretical saturation was achieved; consequently, to reach data sufficiency, interviews were conducted with 25 individuals. Interviews were utilized as the primary method for data collection. Data analysis was performed based on open coding, axial coding, and selective coding systems. To ensure the validity and reliability of the extracted concepts, the derived concepts were shared with the interviewees (member checking), and procedures to establish the dependability of the extracted data in alignment with the research objectives were applied. Overall, the results indicated that the components of the artificial intelligence curriculum model in primary education of the Islamic Republic of Iran consisted of ten dimensions: objectives (6 axial codes), content (4 axial codes), learning activities (4 axial codes), tools (6 axial codes), resources (6 axial codes), time scheduling (5 axial codes), learning environments (4 axial codes), evaluation (5 axial codes), grouping (4 axial codes), and rationale (5 axial codes). Experts’ opinions indicated that the proposed artificial intelligence curriculum model is founded on: skill-based and technology-driven objectives, forward-looking content and digital literacy, practical and innovative activities and tools, rich human and knowledge resources, a technology-driven and blended learning environment, and a multipurpose, forward-looking evaluation system. Components with lower Item-Level Content Validity Index (I-CVI) and Content Validity Ratio (CVR) scores (e.g., affective and attitudinal aspects, innovative instructional design, financial resources, and interests and aptitudes) require modification, revision, or reinforcement to ensure the curriculum is comprehensive and aligned with the future-oriented objectives of artificial intelligence.

This study aims to identify and conceptualize the components of an artificial intelligence curriculum for primary education in the Islamic Republic of Iran. This applied qualitative study employed the classical grounded theory approach. Participants included 25 national experts in virtual education and information and communication technology curriculum selected through purposive sampling until theoretical saturation during the 2023–2024 academic year. Data were collected through in-depth semi-structured interviews and analyzed using open, axial, and selective coding procedures. Credibility and trustworthiness were ensured through participant validation and transferability techniques. Data analysis produced a comprehensive ten-dimensional curriculum model consisting of objectives, content, learning activities, tools, resources, scheduling, learning environment, evaluation, grouping, and underlying logic. Together, these dimensions form a coherent, future-oriented, and systematic framework for the design of an artificial intelligence curriculum in primary education and demonstrate an integrated developmental approach that extends beyond technical skills to cognitive, ethical, social, and cultural growth. The proposed model offers a scientifically grounded and contextually appropriate framework for designing, implementing, and evaluating artificial intelligence curricula in primary education and provides strategic guidance for preparing future generations to engage responsibly and effectively with intelligent technologies.

In response to the current problems in general artificial intelligence (AI) courses in higher vocational colleges, such as limited course coverage, superficial integration of ideological and political education with professional education, and monotonous teaching methods, this study constructs a "double helix" curriculum system intertwining an "ideological-political chain" and a "professional chain," alongside a "trinity" educational system encompassing "knowledge, ability, and quality." This construction is based on the ADDIE model, the Outcome-Based Education (OBE) paradigm, and constructivist theory, in alignment with the requirements of curriculum-based ideological and political education. The findings indicate that this curriculum system facilitates the deep integration of ideological-political education and professional education, effectively enhances students' AI literacy, and provides theoretical support and practical reference for cultivating application-oriented, interdisciplinary, and innovative "AI+X" talents in higher vocational colleges.

To meet the urgent demand for AI talent in energy systems under the "dual carbon" strategy, this paper proposes a digital-twin-enabled, project-driven teaching model for a course on Energy AI. The model addresses a structural "triple disconnect" in current education by targeting key deficiencies, including the gap between theory and practice and the lack of systems thinking and engineering skills. It builds an integrated "data–model–decision verification" framework, supported by a three-step experimental system based on energy-system digital twins, and embeds a project-based learning (PBL) project centered on a virtual power plant (VPP) operation scenario. Within this project, students develop forecasting models for distributed energy resources, design market bidding and dispatch strategies, and validate their effectiveness in a grid-interactive digital-twin VPP environment. Comparative teaching experiments and multidimensional evaluation based on five core competencies demonstrate that the model significantly enhances students’ complex problem-solving ability, innovation awareness, and teamwork skills, and offers a replicable pathway for cultivating high-quality engineers for the new era of energy and power systems.

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in constructivist learning theory and Human–Computer Interaction principles, to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that the AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Therefore, the learning content delivery for each student was continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem.

The integration of Artificial Intelligence (AI) in South African universities presents both opportunities and challenges, particularly within the context of curriculum transformation and decolonisation. This paper critically examines the relevance of AI in relation to the #FeesMustFall movement, which advocates for equitable access to education, and explores how these themes intersect with decolonisation efforts in South Africa. Although AI technologies promise advantages like tailored learning experiences, improved administrative processes, and enhanced research capabilities, they also present issues related to epistemic bias, digital disparities, and the reinforcement of Western-centric knowledge systems. Grounded in empirical research, this study investigates whether AI serves as an aid or an obstacle in South African higher education, with a specific focus on Historically White Universities (HWUs) and Historically Black Universities (HBUs). Using the Diffusion of Innovation (DOI) theory as a framework, the research explores disparities in AI adoption across institutions, analysing infrastructural constraints, policy gaps, and the broader implications of AI for knowledge production. The findings reveal that while HWUs have made significant strides in AI integration due to better funding and international collaborations, HBUs continue to face systemic barriers that hinder equitable access to AI-driven learning tools. Moreover, AI’s reliance on Western datasets and epistemologies risks perpetuating digital colonialism, complicating ongoing efforts to decolonise the curriculum. This paper underlines the urgent need for Afrocentric AI models that align with local contexts and values, inclusive policy frameworks that address the needs highlighted by #FeesMustFall, and targeted investments in digital infrastructure. By doing so, it aims to ensure that AI contributes meaningfully to higher education curriculum transformation and decolonisation in South Africa.

The knowledge points setting of artificial intelligence curriculum has shortcomings in connection between theory and practices. To overcome the problem, this study designs an open knowledge point design model based on knowledge graph. Fist, to promote the construction of the knowledge graph (KG) of curriculums, associated teaching research was analyzed visually. Then the order and hierarchical structure of the knowledge points were defined, and the ontology structure of curriculum knowledge and the relationship between knowledge points and posts were designed as well. Moreover, an overall logic structure for the construction of the open KG of curriculums was proposed. Results demonstrated that high attention should be paid to the construction and concern of teaching teams for artificial intelligence algorithms and the KG of curriculum construction. Additionally, the opening model can strengthen the openness of the KG of curriculums to reinforce the close connections between classroom knowledge and practices. Research conclusions are conducive to understand the existing problems in the KG of curriculums and provide beneficial references to the integration of information technology and education.

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The integration of artificial intelligence (AI) education into medical curricula is critical for preparing future healthcare professionals. This research employed the Delphi method to establish an expert-based AI curriculum for Canadian undergraduate medical students. A panel of 18 experts in health and AI across Canada participated in three rounds of surveys to determine essential AI learning competencies. The study identified key curricular components across ethics, law, theory, application, communication, collaboration, and quality improvement. The findings demonstrate substantial support among medical educators and professionals for the inclusion of comprehensive AI education, with 82 out of 107 curricular competencies being deemed essential to address both clinical and educational priorities. It additionally provides suggestions on methods to integrate these competencies within existing dense medical curricula. The endorsed set of objectives aims to enhance AI literacy and application skills among medical students, equipping them to effectively utilize AI technologies in future healthcare settings.

This article explores how artificial intelligence (AI) is reshaping the transformation of scientific knowledge into school knowledge, a process traditionally conceptualized through the lens of didactic transposition theory. Drawing from curriculum theory, philosophy and sociology of education, and educational technology, the study examines two key dimensions: the use of AI in curriculum development and textbook authorship, and the role of algorithmic systems in selecting and structuring pedagogically relevant content. A conceptual model, “ReKnow-AI”, is proposed to illustrate how AI can be integrated into all stages of knowledge mediation, from scientific production to classroom practice. The study employs a theoretical and conceptual methodology, grounded in interdisciplinary content analysis. It proceeds through a systematic literature review and comparative synthesis of over 60 international sources published between 2020 and 2025, covering educational research, digital pedagogy, and AI-driven instructional design. No empirical data or statistical tools were employed; instead, conceptual modelling was used to identify epistemic shifts in curriculum-making under AI mediation. Findings highlight pedagogical opportunities such as content personalization, dynamic updating, and real-time instructional support. However, they also underline risks associated with algorithmic bias, lack of transparency, and diminished teaching agency. The proposed “ReKnow-AI” model identifies the critical junctures where AI systems influence educational content and advocates for ethical, human-centered oversight. This research contributes a novel perspective by framing AI not merely as a technological aid but as an epistemic actor in curriculum construction. Its practical significance lies in informing curriculum designers, policymakers, and teacher educators about the structural and normative implications of AI-based content mediation in contemporary education.

Artificial Intelligence Curriculum Education(AICE) in Elementary and Middle Schools has been carried out in many parts of the country, but it has also exposed several problems, such as lack of unified teaching guidance, weak teaching ability of some teachers, and educational resources imbalance. The article puts forward some solutions through on-site visits, symposium and other methods, combining with the current local policies and teaching practice ， from the aspects of curriculum standard establishment, teachers' ability development, students' core qualities training, and educational resources equity according to local conditions. It is recommended to unify the cognition based on the new curriculum standard, enrich the teaching practice of hardware and laboratories, optimize the teachers' ability model with interdisciplinary characteristics, combine theory and practice to cultivate students' core qualities, ultimately to promote the better popularization of AICE.

This study aims to explore the application of Generative Artificial Intelligence (GAI) in integrating ideological and political education into development-related courses for international students from developing countries studying in China. Utilizing literature analysis, case study, and instructional de-sign methods, the research examines the current teaching status and challenges, and proposes an AI-assisted teaching path based on constructivist learning theory and Problem-Based Learning (PBL). The findings indicate that GAI demonstrates significant advantages in content generation, situational simulation, and personalized feedback, effectively enhancing classroom interaction and students’ recognition of China’s development concepts. The study concludes that GAI provides innovative tools for ideological and political education among international students, yet attention must be paid to cultural adaptation and human-AI collaboration. Future work should focus on practical application and outcome evaluation to further promote the deep integration of internationalized higher education and value-oriented education in the AI era

: This study investigates the relationship between Linear Systems Theory and Artificial Intelligence within the engineering graduate curriculum. The analysis highlights their significant roles in engineering disciplines, beginning with the fundamental concepts and development trajectories of both Linear Systems Theory and Artificial Intelligence. The study addresses these two fields' complementarity and integration from theoretical research and application development perspectives. Theoretically, they share a similar mathematical foundation and play crucial roles in modeling and predicting real-world problems. From an application development standpoint, leveraging the characteristics and strengths of both can lead to more efficient and accurate implementations. The analysis, conducted from multiple angles and dimensions, concludes that the graduate curriculum in engineering should emphasize the integration of these courses in both theoretical and practical teaching. This approach will help students understand and master relevant tools and technologies, enhance their practical skills and overall competencies, and better prepare them for future developments in the engineering field.

Artificial Intelligence is now progressing in the world. As different field have adopted the new technology, Communication Education is now facing challenges like other sectors. The study focused on the perspectives of PLM Mass Communication faculty in terms of: 1.) How Artificial Intelligence effectively integrated in the curriculum of communication to enhance students' learning? 2.) What is the potential success of Artificial Intelligence in the curriculum? 3.) What are the factors that influence the success of implementation of AI in the program? 4.) In what ways the AI can be personalized in teaching communication subjects? 5.) How AI will help in promoting creativity, critical thinking, problem solving and skill development? The study made use of case study. The data was analyzed using narrative analysis. There are five faculty from PLM-Department of Mass Communication were interviewed. The study was grounded on Wayne k. Hoy Open System Theory. The integration of AI in the courses according to the faculty is for academic contents, validation of works, seminars, student’s narratives, news and social media. Majority of the faculty revealed that AI still have flaws and unstable to use based on the popularity right now. They also tackled that this new technology may somewhat help them, but it should be regulated by the University and Government to properly utilized it. The department identified the factors that influence the success of implementation of AI in the program like laziness to students, abuse, and misuse. They also saw promising impacts if properly regulated like productivity, efficiency, innovation and promoting globally competitive students. AI will be deeply integrated into the field of communication, transforming how we create, distribute, and consume media. AI will enhance efficiency, personalization, and ethical practices while providing deeper insights and predictive capabilities. The focus will be on leveraging AI to augment human creativity and decision-making, ensuring that communication remains impactful, relevant, and responsible in an increasingly digital world.

Research curriculum design is to arrange instructional links in an orderly manner in the course of teaching according to the requirements of course standards and the characteristics of instructional objects,so as to determine the plan of appropriate instructional scheme.The establishment of a reasonable assessment system of teachers' instructional level aims at correctly and reasonably distinguishing the differences of instructional level among teachers,enabling excellent teachers to discover their own advantages in time so as to improve their own shortcomings.Based on the concept of Outcome-based education,this article puts forward a instructional assessment model based on big data from the perspective of the education of new media.In the application effect,it can be seen that with the support of OBE instructional theory,students' creative ability and practical ability are obviously improved.The application of ANN in instructional level assessment is completely feasibleand it can meet the accuracy requirements,providing assistance for the design and practice of research curriculums.

The advancement of Artificial Intelligence (AI) technology has led to a new wave of innovation in education, particularly in curriculum development. This article delves into the extensive potential of AI-assisted curriculum development, providing a comprehensive overview of current advancements, methodologies, and implications of integrating AI into educational content design. By analyzing theoretical frameworks, technological advancements, and empirical research, I demonstrate how AI facilitates the creation of dynamic and personalized learning experiences that cater to the diverse needs of modern learners. I also address the ethical considerations and challenges of implementing AI technologies, such as data privacy, algorithmic bias, and the digital divide. Through interdisciplinary cooperation and ethical use of AI, we advocate for a well-balanced approach that maximizes the benefits of AI while minimizing its risks. By utilizing AI to enhance curriculum design, educators can efficiently prepare students for the complexities of the contemporary world, fostering lifelong learning and adaptability in an ever-evolving global environment.

Abstract:In the context of rapid advancements in artificial intelligence (AI) technology, vocational education, as a crucial pathway for cultivating applied skill talent, is undergoing profound systemic transformation. Educational ecology theory, emphasizing holism, dynamism, diversity, and sustainable development, provides theoretical support for the optimization and innovation of vocational education. Research indicates that AI technology is reshaping the vocational education ecosystem, leading to significant changes in key aspects such as stakeholder roles, resource allocation, teaching models, and external environments. Meanwhile, in the process of dynamic equilibrium and co-evolution, the vocational education ecosystem faces multiple challenges, including inadequate technological adaptability, uneven resource distribution, and lagging policy support. However, it also presents opportunities for curriculum optimization, deeper industry integration, and personalized training. This study systematically analyzes vocational education in the AI era from the perspective of the educational ecosystem. Based on educational ecology theory, it proposes strategies for optimizing the vocational education ecosystem. These strategies aim to enhance the coordination and sustainability of the vocational education ecosystem, providing both theoretical support and practical pathways for its development in the AI era. Through this research, the study further enriches the practical application of educational ecology theory and offers scientific guidance and reference value for vocational education in adapting to technological transformations.

The rise of ChatGPT era will bring great changes to human production and life, and also pose a major challenge to the implementation and reform of basic education curriculum. This will lead to an all-round reform of basic education curriculum theory, basic education curriculum system and basic education curriculum practice. This paper comprehensively analyzes the impact of ChatGPT on the basic education curriculum and the challenges faced in the educational transformation from a systematic perspective. The technical characteristics of ChatGPT and its advantages in enabling basic education curriculum construction are deeply studied. At the same time, the challenges facing the construction of digital educational resources are analyzed in detail. The paper also puts forward some suggestions on basic education knowledge system, curriculum implementation, teaching methods, teachers and students' digital literacy and so on.

As Generative Artificial Intelligence (GenAI) transforms industries, including tourism and hospitality, there is an urgent demand for professionals with both domain expertise and digital proficiency. However, traditional tourism curricula often lack systematic AI training, creating a digital divide for non-STEM students who face cognitive barriers when approaching technical AI courses designed primarily for computer science majors. This paper addresses this gap by proposing a specialized AI Literacy curriculum framework grounded in the TPACK (Technological Pedagogical Content Knowledge) model, specifically tailored for undergraduate Tourism Management majors. This research advances computer technology in education through three computational innovations: (1) a formalized algorithmic instructional model that transforms pedagogical principles into reproducible computational workflows using iterative feedback loops for mastery-based learning (Algorithm 1), (2) a quantitative competency assessment framework employing multi-dimensional weighted evaluation for objective measurement of AI literacy development (Equation 1), and (3) a data-driven curriculum optimization system validated through systematic pre-post statistical analysis. The curriculum integrates theoretical cognition, practical tool application (including ChatGPT, Midjourney, and other GenAI tools), and ethical reflection into a 32-hour hybrid course structure combining lectures and laboratory practice. Through systematic evaluation including pre-post survey data (n=87) using validated five-point Likert scales and analysis of student capstone projects ranging from cultural heritage tour designs employing spatio-temporal folding concepts to data-driven niche market analysis targeting Generation Z consumers, this study demonstrates that the proposed framework effectively enhances student capacity to solve complex industry problems using AI tools while fostering human-AI collaboration mindsets. Survey results show 94% of students reported significant confidence gains (pre-course M=2.1, SD=0.8; post-course M=4.3, SD=0.6; paired t-test p<0.001), with 89% reporting reduced technology anxiety. These computational methodologies enable automated curriculum evaluation, algorithmic performance tracking, and data-driven instructional refinement, advancing intelligent educational system design. The findings provide a replicable model for interdisciplinary AI education in liberal arts disciplines facing similar digital transformation challenges.

The increasing integration of generative artificial intelligence into educational practices has introduced new challenges for student agency, particularly in subjects involving normative and epistemic authority such as Islamic Religious Education. While the Merdeka Curriculum emphasizes learner autonomy, limited attention has been given to how this autonomy operates when students increasingly depend on AI-generated religious explanations. This study investigates how Madrasah Aliyah students construct digital religious agency through interactions with generative artificial intelligence in Islamic learning contexts. This study adopts a qualitative design using Interpretative Phenomenological Analysis. Data were collected through in-depth interviews, focus group discussions, and digital artefact analysis involving fifteen students and three Islamic Religious Education teachers in Bogor, Indonesia. Findings identify three typologies of digital religious agency. First, pragmatic-instrumental agency emphasizes efficiency-oriented use of AI. Second, explorative-critical agency involves verification, comparison, and reflective engagement. Third, passive-acceptive agency reflects uncritical reliance on AI-generated knowledge. Results show that although the Merdeka Curriculum legitimizes autonomous engagement with digital resources, the absence of structured guidance on artificial intelligence literacy often leads to superficial autonomy and epistemic dependence rather than reflective independence. Student agency in AI-mediated religious learning should therefore be understood as a multidimensional capacity encompassing technical competence, epistemic awareness, and evaluative judgment. Integrating critical artificial intelligence literacy into Islamic Religious Education is essential to ensure learner autonomy contributes to responsible and informed religious understanding in the digital era.

Artificial intelligence (AI) is becoming a core educational concern in undergraduate medical education as AI-enabled tools increasingly shape clinical workflows, learning environments, and patient care. The challenge is no longer simply whether AI should be included in the curriculum, but how AI literacy should be bounded for undergraduate learners and translated into teachable, observable, and assessable educational outcomes. This focused conceptual narrative review synthesized literature on AI literacy and related constructs in undergraduate medical education, using a structured search and interpretive synthesis with competency-based medical education (CBME) as an interpretive lens. PubMed and ERIC were searched for English-language literature from 1 January 2020 to 15 April 2026. Local screening records identified 94 standardized bibliography records, 66 records screened after deduplication, 40 full-text reports assessed, and 30 publications contributing to the final synthesis. Five recurring domains were identified: Foundational AI knowledge; applied clinical interpretation and use; data literacy and critical appraisal; ethics, law, and professional responsibility; and human–AI collaboration and professional formation. Through a CBME lens, these domains can be translated into learning outcomes, contextualized tasks, observable performances, and programmatic assessment evidence. The literature most strongly supports conceptual clarification, domain identification, and curricular translation, whereas evidence for longitudinal development, observable performance, and validated undergraduate assessment remains limited. The proposed framework, examples, milestones, and rubric anchors are synthesis-informed design propositions that require empirical validation before high-stakes use.

This paper reports findings from a mixed-methods study examining the integration of Generative Artificial Intelligence (GenAI) literacy within an undergraduate Business Management programme. Drawing on Zhou and Schofield’s (2024) AI literacy framework, AI-integrated learning activities were embedded across each year of study to support four dimensions of AI literacy: ’Know and Understand’, ‘Use and Apply’, ‘Evaluate and Create’, and ‘AI Ethics’. Data collected through surveys and focus groups suggest that students perceived curriculum-embedded AI activities as supporting AI literacy development; however, outcomes varied. Year 1 students reported the strongest development in the Know and Understand dimension, while Year 2 students demonstrated greater development in Use and Apply, and in Evaluate and Create. Year 3 students continued to emphasise foundational understanding, highlighting the need for greater scaffolding of advanced competencies. Engagement with AI Ethics remained comparatively limited. The study demonstrates the importance of a strategic, curriculum-wide approach to AI literacy development.

Artificial intelligence (AI) is increasingly used to predict materials properties, prioritize experiments, and generate hypotheses in materials science. The limiting challenge is no longer only algorithmic capability, but also whether students and educators can use AI with domain-specific scientific judgment. This conceptual analysis argues that AI education for AI-powered materials discovery should move beyond tool access and surface-level interaction with generative systems toward a workflow-aligned model of AI literacy. The analysis links AI literacy to core materials-informatics competencies: data provenance, domain-specific featurization, model validation, uncertainty quantification, physics-informed reasoning, reproducibility, and experimental feedback. It also advances an outcome-oriented equity perspective in which institutions evaluate not only access and participation, but also comparable learning gains, transfer of learning, learner confidence calibration, persistence, and research readiness across student subgroups. The article synthesizes relevant evidence, identifies cognitive and ethical risks such as off-loading and cognitive surrender, and proposes a dual-track curriculum model, implementation recommendations, reusable curriculum artifacts, and a set of testable propositions for future study. The central goal is to prepare students to become better scientists—not merely more efficient users of AI tools.

This paper proposes a conceptual framework for integrating Artificial Intelligence (AI) into the curriculum. It builds on previous conceptual papers, which provided initial suggestions on integrating AI into teaching. The approach to developing the conceptual framework includes drawing on existing frameworks, AI literature, and case studies from the Queen Mary University of London President and Principal, an AI literacy-funded project. The opinion piece aims to advance our thinking on creating a teaching and learning toolkit to support educators in integrating AI into their teaching, enhancing students’ AI literacy and skills. This paper has two main objectives: first, it develops an AI literacy conceptual framework to support educators in integrating AI into their teaching, and second, it provides suggestions on how to engage with it.

Artificial Intelligence (AI) has become a defining force in education, transforming how knowledge is created, evaluated, and shared. However, universities still face challenges in integrating AI literacy effectively into their curricula. Current efforts, opinions, and ideas vary widely. Some concentrate on technical skills, while others emphasise ethics or social impacts, often without pedagogical alignment or supporting research. This study offers a Structured Literature Review (SLR) of 87 peer-reviewed sources (2018–2025) from Scopus-indexed journals, including Computers & Education, AI & Society, British Journal of Educational Technology, Nature Machine Intelligence, and the International Journal of Artificial Intelligence in Education. The SLR synthesises conceptual, pedagogical, and governance perspectives to create the AI Literacy Design Matrix (AILDM). This matrix serves as a comparative framework identifying four interconnected curriculum design dimensions: conceptual, ethical, productive, and participatory. The review reveals that, although higher education worldwide acknowledges the importance of AI literacy, most programmes remain fragmented, with limited integration of ethics or civic aspects. The AILDM proposes a framework for designing balanced curricula that foster understanding, creativity, and responsibility. The paper concludes that AI literacy should be regarded as a civic and epistemic infrastructure, and therefore integrated across disciplines rather than confined to computer science.

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.

This study first employs a meta-analysis to explore and analyze global literature on AI literacy, resulting in the development of a Global Artificial Intelligence Literacy Framework (GAILSF). Next, we conduct a matching analysis between the framework's indicators and the “AI Curriculum Development Standards for Primary and Secondary Schools” issued by the Chinese Society of Education to validate the framework's reliability further. Additionally, based on the application and innovation indicators of the framework and in the context of GAI tools, this study develops an assessment of middle school students' AI application skills in China, aiming to promote the standardized evaluation of AI literacy in education. Through analysis of the latest relevant research and multiple expert discussions, we extend the application and innovation indicators into measurable assessment criteria and design contextualized and gamified computer operation assessment items.

We analyzed 74 OECD documents to identify core competencies of AI literacy, using the KSA framework—Knowledge, Skills, and Attitudes as the analytical basis. SBERT embedding and semantic-based clustering techniques were applied. The analysis identified 27 core competencies, with 9 competencies in each KSA category. In the knowledge domain, we identified competencies such as ‘Open Knowledge Sharing’ and ‘Comprehensive AI Educational Competency.’ In the skills domain, ‘Technology-based Problem Solving’ and ‘Creative Critical Thinking’ were confirmed. In the attitudes domain, competencies included ‘Empathetic Social Awareness’ and ‘Ethical Technology Consideration.’ We also proposed evaluation elements for each category; AI information interpretation and ethical judgment abilities for the knowledge domain; problem-solving and critical thinking abilities for the skills domain; and empathy and ethical responsibility for the attitudes domain. This study provides empirical criteria for AI education and policy development, supporting curriculum design and the construction of competency assessment systems.

Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), creating a critical need for AI literacy in management education. In Morocco, this global challenge is compounded by a persistent digital skills gap and the absence of a national AI strategy, creating a double gap for its higher education system. Grounded in an integrative review methodology, this paper develops a pedagogical framework through the triangulation of evidence from three distinct streams: a review of academic literature on AI in HRM and pedagogy, a benchmark of leading international university curricula, and an analysis of the Moroccan institutional context. The primary contribution is the HR-AI Bridge Framework, a conceptual model structured around three core pillars: Strategic Application, Ethical Vigilance, and Human-Machine Collaboration. This model offers a structured approach for Moroccan universities to modernize their HRM curriculum. It also contributes a contextualized, non-Western perspective to the global discourse on AI literacy, proposing a pathway to cultivate future-ready leaders capable of acting as the strategic and ethical architects of a human-centric digital transformation.

Artificial Intelligence (AI) is a transformative force in the 21st century, offering significant potential to revolutionize various sectors, including education. However, the disparity in AI literacy between developed and developing nations poses significant challenges to achieving equitable global progress. This review paper aims to address these challenges by proposing a comprehensive AI literacy framework tailored for developing nations and outlining strategic implementation plans. The paper begins by defining AI literacy and emphasizing its critical importance for individuals and societies, particularly in the context of developing nations. It provides an in-depth analysis of the current state of AI literacy in these regions, identifying key barriers such as limited infrastructure, insufficient educational resources, and policy gaps. A detailed AI literacy framework is proposed, focusing on essential competencies and skills, curriculum integration, and the pivotal role of educator training. Strategies for effective implementation are explored, including policy recommendations for governments and educational institutions, the role of public-private partnerships, and the utilization of online educational platforms to enhance accessibility and inclusivity. The review highlights successful case studies from various developing nations, extracting best practices and lessons that can be adapted and replicated. Methods for assessing the impact of AI literacy programs are discussed, emphasizing the long-term benefits for both individuals and the broader society. Finally, the paper considers future directions and innovations in AI literacy, suggesting ways to improve and adapt the framework to meet evolving needs continuously. By thoroughly reviewing the existing literature and offering actionable strategies, this paper aims to contribute to the global effort to promote AI literacy and ensure that developing nations are not left behind in the AI-driven future.

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The rapid advancements of artificial intelligence (AI) in healthcare have triggered a significant literacy gap among nursing professionals, raising concerns about implementation, ethical use, and impact on practice. This article introduces the AI-ABCs framework as a structured approach to building foundational AI literacy among nurse leaders, educators, and healthcare professionals. The framework was developed through a comprehensive literature synthesis and expert consultation in nursing informatics to identify essential components of AI literacy for healthcare professionals. The AI-ABCs framework provides an accessible entry point for AI literacy through three components: A-AI-Basics, B-Benefits and Challenges of AI, and C-Core Components and Terminologies in AI. The AI-ABCs framework offers nurse leaders and educators a structured approach to developing foundational AI literacy necessary for guiding policy development, curriculum integration, and ethical implementation in healthcare and educational settings, aligning with recommendations from key professional organizations.

The emerge of Generative Artificial Intelligence (GenAI) sparked diverse reactions within the educational sector, from enthusiasm to prohibition. The core question is no longer whether educators should use GenAI, but rather what skills, knowledge, and mindset they need to use AI effectively and responsibly. Educational AI literacy focuses on the competencies educators must develop to understand, utilize, and critically evaluate AI in teaching, learning, and assessment. However, fostering educational AI literacy requires more than simply identifying and defining necessary competencies; existing literature does not adequately address providing a comprehensive curriculum for developing AI literacy in educational contexts. Drawing on an integration of conceptual theory, empirical research, and the author's extensive experience in faculty development across diverse international contexts, this conceptual paper introduces a holistic, participatory, and inquiry-driven curriculum framework for developing AI literacy among teacher educators in higher education. The synergy of these three sources of knowledge ensures a theoretically grounded and practically relevant foundation for the framework's design. Developed through an iterative process of design, implementation, and evaluation, the framework offers structured guidance for creating, embedding, and assessing AI literacy initiatives responsive to the unique needs and institutional contexts of higher education settings. Rather than prescribing a fixed hierarchical sequence, the framework is intentionally designed as a flexible blueprint—an open-ended roadmap, where the curriculum's depth and breadth can be adapted to an institution's AI-readiness and its broader mission regarding educators’ expected AI proficiency or mastery. Additionally, the analytical study addresses potential implementation challenges, strategies to mitigate them, and implications for stakeholders.

This paper explores the need to integrate artificial intelligence (AI) proficiency into undergraduate psychology education, building on the framework proposed by Robinson et al. Although AI presents both significant opportunities and societal risks, psychology majors seem to be well-positioned to address these challenges through their competencies in ethics, critical thinking, and the study of human behavior. AI literacy is essential for preparing graduates to navigate a rapidly changing workforce. By extending established curricular documents and guidelines, such as the American Psychological Association's Guidelines for the Undergraduate Psychology Major 3.0, institutions can enhance the relevance and value of psychology degrees. However, successfully integrating AI literacy into the existing psychology curriculum requires both practical pedagogical strategies and the consideration of the philosophical challenge of preserving the human-centered focus of psychology education.

Rapid advances in artificial intelligence (AI) are reshaping curricula and work, yet technology and engineering education lack a coherent, critical AI literacy pathway. In this study, we (1) mapped dominant themes and intellectual bases and (2) compared AI literacy between secondary technical students and pre-service technology and engineering teachers to inform curriculum design. Moreover, we conducted a Web of Science bibliometric analysis (2015–2025) and derived a four-pillar framework (Foundational Knowledge, Critical Appraisal, Participatory Design, and Pedagogical Integration) of themes consolidated around GenAI/LLMs and ethics, with strong growth (1259 documents, 587 sources). Phase 2 was a cross-sectional field study (n = 145; secondary n = 77, higher education n = 68) using the AI literacy test. ANOVA showed higher total scores for pre-service teachers than secondary technical students (p = 0.02) and a sex effect favoring males (p = 0.01), with no interaction. MANCOVA found no multivariate group differences across 14 competencies, but univariate advantages for pre-service technology teachers were found in understanding intelligence (p = 0.002) and programmability (p = 0.045); critical AI literacy composites did not differ by group, while males outperformed females in interdisciplinarity and ethics. We conclude that structured, performance-based curricula aligned to the framework—emphasizing data practices, ethics/governance, and human–AI design—are needed in both sectors, alongside measures to close gender gaps.

The integration of artificial intelligence (AI) in Islamic Religious Education (IRE) requires a conceptual framework for the development of a curriculum that is context sensitive and in harmony with ethical values and the Islamic education system. This is a challenge that is not only technological but also epistemic and ethical, because religious knowledge requires the validity of sources, contextual interpretation, scientific authority, and the guidance of teachers. This research aims to construct the dimensions of Islamic AI Literacy as the basis for the development of an adaptive IRE curriculum in the Islamic Religious Universities. This study uses a qualitative approach with an integrative literature study method. This method is used to critically synthesize various publications related to AI literacy, Islamic Religious Education, and curriculum development from 2020 to 2026. The results of the study show that Islamic AI Literacy consists of six operational dimensions: (1) information access literacy, (2) AI boundary literacy, (3) critical prompting literacy, (4) digital tabayyun, (5) academic integrity, and (6) literacy of scientific authority. These dimensions are based on Islamic principles such as adab, amanah, 'adl, maslahah, tabayyun, as well as maqāṣid al-sharī'ah, monotheism, and morality. The construction roadmap developed describes the four main stages of theoretical foundation, dimension identification, formulation of indicators and curriculum implications, and implementation and evaluation.

The rapid integration of Artificial Intelligence (AI) into academia and industry necessitates a fundamental transformation of higher education curricula. Moving beyond purely technical proficiency, this paper proposes a comprehensive framework for embedding AI Literacy and Critical Discourse Analysis (CDA) as transdisciplinary core competencies, positioning AI outputs not as neutral tools but as ideologically laden discourse requiring rigorous scrutiny. Drawing on a qualitative multiple-case study involving undergraduate Communication (n=45) and Business (n=38) courses at a Zambian university, this research demonstrates how a pedagogy synergistically combining CDA with hands-on AI interaction fosters deeper digital citizenship, enhances critical thinking, and improves employability skills. Over a three-week intervention, students applied Fairclough’s three-dimensional CDA model to AI-generated press releases and marketing content. The findings reveal three transformative outcomes: first, students adeptly identified embedded societal biases such as gendered leadership stereotypes; second, they reconceptualised AI from an authoritative “oracle” into a fallible, dialogic partner requiring ongoing interrogation; and third, they developed sophisticated, ethically-informed prompting strategies that directly corrected identified biases, a synergistic loop between critique and creation. This approach ensures graduates emerge as critical, innovative, and ethical producers rather than passive consumers of technology. The paper recommends curriculum design, including transdisciplinary modules, faculty development, open-access resources, and reformed assessment strategies that reward critical engagement over mere output generation.

Objective: The integration of Artificial Intelligence (AI) into global healthcare necessitates corresponding advancements in health professions education. This study aimed to develop a comprehensive, evidence-based AI literacy curriculum specifically tailored for physiotherapy students in India, where such training is currently absent. Methods: A systematic curriculum development process was employed, guided by Kern’s sixstep framework. This involved a literature review of global AI applications in physiotherapy and healthcare education, a targeted needs assessment identifying gaps in current Indian physiotherapy training, and synthesis and contextualization to adapt international evidence for the Indian healthcare setting. Results: The outcome is a detailed 12-week, 36-contact-hour curriculum titled 'Artificial Intelligence Literacy in Physiotherapy Practice'. The curriculum consists of five sequential modules emphasizing low-cost tools, Indian case studies, ethical reasoning, and hands-on exercises. Conclusion: This paper provides a rigorously developed, ready-to-implement framework to address the critical gap in AI literacy within Indian physiotherapy education. Future research should evaluate its effectiveness in improving student competencies and readiness for AIintegrated healthcare.

Generative AI increasingly participates in curriculum design, yet the resulting syllabi, learning outcomes, and assessments may reproduce subtle epistemic injustices that existing governance instruments fail to detect. We propose an adaptive and auditable framework that learns a bias and fairness checklist for generative AI curriculum artifacts rather than treating such a checklist as a static set of rules. The proposed method encodes curriculum texts into two disentangled latent subspaces, one capturing pedagogical utility and the other capturing epistemic justice violations, through a contrastive objective combined with intersectional adversarial debiasing. A psychometric utility-preserving penalty constrains this separation so that debiasing does not degrade instructional quality. Each checklist item is then evaluated by an evidential deep learning head that outputs a Dirichlet distribution over compliance states, hence yielding calibrated violation probabilities and explicit uncertainty estimates. An uncertainty-aware routing mechanism subsequently directs low-confidence drafts to a Faculty Expert Panel, whose decisions and justification notes are returned to a fine-tuning dataset. This feedback loop updates the checklist definitions and model weights iteratively, therefore allowing the governance instrument to adapt to newly identified forms of subtle bias. The main contribution is a mathematically grounded pipeline that couples representation disentanglement, evidential uncertainty quantification, and human expert feedback within a single auditable process. For curriculum governance, the framework offers transparent violation probabilities, traceable expert interventions, and a documented evolution of fairness criteria, which together support accountability in generative AI-assisted educational design.

This study verifies the ability of large language models (LLMs) to generate a curriculum and develop syllabi for specific courses. We prompted four models to generate two sets of curricula for a bachelor’s degree in Economics and Management. We also generated syllabi for the courses included in the curriculum. We chose five Polish public economics universities offering those degree programs for comparison. Four LLMs were used in this experiment: ChatGPT-3.5, ChatGPT-4, Google Bard, and Gemini. Two of them are multimodal models. The study used an iterative approach, increasing the detail of the prompt in each iteration. The results show that the more specific prompt is given to the LLM, the less accurate the results are. Moreover, the experiment shows that none of the LLMs developed a complete curriculum at a level comparable to that generated by humans. However, LLMs can significantly help create a curriculum and develop syllabi by humans, provided that there is close human–artificial intelligence (AI) collaboration. The results obtained from the AI-assisted curriculum design differ depending on the model. By analyzing the differences between the tools and the real degree programs and syllabi, we determined that multimodal models are better suited for this task than older models.

Generative AI (GenAI) presents new opportunities for learner-centred teaching. This study explores its potential in co-creating learning personas—fictional yet research-informed representations of students – for energy digitalisation education, a field that attracts learners from diverse backgrounds. We document the process of generating nine learner personas with GenAI and evaluating their quality through expert review and benchmarking against publicly available LinkedIn profiles of professionals in similar roles. Our findings indicate that GenAI effectively differentiates professional roles, captures key job-related challenges, and reflects learner motivations, making it a valuable tool for curriculum design. However, critical limitations persist, with GenAI creating overly idealised professionals, lacking diversity in the supporting AI-generated images, and overlooking some nuanced real-world complexities. These challenges highlight the need for human oversight to ensure authenticity, inclusivity, and ethical depth. Based on our findings, we provide recommendations for co-creating personas with GenAI for curriculum design.

Despite increased recognition of the importance and need for pedagogical training for public health and health promotion instructors in best-practices and inclusivity, formal training is often overlooked. This disregard for pedagogical training necessitates exploration of alternative and innovative approaches to enhance teaching and learning such as generative AI. This paper describes applied uses of generative AI, specifically ChatGPT, to enhance pedagogy in public health and health promotion education in the areas of curriculum design, instructional strategies, assessment and feedback, and diversity, equity, and inclusion. Generative AI as a supplemental tool shows immense promise for improving teaching and learning, however, inherent limitations and ethical considerations require caution and continued scrutiny.

Research on the application of Generative AI to education has received wide Attention. Researchers find that the rise of Generative AI provides a technological driver for innovation in education. Researchers also found that the characteristics of Generative AI provide a theoretical foundation and practical direction for its deep integration into the secondary school intelligence education platform. However, there is very little empowerment for a particular discipline. Therefore, this paper analyzes the design of generative AI-enabled secondary school mathematics curriculum, resulting in immersive scenarios, optimizing innovative teaching models, and enhancing personalized diagnostic feedback. This paper suggests, in combination with traditional teaching methods, that the development of a human-generative AI mathematical knowledge construction system plays the interaction of mathematics teachers’ learning characteristics and technical capabilities.

Addressing a developing yet mostly uncharted field of educational technology, this paper examines the present status of AI in higher education's automated curriculum creation. ML is becoming more popular in the field of education, but there is a dearth of studies specifically examining its impact on the creation of courses at the university level. The uses of advanced algorithms like TF-IDF for academic feature extraction and data pretreatment approaches in general have received very less research focus. it filled this knowledge vacuum by reviewing the literature extensively and then using network analysis to identify trends and recurrent themes in curriculum design using ML. Our new WSA-CNBiRN model, which is based on a quantum water strider algorithm, is designed to improve the automation of curriculum production and is based on these discoveries. In order to properly define and categorise different kinds of educational activities, this model was developed. The WSA-CNBiRN method demonstrated encouraging results in experimental assessments, reaching a high performance accuracy of 96.44%. These results highlight the AI's ability to revolutionize higher education's curriculum development and highlight the need for ongoing study in this dynamic field to back scalable, data-driven instructional design.

The article presents the possibilities of using generative artificial intelligence (GAI) services to optimize the process of developing working programs for disciplines (WPD) in higher education. Various types of prompting are considered. An experimental generation of WPD was conducted using the tools GigaChat, DeepSeek, and ChatGPT, along with an expert evaluation of the results. Recommendations for the effective application of these services are proposed.

The increasing presence of Artificial Intelligence (AI) and Generative AI (GenAI) tools in education highlights the need for teachers to develop specific digital competencies for effective integration into curriculum planning. This study aimed to design and psychometrically validate a diagnostic instrument, developed by the authors, grounded in an extended version of the Technology Acceptance Model (TAM), in order to assess in-service teachers’ behavioural intentions and its relationship with teachers’ pedagogical digital competencies for the integration of GenAI tools in curriculum design. A sample of 434 in-service teachers from the Dominican Republic participated in the study. The model included factors such as Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, Self-Efficacy, Attitude of Use, and Behavioural Intention, analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results confirmed all hypothesized relationships, with Behavioural Intention emerging as the main predictor of digital competence for using GenAI tools in curriculum planning. The model demonstrated strong reliability, convergent and discriminant validity, and good explanatory power. The findings emphasize the importance of teachers’ motivation, self-efficacy, and perceived benefits in adopting AI technologies. Educational policies and teacher training programs should place particular emphasis on enhancing these dimensions to foster effective pedagogical use of GenAI tools in teaching practice.

This study explores how generative artificial intelligence (AI) can be effectively integrated into visual design education, focusing on 3D design courses. As tools like Midjourney, DALL·E, Runway, and Blender AI plugins become more common, design curricula must adapt to these emerging technologies. Through case studies, literature review, and focus group discussions with educators and professionals, the research examines how generative AI supports idea development, enhances visual creativity, and automates repetitive tasks. The findings suggest that AI can strengthen students’ creative workflows, but also highlight the need for education that combines technical skills with diverse awareness. This study offers practical insights for designing AI-integrated 3D design curricula for the next generation of designers.

Background/Objectives: This study explores the effectiveness of a "multi-skill integrated, long-term project-based learning" (PjBL) approach within a generative artificial intelligence (GenAI) course in vocational colleges. Methods: Grounded in constructivism and authentic learning theory, the study designed an eight-week "AI-assisted short online novel creation" program. This curriculum integrated multiple skills-including AI writing, painting, and data analysis-resulting in the publication of novels on a real-world platform to foster authentic engagement. A quasi-experimental design was employed to compare an experimental group (n = 49) undergoing this long-term integrated instruction against a control group (n = 66) engaged in short-term projects. Results: It indicated that the long-term PjBL approach yielded significantly higher task completion rates (98.0% vs. 78.8%, p < 0.01) and demonstrated superior comprehensive AI literacy compared to the short-term approach. Conclusions: These findings suggest that authentic, long-term complex tasks significantly enhance student efficacy and skill acquisition, providing a valid paradigm for the curriculum design of GenAI courses in vocational education.

Static curricula frequently fail to accommodate individual differences in learning pace and style, creating a critical gap in technology-enhanced learning environments. This study investigates the impact of a Generative AI-driven adaptive curriculum on student performance and engagement. The study was conducted with 270 students enrolled in an “Innovation and Digital Technology for Learning” course at Khon Kaen University, divided into two groups: one following a static curriculum and the other using the AI-generated dynamic curriculum and randomly assigned via stratified randomization to a static curriculum (control group, n = 135) or a Generative AI-driven adaptive curriculum (experimental group, n = 135). The AI-driven system adapted learning materials in real-time. The experimental group demonstrated significantly superior outcomes: post-test scores improved by 24.2% versus 10.2% in the control group (t(268) = 8.43, p < .001, Cohen’s d = 1.03), with higher engagement intensity (9.1 vs. 5.6 h/week), lower cognitive load on the NASA-TLX (M = 38.4 vs. 52.1), and stronger complex problem-solving performance (85.6% vs. 68.2%). The System Usability Scale (SUS) yielded a score of 92.5 (SD = 6.8), indicating excellent usability. These findings suggest that Generative AI-driven adaptive curricula can meaningfully improve student outcomes in technology-enhanced learning contexts, though replication across diverse settings is needed. Future studies should investigate the scalability of AI-driven curricula across disciplines and longer-term educational outcomes.

Generative Artificial Intelligence (GenAI) is transforming education by reshaping curriculum design, individualized learning, and professional collaboration. This interpretive study examines how seventy preservice teachers engaged with GenAI through structured learning experiences focused on lesson planning, content development, and reflective practice. Participants, all fourth-year PK–Grade 5 and intervention specialist candidates at the University of Dayton, completed a co-taught seminar led by methods professors in science, social studies, language arts, and mathematics. Data sources included initial reflections on GenAI’s role in education, documentation of prompt exploration, and ongoing reflections during the seminar. Thematic analysis revealed a shift in preservice teachers’ understanding from viewing GenAI as a simple lesson-planning tool to recognizing its potential for differentiation, assessment, and parent communication. Participants demonstrated increasing sophistication, ethical awareness, and confidence in using GenAI to enhance instruction. Findings indicate that intentional professional development enhances preservice teachers’ GenAI literacy and confidence, supporting thoughtful integration of emerging technologies into teaching practice. The study advocates for embedding GenAI training within teacher education programs to cultivate innovation, ethical practice, and inclusivity in future classrooms.

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Generative artificial intelligence (GenAI) is rapidly integrating into educational practices, yet its impact on teacher professional practice within the highly context and relationship-dependent field of early childhood education remains poorly understood. This study explores how GenAI functions as a "digital scaffold" to support novice preschool teachers in de-signing contextualized, kindergarten-based curriculum within the Chinese context. Through in-depth interviews and thematic analysis with 10 teachers, we discovered GenAI's dual potential: it empowers educators by providing instant re-sources and inspiration, yet its "decontextualized" outputs create tension with the contextual and relational core of early childhood education. Crucially, teachers demonstrated robust professional agency by critically evaluating and localizing AI-generated content. This process of "mediated reflection" catalyzed their professional knowledge development. Ultimately, AI integration does not replace teachers but re-configures their professional roles toward higher-value, human-centered core competencies-situational judgment, ethical decision-making, and emotional connection-which form the bedrock of resilient and sustainable educational ecosystems. Our findings underscore that responsible AI integration in education must prioritize sup-porting teacher agency and well-being to enhance systemic sustainability.

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Generative artificial intelligence (GenAI) has moved from an emerging educational tool to a structural challenge for science, technology, engineering, and mathematics (STEM) higher education. This narrative review argues that the most consequential effect of GenAI is not the automation of existing teaching practices but the need to redesign curricula, learning outcomes, pedagogies, and assessment around disciplinary judgment, critical verification, intellectual independence, and transparent, ethical use of GenAI. Its distinctive contribution is to frame GenAI as a problem of curriculum and assessment validity rather than primarily as a question of tool adoption or academic integrity. Because widely available systems can generate code, solve quantitative problems, summarize literature, draft laboratory reports, and produce fluent scientific prose, conventional submitted artifacts have become weaker indicators of the reasoning and competence they are intended to demonstrate. The review therefore examines the full programme-to-classroom pathway, connecting definitions of graduate competence with course design, classroom and laboratory practice, assessment, feedback, faculty capability, technology adoption, and iterative evaluation. The analysis integrates cognitive load theory, constructive alignment, constructivist perspectives, and frameworks of faculty capability and technology adoption. The biological sciences serve as a recurring disciplinary case because they combine conceptual knowledge, laboratory practice, computational analysis, and ethical decision-making, and are also being transformed by AI-based scientific methods. A worked cell biology example, structured using the Analysis, Design, Development, Implementation, and Evaluation model, operationalizes the review’s conceptual argument and demonstrates how GenAI integration can translate into needs analysis, outcome specification, resource development, blended laboratory implementation, assessment, and iterative redesign. The resulting design logic is generalized into a transferable five-step template for STEM curriculum redesign, with recommendations at programme, course, and institutional levels.

Abstract Generative artificial intelligence (GenAI) is simultaneously reconfiguring occupational task allocation and the evidentiary conditions under which competence is judged in technical and vocational education and training (TVET). By generating text, explanations, and recommendations, it lowers the cost of producing fluent outputs while weakening the extent to which final products alone demonstrate learners’ independent capabilities. This article uses an integrative conceptual synthesis to connect research on vocational competence, digital competence, digital work, constructive alignment, boundary objects, and evidence-centered assessment. It develops the argument that responsible digital mediation should be treated as a transversal core capability: learners must decide when to use, constrain, or reject generative tools on the basis of task fit, comparative advantage, verification feasibility, and protection of competence evidence, while retaining responsibility for outcomes. The article positions the competence map as a secondary, intermediary translation mechanism in the curriculum-making middle space. In a minimal sufficient form, the map links occupational tasks, competence claims, learning outcomes, activities, tool boundaries, and assessment evidence without replacing existing occupational standards, competency frameworks, curriculum maps, or rubrics. An illustrative Smart Warehousing and Data Operations prototype shows that deterministic systems remain preferable for structured calculation and transaction processing, whereas GenAI can expand the option space in verifiable episodes of hypothesis generation, interpretation of unstructured information, and audience-sensitive communication. The contribution is conceptual rather than causal: it reframes GenAI as a dual disruption of work and evidence and specifies conditions under which curriculum use is educationally defensible and administratively proportionate.

Generative Artificial Intelligence (GAI) holds significant potential to advance the field of social work, yet it brings forth considerable challenges and risks. Key concerns include the legal and ethical ramifications of GAI application, as well as its effects on the vital human connections inherent in social work. Nonetheless, educators in this field must ready their students for the evolving digital environment, ensuring they are adept at employing GAI thoughtfully, skillfully, and ethically. This article will explore the integration of GAI knowledge and skills within educational settings. It will feature a case study detailing the author’s redesign of community welfare and social work degree assignments to include GAI within a community welfare/social work undergraduate course in Queensland, Australia. The discussion will extend to curriculum and assessment development processes aimed at leveraging GAI to enhance student learning, knowledge retention, and confidence in applying GAI within their academic and professional pursuits. Furthermore, the article will examine the implications for curriculum and assessment design, emphasizing the importance of clear learning objectives, the creation of specific, intricate, and contextualized assessments, the necessity for students to critically evaluate GAI outputs, and the challenge of presenting GAI with tasks beyond its capabilities.

This paper discusses the potential of implementing Generative AI in the fashion design curriculum. AI literacy has become essential for several industries, and its integration into educational programs is becoming increasingly vital. This work uses the theoretical framework of technological, pedagogical, and content knowledge (TPACK) as the guiding principle and designed generative AI content for designing process applicable to apparel design programs. Adobe Firefly, Photoshop, and Illustrator were the core tools used to develop a digital design collection, beginning with a cover page and concluding with realistic garment visuals, with AI as the central implementation tool. Mixed-method and reflexive thematic analysis of the results showed that structured instructional exposure increased students’ confidence in using AI while shaping their views on creativity, efficiency, authenticity, and future adoption. Student reflections on the creative speed vs. overall efficiency underscores the importance of developing domain-specific scaffolding in pedagogy. Also, through hands-on exploration, students learned its strength and limitations as fashion designers and expressed AI tools as a supporting tool rather than a replacement to human creativity.

Architectural education is being reshaped as Artificial Intelligence (AI) challenges human-centered conceptions of creativity, authorship, and knowledge production. However, current discussions on AI-supported architectural education remain largely focused on tool adoption, productivity, creativity, and student perception, while the policy implications of AI for curriculum design, studio governance, assessment, educator training, and ethical accountability remain underdeveloped. Addressing this gap, the study develops a policy-oriented posthuman framework for interpreting AI integration in architectural pedagogy and translating it into responsible design education principles. The study adopts a two-stage review design that combines a conceptual framing review of posthuman pedagogy with a systematic synthesis of empirical and pedagogical studies on AI, computational design, and architectural education published between 2010 and 2025. The review identifies three interrelated dimensions of AI-supported posthuman learning: distributed agency, in which design intelligence is shared across students, educators, AI systems, datasets, interfaces, materials, and studio environments; situated knowing, in which AI becomes pedagogically meaningful only when embedded in reflective, material, and context-sensitive design inquiry; and ethical entanglement, in which authorship, bias, accountability, originality, dependency, and environmental responsibility become core educational concerns. Based on these findings, the paper proposes ecological intelligence as a design education policy principle: the capacity to think, design, evaluate, and act responsibly within interconnected human, technological, material, environmental, and institutional systems. The contribution of the study is twofold. First, it clarifies the theoretical relevance of posthuman pedagogy for AI-supported architectural education. Second, it translates this theoretical perspective into a policy-oriented pedagogical framework that can inform curriculum development, studio pedagogy, assessment criteria, and ethical governance in architectural education.

This paper proposes a governance-oriented transformation of fashion design education in the agentic AI era, shifting pedagogy from skill transmission to intelligent orchestration. Using Weifang University of Science and Technology (WUST) as a case, it reconfigures the Four-in-One model (Course–Competition–Project–Job) into an Agent-Aided Design (AAD) ecosystem supported by an agile curriculum governance mechanism and a dynamic curriculum knowledge graph. The framework integrates posthuman distributed creativity, Bourdieu’s field theory (capital conversion), and an AI–TPACK approach to faculty upskilling. A process-oriented quality assurance model (40/30/30) and a “traffic light” integrity protocol ensure accountability. Flagship projects—Qimin Yaoshu digital heritage restoration and wearable-tech pathways via Goertek—articulate a replicable “Weifang Model” toward a “wisdom aesthetic.”

The contemporary world is marked by multifaceted crises-personal, social, and environmental-that raise urgent existential and educational questions. Within curriculum studies, these crises prompt critical inquiries such as "What knowledge is of most worth?" "Whose knowledge is valued?" and "Is knowledge alone sufficient?" While these questions remain central, this article shifts focus to the role of the curriculum scholar in navigating the contemporary moment. Drawing on theoretical insights from thinkers who have explored the nature of contemporaneity, I engage particularly with Giorgio Agamben's (2009) notion of the untimeliness of the contemporary. Building on this theoretical insight, I propose nine provocations for curriculum scholars operating in a temporally complex world. These propositions are not grounded in formal logic; rather, they serve as posthuman invitations to engage with reality in experimental and transformative ways.

This paper interrogates the opportunities and challenges of a borderless curriculum as the alternative to reimagine a better future premised on initial teacher education. The paper comes against the background that curriculum projects remain nationalised, depriving learners and educators of an opportunity to learn from the best educational practices outside their borders. The paper is located in posthumanism, where a borderless curriculum through technology can be positioned to respond positively to human tragedies such as war, systematic racism, human trafficking and conflict. Borderless curriculum involves unlearning in order to learn by harvesting best practices across borders to reimagine a comprehensive initial teacher education that addresses the lived realities of the learners globally. The paper argues that the posthuman era provides a platform for nations to share knowledge in the virtual and blended space to deconstruct prejudices while evoking living and working together across curriculum and spaces to improve initial teacher education.

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: By using visualization software CiteSpace 6.2R6, the literature on curriculum knowledge graph in China National Knowledge Network (CNKI) from 2010 to 2023 was measured and analyzed in terms of the annual number of publications, research institutions and authors, and research topic clustering. It is found that the core research institutions are Central China Normal University, Yunnan Normal University, Huazhong University of Science and Technology, etc., and the exchanges and collaboration between the research institutions and scholars in the academic field are not close enough. The hot research topics include flipped classroom, learning path, entity recognition, data structure, etc., which can be summarized into three theme areas: "teaching", "personalized learning" and "technical support". The future unified research trend is not clear, and resource recommendation is one of the trends in the study of curriculum knowledge graph in China.

In this article we discuss the difference between curriculum studies (as a field of inquiry) in the posthuman condition and posthuman curriculum (studies). The posthuman condition is characterised by both posthumanism and post-anthropocentrism and relates respectively, to how we now define human given humans’ entanglement with new technologies, and the ethical response-ability of humans in intra-action with the more-than-human-world in a context of impending ecological disaster. In this article we shall argue that although Enlightenment humanism has been challenged philosophically/conceptually both in discourses on anti-humanism and posthumanism, humanist approaches to curriculum studies remain with us in the posthuman condition – the ghosts of Dewey, Tyler, Freire, etc., imbue much of curriculum work. However, the posthuman condition also produces posthuman thought that makes it possible to reimagine curriculum studies, that we shall call posthuman curriculum (studies). We shall review different approaches to curriculum studies in the posthuman condition, and then turn our attention to posthuman curriculum (studies). We argue that curriculum (as a vital concept) in posthumanist terms is intelligible and manifests through intra-actions, processes of becoming and experimenting. Set against sedentary states of being that mark curriculum studies in the posthuman condition; becoming, intra-acting and experimenting in posthuman curriculum (studies) are acts , doings in and of this world. The acts and doings in posthuman curriculum (studies) that are mostly written about include: improvisation, theorisation and diffraction. To these we add and specifically discuss quantum tunnelling, tracing, and desiring. Other forms of curriculum experimentation worthy of consideration in posthuman curriculum (studies) but not discussed in the article are queering, imagining, and writing. Towards the end we make the point that although some connections with the past (such as those that haunt curriculum discourses) can be threatening to life, connections of the thick now hold potential and radical openness for newness.

In Freedom Dreams , Robin D. G. Kelley (2002) asserted “without new visions, we don’t know what to build, only what to knock down. We not only end up confused, rudderless, and cynical, but we forget that making a revolution is not a series of clever maneuvers and tactics but a process that can and must transform us” (p. 44). The authors in this issue present such transformative visions, engaging in what activist and writer Annika Hansteen-Izora (2021) has described as “communal dreaming.” Communal dreaming is a collective practice of envisioning futures that transcend the limitations of current societal norms. It involves collaborative thinking, reflection, and creativity to explore possibilities beyond the status quo. This process is rooted in interdependent relationships, communal care, and co-learning, fostering a shared vision of transformation by critiquing existing power structures, challenging marginalization, and harnessing our collective capacity to build a more equitable, inclusive, and just world (Hansteen-Izora, 2021). In uncertain times, dreaming might seem like a luxury; yet, without it, we are destined to repeat the same patterns. Resisting the neoliberal insistence towards destruction and stubbornly pushing for interdependence is, in itself, an act of liberation—one that requires us to dream collectively, not through wishful thinking but through intentional and deliberate action. Critical curriculum studies offers a space for this dreaming, providing an expanse to envision possibilities beyond the status quo, interrogate power structures, and support one another in the pursuit of equity.

South Africa’s curriculum, and education system at large, remains humanistic and dominated by Western Eurocentric ways of thinking, doing and being. The ongoing protest action in higher education has made explicit the inequalities that continue to keep epistemic violence, Eurocentrism, and human exceptionalism intact. Central to this protest action has been the need for curriculum inquiry to reinvent itself and go against the pedagogical grain by advocating for a decolonised curriculum. However, the decolonial turn has largely been a structural exercise of abstract and rhetoric intensity fueled by a language of critique that is entrapped in discursive tendencies that inhibit the type of radicalism needed for it to invigorate alternatives that can challenge dominant education paradigms. This article is inspired by posthuman feminist theory that troubles the logic of advanced capitalism, epistemic injustice and dominant education paradigms. Invoked by new materialism, intra-actions of our becoming within and as part of the world are the deep entanglements of ethics, knowing and being that can spur scholarly activism. Four curriculum studies scholar activists shared their narratives of how they are enacting their curriculum to challenge dominant education paradigms. Their lived experiences as material, relational, embodied and embedded incite posthuman tendencies of what it means to teach to transgress. Experimenting with transversal transgressions this article attempts at a non-anthropocentric, feminist inspired perspective for curriculum inquiry to find alternative pathways for decolonising the curriculum so that it can rise to the challenges of our time.

ABSTRACT The call for ‘bringing knowledge back in’, epitomized by the concept of powerful knowledge coined notably by Michael Young and Johan Muller, has significantly influenced the ‘knowledge turn’ in global educational landscapes. However, the way of thinking and theorizing about knowledge and the curriculum that underpins the project of ‘bringing knowledge back in’ has not received adequate attention in the literature. Using Muller’s article ‘The palimpsests of knowledge’ (this issue) as a starting point and referencing several key works by Young and Muller, this article examines the social realist approach to thinking and theorizing about knowledge and the curriculum, including its promises and limitations. Invoking Pädagogik and Didaktik, the article proposes an alternative approach that addresses these limitations and provides a foundation for articulating a model of a future-oriented, knowledge-rich curriculum—a model particularly pertinent to the ‘knowledge turn’ in the National Curriculum in England. The article concludes by calling for greater engagement with continental traditions of educational theory and theorizing.

This editorial is a culmination of various research on the area of posthuman theorization as applied to the field of education. It also focused on the need for borderless curriculum to circumvent global challenges such as genocide, terrorism among other things. It details the rationale of adopting a post human and borderless curriculum to respond to the ambivalence brought by the corona virus. The special issue gives alternatives which emerged during the pandemic and arms educators and learners with new models of learning that will ensure education system is not disrupted on the even another pandemic emerges. The argument of the special issue is that within the auspices of posthuman and borderless curriculum something else, and new is possible through working and thinking together.

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ABSTRACT This introductory essay presents a special issue that foregrounds school subjects as purpose-built educational enterprises and reconsiders the role of powerful knowledge in national curricula. Framed against the marginalization of knowledge in both global policy reforms and contemporary curriculum theory, it argues for renewed attention to the educational purpose, content, and construction of school subjects by engaging with questions such as: What are the purposes of school subjects? How should powerful knowledge be conceived in the curriculum? How are school subjects conceptualized and constructed? The issue includes four articles examining the purposes and content of school subjects—geography, history, religious education, and biology—in national curricula across Sweden, Finland, and England. It also features two articles exploring changes in business and management education in Poland and the ‘life and death’ of Liberal Studies as a school subject in Hong Kong. This special issue advances two key propositions: first, that school subjects are structured to fulfil multiple academic, civic, social, and personal aims; and second, that powerful knowledge should be understood not only in terms of its epistemic structure but also in relation to the intellectual and ethical capabilities it enables.

Uganda, a country in East Africa, first implemented its competency-based curriculum in February 2020. This curriculum, referred to as the Lower Secondary Curriculum (LSC), replaces the knowledge-based, content-centred, and examination-oriented curriculum, designed in the 1970s mainly to produce human resource for white-collar jobs. This exploratory three-case qualitative study used semi-structured interviews with 21 teachers, eliciting their experiences in the implementation of the LSC. It illuminated potential gains/opportunities such as teacher collaboration, active learning, generic skills development, and enhancement of ICT competences. However, like previous studies on curriculum reform in developing countries, the full benefits of the LSC remain tenuous due to systemic problems. Indeed, the misalignment to the competence-based approaches, reflected through the tendency to revert to traditional teacher-centred methods, was largely attributed to inadequacies in resourcing, limited ICT competences, poor infrastructure, and shortcomings in the training, all of which disproportionately affected the poorly resourced schools. Addressing these challenges requires targeted and differentiated supported systems, including resource allocation, ongoing in-service teacher training, and institutional support for ICT integration. This is critical for plugging the deepening educational inequalities.

ABSTRACT Which historical knowledge is worthwhile teaching? Over the last decades, this question has been answered differently by history scholars. In many Western countries, arguments to avoid Eurocentric and nationalistic curricula challenge the current selections of historical knowledge in the curriculum. Epistemology in history education also changed following the appreciation of historical thinking and reasoning. Yet most scholars agree that a certain basis of orientation knowledge is needed to achieve the key targets of the history curriculum. Consequently, it is difficult for curriculum developers to select knowledge for history teaching. The aim of this article is to scaffold curriculum developers’ content selection by proposing guiding questions. It aggregates and elaborates theories of knowledge in the field of history education. Young and Muller’s concept of powerful knowledge is applied to the subject of history teaching. Arguments for a structural differentiation of vertical and horizontal discourses are presented. Also, the functional aspect of history teaching is elaborated by relating three purposes of education to knowledge selection.

ABSTRACT This article examines history teachers’ experiences as curriculum makers, focusing on how they connect disciplinary knowledge content to students’ lifeworld experience. By examining how teachers transform institutional curriculum guidelines to create educational encounters with disciplinary knowledge content, this article contributes to the ongoing debate regarding whether teaching powerful knowledge demands connections to students’ lifeworld experience. Data were generated from in-depth interviews with 18 upper secondary school history teachers in Norway, and analysed through reflexive thematic analysis. The analysis highlights that history teachers share two key characteristics when connecting content and students’ experiences. Firstly, teachers are ‘bringing the past closer to students’ by emphasising the past’s relevance to contemporary issues. Secondly, teachers are ‘bringing the students closer to the past’ by encouraging reflection on how their own backgrounds shape their understanding of the past. However, the study reveals that making these connections require teachers to balance different considerations related to presentism – an underlying bias towards interpreting the past to meet the needs of the present. The findings suggest that teachers hold markedly different views on how to address the issue of presentism, and the article discusses how their deliberations regarding presentism shape the conditions of their curriculum making.

ABSTRACT Digitalization in education and the continuously growing presence of digital curriculum instruments have been approached as networked governance with a focus on private commercial actors. By comparison, teachers have been perceived as mere users of the digital instruments provided to support them. This study explores the Norwegian Knowledge Promotion reform as a translation of the policy intention to support teachers’ local curriculum work into a digital Curriculum Planning Tool (CPT) with a particular focus on teachers’ pedagogic knowledge. The findings illustrate how the Norwegian Directorate for Education and Training emerges as a boundary actor by delegating to other actors the activities of research and evaluation, consultation and conducting collaborative service design and development of the CPT. Through collaborative practices, teachers’ experiential knowledge contributed to the problematization of the lack of curriculum support, consultation with private consultancy companies that developed the CPT and adjustments to the CPT rather than to the decision-making about digital curriculum support. The network of digital curriculum support making emerges through an entanglement between the programmatic discourse of national curriculum making and practical discourse consisting of local curriculum work, blurring their boundaries and shaping a new space of networked governance.