高等教育需要更好的人工智能体验，而不是更多的人工智能工具

原文标题: Higher education needs better AI experiences, not more AI tools
来源: eCampusNews | 发布时间: 2026-08-12
原文链接: 点击阅读原文

Key points:

• As AI capabilities advance, maintaining educator agency remains essential

• A strategic roadmap for AI in higher education

• Why governance matters more in the age of AI

• For more on AI’s higher education evolution, visit eCN’sAI in Educationhub

Artificial intelligence is becoming increasingly embedded across higher education. As AI policies continue to evolve, faculty are gaining greater clarity and opportunity to responsibly use new tools, including AI-powered assistants, content generation tools, chatbots, and a growing number of AI-enabled applications. Technology providers continue introducing new capabilities, while institutions are working to understand where AI can create meaningful value for teaching and learning.

For most colleges and universities, access to AI tools is no longer the barrier. The real work is building the clarity, confidence, and shared responsibility to use them well. The question now is whether those tools fit the realities of teaching and learning. In my experience, faculty are often less interested in adding another platform than they are in finding ways to reduce friction within the work they already do.

What I hear from faculty is remarkably consistent. A few things decide whether AI becomes part of everyday practice or stays another login faculty forget they have

What faculty find most useful

Faculty value AI when it helps with specific instructional tasks.

Quiz generation and feedback are part of the everyday work through which educators guide learning. The opportunity should not simply be to produce questions or comments more quickly. It is to give faculty a strong starting point while keeping their judgment, voice, and instructional intent at the center. Educators should remain responsible for reviewing and refining what is created, deciding what fits their learners, and ensuring that every interaction supports the goals of the course. When designed this way, AI does not replace the deeply human work of teaching. It helps educators create more opportunities for learners to practice, understand their progress, and move forward with greater confidence.

This is AI working the way it should: it makes work lighter without taking the judgement out of it.In a study conducted with the Online Learning Consortium, students reported using AI to generate ideas, create practice questions, receive feedback, and better understand course concepts. The findings suggest that both students and educators see the greatest value in AI when it supports learning activities rather than attempting to replace them.

Faculty are not asking AI to teach their courses. They are looking for support with the work that surrounds teaching.

And when AI takes on work faculty already must do, the point is not speed for efficiency’s sake. It is what the reclaimed time makes room for: the relationships, feedback, and judgment that impacts learning.

Why context matters

Trust depends on context.

Faculty want AI systems to work from course materials, learning objectives, rubrics, and other instructional resources. They also want the ability to review, revise, and verify outputs before those outputs reach students.

Students are asking for that guidance as well. The same research conducted with the Online Learning Consortiumfound that students want more direction from faculty on how to use AI effectively in their learning. That places even greater importance on AI tools that operate within course materials, learning objectives, and educator oversight rather than functioning as standalone systems.

AI can support teaching and learning, but educators need confidence that recommendations and content reflect the goals and requirements of their courses.

Where adoption is gaining traction

The clearest lesson I’ve learned from the past two years is a simple one. AI gains traction when it stops being a separate experiment and becomes an integrated part of the teaching and learning experience.

TheTime for Class 2026reportshows that individual AI use is no longer the primary threshold. More than half of administrators, faculty, and students now use AI at least weekly, and many are already paying for AI tools out of pocket. The opportunity, then, is not simply to expand access. It is to help institutions move from scattered use to purposeful, supported, and institutionally aligned practice.

Faculty are already navigating complex ecosystems of courses, content, assessments, communication tools, student data, and support processes. When AI is introduced as yet another standalone system, it can add friction to work that is already stretched. The report notes that faculty spend an average of 34 hours per week on course-related work, with only a small portion of that time spent delivering instruction. That means adoption is more likely to take hold when AI reduces the burden of teaching rather than adding another layer to it.

This is why integration matters. Faculty who are using AI to redesign assessments — what the report calls “Integrators” — are seeing stronger signs of student engagement than peers who respond primarily through restriction or proctored control. To me, that is the whole point, AI matters most when it strengthens learning design, not just faster content creation or tighter compliance.

Embedding AI into familiar learning environments allows educators to access support where teaching and learning are already happening. It can reduce context switching, make responsible use more visible, and help AI feel like a natural extension of existing practice rather than a disconnected initiative. It also gives institutions a better path for moving from policy to practice, especially when policies define the “why” while leaving room for faculty to adapt the “how” within their disciplines.

The real opportunity is not simply to provide AI tools. It is to make AI useful inside the environments educators already trust, tied to the work they are already doing, and aligned to the outcomes institutions care about most: engagement, belonging, assessment quality, and student success.

The role of educator judgment

As AI capabilities continue to advance, maintaining educator agency remains essential.

Faculty want the flexibility to determine when AI is helpful, how outputs should be used, and where human judgment should remain central. They also want transparency into how systems generate recommendations and content.

Successful AI adoption is not about automating educational decisions. It is about providing tools that support informed decision-making while keeping educators in control of the learning experience.

When institutions design AI experiences that respect professional expertise, they create stronger foundations for long-term adoption.

The next phase of adoption

The next phase of AI adoption in higher education is not about whether faculty will experiment with AI. Many already are. The more important question is whether institutions can help that experimentation mature into intentional, effective, and sustainable practice.

TheTime for Class 2026report points to this shift clearly: Personal AI use is becoming table stakes, while the next frontier is depth of use, quality of policy, and the institutional will to move from compliance to capability. That means awareness and access still matter, but they are no longer enough on their own. Faculty need guidance, professional learning, peer examples, and practical support that connects AI use to the real work of teaching, assessment, feedback, and student engagement.

This is where institutional strategy becomes important. Educators need space to build the confidence and fluency to make thoughtful decisions about when, where, and why AI should be used. They also need opportunities to learn from colleagues who are already redesigning assignments, rethinking assessment, and using AI in ways that strengthen—not shortcut—the learning process. To support that work, D2L, WCET, and Opened Culture have developedAI Literacies in Practice, a self-paced Master Class for educators, instructional designers, and those leading AI efforts across their organizations. The course introduces eight interconnected AI literacies and three pillars of AI readiness—pedagogy, operations, and governance—providing a practical starting point for institutions seeking to move from general awareness to more purposeful and responsible practice.

Higher education does not need AI for its own sake. It needs AI that supports educators, strengthens learning experiences, and fits naturally within the work of teaching.

The institutions that make the greatest progress will not be the ones with the most tools. They will be the ones that build the clearest conditions for responsible use: thoughtful policy, trusted platforms, meaningful professional learning, and sustainable practices that help faculty and students use AI well.

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Dr. Cristi Ford is the Chief Learning Officer at D2L.

• Schools are building AI rules before they know the destination- August 17, 2026

• Cohort connections matter: Strategies to help graduate students persist and succeed- August 14, 2026

• Higher education needs better AI experiences, not more AI tools- August 12, 2026

本报道由 AI 助手自动抓取、翻译并发布。

主题演讲之外：人工智能在高等教育中的战略路线图

原文标题: Beyond a keynote speaker: A strategic roadmap for AI in higher education
来源: eCampusNews | 发布时间: 2026-08-10
原文链接: 点击阅读原文

Key points:

• AI is transformational, but it is not a magic solution that automates the struggle of learning

• The pedagogy gap: Redefining the role of faculty and AI in higher education

• Colleges are adopting AI faster than they can govern it

• For more on higher education and AI, visit eCN’sAI in Educationhub

The atmosphere inside the Global Connect/American Accounting Association conference at Caesar’s Palace in August 2026 was a study in contrasts. Outside, the Las Vegas sun was punishing, an environment so relentless that I joked that the swag bags should have swapped out sunblock for flame retardant. Inside, however, the climate was controlled, though a different kind of heat was beginning to rise. This was the figurative fire of artificial intelligence, a technological shift that has left many faculty members feeling as though they are standing in the middle of a desert without a map.

As a professor who has navigated frontier models since the introduction in 2023, I recognize that my role has shifted from a mere instructor to a sherpa or an architect, guiding others through a landscape where information asymmetry is being obliterated. As the opening keynote speaker and to ground the audience before tackling this disruption, I shared an anecdote. I happen to reside just less than 30 minutes away, but I couldn’t resist staying at the hotel anyway, and on the concierge floor last night. You know why? “Those towels. They are so thick and luxurious. I could hardly get my suitcase closed this morning.” Some deadpan silliness completely put the audience at ease as we all roared with laughter.

It served a strategic purpose. In the world of digital pedagogy, humor is a vital tool for reducing transactional distance, which is the psychological and communicative gap between the instructor and the learner. By opening with a shared laugh, we lowered the collective anxiety of the audience, preparing the group for a session of group therapy for the modern professor.

I stand before you as someone who understands the competitive forces at play in our industry. As we transition from the physical heat of the Nevada desert to the psychological heat of rapid technological obsolescence, we must recognize that the fire is already in the classroom. The question is no longer whether we will engage with it, but whether we have the strategic framework to lead our students through the flames before our current curricula turns to ash.

The Wile E. Coyote syndrome: Navigating the pace of obsolescence

The speed of AI development creates a unique psychological burden for educators, a phenomenon I liken to the Wile E. Coyote syndrome. We are perpetually chasing a technological Roadrunner that only seems to gain speed, leaving us in a cloud of dust just as we think we have grasped a new concept. I admit that I have spent nights staring at the ceiling, wondering if the curriculum I prepared yesterday will be obsolete by the time I wake up. This is the Ground Zero state of education today: a world where the frames of knowledge are being redrawn in real time.

For a university leader, this pace is not just a pedagogical hurdle; it is a strategic threat to the perceived value of a four-year degree. For those in fields like accounting, this creates a profound friction. Accounting is fundamentally deterministic. It is a discipline of precision where one plus one must equal two, and the ending balance must tie out with absolute certainty.

Conversely, Large Language Models (LLMs) are probabilistic. They are high-speed guessing machines that predict the next word based on statistical likelihood derived from billions of records. They can be amazingly accurate but do sometimes faulter. When an AI confidently states that Christopher Columbus sailed the ocean blue in 1892, it is not lying in the human sense; it is making a statistically confident guess that happens to be wrong. This non-determinism means that the same prompt can yield different results every time. For a profession built on standardized correctness and tie-outs, this is maddening.

The anxiety of overnight obsolescence is real, but we must realize that while technology moves at a breakneck pace, the foundational principles of human learning remain immutable. We cannot let the statistical guessing of a machine dictate the rigor of our discipline, yet we cannot ignore that the machine is now a permanent fixture in the professional world our students will enter.

Media as vehicles: Grounding AI in instructional design

To find our footing, we must look back to the foundational debates of instructional design, specifically the 1990s discourse between Richard Clark and Charles Kozma. Clark famously argued that “media are mere vehicles” that deliver instruction, suggesting they do not influence student achievement any more than the truck that delivers the groceries. This metaphor is our North Star in the age of AI. The technology (the truck) is secondary to the content and the pedagogical method (the groceries). If our students are not reaching mastery, we must look at the nourishment we are providing rather than simply blaming the delivery vehicle.

AI is challenging our instructional designs because it has exposed the functional obsolescence of many traditional assignments. For decades, we relied on certain containers to measure knowledge: the five-page essay, the take-home problem set, or the research paper. We believed these containers held evidence of critical thinking. However, AI has shattered these containers. If a student can generate a coherent essay or solve a complex problem set by feeding a prompt into a machine, then the assignment was never truly measuring critical thinking; it was measuring a task that a machine can now perform better and faster.

The strategic takeaway for faculty is uncomfortable but necessary: if an assignment can be completed entirely by an AI, it is now likely failing to measure true synthesis and high-level evaluation. The old groceries are spilling out of the truck because the truck has changed, and we must redesign our containers to ensure they still hold value in an era where basic content generation is a commodity.

The graveyard of predictions: Maintaining perspective in the age of AI

History is littered with technologies that were prophesied to replace the teacher. Maintaining a sense of historical humility is essential to avoid modern alarmism. In 1922, Thomas Edison predicted that talking films would replace textbooks and teachers within a decade. He envisioned a world where Physics 101 was delivered by a screen, yet we ended up with the Cineplex for entertainment while the teacher remained central to the classroom. Similarly, in the mid-20th century, television was hailed as the classroom of the future. Programs like Sunrise Semester offered one-way lectures via broadcast, but the lack of feedback and engagement led to abysmal completion rates and the eventual realization that passive consumption is not the same as learning.

The graveyard of technological predictions continued into the digital age. In the 1990s, major publications dismissed the internet as a useless invention, calling it fritter-ware because it was seen merely as a way to fritter time away. They failed to see how it would eventually transform the world and education alike. More recently, one of the largest school districts in the country spent 1.3 billion dollars on an iPad initiative, only to realize within two months that the devices were not aligned with curriculum needs. In 2005, there were claims that students would tweet themselves into mastery, yet social media created more distraction than deep inquiry. We must recall the words of the banker who once told Henry Ford that “the horse is here to stay,” dismissively labeling the automobile a fad. We must avoid that same trap.

AI is transformational, but it is not a magic solution that automates the struggle of learning. We cannot evaluate this technology based on its worst possible use case, just as we do not abandon water because people can drown in it. We must integrate AI into curriculum and must begin from the instructional design of each course.

Watching the donut: AI as the new broadband

The folk singer Burl Ives once sang, “Watch the donut, not the hole.” In our context, the hole represents cheating, the hallucinations, and the fear of replacement. The donut is the positive potential for deep inquiry and enhanced productivity.

I believe we are currently experiencing a Digital Divide Redux, where AI has become the new broadband. Just as we transitioned from screeching dial-up to high-speed fiber in the 1990s, we are moving into an era of high-speed intelligence. If our students do not have access to professional-tier AI tools, they are essentially stuck on intelligence dial-up while the rest of the professional world moves at gigabit speeds. This creates a massive equity risk for universities that ignore the budgetary challenges of providing these tools.

However, we must also be clear about the limitations of current LLMs. I often describe AI as having AI Alzheimer’s, which refers to its limited context memory. It often forgets what you told it ten minutes ago in a long chat thread, leading to inconsistencies that frustrate the uninitiated. Furthermore, we now live in a Zero-Click Reality driven by tools like Google Gemini. When search engines provide an AI summary at the top of the page, it destroys traditional research habits. This zero-click shift is a literacy crisis. If the summary replaces the source, students stop the inquiry process at the first paragraph. This destroys websites that provide deep content and, more importantly, it destroys the student’s ability to engage with nuance.

To illustrate how the media sensationalizes these risks, consider the recent reports about Anthropic. The press treated the situation like a rabid dog running loose in a neighborhood after an AI model accessed corporate sites. In reality, a developer simply left an API door open during testing. The model did exactly what it was programmed to do; it was a human error of oversight, not a sentient machine on a rampage.

Deconstructing the digital native: Literacy and agency

There is a persistent myth that today’s students are digital natives. While they can swipe a phone or navigate social media with surgical precision, they often lack productivity literacy. Most freshman students arrive knowing almost nothing about the professional and productive use of technology. Digital literacy skills are inextricably linked to student academic success. Students are often lost in Excel, unable to construct basic formulas or functions, and are often unaware of the citation and reference tools built into Microsoft Word, as just a few examples.

This gap between social media fluency and professional productivity is where we, as educators, must step in. The new AI Literacy is not a replacement for digital literacy but a vital subset of it. It requires teaching students two critical skills: Corroboration and Ethical Agency. Corroboration is the habit of checking the confident, probabilistic answers of an AI against reliable sources. Ethical Agency is an understanding of when to use a tool to assist thought rather than replace it.

We must remind students of the “168 hours” concept. Elon Musk has the same 168 hours in a week as they do. If Musk can use those hours to build rockets and revolutionize industries, what is the student doing with their 168 hours? Our role as professors is to teach students to own their learning and use these tools to build their own metaphorical rockets, not just to bypass the struggle of a homework assignment.

The architect’s toolkit: Walled gardens and syntopical reading

Our professional role is shifting from the sage on the stage to the architect of learning experiences. One of the most powerful tools in this new toolkit is Gemini Notebook, formerly known as NotebookLM. This tool allows us to create Walled Gardens of content. Instead of letting AI roam the entire internet (where it might drink from contaminated data sources), we can force it to drink only from the water sources we provide, such as our curated PDFs, lecture videos, and trusted datasets.

This enables what Mortimer Adler called Syntopical Reading in his seminal work, “How to Read a Book.” Syntopical reading is the highest form of reading, where one reads across multiple sources to form a unique synthesis. AI can help students navigate these complex sources by integrating multiple sources from which students can read and gather differing information. Also, by providing summaries and answering learner questions in real-time, effectively serving as a cognitive scaffold. It can even transform a dense lecture into a podcast, creating a Professor in a Box that students can listen to while driving or cleaning. This caters to different learning styles (Auditory vs. Kinesthetic) without compromising the rigor of the content. By providing the vehicle, we ensure the groceries actually reach the student, narrowing the transactional distance by providing 24/7 feedback when the human instructor is unavailable.

Case study: The evolution of Captain Excel

To illustrate this shift, let us look at a specific learning problem: the 99% Pilot analogy. In accounting, total accuracy is required. I tell my students that if a pilot lands successfully 99% of the time and today is their 100th flight, you have a problem. For years, I used a self-grading spreadsheet I authored, called Captain Excel to teach complex functions. I demanded 100% accuracy, and it was a highly effective tool until AI made it functionally obsolete almost instantly. Students simply fed the workbook to the AI and received every formula instantly.

Notably, I did not ban the technology; I rebuilt the experience to leverage the new opportunities provided by AI.  Using new AI development tools, which allow faculty with zero technical ability to build functional applications, I created a custom app that functions as a textbook/manual and a practice arena. Crucially, I included a Socratic AI Button. When students are stuck, they do not get the answer; they get a hint or an explanation of the why behind the formula. I made sure to include multiple questions for every function so they must demonstrate mastery through iteration.

The result was transformative. Students told me they had more fun than they ever had in a spreadsheet, and they left the course as Excel experts, not just copy-pasters who knew how to trick a system. This has become my clearest example of how AI can render old designs obsolete while opening the door to something stronger. In the keynote, my examples of AI-driven course redesign, including courses where students design and build software applications, struck a powerful chord with the audience.

The integrity pivot: Moving beyond the integrity police

The strategy of policing AI is a failed one. You cannot be the Integrity Police for 100 students when you only have 168 hours in a week. Instead, we must focus on narrowing the transactional distance through guidance, trust, and agency. The signature of AI writing is often unmistakable: a monotonous rhythm of sentences that are all the same length. It is a bizarre contrast when a student who speaks like Rocky Balboa suddenly writes like William Shakespeare.

I once held an Amnesty Day after discovering a massive wave of AI-assisted cheating on a writing task. The line outside my office looked like a British soccer match, a sea of contrite faces waiting to admit their shortcuts. It was not about punishment; it was a conversation about agency. When students recognize that cheating destabilizes their own intellectual growth, the dynamic changes. However, the future of assessment must also evolve. We may see a return to blue books and oral exams, though these are difficult to scale. A more viable path may be the implementation of local or regional Exit Exams or proctored exam centers with participation within school walls, or with other surrounding institutions.

During the keynote, I shared how faculty can share proctoring exam sessions across many different exam types, with little cost or effort by the school. Schools can create management systems using low code or no code AI to easily build the infrastructure to support curriculum exam needs.  By bringing the final proof of knowledge back into a supervised environment, we can blunt the desire to cheat and allow the rest of the course to focus on the process of learning.

Heterophily and the road ahead

Our mission is to move from Homophily (where birds of a feather flock together) to Heterophily (bringing unlike minds together). In higher education, we often silo ourselves within our departments. However, true innovation in the AI era will come from cross-departmental and interdisciplinary collaboration. We need to talk to the humanities folks, the engineers, the philosophers, and even that cranky old guy with patches on his elbows who has been teaching the same way since 1978. We must bridge these gaps to understand how this technology impacts the human condition. We must embrace the “Wicked Opportunities” described by Robin Lake of Arizona State University. This is the donut, the chance to deepen understanding and use our brains at higher levels.

As the comedian Lily Tomlin once quipped, “We are all in this together by ourselves.” You have the academic freedom to design your own path, but you are not alone in the struggle.

My mandate to you is to start small. Pick one learning problem in your syllabus that is currently broken by AI and solve it with a new tool or a new design. Build a library of success one step at a time. The road to success is under construction, and we have the professional tools to architect a future that is better than the past.

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Dr. Mark Taormino is a professor in the Computing & Information Technology Department at the College of Southern Nevada. His research interests are focused on Instructional Technology and using technology as an instructional and learning catalyst. In addition to teaching computer and technology courses, he works closely with faculty to integrate pedagogy and technology into teaching practice.

• Schools are building AI rules before they know the destination- August 17, 2026

• Cohort connections matter: Strategies to help graduate students persist and succeed- August 14, 2026

• Higher education needs better AI experiences, not more AI tools- August 12, 2026

本报道由 AI 助手自动抓取、翻译并发布。

AI正在从教育行业的”技术变量”变成”行业常量”

这一周最出圈的新闻其实不在教育圈——OpenAI发了GPT-5。但发布会上的一个细节让我心里咯噔了一下：Sam Altman让GPT-5五分钟搭了一个语言学习App。五分钟。这比它写代码、做数学题、回答医学问题更让我在意，因为它直接扎进了教育科技最成熟的商业赛道。而这周发生的其他几件事——Duolingo发财报、叫叫做AI启蒙课、有道预告新硬件、Sal Khan推AI教育网络——单独看都只是行业日常，放在一起，就是一张教育AI加速渗透的地图。

GPT-5演示「五分钟搭语言学习App」，Duolingo的护城河还深吗？

北京时间8月8日凌晨，OpenAI正式发布GPT-5。Altman用”博士级专家”来形容这个新模型，发布会上的编程演示确实震撼：两分钟搭网站，五分钟做出一款带卡片式界面、互动功能和进度追踪的语言学习App。

这个演示虽然只是秀肌肉，但它传递的信号很清楚：大模型做语言教学的门槛正在急速降低。如果连OpenAI自己都能随手生成一个像模像样的语言学习产品，那些靠”AI+语言学习”讲故事的创业公司，故事还讲得下去吗？

巧合的是，就在两天前，8月6日，Duolingo发布了2025年Q2财报。数据看起来相当漂亮：营收2.52亿美元，同比增长41%；日活用户4770万，增长40%；净利润4480万美元，增长84%。CEO Luis von Ahn在电话会上提到”Energy机制”和”国际象棋课程”等新产品表现出色。

但市场给出了一个微妙的反应。财报发布当天，Duolingo股价并没有因为漂亮数字大涨，而GPT-5发布会后，多邻国股价进一步承压。资本市场的逻辑很简单：当OpenAI、Google这些巨头开始把语言教学当作大模型的”标配功能”，一家专门做语言学习的公司，还能靠什么维持40%以上的增速？

我的看法：Duolingo的护城河从来不只是技术，更是它沉淀了十多年的用户行为数据和游戏化学习设计。GPT-5五分钟搭出来的App，功能上可能不比Duolingo差多少，但它缺了最核心的东西——让几千万人每天心甘情愿打开App打卡的”钩子”。这不是大模型能力能替代的。但危险也确实存在：如果大模型驱动的免费语言学习工具体验越来越好，用户为什么还要付费订阅？Duolingo接下来的仗，不是跟GPT-5打，是跟自己”免费+广告”模式的可持续性打。

儿童AI启蒙突然成了香饽饽：叫叫入场，有道加码

8月8日同一天，国内少儿数字阅读品牌叫叫开了一场”全AI线上星际发布会”，推出《AI启蒙与应用》课程，面向5-9岁儿童。课程设计挺有意思：把AI知识包装成冒险故事，孩子扮演”超级英雄”收集能量宝石、击败”数据怪”。

往前翻两天，8月6日的ChinaJoy CDEC高峰论坛上，网易有道高级副总裁刘韧磊宣布，新一代AI答疑笔将在8月下旬发布，会更强调”思考过程可视化”和分步骤引导。

两件事放在一起看，能看出一个趋势：儿童AI教育产品正在从”用AI教传统学科”转向”教孩子理解和使用AI本身”。这个转向的逻辑很直接——教育部已经明确2030年前中小学基本普及AI教育，政策窗口期打开了，但学校端的课程体系和师资都还没准备好，企业端的机会就来了。

叫叫的切入角度挺聪明：不试图教5岁小孩什么是神经网络，而是把AI变成一个可以互动、可以”玩”的对象。这让我想起一个老问题：我们到底该在什么年龄、以什么方式让孩子接触AI？如果AI将来会像现在的手机一样渗透进日常，那”AI素养”确实应该从娃娃抓起。但产品设计要特别小心——孩子的认知发展阶段决定了，重要的不是”会用AI”，而是建立一种健康的、有边界的、不盲从的”人机关系”。叫叫用故事化、游戏化的方式做这件事，方向是对的，关键看后续内容深度能不能跟上。

有道那支答疑笔的升级方向也值得关注。”思考过程可视化”这个功能本质上是在对抗一个家长最担心的场景：孩子拿AI直接抄答案。如果答疑笔能把解题的中间步骤展开，引导孩子自己推导，那它就真的在扮演”老师”而不仅仅是”答案机”。这个定位的差异，可能决定AI教育硬件到底是昙花一现还是长青品类。

Khan Academy创始人走进俄亥俄：一场”AI教育落地”的田野实验

8月7日，Khan Academy创始人Sal Khan第一次踏进俄亥俄州。这不是一次普通的名人访问——俄亥俄州刚刚成立了”Ohio AI Education Network”，是一个全州范围、从K-12延伸到高等教育的AI教育推广计划，Khan Academy是核心合作伙伴。

Khan在俄亥俄州商会面对满场的政商学界人士说了一句话：”我们正进入一个获取答案比以往任何时候都快的时代，但真正的力量在于引导学习者思考、解决问题和创造。”这句话几乎可以当成本周所有教育AI新闻的注脚。

让我有感触的是这个项目的落地方式。它不是硅谷团队关起门来打磨产品再”推向市场”，而是跟具体州的学区、企业、教育服务中心深度绑定。Tipp City学区的AI学习实验室已经跑起来了，联合了NWN、Intel等企业资源。这种”社区嵌入”的模式，可能比任何酷炫的产品Demo都更接近AI教育真正落地的样子。

对比一下国内的情况：我们的AI教育政策力度很大，顶层设计也很清晰（比如国务院”人工智能+”行动意见明确提到”智能学伴、智能教师”），但从政策到课堂的”最后一公里”仍然漫长。Khan Academy在美国一个州做的事——不是卖产品，而是和学区共建基础设施、培训教师、调整课程——也许是我们应该认真参考的路径。

融资线：1200万和2亿，两笔钱流向了不同的方向

本周教育科技领域有两笔融资值得记一笔。

前学而思网校总经理刘庆逊创立的瓦拉英语，拿到了北极光创投、顺为资本和好未来集团等合投的1200万美元。产品思路是”AI情境学习”——用大模型驱动剧情对话和题目生成，在虚拟世界里做沉浸式英语学习。老实说，这个思路在2023年AI教育第一波热潮时就有不少团队提过，但大多停留在Demo阶段。瓦拉能拿到这笔钱，说明至少在产品化上走出了实质性一步，也有好未来的背书加成。

另一个数字更大：AI玩具公司跃然创新(Haivivi)完成2亿元A轮融资，由中金资本、红杉中国、华山资本、愉悦资本联合领投，这是目前AI玩具领域最大的一轮融资。跃然创新做的是一个更”软”的品类——不是学习机，不是答疑笔，而是陪伴型AI玩具，面向的是亲子互动和情感陪伴场景。

两笔钱一个投”学”、一个投”玩”，这个分野让我想到一个老话题：教育科技到底应该更像”教育”还是更像”科技”？瓦拉英语显然是前者，瞄准的是非常确定的”学英语”刚需；跃然创新赌的是后者——AI作为一种新的交互媒介，可以在”玩”的过程中自然发生学习。两者的产品逻辑截然不同，但最终的用户都是孩子。谁能在家长的钱包和孩子的注意力之间找到一个可持续的平衡点，谁就更可能跑出来。

收束：这一周在告诉我们什么

本周最大的感受是：AI正在从教育行业的”技术变量”变成”行业常量”。

GPT-5的发布不再让教育行业恐慌（毕竟GPT-4出来的时候已经恐慌过了），而是直接引发了商业层面的连锁反应：Duolingo股价承压、语言学习赛道的创业者重新思考壁垒。GPT-5当然不是来抢教育公司饭碗的，但它强势地重新定义了”AI能做什么”的基线。当这个基线上移，所有教育产品都必须重新回答同一个问题：你的价值里，有多少是大模型覆盖不了的？

与此同时，叫叫做AI启蒙、有道做详解疑笔、Khan Academy做学区落地——大家都在各自的细分领域里埋头干活。这比去年那种”All in AI””颠覆教育”的口号式冲锋健康多了。

教育的变革，从来不是被某个技术”瞬间颠覆”的。它更像是水滴石穿——本周这些看似零散的新闻，每一滴都在同一个方向。等到哪天回头看，才会意识到，石头已经穿了。

素材来源清单

存档您的学术旅程：保存课程作业和课程文物

原文标题: Archiving your academic journey: Preserving coursework and program artifacts
来源: eCampusNews | 发布时间: 2026-08-07
原文链接: 点击阅读原文

Key points:

• Archiving academic coursework protects the long-term value of your degree

• How should we evaluate professorial work?

• A 60-second skill to advance faculty careers and student success

• For more news on preserving coursework, visit eCN’sCampus Leadershiphub

Nearly every week, I get queries from potential students about obtaining credit for previous coursework towards a degree or licensure. Often, they are unable to provide detailed records of their previous academic work beyond the transcript. Recently one student was told by her former institution that they could not send her a syllabus as she was no longer a student. Unfortunately, though the transcript is the university’s official record of student activity, it rarely contains enough information to flesh out what subject matter an individual courses included. That is information that is often needed to make a determination as to the compatibility of coursework between institutions.

When a student is embarking on a degree program, whether undergraduate, master’s, or doctoral, they are making a significant investment of time, intellect, and financial resources. While most students focus primarily on completing assignments, passing exams, and crossing the finish line at graduation, far fewer think about what happens after the diploma is awarded. As an advisor, recommend that your students engage in archiving materials that are representative of their academic work.

Years down the road, you may apply for professional licensure, seek board certifications, transfer credits, apply to advanced graduate or post-doctoral programs, or undergo employment credential verification. In these instances, an official university transcript is often not enough. Review boards and accrediting bodies frequently require concrete evidence of the specific competencies, clinical hours, software proficiencies, or theoretical scope covered in your past classes.

To safeguard your academic achievements and avoid the frantic scramble of requesting archived files from past professors or defunct university departments, every student should establish a personal academic archive. Below is a comprehensive guide to the critical documents and artifacts you should preserve throughout your plan of study.

Program foundations and course mapping

The formal framework of your degree program provides context for every course you complete. Keeping high-level program documentation establishes the legal and structural parameters of your education at the time you were enrolled. Even though universities generally keep this information for their own records, obtaining copies later might be difficult, if not impossible. Some of the documents to retain would include:

• Program handbooks and field-specific guidelines:Degree requirements, accreditation standards, and institutional policies change over time. Retaining the program handbook from your matriculation year proves what rules and competencies governed your specific cohort as you earned a degree.

• Approved plan of study or degree audit:Keep copies of your official signed plan of study, including any approved course waivers, substitutions, or transfer credit evaluations. This documents the formal agreement between you and the institution regarding your path to graduation.

• Official course syllabi:If you save only one type of document, make it your full, unabridged course syllabi. A standard transcript lists only a course title (e.g.,EDUO 802: Quantitative Research) and a grade. A complete syllabus, however, details:Course descriptions and learning objectives.Required textbooks, article reading lists, and software tools.Week-by-week topic breakdowns and instructional hours.Grading rubrics and major assignment descriptions.

• Course descriptions and learning objectives.

• Required textbooks, article reading lists, and software tools.

• Week-by-week topic breakdowns and instructional hours.

• Grading rubrics and major assignment descriptions.

• Course descriptions and learning objectives.

• Required textbooks, article reading lists, and software tools.

• Week-by-week topic breakdowns and instructional hours.

• Grading rubrics and major assignment descriptions.

Core academic artifacts:

Beyond the administrative outlines, reviewing agencies often want to see proof of the caliber and scope of the work you produced. Programs often ask for writing samples. Using past academic writing may save time and provide a high-quality example that has been critically reviewed. Save final, polished versions of your major academic output.

• Research papers, literature reviews, and capstones:Retain final drafts of all major research papers, annotated bibliographies, literature reviews, and capstone projects. These demonstrate your writing proficiency, methodological knowledge, and specialized subject-matter expertise. They might be used as foundations for future work as well.

• Presentations and defense slides:Save slide decks, poster presentation files, and recording links for major oral presentations, seminar leads, or thesis/dissertation defenses.

• Data sets, code, and analytical projects:If your coursework involves data analysis, software development, or statistical modeling, preserve your clean data files, syntax/code scripts (e.g., Python, R, SQL, SPSS), and final analytical output reports.

Practical, clinical, and fieldwork records

For students in disciplines such as education, nursing, counseling, social work, or engineering, practical application is a critical component of degree completion. Licensing boards are notoriously strict regarding field application documentation. Maintaining copies of practicum or internship logs is a good idea.

• Logbooks and verified field hours:Maintain detailed logs of all clinical, internship, practicum, or student-teaching hours. Ensure these logs include dates, specific settings, populations served, and direct supervisor signoffs.

• Supervisor evaluations and rubrics:Keep copies of all midterm and final field performance evaluations completed by site supervisors and university faculty mentors.

• Certificates of completion:Store documentation for specialized workshops, compliance modules (e.g., CITI research ethics training, HIPAA training), or safety certifications completed as part of your coursework.

Other materials:

If you served as a teaching assistant (TA) or research assistant (RA), save offer letters, course descriptions you assisted with, lecture materials you created, and student evaluation summaries.

Recommended archival best practices

Simply downloading files to a temporary downloads folder is a recipe for lost data. Implementing a clean, resilient archiving system while you are still in school will save you hours of effort later. Organize your digital archive by institution, program, and course code. Use clear, standardized file names that include dates or course codes. Standardize documents into commonly archived formats. Convert propriety formats (e.g., Apple Pages, Google Docs) into universal, long-term formats such as PDF/A for documents, CSV for tabular data, and JPEG/PNG for visual artifacts. The PDF format preserve formatting, fonts, and embedded images regardless of future software updates and is probably the best way to save documents intended for archival purposes.

Backup strategy

Do not rely solely on your university-provided cloud storage (such as Google Drive or OneDrive), as institutional accounts are frequently deactivated or purged a few months after graduation. Keep multiple copies of your archive in both local and cloud based formats if possible.

Your coursework artifacts are more than just steppingstones to a grade; they constitute a comprehensive portfolio of your intellectual and professional development. By systematically archiving your syllabi, major assignments, field logs, and program documentation today, you protect the long-term value of your degree and ensure you are fully prepared for any future credentialing, licensing, or career opportunities.

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• Archiving your academic journey: Preserving coursework and program artifacts- August 7, 2026

• How should we evaluate professorial work?- July 13, 2026

• Transparency appendices may be the next essential AI disclosure practice in higher education- June 29, 2026

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学校在知道目的地之前就开始构建人工智能规则

原文标题: Schools are building AI rules before they know the destination
来源: eSchoolNews | 发布时间: 2026-08-07
原文链接: 点击阅读原文

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Key points:

• Career durability depends on fundamentally human skills that give students the flexibility and adaptability to adjust to changes

• Leading with AI and technology in the age of personalized learning

• How districts can build a shared AI structure

• For more news on schools and AI pathways, visit eSN’sDigital Learninghub

America’s schools are moving quickly to respond to the rise of artificial intelligence, with parents, teachers, administrators, and lawmakers working to wrap their arms around what this means for student education.

But regulation that comes ahead of a clear definition of preparedness may set students back in the AI race. For the future of America’s students — and economy — leaders need to first define what the future of career preparedness looks like.

The speed at which education leaders have moved to respond to AI has been nothing short of remarkable. Just a few years ago, there were no states with formal guidance on AI in K-12 education. Today,34 states and Puerto Ricohave issued some sort of AI guidance for schools. More than 70 bills about AI in the classroom have been introduced across 27 statesthis year alone,as policymakers debate every aspect of AI, from classroom restrictions and student privacy, to graduation requirements and teacher training.

Yet as schools debate every part of AI policy, including who is and isn’t allowed to use it, how they’re allowed to use it, and when they’re allowed to use it, they’re skipping over a more fundamental issue. What do students actuallyneedto learn in order to be successful for the rest of their lives post-graduation?

It’s a question that should fundamentally drive most education policy decisions, especially when it comes to students in the final years of high school and in college. Before schools can determine what students should avoid, they need to first understand what needs to be encouraged based on the demands of employers and the changing business landscape.

This isn’t about letting businesses dictate education policy. We’ve seen industry lead students astray before. The bootcamps and computer science degrees students not long ago were told would lead to higher paying jobs have nowfizzled out— a short-term need now rendered obsolete by the advancement of AI.

The mistake we made then was over-indexing on short-term signals. Several years ago, there was high demand for computer engineers, so education leaders gave students one set of skills needed for one defined future.

Then things changed.

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Scott Laband is the President and CEO of Colorado Succeeds, a nonprofit, nonpartisan coalition of business leaders committed to improving the state’s education and workforce system.

• What it really takes to accelerate adolescent literacy across our district- August 20, 2026

• Uncovering the college and career readiness shifts that exceptional school systems are making- August 19, 2026

• Today’s classroom isn’t broken–but it wasn’t built for today’s top students- August 18, 2026

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学校设计是否有助于或阻碍青少年学习？

原文标题: Does school design help or hinder adolescent learning?
来源: eSchoolNews | 发布时间: 2026-08-04
原文链接: 点击阅读原文

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Key points:

• Physical environment and school design can either support or constrain the types of learning experiences adolescents need

• How one school reimagined learning spaces–and what others can learn

• What K-20 leaders should know about building resilient campuses

• For more news on school design, visit eSN’sEducational Leadershiphub

Adolescence is one of the most consequential periods of human development, yet the environments designed to support it often fail to reflect its complexity. Spanning ages 10 to 18, this stage is marked by rapid neurological, emotional, and social transformation. During this time, the brain is not simply maturing; it is being actively rewired in response to experience. As a result, learning environments are not neutral settings but active participants in shaping how adolescents think, engage, and develop.

Neuroscience highlights adolescence as a period of profound plasticity. Gray matter is pruned to strengthen efficiency, while white matter increases to accelerate neural communication. At the same time, the brain develops unevenly: subcortical regions associated with emotion and reward mature earlier than the prefrontal cortex, which governs decision-making and self-regulation. This imbalance contributes to heightened sensitivity to peers, increased risk-taking, and a strong drive for exploration and identity formation. These are not deficits but adaptive features that prepare young people for adulthood. However, they also make adolescents particularly responsive to environmental conditions, both positive and negative. This raises a critical question: Do our schools align with how adolescents are wired to learn?

Traditional middle and high school models, which are often characterized by rigid schedules, passive instruction, and limited autonomy, can conflict with adolescents’ developmental needs. In environments where students have little control, minimal opportunity for social learning, and few meaningful connections, stress can increase while engagement declines. Given that adolescents are especially sensitive to social evaluation and emotional context, such conditions may undermine both well-being and academic outcomes. Research in developmental science suggests that effective learning environments for adolescents must address more than cognitive performance alone. Learning at this stage is inherently social, emotional, and experiential. Students can benefit from environments that foster belonging, safety, and meaningful relationships with both peers and adults. The concept of “mattering,” feeling valued and able to contribute, helps motivation. When students feel recognized and supported, they are more likely to take risks, persist through challenges, and engage deeply in their learning. Conversely, environments that prioritize performance over connection can exacerbate anxiety and disengagement.

Adolescents also require opportunities to build autonomy and self-regulation. As executive function skills are still developing, students benefit from structured opportunities to make choices, manage their time, and take ownership of their learning. This is particularly important in a broader cultural context where increased digital engagement and reduced independence may limit real-world experiences. Schools, therefore, play an important role in providing opportunities for exploration, collaboration, and manageable risk-taking. The heightened drive for novelty and exploration can be leveraged through hands-on, project-based, and real-world learning experiences. When students engage in meaningful work that connects to their interests and future aspirations, motivation and depth of understanding can increase. These experiences also support identity formation, helping students develop a sense of purpose and direction.

Design, in this context, becomes a powerful tool. The physical environment can either support or constrain the types of learning experiences that adolescents need. Frameworks such as Universal Design for Learning emphasize flexibility, choice, and accessibility, while Trauma-Informed Design highlights the importance of safety, belonging, and emotional regulation.

Together, these approaches position the built environment as integral to developmental outcomes. Specific design strategies can directly support adolescent needs (see figure). Access to daylight and nature, acoustic comfort, and clear wayfinding can reduce stress and support focus. Flexible spaces that accommodate both collaboration and retreat allow students to navigate social interaction and privacy. Opportunities for personalization and cultural expression reinforce identity and belonging. Equally important are spaces that support hands-on, interdisciplinary learning and connections to real-world contexts.

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Heidi Neumueller, AIA, NCARB, LEED AP, is a Principal and PK–12 Education Market Leader at Cuningham. With over 20 years of experience in educational design, she is dedicated to creating safe, inclusive, and trauma-informed environments that support student well-being and strengthen community connections.Amy Frye is an Associate Principal and National Research Director at Cuningham. She leads Cuningham’s cross-disciplinary research strategy, aligning emerging trends and strategic growth areas to strengthen client outcomes across all market sectors. Amy has contributed to more than 50 peer-reviewed publications and industry reports and delivered over 30 speaking engagements.

• What it really takes to accelerate adolescent literacy across our district- August 20, 2026

• Uncovering the college and career readiness shifts that exceptional school systems are making- August 19, 2026

• Today’s classroom isn’t broken–but it wasn’t built for today’s top students- August 18, 2026

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