高等教育中人工智能快速转型的导航与C·爱德华·沃森

今天，在《高等教育教学》播客第602期节目中，我们将与C一起探讨人工智能在高等教育中的快速变革。

Today on episode number six zero two of the Teaching in Higher Ed podcast, navigating AI's rapid transformation in higher ed with C.

爱德华·沃森。

Edward Watson.

由创新学习制作，最大化人类潜能。

Produced by Innovate Learning, maximizing human potential.

欢迎收听本期《高等教育教学》。

Welcome to this episode of Teaching in Higher Ed.

我是邦妮·斯塔霍维亚克，这里是我们探索如何更有效地促进学习的艺术与科学的空间。

I'm Bonnie Stahoviak and this is the space where we explore the art and science of being more effective at facilitating learning.

我们还会分享提升工作效率的方法，以便在生活中获得更多的平静，并更专注地陪伴我们的学生。

We also share ways to improve our productivity approaches so we can have more peace in our lives and be even more present for our students.

在本期节目中，我邀请到了C。

In this episode, I'm joined by C.

爱德华·沃森博士。

Edward Watson, Ph.

D，美国大学协会数字创新副总裁，以及美国大学协会人工智能、教学法与课程研究所的创始主任。

D, Vice President for Digital Innovation at the American Association of Colleges and Universities, and founding director of AAC and U's Institute on AI, pedagogy and the curriculum.

埃迪长期以来一直走在帮助院校应对新兴技术的前沿。

Eddie has long been at the forefront of helping institutions navigate emerging technologies.

他最近的作品《人工智能教学》第二版、《引领颠覆》和《学生人工智能指南》，为塑造高等教育的变革提供了实用且富有洞察力的视角。

And his recent work, Teaching with AI, the second edition, Leading Through Disruption and the Student Guide to AI, offer a practical and informed perspective on the changes shaping higher education.

正如您将听到的，我们的对话追溯了我们在各个校园中所经历的AI快速变化，从最初对作弊的担忧，到更广泛的关于人工智能素养和学科整合的问题。

Our conversation, as you'll hear, traces the rapid changes that we've been experiencing with AI on our various campuses, from the concerns that came very quickly about cheating to more broader questions around AI literacy and discipline specific integration.

那么，如何帮助学生为一个正由人工智能塑造的劳动力市场做好准备呢？

And what does it look like to help prepare students for a workforce being shaped by artificial intelligence?

埃迪和我探讨了教师如何应对人工智能模型令人困惑的快速变化节奏，代理工具时代学术诚信的含义，以及他与合著者何塞·鲍文为何认为未来几乎每份工作都需要一定程度的人工智能能力。

Eddie and I explore how faculty can respond to the disorienting pace of change in AI models, what academic integrity looks like in an era of agentic tools, and why he and his coauthor Jose Bowen say why nearly every job will require at least some level of AI competence in the future.

我们深入探讨了他对新兴人工智能素养框架的分析、我们在作业设计中所面临的张力，以及院校在适应过程中必须优先考虑的事项。

We dig into his analysis of emerging AI literacy frameworks, the tensions we experience around assignment design and what institutions must prioritize as they adapt.

埃迪·沃森，欢迎再次做客《高等教育教学》。

Eddie Watson, welcome back to Teaching in Higher Ed.

邦妮，谢谢你再次邀请我。

Bonnie, thanks for having me back.

能再次和你对话真是太好了。

It's so good to get to be in conversation with you again.

我一直在期待这次交流。

I've been so looking forward to this time.

我需要你为我们稍微梳理一下。

And I need you to orient us a little bit.

我们上次谈话已经有一段时间了。

It's been a minute since we talked.

我们上一次对话是在2024年，那时以来发生了太多变化。

We we last spoke in the 2024, and so much has changed.

所以，如果你能先为我们简要回顾一下我们走过的历程，我会非常感激。

So I'd love if you would just start out with us giving a little bit of an arc of kind of where we've been.

这样做总是有风险的，但请你大致说说我们如今所处的位置。

And it's always risky to do this, but kind of sort of where we stand today.

但我们都知道，如今的情况依然充满变化，感觉如此。

But we also both know where we stand today is still a lot of change, it feels like.

但请你给我们大致讲讲这个演变过程。

But give us a little sense of that arc.

我认为，这个演变过程几乎是‘越变越不变’。

Well, I think the arc is almost one of the more things change, the more they stay the same.

我的意思是，如果回溯到三年前，当生成式人工智能首次进入公众视野时，对我们来说，第一个学期是探索阶段。

I mean, if you think back over three years now since generative AI burst onto the scene, at least for the broader public, the first semester for us was discovery.

等等，这东西真的存在？

Wait, this exists?

但学生们知道这个吗？

But do students know about this yet?

它到底能做什么？

What can it really do?

当我们逐渐了解它真正能做什么后，接下来的学年，焦点几乎就转向了关于作弊的讨论。

And then once we got a sense of what it really could be used for, then it's almost like the next academic year, it was really kind of about this conversation around cheating.

所以在前十八个月里，是探索阶段，然后是担忧。

So for the first eighteen months, it was discovery and then concern.

但就在我们在这个高等教育的小圈子里进行这些讨论的同时，职场世界正在各个学科和情境中迅速采用生成式人工智能。

But then at the same time that we were having these conversations within sort of our higher ed bubble, the world of work was rapidly adopting generative AI in lots of different disciplines and contexts.

因此，大约在十八个月后，出现了这种转变，机构开始意识到：等等，这似乎并不是下一个《第二人生》。

So then there was this shift, probably eighteen months in, where institutions started thinking about, oh, wait a second, this seems like it's not the next second life.

它真的会长期存在，而职场正在以许多有趣的方式探索如何应用它。

This is really here to stay, and that the world of work is figuring out how to implement this in a lot of interesting ways.

这是否是我们需要教会学生掌握并熟练运用的技能？

Is this something that we need to indeed teach our students to do and to do well?

于是，讨论的范围发生了转变或扩大，不再仅仅局限于如何阻止学生使用人工智能，而是扩展到：我们是否需要培养学生的AI素养和AI技能？从课程角度来看，这又意味着什么？

So then there was this shift or broadening of the conversation to not just how do we prevent students from using AI to, wait, do we need to engender AI literacy, AI skills, and what does that look like from a curricular standpoint?

这种理念逐渐被融入通识教育，或作为新生体验的一部分，也可能成为学生毕业前、即将步入职场或进入人生下一阶段时的顶点课程内容。

And this notion of building that into maybe general education or part of a first year experience or maybe the capstone components within right before students graduate and indeed join the world of work or go on to the next stages of their life.

因此，讨论的范围扩大了：不是简单地将AI排除在课堂之外，而是适当地加以管控，但同时我们也确实需要在某些地方培养这些技能。

So the conversation broadened to, well, not keep it out of the classroom, but keep it out appropriately, but then there's places that we do indeed need to be engendering these skills.

因此，关于课程教学法的讨论范围扩大了，我们教师自身也可能利用它来提升我们工作的质量，或在某些方面节省一些时间。

So broader conversation about curriculum pedagogy and then how we ourselves, faculty, might leverage it to maybe improve the quality of what we're doing, maybe save us some time here or there.

但尽管讨论范围已经扩大，AAC&U确实举办了关于课程中AI教学法的研讨会，专注于帮助机构实现这类课程与教学上的转变。

But while that's sort of been the broadening of the conversation, and indeed, AAC and U has an institute on AI pedagogy in the curriculum that has been focusing on helping institutions make these kinds of curricular pedagogical shifts.

然而，最近我们对教师进行了一项调查，他们表达的首要关切是学术诚信问题。

However, recently we did a survey of faculty and the number one concern that they expressed was academic integrity.

尽管情况已经发生变化，讨论也更加广泛，但那些每周都身处课堂的我们，依然在应对生成式AI刚出现时就引发的学术诚信挑战。

So while things have changed and the conversation has broadened, those of us that are in the classroom from one week to the next are still struggling with the challenges associated with academic integrity that we first realized when generative AI came onto the landscape.

当然，随着代理式AI的实现，现在又出现了新的挑战——这不再只是被动形式的AI，即你提出问题，它给出答案。

And then, of course, there are new challenges now as agentic AI has become realized where it's not just a passive form of AI where you ask it for something and then it would provide you with an answer.

现在你可以赋予它一项任务，它就能去执行。

Now you can kind of give it a task to perform, and it can go and do that.

这确实包括让代理式AI在学习管理系统中为你完成任务。

And this indeed includes asking an agent, agentic AI, to complete tasks within the learning management system for you.

这完全是不同的情况。

So that's completely different.

并不完全不一样，只是不同于最初生成式AI的推动。

Not completely different, it's different than the initial generative AI push.

因此，我们正在面对新的对话和新兴的挑战。

So we have new conversations and new challenges that are emerging.

但同样地，尽管我们可能从机构层面思考并为劳动力市场做准备，但身处课堂一线的我们，依然在为学术诚信的问题而挣扎。

But again, while we might be thinking institutionally and preparing for the workforce and things like that, those of us in the trenches in the classroom are still very much struggling with questions of academic integrity.

我认为实际上存在两种变化，非常感谢你提到的这两个转变，我认为前者的能力仍然只是传说。

I think about there really being and and thank you so much for rudiness and the arc that two changes that I think the former capabilities are still existing as myths.

而这两种变化之一是，这些大型语言模型现在可以进行搜索。

And those two changes, one being that these large language models can now search.

而以前，我还清晰地记得，埃迪，那时候它只能搜索它训练数据内部的内容。

Whereas previously, I remember so vividly, Eddie, where it would be like, oh, well, it can only search within its model that it's been trained on.

所以对我来说，如果我曾经尝试过，比如问它‘关于埃迪·沃森在高等教育领域的教学，它知道些什么？’，如果它只训练到2021年或2022年，那它就不知道。

So for me, if I ever was experimenting and saying, you know, what did it know about Eddie Watson, you know, coming on teaching in higher ed if it hadn't been if it was only I think of the year might have been, oh, it's only trained up through 2021 or 2022.

因此，如果某件事尚未发生，那么对大型语言模型而言，它就不存在。

So if if it hadn't happened yet, then it didn't exist as far as the large language modules knew.

我接触的许多教职员工仍然认为情况如此，尚未完全理解这些搜索能力如何改变现状。

And a lot of faculty that I talked to believe that that's still the case and haven't quite understood yet how these search capabilities change the picture.

然后，正如你提到的，埃迪，天哪。

And then as you mentioned, Eddie, holy cow.

我的意思是，我非常感激那些愿意在各种社交媒体平台上实时分享他们发现整个学习管理系统时强烈反应的人——我本来想说出这个学习管理系统的名称，但其实具体是哪一个并不重要。

I mean, I've I've I've felt so grateful for the people willing to share almost in real time on some various social media platforms of the visceral reaction that they have discovering their entire I was going to say the name of the learning management system, but it kind of doesn't matter which one I name.

所以我会避免提及具体名称，因为这绝不是针对某个特定系统的评论。

So I'll refrain from naming it because it's certainly not a commentary on that specifically.

但看到这些反应，仅仅是我自己就能感受到背后蕴含的丰富情感，正如你所说，我们还有很多需要消化的东西。

But but like to have watched that and just for me to see the emotions behind that, that there's a lot there, as you said, a lot there for us to process.

在我们继续对话的过程中，你是否还想到其他与此弧线相关、值得人们思考的重要内容？

Is there anything else that's coming to mind for you in terms of this arc that that might be important for people to be thinking about as we continue our conversation?

嗯，当大型语言模型或其开发公司之间建立起实时连接时，这便是一个关键的细微转变。

Well, that that was a key nuance shift whenever there was a live connection between the large language models or the companies that produce those tools.

这些工具过去只是拥有一个封闭且定义明确的数据集，然后他们会公布，比如‘数据截止到2023年4月’，而即使现在已经是9月，此后发生的一切你都无法真正访问或询问。

Those tools themselves, when there used to be just sort of a closed defined data set, and then they would publish like, this is from April 2023, and it might be September, but anything that's happened since then you don't have really access to or really can't ask about.

现在你有了实时访问权限，你可以向大型语言模型提问：纽伦堡在当地剧院的演出时间是什么时候？

Well, now that you have live access, you can go to a large language model and say, what time does Nuremberg play at the local theater?

它能查出电影放映时间之类的信息。

And it can bring up movie times and things like that.

所以这确实是一个重大的飞跃，肯定是在大约一年前发生的，但这也确实是一个很有趣的变化。

So it's definitely a it was a leap forward, for sure, that occurred about a year ago, but that definitely was one of those it's interesting.

这就像有很多渐进式的转变，但每个增量都相当大。

It's like there's a lot of incremental shifts, but the increments are quite large.

因此，当你经历了十次相当大的渐进式转变时，你会真切地感受到：哇，这些工具在很短的时间内发生了如此巨大的变化。

And so when you have 10 reasonably large incremental shifts, it really feels like, wow, there's been this expanse of change within these tools fairly in short amount of time.

谢谢你这么说。

Thank you for saying that.

你提到的另一件事是学术诚信。

And one of the things you mentioned, academic integrity.

我很幸运能够与那些职业生涯和专业经验远早于2022年11月ChatGPT这款基于聊天的大型语言模型发布的人交谈。

And I've been so fortunate to be able to talk to people for whom their career span and expertise spans well beyond November 2022 when ChatGPT, the chat based large language model, got released.

当你谈到这些渐进式转变时，我深深理解那些感到极度迷失的人。

And and in terms of as you're talking about these inter incremental shifts, I feel so much empathy toward people for whom this is so disorienting.

我确实经历过这种迷失感，未来也还会继续感受到自己的困惑。

And I certainly have experienced that and will continue to experience my own sense of disorientation.

这对任何人来说都不容易。

This is not easy for any of us.

我们都没有主动要求过这一切，这一点在你与合著者何塞的下一集对话中也提到过。

None of us asked for this, which in a future episode, dun dun dun with your coauthor, Jose, does mention something about none of us asking for this.

所以我知道你和他对此也有共鸣。

So I know you and he resonate with that with that as well.

但我想说的是，‘错误导向’这个词可能太强烈了，不过在播客访谈中，我不喜欢长时间停顿。

But but I think so I while I do feel empathy, I also think about the the wrong headedness is too strong of a word, and yet I don't like to leave long pauses during podcast interviews.

所以我一时想不出更合适的词，因为‘错误导向’听起来带有评判意味，而这并非我的本意。

So I'm having trouble coming up with it because wrong headedness sounds judgmental in a way that I don't intend for that word to mean.

但如果我们作为教育者，将目标定为避免作弊，而把任何使用人工智能的行为都等同于作弊，你能帮我们拆解一下这种观念吗？

But if we're orienting ourselves as educators toward avoiding cheating, and if cheating equals any use of artificial intelligence, could you help break that down?

也许找一个更好的词，因为你在生活中显然比我更有外交手腕，埃迪。

Maybe find a better word because you're you seem far more diplomatic than I am in life, Eddie.

所以你能帮我找个比‘错误导向’更好的词吗？

And and and so is can you help me find a better word than wrong headedness?

但同时，我们能不能换个角度思考，有什么词可以替代它？

But also, like, how might we instead think about, like, what could replace that?

我会围绕阻止学生作弊来设计我的课程，而作弊等于任何使用人工智能的行为。

I'm going to design my classes around stopping people from cheating and cheating equals any use of AI.

帮我把这个公式拆解一下，也许能找到一个更好的词和一个更明确的目标。

Help me break that formula down to maybe a better word and maybe a better aim.

是的。

Yes.

甚至可以再扩大一点，因为我觉得这更像是一个决策矩阵，而不是非黑即白的二元选择——要么能用，要么不能用。

Even broaden that a little bit further because I think that there's it's like a a matrix of decision rather than this binary of, like, either they can use it or they can't.

我的意思是，也许我们可以从一个更宏观的观念开始，这个观念可能是我们都认同的：无论何种类型的院校，高等教育的一个目的都是为学生毕业后的生活做好准备，这包括职场、终身学习，以及研究生教育等等，而高等教育的目标应当更加深远。

I mean I mean, you can kind of start maybe with the larger the larger notion that maybe we would all agree that regardless of institution type, part of the purpose of higher education is to prepare students for life beyond graduation, which would include the world of work and lifelong learning scenarios and maybe grad school or whatever it might be, but that we have a purpose for higher education that is greater.

这就是我们的理想。

That's the aspiration.

事实上，今天大多数大学生都是为了社会流动而上学。

And indeed, the vast majority of our students that are in college today, they're there for social mobility.

他们希望拥有比父母更好的生活。

They're there to have maybe a better life than their parents did.

这在一定程度上是为了找到一份工作。

It's partly about finding the job.

对许多学生——我们大多数学生而言，这关乎职业发展。

For many the vast majority of our students, it is about career.

如果我们接受这一观点，并看到工作世界如何迅速采用人工智能，那么问题可能变成：我们是否应该教会学生如何良好且合乎道德地使用它？

So if we accept that notion, and if we see how the world of work has rapidly adopted AI, the question then might become, is this something we should be teaching our students to do well and ethically?

许多机构已经得出结论：是的，我们必须将这一点纳入课程中。

And many institutions have concluded that yes, that is something that we have to incorporate into the curriculum.

并不是每个人都在教人工智能，但每个机构都应当有人去教。

Not that everyone's teaching AI, but that at each institution, someone should.

那么问题来了：我们已经确立了这一思路，即应该向学生教授人工智能。

So then the question then is, Okay, we've got that corner or that one line of thinking, right, that we should be teaching AI to our students.

但在另一个极端，或者说矩阵的另一端，存在另一种观点：为什么我们要教学生使用一个可能被他们用来作弊的工具呢？

But then on this other end of the spectrum, on the other end of the matrix maybe, there's this notion, why would we want to teach our students to be power users of a tool that they could use to cheat with, number one?

而且还有第二种担忧：不仅他们可能用它来作弊，它还可能真正削弱他们的学习效果。

But then there's even a second line of thinking, not just that they might use it to cheat with, but that it might actually diminish their learning.

事实上，我认为这或许是高等教育史上首次出现学习目标彼此冲突的情况。

Indeed, I would argue that maybe this is the first time in the history of higher education that we have learning outcomes that are at war with one another.

例如，我们从未说过：不能教学生书面沟通。

For instance, we have never said, we can't teach our students written communication.

因为这会影响他们的批判性思维能力。

That's going to mess with their critical thinking skills.

不，它们实际上是相互增强的，对吧？

No, they actually amplify one another, right?

即使你考虑的是截然不同的领域，你也不会说定量推理不应该在创意课程中教授。

Even if you were looking at disparate ends of things, you wouldn't say that quantitative reasoning is something we shouldn't teach in our creative courses.

不，这些都在协同作用。

No, no, these are all working together.

但将AI作为学习成果，实际上有可能削弱其他学习成果的实现。

But AI as a learning outcome actually has the possibility of diminishing achievement of these other outcomes.

因此，当我们进一步深入探讨作弊这一概念时，作弊的定义变得非常模糊。

And so as we even drill down further to this notion of cheating, the definition of cheating has become really murky.

我的意思是，有一种使用AI的情况，无论你是学生、行政人员、教职员工还是家长，我们都会一致认为这是作弊。

I mean, there's one case use of AI that whether you're a student, administrator, or faculty member, or parent, we would all agree it's cheating.

我去求助AI。

I go to AI.

我让它帮我写这篇五页的论文。

I ask it to write this five page paper.

然后我直接复制粘贴，署上自己的名字，提交上去。

I then copy and paste it and put my name on and turn it in.

每个人都说这是作弊。

Everyone says that's cheating.

但如果你给我这个论文任务，我去使用AI，让它为我提供一份关于如何回应这个提示的详细提纲呢？

But what if I get that paper assignment from you, and I go to AI, and I say, provide me with a detailed outline regarding how I should respond to this prompt.

于是我得到了这份详细提纲，然后作为学生，我把它拆分成一系列微写作任务。

And so I get this detailed outline, then I, as the student, it's a bunch of micro writing assignments.

好吧，我需要一个引言。

Okay, I need an introduction.

好吧，写四句引言。

Okay, four sentences of introduction.

好吧，写一个目的陈述。

Okay, statement of purpose.

好吧，围绕这个写几句话。

Okay, some sentences about that.

好吧，谈谈变量之类的。

Okay, so talk about the variables or whatever.

好吧，这其实就是一系列微写作任务。

Okay, so it's just a bunch of micro writing assignments.

那这是作弊吗？

So is that cheating?

是或不是。

Yes or no.

事实上，我们一年前向管理人员提供了几个情景，结果出现了明显的分歧。

In fact, we provided scenarios to administrators about a year ago, and there was a real split.

大约一半人认为这是作弊，另一半则认为这不算作弊，或者不确定这是否算作弊。

About half said that was cheating, and half said that either it wasn't cheating or they weren't sure whether that was cheating or not.

对于我所分享的这个例子是否构成作弊，人们的看法呈现出明显的双峰分布。

It was this real bimodal distribution about whether this example that I'm sharing is actually cheating.

但也许作弊本身并不是关键问题。

But maybe cheating's beside the point.

我要求获得那个详细提纲，是否很可能削弱了我从这项作业中获得的学习效果？

Is it likely that me asking for that detailed outline might diminish my learning associated with that assignment?

几乎可以肯定。

Almost assuredly.

我并不纠结于论文中该包含什么、不该包含什么。

I'm not struggling with what goes in the paper and what doesn't go in the paper.

我也不纠结于该强调或弱化哪些内容，以及观点的先后顺序。

I'm not struggling what to emphasize and deemphasize, what the order of ideas should be.

事实上，我们给学生布置的每一项评估都是一种教学行为。

And in truth, every assessment we give our students is an act of pedagogy.

学生在撰写论文、团队合作、进行演示，甚至参加多项选择题考试时，都会产生大量的学习。

There's lots of learning that takes place when students write papers or work as a team or deliver a presentation or indeed even taking a multiple choice test.

那么，以这种方式使用AI，是否会削弱该作业所设计的其他学习成果的实现呢？

So is this likely is AI usage in this way likely to diminish achievement of the other learning outcomes that were designed into that assignment?

我认为答案是肯定的。

I think the answer is yes.

因此，这就像一个矩阵，这确实是高等教育面临的一项重大挑战：我们需要培养这些技能，以帮助学生为毕业后的生涯做好准备。

So there's this matrix of like and this really a grand challenge for higher ed, where we need to be engendering these skills to prepare students for life beyond graduation.

但这样做可能会对他们的学习或其他学习成果的达成产生负面影响。

But doing that could actually have negative impacts on their learning or achievement of other learning outcomes.

事实上，不同教师对什么是作弊的看法差异很大，甚至可能以一些微妙的方式被用作作弊，比如让我提供一个提纲，或者我写完整篇论文后再寻求反馈。

And indeed, depending on one faculty member to the next, their perceptions of what is cheating could very much be used for cheating even in somewhat subtle ways, such as provide me with an outline, or I write the whole paper and then I ask for feedback.

我的意思是，AI检测工具不会发现这两种可能被称为作弊的行为，但它们肯定会削弱学习效果。

I mean, the AI detection tools wouldn't catch either of these examples of what might be termed cheating, but definitely could diminish learning.

因此，这是一个非常复杂的局面，这还只是初步的观察，甚至还没谈到具身AI，但这就是我们当前所处的环境，也是许多机构和教师在自己的课堂中正面临的困境。

So it's a really complicated landscape, and that's just sort of a first blush, not even talking about agentic AI, but that's sort of the landscape that we're on and the tensions that many institutions and many faculty members in their own classrooms are struggling with today.

我非常感谢您打破了二元对立的思维模式。

I so appreciate your breaking the dichotomous thinking up.

这种二元思维只会让我们陷入困境，而用连续体、多维度的视角来看待问题，对我们非常有帮助，也能让我们对那些与我们观点不同的人如何形成他们的信念和认知产生好奇，这是一件非常健康的事。

It just gets us into so much trouble and thinking about things on a continuum, but also a multifaceted continuum can be so helpful for us and getting curious about where people who think differently than we might, where they form their beliefs and perceptions and can be such a healthy thing.

你和你的合著者何塞·鲍恩在本书第二版中提出了一个大胆的主张。

You and Jose Bowen, your coauthor, make a bold claim in this book, in this second edition.

AI将改变每一项工作。

AI will change every job.

好了，埃迪，该你了。

All right, Eddie, it's time.

支持你的说法。

Back up your claim.

我的意思是，已经有一些研究显示，这很可能是我们未来的发展趋势。

Well, I mean, there have been studies that have shown that this is likely what we're going to see moving forward.

几年前，一组斯坦福的研究人员分析了联邦职位指数，该指数包含了961个职位描述，他们得出结论：每一个职位描述中至少有一项任务可以由人工智能更好地完成。

I mean, a couple of years ago, a group of Stanford researchers looked at the Federal Job Index, which contains nine sixty one job descriptions, and they concluded that every one of those job descriptions had at least one task that might be done better by AI.

这并不意味着这些工作会消失，但意味着人们越来越期望人工智能成为一项必备技能或组成部分。

That doesn't mean these jobs are going away, but it means there might be more and more of an expectation that AI would be a skill or component that would be required.

实际上，最近AAC&U发布了一份报告，我们每两三年就会进行一次雇主调查。

And actually, recent report from AAC and U, we do these employer surveys every two or three years.

我们已经做了将近二十年了。

We've done them for almost twenty years now.

但在我们最近一次调查中，也就是2025年底发布的那次，我们询问了雇主们。

But in our last iteration, so published late in 2025, we ask employers.

我们收集了超过1000家不同雇主的反馈。

We had over 1,000 different employers.

我们问他们这个问题：对于大学毕业生来说，在校期间发展与人工智能使用相关的技能有多重要？

We ask them this question, how important is it for college graduates to have developed skills related to the use of artificial intelligence while they were in college?

91%的雇主表示，这很重要。

91% of employers say, yes, this is important.

这是学生在进入职场之前应该培养的技能。

This is something that students should develop skills around before they enter the world of work.

因此，这涵盖了就业领域的广泛范围。

So that's a broad swath of the world of employment.

我最近做了一次演讲，有人问我：哪些领域是不受影响的？

I was giving a talk recently, and someone asked me, well, which fields are immune?

哪些领域不会被人工智能触及？

Which fields aren't going to be touched by AI?

我耸了耸肩，说：我不知道，也许是舞蹈领域？

And I shrugged, and I said, I don't know, maybe the field of dance?

然后一位舞蹈教授举起了手，她说：实际上，在我的大三课程中，我们正在使用人工智能进行舞台设计。

And then a dance professor raised her hand, and she said, actually, in my junior level course, we're using AI to do set design.

她描述了自己如何重新构想某些戏剧在未来一千年的样子，以及它们至少在舞台设计方面可能会如何被重新定义。

And she described how she was re envisioning certain plays 1,000 into the future and how they might be reframed in terms of set design, at least.

因此，我很难找到任何我们可能看不到这种变化的角落。

So I'm hard pressed to find corners where we may not see that.

另一项来自宾夕法尼亚大学沃顿商学院在2025年底进行的研究调查了员工，结果显示，超过80%——五分之四的员工表示，他们在工作中每周至少使用一次生成式AI。

Another study from the Wharton School of Business in late twenty twenty five that surveyed employees, and over 80, over four out of five employees, are now reporting that they're using generative AI at least once a week in the work that they do.

所以，这仅仅是工作世界中广泛存在的一种现象。

So that's just a broad swath of the world of work.

因此，我们做出了一个大胆的断言。

And so we make the bold claim.

我敢打赌，肯定有人会说，啊，这个领域不会受到波及。

I bet there's someone somewhere that can say, ah, this isn't going to touch this field.

而这种情况确实可能存在。

And that indeed might be the case.

但我认为，工作世界中的绝大多数职位都将以某种方式——无论是轻微还是显著——受到生成式AI的影响。

But I think the vast majority of positions in the world of work are going to be touched in either small or large ways by generative AI.

你和何塞已经与这么多不同背景的人交流过，你们是如何思考这究竟是关于技能的？

As you and Jose have talked to so many people across all these different contexts, how are you thinking about is it skills?

是关于素养的吗？

Is it literacy?

是关于能力的吗？

Is it competencies?

你们是如何考虑在这些独特情境下、不同学科、不同机构、不同教学风格中定义各种技能的？

How how have you kind of considered what it looks like to try to define different sets of skills in these unique contexts, different kinds of disciplines or different kinds of institutions, different types of teaching styles?

这是一个有趣的问题。

Well, that's an interesting question.

我的意思是，何塞和我收集了所有出现过的框架——姑且这么称呼吧。

I mean, Jose and I, we collected all of the frameworks, for lack of a better word, that have emerged.

大概有九个框架在高等教育或更广泛的教育领域中获得了真正的关注。

There's maybe nine that seem to have gained some real traction across higher ed or more broadly the world of education.

联合国教科文组织就有一个。

I mean, UNESCO has one.

开放大学也有一个。

The Open University has one.

巴纳德学院早些时候也有一个。

Barnard College had an earlier one.

斯坦福大学也有一个。

Stanford has one.

市面上还有不少这样的框架。

There's a number of them that are out there.

他和我试图构建一个元模型，来分析这些其他工作，找出它们的共同点以及哪些部分至关重要。

And he and I tried to develop a bit of a meta model that looked at all of this other work and what are all of the commonalities and which pieces seem to be really important.

有些模型包含了一些独有的特征，没有在其他地方出现，而还有很多元素是我们所见的所有模型中都共有的。

I mean, there are some that had a discrete feature that didn't show up anywhere else, and then there's lots of elements that were across all the models that we've seen.

所以，我想，对于人工智能素养，会有许多不同的理解方式。

So, I mean, there's going to be, I guess, a lot of different ways to conceive of AI literacy.

而目前市面上的大多数模型都以一种通用的方式谈论人工智能素养。

And most of these models that are out there talk about it in a generic version of AI literacy.

但我认为，这种人工智能素养的愿景还有一个配套方面，那就是在学科内部，因为各种工具、策略甚至伦理实践都会因学科而异。

But I think there's a companion to that vision of AI literacy that is within the discipline because there are many tools and strategies and even ethical practices that will vary from one discipline to the next.

或者有些工具是某个学科特有的。

Or there are tools that are very specific to that discipline.

我的意思是，即使是护理领域与全科医生之间的区别，全科医生的记录员也被视为一种积极的工具，因为这样能保持更多眼神交流，让你在面对就诊者时更能专注投入。

I mean, even the field of nursing versus primary care physicians, note takers for primary care physicians are seen as a positive tool because there's more eye contact, and you can be more present with someone that's in your office.

但我听说，对于护士在巡房时使用记录工具，反而被认为是一种干扰，因为她们在必须倾听和处理信息时，注意力反而比现在更集中。

Where I've heard feedback about note taking tools for nurses making rounds that actually it serves as a distraction, that they actually don't pay as close attention as they were when they had to listen and process.

换句话说，她们可能不如以前那样熟悉自己当班期间所负责的病人了。

So in other words, they don't know the people on their floor during their shift maybe as well as they did previously.

因此，即使在两个看似非常接近的领域——护理和家庭医生——中，对最佳实践的看法也存在差异。

So even what is best practiced in two fields that seem really close together, nursing and maybe your family doctor, there's different views.

所以，存在一种通用版的人工智能素养，其中可能有一些共性，这些共性或许应该纳入通识教育课程中教授。

So there's a generic version of AI literacy that there's probably some commonalities about that maybe that's what belongs or should be taught within the general education curriculum.

但我们也可能需要在专业课程中另设一个部分，向学生传授在各自职业领域中使用生成式AI的工具、实践、规范与伦理。

But probably we need to also have a second place within the major where we teach our students the tools and the practices and the norms and the ethics of using generative AI within that world of work.

当我们上次讨论这本书的第一版时，我记得我们曾谈到过这个话题。

When we last spoke for the first edition of the book, I remember us coming across the conversation.

当时大概是说：天哪，刚刚发生了什么？

It was something like, holy cow, what just happened?

我不确定我是不是真的说了‘天哪’。

I don't think I quite said holy cow.

大概是说：到底刚刚发生了什么？

It was something like, what just happened to like?

然后我记得我们对话的最后三分之一，都是围绕那些让你惊叹‘哇哦’的事情。

And then and then I remember the closing third of our conversation were around things that were making you go, oh, wow.

就像什么？

Just like what?

什么？

What?

你知道吗，埃迪，那种感觉一直深深伴随着我。

You know, and I I can't even begin to tell you, Eddie, the way that that just carried with me.

这真的不只是我一个人的感觉。

It was really and not just me.

我的意思是，当时有很多人联系我，说你当时所兴奋的那些事情非常引人入胜，激起了很多人的兴趣。

I mean, so many people would contact me and say some of the things that you were excited about at the time were very intriguing and got a lot of people curious.

那么，有没有什么事物，无论是你作为一位使用计算机、从事相关工作的人在个人探索中发现的，还是你在高等教育中指导他人时所接触到的？

So are there any things that are either coming with your own personal exploration as a as a human being who uses computers and and has a job or within your own teaching that you do in leading people in higher education?

有没有什么让你现在感到‘哇，这真有用’或‘真令人惊讶’的事情浮现在你脑海中？

Anything that's making you go, wow, this is, you know, this is pretty, you know, useful or surprising or anything like that that comes to mind today?

嗯，我想其中一个例子可能比较个人化，而另一个例子则可能触及所有机构。

Well, I guess one example is is maybe quite personal to me, and then there's another example that I think is likely to touch all institutions.

但就我自身使用AI而言，AI的声音带有一点傲慢。

But for me and my own use of AI, AI has a voice that has a little bit of an arrogance.

每当你向它提问，它都会说：‘很高兴你问了这个问题。’

Like, anytime you ask it for anything, it'll say, well, I'm glad you asked.

然后它会给出一个非常自信的答案。

And then it's got a very confident answer that it provides us.

我发现，无论你向AI问什么，它都会给出一个合理的回应。

And I found that anytime you ask AI anything, it's going to give a reasonable response.

哦，这听起来不错。

Oh, that sounds good.

我很快发现，如果我把AI作为我流程的第一步，我就会迷失方向，因为我听到了那个回应。

And I guess I very quickly discovered that if I go to AI as the first step in my process, I kind of get lost because I hear that response.

我觉得，这还不错。

I go, that's pretty good.

于是我就继续往前走了。

And so I kind of move on.

现在对我来说，我从不在流程的开始阶段使用AI。

And now for me, I just never include AI in the beginning of my processes.

我会先做事，确保自己专注，并把自己的想法表达出来。

It's like I do work first to kind of make sure that I'm present and I have my ideas out there.

然后，就像我可能会向你、何塞或朋友、同事寻求反馈一样，我会去问AI，获得一个额外的视角，再决定是整合还是舍弃。

And then in the same way that I might go to you or Jose or a friend or colleague to ask for feedback on something, then I might go to AI and then get an additional perspective that I then either synthesize or discard.

所以对我来说，我发现这确实有点令人担忧，但就我个人而言，我明确意识到这种感受是非常真实的。

So I guess for me, I just found that it's a bit of a concern, but for me personally, I definitely recognize that it was very real.

我们很容易迷失方向。

It might be easy for us to get lost.

我们的想法，我们人类特有的、高度个性化的见解。

Our ideas, our human, very specific to us.

以第一人称为中心，比如我因为太容易采纳AI的观点而失去了自己的视角。

First person central, like me losing my perspective because it's just so easy to go with the AI perspective.

所以我的想法、我的思考、我的创造力可能会被淹没。

So my ideas, my thoughts, my creativity could get submerged.

因此，对我来说，一种实践方法，也是我推荐给学生和教职员工的，就是先自己动手，然后再寻求反馈，而不是直接求助于AI。

So as a practice for me, and really what I recommend for students and faculty is maybe you start first and then get feedback rather than going straight there.

当然，这取决于具体任务，对吧？

Of course, it depends on the task, right?

如果某项任务无关紧要，你只是需要完成它，那么在这种情况下，直接使用AI也无妨，因为你对这项任务的创造性并不看重，也不太重视自己的创意。

If it's something that there's just it doesn't matter much, you just need to get it maybe going straight to AI in that case where your inventiveness isn't necessarily valued, that you don't value your inventiveness very much around that task.

比如给院长回封邮件之类的，你只需要礼貌地回复一下，嗯，好的。

Like an email response to the dean or whatever, you just need to have a polite response that, yeah, okay.

但我想这一点在于，要意识到如果我们允许AI成为船长并率先行动，而不是我们自己先行动、打磨想法，再在后续步骤中请AI介入，我们很容易在与AI的合作中迷失自我。

But I guess that's one thing, was just this notion of recognizing how easy it might be for us to lose ourselves in our collaboration with AI if we allow it to be the captain of the ship and go first, as opposed to us going first and refining our ideas and then maybe as a later step in the process asking AI to come in.

这只是一个个人习惯，不过我也听到其他人表达了类似的观点。

So that's just a personal practice, though I've heard others echo that notion as well.

所以这是第一件事，但当我2022年11月第一次看到AI时，是我儿子卡特给我展示的，我脱口而出的第一句话是：天啊，不好了。

So that's one thing, but I guess the thing that has made me go I first when I first saw AI in November 2022, my son Carter showed it to me, and the first words out of my mouth were, oh, no.

我并没有兴奋于自己能用它做什么。

Wasn't excited about what I could do.

我立刻意识到它将带来的学术诚信问题，以及它可能给我带来的工作负担，还有对高等教育可能造成的冲击。

I instantly saw the academic integrity challenges it was going to bring forward and the work that that might cause for me and the disruption for higher ed that was likely to come along.

每当有人建议我尝试OpenAI的智能网页浏览器时，我都有同样的反应。

And I had the same response whenever someone gave me some advice to try the agentic web browser from OpenAI.

而我又一次感到：哦，这真的不太对，我明白它在许多不同场景中是有价值的。

And it was, again, another like, oh, this is really not I see the value in this for many different settings.

但对于高等教育而言，这将是一场全新的学术诚信噩梦。

But for higher ed specifically, this is going to be a new academic integrity nightmare.

事实上，我预测到2026年，我们讨论的将是：如何解决这个问题，尤其是那些提供完全在线异步课程的机构。

In fact, predict that 2026, this is what we're going to be talking about is how do we solve this, those that are offering fully online asynchronous programs of study.

我认为，那些在高度成熟的代理式AI出现时，将面临最大挑战的，正是这些机构。

Those are the ones that I think are going to have the greatest challenges associated with the emergence of fairly realized agentic AI.

我喜欢听你谈论这些，所有这些截然不同的情感交织在一起。

I love I love hearing you talk about those, like, just all the juxtaposition of all these different feelings.

我也听出你说过，有些情感是成弧线和波浪般出现的，但我们也可能在这场关于AI的对话中同时感受到十七种不同的情绪，因为AI对我们交叉身份的影响实在太模糊、混乱而复杂了。

And and I also hear you saying some of them came in arcs and waves, but, also, we can probably have 17 different feelings in just this conversation about it because it is so murky and muddy and messy as far as, how it impacts our intersectional identities.

我希望在我们进入建议部分之前，你能就未来谈一谈，为这一部分画上句号。

I I would like you to close this part of the episode before we get to those recommendations with talking a little bit about the future.

哇。

Wow.

我认为，任何能准确预测未来的人，大概早就不再上播客了。

I think anyone that could accurately predict the future, they probably are no longer on podcasts.

他们正躺在某个海滩上，因为他们已经能够做出这些预测。

They're on a beach somewhere because they've been able to make those predictions.

我觉得，曾经模糊的未来，现在似乎变得清晰了一些。

I feel like what was once a murky future, I think there's some clarity.

再次强调，高等教育的一部分使命是为学生毕业后的生活做准备，我认为大多数机构都会致力于构建课程体系，让学生在毕业后获得所需的技能。

Again, this notion that higher ed, part of our mission is around preparing our students for life beyond graduation, I think most institutions are going to lean into developing curricular structures where their students will gain the skills that are needed once they graduate.

所以，我认为我们会越来越多地看到这一点。

So I think that's something we're going to see more and more of.

我的意思是，AAC和你们，我们专门设立了人工智能教学与课程研究所，以帮助各高校实现我们前方的目标。

I mean, AAC and you, we have this Institute on AI Pedagogy and the Curriculum specifically to help campuses toward those goals that lie ahead of us.

但追赶某样东西和把事情做好是两回事。

But there's chasing something versus doing something well.

我认为我们目前所处的位置就是：我们该如何把这件事做好？

And I think that that is the position that we're in, is sort of like, how do we do this well?

我们必须灵活应对，但同时也必须把这件事做好。

And we have to move with agility, but we also need to do this well.

在高等教育中，我们非常擅长课程改革。

And one thing that we're really good at in higher ed is curriculum reform.

但我们不擅长快速进行课程改革，我们从未为变化如此迅速的事物设计过课程。

What we're not good at is curriculum reform fast, and we've never built a curriculum for something that's changing so quickly.

我们可能需要两年时间来制定一套优质的课程、课程描述、教学大纲等所有这些优秀的内容，然后才准备好推出。

We might spend two years to put together a good curriculum and courses and course descriptions and syllabi and all of those great things, and then now we're ready to launch it.

而就在代理式人工智能出现的另一边，又迎来了一次重大的技术变革。

And then what's on the other side of agentic AI emerges, there's another big technological shift.

因此，我们被要求完成一项前所未有的任务。

So we're being asked to perform a task that we've never been asked to do before.

以有意义的方式跟上这种变化速度，真正为我们的学生服务。

Keeping up with this rate of change in a meaningful way that actually serves our students well.

在未来几年，这就是我们面临的前景，但同时我们还要应对与代理式人工智能合作的新挑战，思考如何确保我们的课程和学位在这些新兴工具面前保持完整性。

That's what's ahead of us in the next couple of years, but then we also have new challenges working with agentic AI and discerning how to ensure the integrity of our curriculum and our degrees in light of tools such as this that have emerged.

这些就是我们面临的挑战。

So those are the challenges.

我的意思是，我认为在未来两年里，我能预见这将是我们要开展的工作，也是我们面临的总体挑战。

I mean, I think for the next two years, I can see that that's the work that we're going to be doing and the overarching challenge that's ahead of us.

说实话，我们还没有解决三年前出现的生成式AI挑战。

And to be honest, we really haven't solved the generative AI challenge that emerged three years ago.

对于所有在高等教育中授课的人来说，这仍然是一个棘手且重大的挑战。

It's still a problematic and a grand challenge for everyone that that teaches a class in higher education.

这是节目里我们各自分享推荐内容的环节。

This is the time in the show where we each get to share our recommendations.

埃迪，你刚才提到了我的推荐。

And Eddie, you mentioned mine a little bit ago.

我在一份来自AAC以及你所在机构的报告中找到了一些希望。

I found some hope in a report that came out from AAC and you, the organization that you work with.

我只读一下摘要，然后我很想听听你的分享，因为你对这份报告比我熟悉得多。

And I'll just read the headlines, and then I'd love to have you share because you're far more familiar with it than I am.

我觉得听你分享一点会很有趣。

I think it'd be fun to hear you share a bit.

但标题是：新全国调查发现雇主对高等教育充满信心。

But so the headline goes, New National Survey finds strong employer confidence in higher education.

实际上，我认为你并没有提到这份报告。

I believe, actually, it's a it's a you didn't mention this report.

你提到了另一份报告，但那份报告是关于不同主题的。

You you mentioned another report, but but that came out around a different topic.

所以，是的，新全国调查发现雇主对高等教育充满信心，调查结果表明通识教育成果与不断变化的劳动力需求之间存在明确的契合。

But so, yeah, New National survey finds strong employer confidence in higher education findings show clear alignment between liberal education outcomes and evolving workforce needs.

我们很少能听到令人高兴的消息。

And we don't often get happy store.

我的意思是，也许‘高兴’这个词不太准确，但我们很少能看到这样的证据：至少，这里有一些迹象表明，我们所关心的公共利益、以及包含博雅教育的优质教育，确实具有价值。

I mean, maybe happy is not the right word, but we don't often get to see like, wow, there's we're seeing some at least there's there's some evidence here of some value and and value around things that I know those of us that that care about the common good and about what it means to have a wonderful education that includes, you know, a liberal arts education within it.

告诉我们这些发现是什么吧？它们是否像带给我希望一样，也给你带来了希望？

Tell us what these findings were, and is it bringing you any hope the way it's bringing me some?

嗯，是的，这确实给我带来了一些希望。

Well, yeah, it definitely does bring me some hope.

这项研究实际上也包含了一些关于人工智能的子问题。

And this actually is the same study that has some questions nested about AI.

这项调查的结构相当广泛，涵盖了众多不同的话题。

The survey was pretty sort of broad structured to cover a lot of different topics.

但高等教育目前处境艰难，有些挑战我们部分负有责任，比如学生债务危机，但毫无疑问，它已经变得高度政治化，某些圈子对高等教育价值的贬低以及对它的表述方式也在加剧。

But I mean, higher ed is fairly embattled, and there are some challenges that maybe we partially own, such as the student debt crisis, but certainly it's become very politicized, and there's been this diminishing of the value of higher education in some circles and how things have been framed.

所以，天啊，我刚想到一个比喻，但我真讨厌这个比喻，但可以说，我们产品最忠实的消费者——也就是雇佣我们学生的那些人。

So guess, boy, don't even Oh, I hate this metaphor that just came to my head, but kind of the greatest consumers of our product, if you will, those that hire our students.

这样说好多了。

That's much better.

但我刚说出口，就讨厌这个比喻了。

I hate that metaphor now that I've just said that out loud.

但他们对我们所做的工作感到满意。

But they're pleased with what we're doing.

我的意思是，学生们离开校园后去工作，他们准备充分，接受了良好的教育，能够胜任毕业后的各项工作。

I mean, the students are They leave our campuses, they go to work, they're positioned well, they have been educated in such a way that they can do the work that awaits them beyond graduation.

而那些最能评判这一点的雇主——不是政客，也不是父母，而是那些需要分配任务并管理我们前学生的管理者——发现他们所接收的学生在企业与机构中都做好了充分的准备。

And the employers who would be the best judge of that, not politician, not parents, but those that are having to assign tasks and manage our former students, they're finding that students that they're receiving are well prepared for the work within their businesses and their institutions.

因此，在这个更广阔的背景下，这是一个非常积极的数据点：我们确实有很多方面做得非常好，几十年来一直如此，而且显然我们仍在持续做得很好。

So that's a very positive data point on this broader landscape is that there are things that we do quite well and that we've done quite well for decades and decades and that we're obviously continuing to do quite well.

我的第二个建议是，我正在不按顺序重新录制节目，但我对此深信不疑。

My second recommendation is I am rerecording episodes out of order, but I'm still I just feel so strongly about it.

我得推荐一个播客的第二季，等你们在接下来几周听到布莱恩·亚历山大时，我再推荐第一季。

I got to recommend season two of a podcast that I'll recommend season one of when you all get to hear Brian Alexander in the coming weeks.

欢迎再次回到播客。

Come back on the podcast.

这个播客叫《壳牌游戏》，我会让你先听听布莱恩·亚历山大那期，了解一下第一季的内容。

So the podcast is called The Shell Game, and I'll let you listen to the episode with Brian Alexander a little bit about what season one was all about.

简而言之，它讲述的是一位记者兼企业家，他出去创办了一家由AI员工组成的公司。

Essentially, it is about the brief version of it is it's a guy, a journalist that and also someone who is more entrepreneurial goes out and starts a company with AI workers.

如果听到这里你已经皱眉了，心想：‘谁会想听这个？’

And if that just made you bristle and go, why would anyone ever wanna listen?

我跟你说，我必须读一下我朋友杰姬发给我的一条信息，她也和我一起听了第二季，因为我真的必须读出她的话。

I'm I'm telling you, I have to actually read a text from my friend, my dear friend Jackie, who also had listened to season two along with me, because I just have to read her her words.

这是她发给我们一群人的消息。

So this was a text to a group of us.

她说：我已经听完了《壳牌游戏》第二季，以下是她所有的评价，埃迪。

She says, so I've now listened to season two of the Shell Game podcast, and here is all of her descriptions, Eddie.

引人入胜、鼓舞人心、 hilarious（令人捧腹）。

Fascinating, inspiring, hilarious.

真的很好笑。

It is hilarious.

我可以证实这一点。

I can back that up.

令人不安、发人深省、令人精疲力尽、富有挑战性、吓人、引人入胜。

Disturbing, thought provoking, exhausting, challenging, scary, intriguing.

我喜欢这个播客的一点是，所有这些感受交织在一起，正是我的真实体验。

One of the things I love about this podcast is all of those feelings all mishmash together is what my experience has been.

在我的教学、工作以及对人性意义的理解中，我都曾感受到过这些情绪。

I have felt those things terms of my own teaching and my own work and my own sense of what it means to be human.

她能用这么多形容词精准地概括出这些感受，这真是太美妙了。

And it was so delightful that she could put she could pack so much of those adjectives.

我想，是的，当我聆听这些剧集时，我也感受到了这些情绪。

I thought, yes, I feel those things while I am listening to these episodes.

我也认为，我们很多人都会有共鸣，因为我们都在经历‘这究竟意味着什么’的过程中，感受到过这些情绪。

I also think so many of us would resonate because we have felt those things as we've been, you know, going through this experience of what does this mean?

这会带来什么改变？

What does this change?

什么没有改变？

What hasn't changed?

你知道的，我们不想过度沉迷于这种炒作。

You know, not wanting to buy too much into the hype.

我们依然是人类，而人类是独特而美好的。

And we are still humans and humans being very unique and wonderful.

所以，我只是觉得这真的很美好。

And and so I just it's just it's delightful.

我要提醒你，如果你决定听的话，你会产生强烈的情感反应。

I I will warn you if you do decide to listen that you will have visceral feelings.

不过，我认为重要的是我们要认识到，刚刚发生的事情究竟是什么引起的。

I think that's important, though, that we recognize what is it about that that just happened.

罗伯·帕克也在这个聊天中提到，他的妻子杰基的丈夫说，他得放慢节奏听这些剧集，真的很难听下去，你知道吗？

That is Rob Park, also on this chat, said he has to take these his his Jackie's husband had had mentioned he had to take the episodes a little slower, really hard to listen to, you know?

所以你可能需要放慢节奏。

So you might need to take it slow.

你可能会像杰基和我一样一口气听完，但我确实认为，这是一个重要的机会，让我们反思：为什么会这样？

You might be binging like Jackie and I, but I do think this is an important opportunity for us to be able to reflect on, well, why is it?

是什么触动了我内心深处的东西？

What is it that that's bumping up against in me?

我希望你们在听的时候能告诉我，我的意思是，我迫不及待想和大家重新连接，让我们所有朋友聚在一起，好好聊聊这个非常特殊的情况。

And I hope that you'll let me know if you're listening or I mean, because I can't wait till we're all caught up with each other so all of us friends can get together and have a a good conversation about this very unusual circumstance.

每一集我都想立刻坐下来和别人聊聊。

And every episode, I just wanna sit down immediately and talk to people about it.

我很久很久没有觉得一档播客如此令人上瘾了。

I have not felt this binge worthy of a podcast in a very, very long time.

它会让你非常好奇，想知道下一集会发生什么。

It does get you very curious to find out what on earth is gonna happen next in the next episode.

所以我想我把话筒交给你，埃迪，你想推荐什么都可以。

So I guess I'll pass it over to you, Eddie, for whatever you'd like to recommend.

嗯，这和媒体与高等教育的领域完全无关。

Well, something sort of way out of the domain of media and higher education.

自从这本书第一版以来，我就经常在路上，吃了很多路边小吃。

So really since the first edition of the book, I've been on the road a lot and eating a lot of road food.

每当我回家时，我都想自己做饭，吃些新鲜的食物。

And whenever I'm home, I want to cook and have fresh food.

一位朋友推荐了，因为我不一定是个好厨师，对吧？

A friend of mine recommended, because I'm not necessarily the best cook, right?