这是我近期看到最有价值的文章，分享给大家 来自沃顿商学院对 AI 时代如何进行开发的讨论

他们打造了一个 AI 工作室（Generative AI Studio），来观察学生们如何通过 AI 去制作项目。

他们观察到，有两种典型的协作模式会限制学生的创造力。

1. 学生们总是随时准备行动，迫不及待地把想法变成现实，但是这会过早地将他们的精力集中在执行上，而非发明创造上。
2. 当学生们在一起讨论的时候，群体思维迅速出现，它会让平庸的想法被不断附和、放大，而真正原创的想法反而变少了

同时，学生们对待 AI 的态度也非常典型：

1. 学生把 AI 当成自动售货机，以为丢一句简单提示词就能得到完美成品，不会去管理 AI 随机性，没有耐心通过持续沟通、补充上下文、逐步纠偏来得到好结果。
2. 把 AI 当成只会写代码的工具。有一位学生想做一款健身 APP，他一开始就沉浸到如何设计数据库、保存训练数据上面，没有思考如何用 AI 去创造那些普通 APP 做不到的体验。

对此，沃顿商学院的教师们提出了解决方案

判断力、品味、创造力和批判性思维仍然是 AI 时代最宝贵的特质，他们从这一点出发，设计了 Crit（作品评议）以及 Charrette（一日限时创作）

Crit——培养判断力

你可以把它理解成作品互评论，有两种形式：

1. 1 对 1 crit：导师直接给反馈，指出哪里需要加强、哪里是亮点、需不需要换方向、要不要返工等细节。
2. 小组 crit：在导师的主持下，让同伴之间进行互评

不管哪种形式，开场都一样：学生先展示作品，然后向全场征集反馈。展示时要说清五件事——

1. 我的意图（想做什么）
2. 我的制作过程
3. 过程中有什么新发现
4. 冒出了什么新问题
5. 还剩下什么挑战

为了控制评论的质量，避免把互评变成宣判大会，导师要求所有的评价意见以这三种句式开头：

1. “I notice…”（我注意到……）——先慢慢看、仔细看，描述你想讨论的东西，而不是直接下结论
2. “I wonder…?”（我在想……？）——用问题开启对话，而不是用判决关闭对话
3. “What if…?”（如果……会怎样？）——把替代方向当作“可能性“提出来，帮对方推进他自己的目标或抓住意外机会

一个实际的例子是： 一个学生想做健身 App，他的思路很常规：问卷调查 + 询问健身房器材 + AI 出训练计划。在 Crit 中有人追问：“AI 能不能做出普通 App 做不到的体验？“这个问题直接推翻了预设：不需要器材数据库，用户拍一张眼前环境的照片，AI 就能理解现场条件、生成真正个性化的训练方案。 这个例子非常有说服力，因为我经常去酒店的健身房，如果有这样的 APP，我真的会使用的。

拓展起来，我们到团队或者公司里面，其实也可以用这种方式去互评，非常有参考价值。

Charrette —— 培养创作流程

Charrette 的过程一共有 6 步

1. 开场设定问题
2. 个人独立用纸笔发散大量粗糙想法
3. 全班梳理筛选，对上一步的想法进行评审，最后决定开发哪些产品
4. 2–3 人小队只做规划，不涉及任何执行
5. 使用 AI，限时两个小时做出原型
6. 全班展示评议

整个过程限定在 8 个小时，其中制作部分最多只有两个小时。

这是整篇文章我最喜欢的部分，如果你做过开发，你很容易发现自己花了大量的时间在执行工作上，甚至好几天好几夜都停不下来。但是别人问起你有没有产品原型可以体验的时候，你经常发现自己拿不出来。

Charrette 的目的是，让学生们意识到，AI 时代规划的工作是比执行工作更重要的，因为 AI 能够帮你去执行，你不应该再把自己放在执行者的位置上。但是 AI 却不能取代你的创造力和批判性思维，所以你更应该给自己一些时间，去思考问题，思考解决方案，而不是急着去动手。

非常棒的文章，值得分享给大家，原文链接：

https://gail.wharton.upenn.edu/research-and-insights/enduring-methods-emerging-medium/…

Enduring Methods for an Emerging Medium: The Generative AI Studio

Source: https://gail.wharton.upenn.edu/research-and-insights/enduring-methods-emerging-medium/ Upskilling efforts are teaching workers to use generative AI tools, but the rise of agentic work is driving a call for enduring skills: judgement, taste, creativity, and critical thinking. We have not seen a pedagogical approach designed to develop these skills in a way that could realize generative AI’s full potential and the economic and human value that could come with it. So, we built one, and it begins with changing how we define generative AI itself.

Generative AI is more than a tool for productivity and efficiency. It can produce stories, images, music, and interactive experiences that stir human emotion. It is stochastic and, like paint or film, what it produces is heavily shaped by the person who directs it. These are the qualities of a creative medium, not a tool. So, we treated it as one, applying the time-tested methodologies of art and design education to teach students to work with it.

In the Generative AI Studio, students pursue individually driven projects with no right answer, only the potential to serve a need or create a meaningful human experience. Two practices were core to the educational experience: the critique, which develops discernment, and the charrette, which develops a productive creative process. Together they enabled students to create an unexpected array of AI-driven experiences and surfaced insights that hold lessons well beyond the classroom.

Why The Arts?

Art and design education leverages repeated cycles of creation, critique, and revision to build the very skills that working with generative AI demands: imagination, technique, process, taste, judgement, and critical thinking.

The Crit: Developing Discernment

Crits center on the assessment and discussion of creative output. Their purpose is to help the student clarify their own goals and identify ways to build on what is already strong within the project. Through repeated crits, students gain multiple perspectives on their work, build a shared critical vocabulary, and develop the ability to discern what success looks like while shaping their own taste. (How we structure and facilitate crits, including the norms that keep them psychologically safe, is covered on our [Pedagogy page].)

The Charrette: Developing Process

Discernment is only one half of the picture. The other is process: the question of how we approach our work with generative AI

We observed two patterns in how our students worked that were clearly limiting their creativity.

• Our students were poised for action and eager to make ideas real. This was often a strength, except when it prematurely focused their energy on execution, rather than invention.
• When students brainstormed all-together, as often happens in a class or a meeting, groupthink emerged quickly, reinforcing weak ideas, and producing fewer truly original ones.

The charrette, a single-day exercise in developing novel solutions to a specific problem within a fixed window of time, addressed both issues.

Anatomy of a One-Day Charrette: From Idea Generation to Project Presentation in 8 hours.

1.Setting the Stage

The problem is framed, new tools are introduced, and the criteria for success are made clear.

Whole Group

2.Divergence

Individuals generate as many rough ideas as possible, none prejudged. The work stays deliberately analog: paper, post-its, and whiteboards.

Each Student Alone

3.Sense Making

A mini-crit of the ideas. Patterns surface, and instructors review all of the analog work to select the ones worth developing further.

Whole Group

4.Convergence

Small teams are formed, each takes one curated idea. This stage is planning only - no code yet.

Teams of 2 or 3

5.Rapid Prototyping

Up to two hours to make a working prototype. The thinking is already advanced; here thebuild tools come into useand code serves the idea.

Teams of 2 or 3

6.Assessment

A closing crit where prototypes are presented. Seeing one problem solved many ways makes visible the range of possibilities.

Whole Group

Afterward, the work was more varied and inventive, students’ expectations for themselves changed, and they left with a concrete ideation process they could carry into other settings.

What Does Success Look Like?

Core to the crit process was a set of criteria that we used to assess our work.

• Intrinsic AI A clear, essential role for generative AI, without which the project would lose its distinct value.
• Aesthetic Elements The effective use of aesthetic elements, including image, color, typography, composition, time, motion, and sound.
• User Interface A logical user experience that serves the project goals and maximizes the distinct value created by generative AI.
• IT Architecture A logical and efficient digital infrastructure.
• Data Security & User Safety Security and safety regarding data, privacy, and unintended consequences on others.
• Ethics Ethics regarding the use of images, music, video, and text produced by generative AI, or other creators.

One student project revealed how the crit can spark innovation. They were building a fitness app for frequent travelers who need to adapt their exercise routine to their current environment. The initial iteration was designed like most apps: it had simple click-through, multiple choice surveys that enabled the system to pull a sub-set of options from established data sets to provide the user with a “custom” set of solutions. In pursuit of this, the student focused first on acquiring access to gym equipment data bases rather than developing the user experience. AI was being treated as a tool to rapidly write the code that would drive the app but not part of what made the app unique.

In the crit, a new question emerged that challenged this approach: what if generative AI could enable experiences that ordinary app logic could not? Could it create truly personalized solutions? Through our probing of preconceived notions, like the need to have a database of equipment from which a workout could be planned, and asking open-ended questions about what might be possible, the survey gave way to a live exchange between the user and the AI. A simple photo of the user’s immediate environment was all that was needed for the system to create a truly adaptable workout plan. The AI was no longer just a tool to build the app; it became a medium through which the traveler’s unique conditions could be understood and turned into a workout.

Embracing Process

Most students initially approached generative AI expecting that a simple input would produce a polished result, like putting a dollar into a vending machine and expecting it to deliver a bag of chips. As the crits helped students become more discerning regarding the quality of AI’s output, they realized that they needed to learn to manage generative AI’s stochastic nature and jagged edge to get better results. So, we taught them to treat prompting as a management process that centers on good communication and collaboration: explaining to the model what they were trying to accomplish, sharing the questions they were wrestling with, and asking the model to help shape and improve its own instructions. Over time, students began to approach generative AI more like a member of their staff, providing direction, context, and ongoing course correction. As they did, the quality of their output rapidly and significantly improved.

Invest in Iteration

We also had to pull students away from building too early, and teach them to invest in the iterative process of idea development. When they focused on the code first, which was their first instinct, their core concepts remained underdeveloped while the architecture and code base became solidified, triggering the commitment bias that makes it challenging to reconsider the original idea. When students stopped treating generative AI mainly as a coding system and instead approached it first as a medium for shaping human experience, the work that resulted was more distinct and compelling, and the build that followed was more efficient and effective.

By the end of the semester, students had advanced both the creativity of their ideas, and the forms that their projects took, moving well beyond the chat interface. Their projects involved speech, vision, robotics, XR environments, and dynamic interfaces. This included a voice-driven system for shaping three-dimensional objects in a virtual environment, a city guide that curated experiences for travellers that were informed by residents’ own stories, and a system that renders current events into age-appropriate, educationally sound, politically neutral comics for children.

Key Takeaways

The crit is a path to discernment.

Structured critique helps students clarify their goals, build a shared critical vocabulary, and learn to weigh feedback, while shaping their taste and definition of what success looks like.

The charrette develops process.

A fixed-time cycle of divergence and convergence gives students a concrete ideation process they can carry into other settings, and counters the pull toward premature building.

Management skills matter.

Learning to work with generative AI means learning to manage it: providing direction, context, and ongoing course correction, and developing the instructions themselves as an early product of the process.

Building too early is a trap.

While generative AI makes coding almost instantaneous, and prototyping faster than ever, hardening the technical structure around a weak or untested idea stalls meaningful innovation.

Enduring methods can unlock emerging technologies.

Art and design education has leveraged studio-based instruction to cultivate enduring skills for generations. When we treat generative AI as a creative medium rather than a productivity tool, those methods apply directly, preparing students not just for this technology, but for whatever comes after it.