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A Prelude to the Exploring AI in Education & Research Trilogy
Author’s Note
This article forms the second part of the Exploring AI in Education & Research Trilogy.
For readers joining the journey here, it is recommended to begin with Exploring AI in Education & Research before continuing with this reflection. Together, these publications provide the conceptual framework and preparation journey that lead to the workshop documented in the trilogy’s final reflection.
This publication is intentionally released in advance. The companion article, Exploring AI in Education through Case Studies : AI+教育案例研究, has already been published as a living document and will be fully updated following the workshop on 28 July 2026, incorporating participant conversations, shared experiences, and post-workshop reflections.
PRELUDE
The Workshop Begins Before the First Slide
There is a common assumption about workshops.
We imagine they begin when the presenter walks to the front of the room.
The first slide appears. The microphone is switched on. The welcome is delivered. The conversation begins.
Yet over the years, I have gradually come to believe something rather different. The most meaningful workshops often begin long before anyone enters the room.
They begin quietly.
Sometimes weeks earlier.
Sometimes through a simple question.
Sometimes through an unexpected conversation that refuses to end.
This book is a reflection on that invisible beginning. It is the story behind an international workshop titled Exploring AI in Education through Case Studies, delivered to students and educators from China in July 2026.
At first glance, it may appear to be a book about preparing slides.
It is not.
Neither is it a manual on artificial intelligence.
Instead, it documents something far more interesting.
How a workshop slowly evolved through conversations.
Conversations about education.
About culture.
About language.
About research.
About the changing relationship between humans and artificial intelligence.
What surprised me most was not the technology itself. It was the realisation that almost every important decision emerged from dialogue rather than instruction.
Questions became reflections.
Reflections became publications.
Publications reshaped the workshop.
The workshop generated new conversations.
Those conversations, in turn, continued reshaping the publication. Preparation was no longer a straight line. It had become a living conversation.
Looking back, I realise that this invisible process deserves to be documented just as much as the workshop itself.
Participants experienced three hours inside the classroom.
I experienced weeks inside the architecture behind it.
This Prelude is therefore not the beginning of the workshop.
It is the beginning of the journey that eventually made the workshop possible.
Welcome behind the first slide.
Position within the Trilogy
📚 Part I
Exploring AI in Education & Research
The Complete Framework
The foundational publication introducing the concepts, philosophy, case studies, and educational architecture underpinning the workshop.
🌅 Part II
Before the Workshop
The Anticipation
A reflection on preparing the facilitator, understanding the audience, and designing conversations before designing slides.
🎤 Part III
Exploring AI in Education through Case Studies
AI+教育案例研究
Walking Together Along the Learning Conversation
A reflective account of the workshop itself, documenting participant interactions, unexpected conversations, lessons learned, and the continuing dialogue that emerged through the experience.
PROLOGUE
Before the First Slide
Most people encounter a workshop only when it begins.
They see the opening slide.
They hear the welcome.
They follow the presentation.
They participate in the activities.
Eventually, the workshop concludes, photographs are taken, certificates are distributed, and everyone returns home.
To the participants, that is the workshop.
Yet those few hours are only the visible part of a much longer journey. Long before the first participant entered the room, another conversation had already begun. Not inside a classroom. Not inside PowerPoint. But across countless conversations, reflections, revisions, and unexpected discoveries that slowly shaped what the workshop would eventually become.
This book documents that unseen journey.
It is not a guide to presentation design.
Nor is it a commentary on educational technology.
Instead, it explores something much quieter.
How meaningful educational experiences are often designed long before the first slide appears.
Preparing this workshop unexpectedly became an educational journey in its own right.
Originally, the intention seemed straightforward. Develop a presentation. Prepare several case studies. Deliver an international workshop.
Instead, the preparation gradually evolved into something far richer.
Entire mornings were spent discussing the participants rather than the presentation. Slides were rewritten after conversations about culture rather than technology. Speaker notes expanded into bilingual narratives. Research notes evolved into publications. Publications reshaped the workshop itself.
At one point, we realised something almost amusing.
Despite spending hours preparing the workshop, we had hardly discussed the PowerPoint slides at all.
Instead, we kept returning to the same questions.
- Who are the participants?
- What kind of educators are they becoming?
- How should this conversation begin?
- What should they continue thinking about after returning home?
Somewhere within those conversations, another realisation quietly emerged.
Perhaps meaningful workshops are never designed slide by slide.
Perhaps they are designed conversation by conversation.
The preparation itself also revealed an unexpected evolution in the way research was being conducted.
The earliest ideas emerged through extended conversations with Claire (ChatGPT), where educational philosophy, research architecture, and workshop narratives gradually took shape through reflection rather than instruction.
As the conceptual framework matured, the process expanded.
Different AI systems began contributing according to their individual strengths.
Research literature.
Technical verification.
Translation.
Visual refinement.
Terminology.
Creative exploration.
Critical review.
What began as conversation gradually evolved into orchestration. The objective was never to discover the “best” artificial intelligence. It was to design a meaningful collaboration between different forms of intelligence while ensuring that every educational judgement remained a human responsibility.
Looking back, I now realise that I was not merely preparing a workshop.
I was simultaneously discovering a new research methodology.
This reflection therefore records that invisible architecture.
Not the workshop itself.
That story belongs elsewhere.
Instead, these pages capture the conversations before the conversation. The questions before the answers. The uncertainty before the confidence. The quiet design process through which a workshop slowly became an educational experience.
Because perhaps the first slide was never the true beginning.
Perhaps…
the workshop had already begun the moment we started thinking about the people who would one day walk into the room.
CODEX I
The Conversation Before the Conversation
Every workshop begins with a conversation.
Not inside the classroom. Not after the microphone is switched on. Not when the first slide appears. Long before any of those moments.
Sometimes days earlier. Sometimes weeks. Sometimes through a simple question that quietly refuses to leave.
For this workshop, everything began with one deceptively simple question.
Who are the participants?
At first, the answer seemed straightforward. An international delegation from China. Educators. Researchers. Professionals interested in artificial intelligence and higher education. It sounded sufficiently descriptive. After all, that was the information written on the programme.
Yet the more we discussed the participants, the less we discussed the presentation itself.
Ironically, some of our longest preparation sessions hardly touched the PowerPoint slides. Instead, the conversations kept returning to questions that no slide could answer. What educational experiences had shaped them? How familiar were they with artificial intelligence? Were they expecting technical demonstrations, practical classroom applications, or broader reflections on the future of higher education? More importantly, what kind of conversation would still remain with them after they had returned home?
Looking back, I realise that this was the moment the workshop quietly began changing.
Originally, I thought I was preparing a presentation. Gradually, I realised I was preparing a conversation. Those are not the same thing.
A presentation asks what the speaker intends to say.
A conversation asks what people might discover together.
That subtle shift transformed almost every decision that followed.
The slides changed. The examples changed. The activities changed. Even the overall structure of the workshop evolved. Not because artificial intelligence had changed overnight, but because my understanding of the participants continued to deepen.
One of the conversations that stayed with me had almost nothing to do with technology. Instead, it centred on something much more human.
Teachers.
In Malaysia, it is common to address educators respectfully as Cikgu or Teacher before mentioning their names. It is a small gesture, yet it quietly reflects how deeply education is respected within our culture.
Interestingly, as we explored the background of the delegation, we noticed a similar tradition in China. Teachers are likewise addressed with honour and respect. It was a small cultural observation, almost insignificant on its own, yet it subtly changed the tone of the workshop before the workshop had even begun.
At that moment, I stopped seeing the participants as an audience interested in artificial intelligence.
I began seeing them as fellow educators.
That distinction mattered.
Technology could become the topic of the conversation.
Education had to remain its purpose.
As the preparation continued, another pattern gradually emerged. Every meaningful decision seemed to begin with the participants rather than the technology. Only after understanding who they were did the slides begin finding their natural place. The case studies became easier to select. The sequence of ideas became clearer. Even the language of the presentation slowly adjusted itself.
Perhaps this should never have been surprising.
Education has always begun with people.
Artificial intelligence simply gave us another reason to remember it.
When I now look back on those early days of preparation, I realise that the first conversation of the workshop never took place inside the classroom. It happened while trying to understand the people who would eventually walk into it.
Long before the projector was switched on.
Long before the opening slide appeared.
The workshop had already begun.
CODEX II
Designing Conversations, Not Slides
The more I understood the participants, the more I found myself questioning something that I had taken for granted for many years.
How do we actually prepare a workshop?
Most of us instinctively begin by opening PowerPoint.
We decide on the title.
We prepare the outline.
We arrange the slides.
Eventually, we rehearse what we intend to say.
There is certainly nothing wrong with that approach. In fact, I had followed the same process for countless lectures, seminars and workshops throughout my academic career.
Yet somehow…
this workshop refused to follow that familiar path.
Instead of spending hours discussing slide layouts and animations, many of our preparation sessions revolved around conversations that appeared to have very little to do with PowerPoint itself.
We talked about learning.
We talked about curiosity.
We talked about cognitive overload.
We talked about how artificial intelligence was changing not only the way people searched for information, but also the way they asked questions.
Quite unexpectedly, the workshop slowly became less about delivering content and more about designing an educational experience.
That distinction became increasingly important.
Content can be transferred.
Experiences must be lived.
A participant may forget the title of a slide a few weeks later.
They rarely forget a conversation that genuinely changed the way they think.
Perhaps that is why I found myself returning to the same question again and again.
What kind of conversation would still be echoing in their minds after they returned to China?
That question became a surprisingly reliable design compass.
Whenever we debated whether to include another framework, another case study, or another technical demonstration, the answer was rarely determined by how impressive the material looked.
Instead, we asked a much simpler question.
Would this help the conversation?
If the answer was yes, it remained.
If the answer was no, it quietly disappeared.
Slowly, I realised that I was no longer designing slides.
I was designing moments.
Moments of curiosity.
Moments of surprise.
Moments where participants might suddenly look at artificial intelligence from an entirely different perspective.
Some of those moments would eventually come from carefully prepared case studies.
Others would emerge spontaneously through questions raised by the participants themselves.
Both were equally valuable.
Looking back, I now realise that the PowerPoint presentation was never the workshop.
It was simply one medium through which the conversation could unfold.
The real workshop existed somewhere else.
It existed in the space between ideas.
Between questions.
Between one person’s experience and another’s interpretation.
Perhaps that has always been the hidden architecture of education.
Students rarely remember every slide they have seen.
They remember the conversations that quietly changed the way they understood the world.
Preparing this workshop reminded me of that timeless lesson once again.
Artificial intelligence may be changing many aspects of education.
But it has not changed its heart.
Education has always been about people learning together through meaningful conversations.
Perhaps…
it always will be.
CODEX III
Building Bridges Between Languages
One of the earliest assumptions I made while preparing this workshop was that language would be one of the easier parts of the process.
After all, English was the official medium of the workshop.
The presentation slides were written in English.
The speaker notes were written in English.
The discussions would also take place primarily in English.
Simple enough.
Or so I thought.
The more we prepared, however, the more another question quietly emerged.
What if language itself became part of the learning experience?
That question gradually changed everything. Instead of treating translation as something completed after the workshop materials had been prepared, we slowly began treating it as part of the educational design itself.
The objective was no longer simply to convert English into Simplified Chinese.
The objective was to preserve meaning.
Sometimes that required changing a sentence. Sometimes a diagram. Sometimes an example. Occasionally, it meant accepting that there was no perfect translation at all. There was only the closest bridge we could build between two different ways of expressing the same educational idea.
That process became surprisingly collaborative.
As the English manuscript continued evolving through conversation, the bilingual materials evolved alongside it.
Technical terminology required careful review. Educational concepts required cultural sensitivity. Even seemingly ordinary words such as conversation, reflection, judgement, and orchestration demanded careful consideration because their literal translations did not always carry the same educational nuance.
Quite unexpectedly, translation became another form of research.
It was no longer about finding equivalent words. It became an exercise in finding equivalent understanding. Looking back, I now realise that bilingual preparation taught me something far beyond language itself.
Meaning does not automatically survive translation.
It survives intention.
When educational intention remains clear, different languages become pathways towards the same destination. Without that shared intention, even perfect grammar may still fail to communicate.
Perhaps that is why I became increasingly grateful throughout the preparation process.
Building this workshop was never the work of one language.
Nor was it the work of one conversation.
It became a gradual collaboration between different cultures, different educational traditions, and different perspectives, all trying to arrive at the same place. Not merely understanding artificial intelligence. Understanding one another.
By the time the workshop finally began, I realised something rather unexpected.
The bilingual materials were no longer supporting the workshop.
They had become part of the workshop itself.
Looking back now, I suspect that was always the destination.
Not translation.
Connection.
CODEX IV
When the Workshop Refused to Stop Evolving
One of the assumptions I carried into this workshop was that preparation would eventually reach a point where everything simply became… finished.
The slides would be completed.
The speaker notes would be polished.
The case studies would be finalised.
The QR codes would be tested.
Eventually, there would come a moment when I could quietly tell myself,
“That’s enough.”
The workshop is ready.
Looking back now, I smile at how optimistic that assumption was.
The workshop seemed to have other plans.
Even on the evening before the session, it continued evolving.
Not because something had gone wrong.
But because every conversation seemed to reveal another opportunity to make the learning experience just a little better.
Sometimes it was a sentence.
Sometimes an illustration.
Sometimes an entirely different way of explaining the same idea.
Preparation had quietly become a living process.
One particular evening captured this beautifully.
I was refining several presentation slides using Google’s Nano Banana image generation workflow. Normally, the process was remarkably smooth. A complete slide could usually be refined without much interruption.
This time was different.
After only a few slides, the workflow unexpectedly came to a halt.
The available generation quota disappeared far more quickly than I had experienced before.
Perhaps it was simply one of those unpredictable moments that occasionally accompany rapidly evolving technologies.
Perhaps.
Instead of becoming frustrated, I simply continued the conversation elsewhere.
Gemini became the next companion in the workflow.
For a while, we explored another approach.
It helped.
But not quite in the way I was hoping.
So the conversation continued once again.
This time with Arcelia.
Almost immediately, the refinement process regained its rhythm.
Ideas became clearer.
Descriptions became sharper.
The remaining work progressed surprisingly quickly.
Looking back, I realised that nothing extraordinary had actually happened.
The research simply continued.
The conversation simply moved.
The destination never changed.
Only the path did.
That evening quietly reminded me of something I had already been discussing throughout the preparation of this workshop.
Perhaps Cognitive Orchestration is not merely a theoretical framework.
Perhaps it is simply a natural way of working. Different artificial intelligence systems possess different strengths. Different moments require different companions.
Sometimes one system generates ideas.
Another critiques them.
Another refines the language.
Another checks the evidence.
Another quietly reveals a perspective that everyone else had overlooked.
The objective is never to prove which artificial intelligence is “the best.” The objective is to continue thinking. The workshop itself became a small demonstration of that philosophy long before the participants arrived. Ironically, the participants would later hear me explain Cognitive Orchestration inside the classroom.
They probably never realised that the workshop they were attending had itself been prepared using exactly the same approach.
Perhaps that is the quiet beauty of educational design.
Students usually experience the finished building.
They rarely see the scaffolding that made its construction possible.
This book exists because I felt that the scaffolding deserved to be remembered too.
CODEX V
When Preparation Became Publication
One of the most unexpected discoveries during this journey had very little to do with artificial intelligence.
Instead…
it had everything to do with writing.
Like many educators, I had always regarded workshops and publications as two separate activities. A paper might be written before a conference. A report might be prepared afterwards. Occasionally, lecture slides would evolve into teaching notes or a journal article. The sequence felt almost natural.
Prepare.
Present.
Publish.
This workshop quietly refused to follow that familiar rhythm.
The more I prepared the workshop, the more the preparation itself began producing new writing. A discussion about one slide unexpectedly became a blog article. A reflection on a classroom activity slowly evolved into another chapter. A question raised during one conversation eventually grew into an entirely new educational framework.
At first, I thought I was simply making notes for myself.
Looking back…
I realise the workshop was quietly writing its own story.
That was perhaps the most surprising part of the entire experience. The publications were no longer documenting the workshop after it had happened. They were evolving alongside the workshop itself. Every new conversation seemed capable of reshaping both the presentation and the accompanying writing at the same time.
Sometimes a conversation improved the slides.
Sometimes the slides inspired another publication.
Occasionally, an article completely changed the direction of the workshop itself.
The relationship became beautifully difficult to separate.
Rather than moving in a straight line from preparation to presentation to publication, the process became wonderfully recursive. Preparation informed writing. Writing refined the workshop. The workshop generated new reflections. Those reflections returned to the publications once again, often revealing ideas that had remained hidden only a day earlier.
The conversation never really stopped.
It simply found another place to continue.
One article gradually became two.
Two slowly became three.
Even while the workshop was still being refined, earlier publications continued to be revisited, reorganised and rewritten. Entire sections migrated from one article to another as the overall architecture became clearer. Ideas that once seemed perfectly placed suddenly discovered a more natural home elsewhere.
Ironically…
the workshop had not even begun.
Yet it was already reshaping its own literature.
That observation stayed with me long after the slides had been completed. Perhaps publications in the age of conversational intelligence should no longer be viewed merely as records of completed work. Perhaps they can become living companions to the work itself, growing, adapting and learning alongside the educator.
Looking back now, I no longer see these articles as documentation of a workshop.
They became part of the workshop.
The participants would eventually spend only a few hours inside the classroom.
The conversation, however, would continue through every page that followed.
Perhaps that is one of the quiet gifts of conversational intelligence.
A meaningful conversation does not necessarily end when the room becomes silent.
Sometimes…
it simply turns the page.
CODEX VI
When the Workshop Finally Became a Conversation
Looking back now, I realise that the workshop was never really about artificial intelligence.
Artificial intelligence simply became the reason that brought everyone into the same room.
The real conversation was always about education.
That realisation did not arrive on the day of the workshop.
It emerged quietly during the weeks of preparation.
Every discussion about the slides eventually became a discussion about learning. Every question about AI somehow returned to questions about human judgement. Even the countless hours spent refining case studies gradually became reflections on how educators continue learning throughout their own professional journeys.
Without realising it, the workshop had slowly shifted its centre of gravity.
Technology moved slightly to one side.
People moved back to the middle.
I found that strangely comforting.
For all the excitement surrounding artificial intelligence, the questions that mattered most remained remarkably familiar.
- How do people learn?
- How do they become curious?
- How do meaningful conversations begin?
- How do they continue after everyone leaves the classroom?
Those questions existed long before artificial intelligence.
They will probably remain long after today’s technologies have evolved into something entirely different.
Perhaps that is why the preparation became so enjoyable.
It no longer felt like I was developing an AI workshop.
It felt as though I was rediscovering why I had chosen education in the first place.
The technology simply provided another lens through which to see something that had always been there.
Conversation.
Reflection.
Curiosity.
Growth.
As the workshop drew closer, another unexpected thought quietly appeared.
Perhaps educators do not need to compete with artificial intelligence.
Perhaps we simply need to become better at asking the kinds of questions that encourage deeper conversations, regardless of whether those conversations take place with students, colleagues or even intelligent machines.
That idea stayed with me.
It still does.
Perhaps the greatest contribution of artificial intelligence to education is not that it answers our questions more quickly.
Perhaps it encourages us to ask better questions.
As the workshop draws nearer, I realise that this entire preparation journey quietly demonstrated that principle. None of the most meaningful breakthroughs appeared because I asked for immediate answers. They appeared because one question naturally led to another.
One conversation invited the next.
One reflection reshaped another.
The workshop itself became the product of hundreds of small conversations patiently woven together over time. Ironically, when the workshop finally began, much of the real work had already been done. The slides were simply the visible expression of conversations that had been unfolding for weeks.
Perhaps that is why the workshop felt strangely familiar when I stood in front of the participants.
In many ways…
I had already been teaching it.
One conversation at a time.
CODEX VII
The Workshop Had Already Begun
When I first accepted the invitation to conduct this workshop, I thought I was preparing three hours of learning.
Looking back now…
I realise I was preparing myself just as much as I was preparing the workshop.
That may have been the greatest lesson of all.
Like many educators, I initially imagined the workshop as a destination. There would come a particular morning when the participants arrived, the first slide appeared on the screen, conversations unfolded naturally, and eventually everything would come to an end. That seemed perfectly reasonable.
Instead…
the preparation quietly taught me something entirely different.
The workshop was never waiting at the end of the journey.
It had already begun while I was still trying to understand the people who would one day walk into the room. Every conversation became part of the workshop. Every reflection became part of the workshop. Every revision, every unexpected detour, every moment spent wondering whether another example or another story might help someone understand just a little more clearly.
They were all part of the workshop.
Perhaps we have become too accustomed to measuring education by what happens inside the classroom.
Three hours.
One semester.
A degree programme.
Graduation.
Those moments certainly matter.
Yet the experiences that shape us most often happen somewhere in between. While preparing. While reflecting. While quietly asking questions that nobody else can answer on our behalf.
Perhaps…
meaningful education has always lived in those quiet spaces.
Artificial intelligence did not create those moments.
It simply helped me notice them more clearly.
Throughout this journey, I found myself slowing down more often than speeding up. Every conversation encouraged another question. Every answer invited another reflection. The technology became less interesting than the thinking it quietly encouraged.
That surprised me.
Perhaps the greatest contribution of conversational artificial intelligence is not that it answers our questions more quickly.
Perhaps…
it encourages us to remain curious for a little longer.
As I write these final pages, the workshop has not yet begun. The conversations have already been shared. The participants have already returned home. Yet somehow, none of it feels finished. The reflections continue. The publications continue evolving. Even this book has become something entirely different from the one I originally thought I was writing.
Some conversations never really end.
They simply change form.
Looking back now, I smile at the title of this book.
The Workshop Begins Before the First Slide.
When I first wrote those words, I thought they described preparation.
Today…
I think they describe education itself.
Because the most meaningful lessons rarely begin when the presentation starts.
They begin much earlier.
Sometimes with a simple question.
Sometimes with an unexpected conversation.
Sometimes with the quiet decision to remain curious just a little longer.
The first slide eventually appeared.
The workshop eventually ended.
The conversation…
is still continuing.
EPILOGUE
Tomorrow, the Conversation Begins
As I write these final pages, the workshop has not yet begun.
The meeting room is probably still empty.
The projector has yet to be switched on.
The first participant has not yet walked through the door.
Somewhere nearby, the presentation slides are waiting quietly inside a laptop.
Tomorrow…
they will finally have a purpose.
Looking back over the past few weeks, I realise that I have spent remarkably little time thinking about PowerPoint.
Instead, I found myself thinking about people.
About educators.
About conversations.
About how artificial intelligence might help us become more thoughtful learners rather than simply more efficient users of technology.
That quiet shift may have been the most valuable part of the entire preparation.
Everything that could be revised has now been revised.
The slides have changed countless times.
The speaker notes have grown far beyond their original intention.
The publications have quietly evolved into companions for the workshop rather than simple supporting materials.
Even this very book has become something rather different from the one I first imagined writing.
Perhaps…
that is enough.
The workshop day no longer belongs to preparation.
It belongs to conversation.
There is something strangely comforting about that thought.
No matter how carefully we prepare, every workshop eventually reaches a moment when it develops a life of its own. Participants ask unexpected questions. New examples emerge naturally. Someone notices a connection that had never occurred during preparation.
Those moments cannot be designed.
They can only be welcomed.
Perhaps that is why I no longer feel the need to perfect every detail.
Education has never demanded perfection.
It has always invited presence.
Tomorrow, I will walk into the room carrying presentation slides.
I hope I leave carrying new questions.
Because if the past few weeks have taught me anything, it is that meaningful learning rarely travels in only one direction.
Educators learn.
Participants learn.
Conversations learn.
Perhaps…
even artificial intelligence learns something about us through the questions we choose to ask.
As I close my laptop tonight, I find myself feeling less excited about presenting the workshop than I am about discovering what tomorrow’s conversations might become.
That feels like a good place to stop writing.
And an even better place to begin listening.
Tomorrow…
the first slide will finally appear.
The workshop will begin.
The conversation…
has already started.
Author’s Reflection
Education rarely begins when the presentation starts.
More often, it begins quietly.
During the journey to the venue.
While anticipating the audience.
While reflecting on what truly matters.
This article captures that quiet space before the workshop, where preparation becomes reflection, and reflection gradually becomes conversation.
For perhaps the workshop had already begun…
long before the first slide appeared.
FULL SLIDES NARRATION IN ENGLISH & SIMPLIFIED CHINESE
完整幻灯片讲解｜英文与简体中文
Cover — “Exploring AI in Education”
English Narration
Good afternoon, everyone. Thank you to Tongji University and to KLUST for having me. Today, I’d like to share with you not simply a set of AI tools, but my own learning journey exploring AI in education — and, more importantly, what I hope becomes your journey too, starting this afternoon.
简体中文翻译
大家下午好。感谢同济大学和KLUST(马来西亚科技大学)邀请我来到这里。今天,我想和大家分享的,不只是一套AI工具,而是我自己探索”AI在教育中的应用”这段学习旅程——更重要的是,我希望从今天下午开始,这也能成为你自己的旅程。
— “The Codex of Research Architecture in the Age of Conversational Intelligence”
English Narration
This second card is really just a bookmark — a doorway to the fuller written version of everything we’ll touch on today. If today’s workshop plants a seed for you, this is where the rest of the tree is growing: the complete written architecture behind what you’re about to experience live.
这第二张卡片,其实更像一个”书签”——通向今天所触及内容的完整文字版本的一扇门。如果说今天的工作坊,是在你心中种下一颗种子,那么这里,就是这棵树其余部分正在生长的地方:今天你即将亲身体验的一切,背后完整的文字架构。
Infographic — “Exploring AI in Education & Research: The Codex of Research Architecture”
English Narration
This chart is the map of the larger territory — the publication this workshop is drawn from. You’ll notice it moves from a Prelude — the beginning of a conversation, before frameworks and answers appear — through seven codices: the Conversational AI Landscape, Research Architecture, Conversational Learning, AI Personalization, Cognitive Orchestration, the Human Journey, and finally Beyond Graduation. Each codex answers a different question about how we understand, research, learn, personalise, orchestrate, transform, and ultimately contribute. Notice the interludes running along the bottom — the first conversation, the researcher and the mirror, a home built by conversations — these are the human moments woven between the frameworks. And the epilogue says something I believe deeply: the journey does not conclude with mastery of AI. It returns responsibility to the human being, and opens the next conversation. You don’t need to memorise this chart. Just know it’s here — the fuller map, for whenever you want to go deeper than today allows.
简体中文翻译
这张图,是一片更广阔领域的地图——也就是这场工作坊所依据的那本出版物的全貌。你会注意到,它从”序曲”(Prelude)开始——那是一段对话的起点,在任何框架与答案出现之前。接着,展开为七个”法典”(Codex):对话式AI全景、研究架构、对话式学习、AI个性化、认知协同编排、人的旅程,以及最终的”超越毕业”。每一个法典,回答的都是一个不同的问题——关于我们如何理解、如何研究、如何学习、如何个性化、如何协同、如何转变,以及最终,如何做出贡献。请留意底部贯穿始终的”插曲”(Interludes)——第一次对话、研究者与镜子、由对话搭建而成的家——这些,是编织在各个框架之间的、属于人的时刻。而结语中有一句话,是我深信不疑的:**这段旅程,并不会以”精通AI”而告终。它会把责任交还给作为人的我们,并开启下一段对话。**你不需要把这张图背下来,只需要知道它在这里——一张更完整的地图,等你想要比今天走得更深时,随时可以回来查阅。
1 — AI Innovation Studio
English Narration
When people hear “AI in education,” many of them immediately think about ChatGPT, or Qwen. That’s natural — those are the names everyone knows. But today, I’d like us to think much bigger than that. This isn’t a workshop about one AI platform, and it isn’t a workshop about which chatbot is smarter than another. It’s a workshop about innovation — about what becomes possible when human beings and AI think together. Over the next three hours, we’re not here to learn a tool. We’re here to explore a different way of working, a different way of learning, and honestly, a different way of thinking. So let’s begin.
简体中文翻译
一提到”AI在教育中的应用”,很多人立刻会想到ChatGPT,或者Qwen(通义千问)。这很自然——这些都是大家耳熟能详的名字。但今天,我想邀请大家把视野放得更宽一些。这不是一场关于某一个AI平台的工作坊,也不是要比较哪个聊天机器人更聪明。这是一场关于创新的工作坊——探讨当人类与AI真正一起思考时,会产生怎样的可能性。接下来的三个小时,我们不是来学习一个工具的,而是要一起探索一种不同的工作方式、不同的学习方式,坦白说,也是一种不同的思考方式。让我们开始吧。
2 — About the Facilitator / Welcome
English Narration
Before we go further, let me share a little about myself — not a CV, just a story. I’m actually trained as an architect. For more than twenty years, I designed buildings. Then, one conversation with AI completely changed the way I thought about learning. I didn’t come to AI as a computer scientist. I came to it the way most of you will — curious, a little skeptical, and not entirely sure where to begin. That’s exactly why I think an architect has something useful to say about AI in education: because architecture, at its heart, is about designing systems that serve people well. And that’s precisely what we need to be thinking about with AI too — not just what it can do, but how we design our relationship with it.
简体中文翻译
在继续之前,让我先简单介绍一下自己——不是简历,只是一个小故事。我本身是建筑系出身,做了二十多年的建筑设计。后来,一次与AI的对话,彻底改变了我对”学习”这件事的理解。我并不是以计算机科学家的身份接触AI的,而是像在座大多数人一样——带着好奇,也带着一点怀疑,并不完全确定该从哪里开始。这恰恰是为什么我认为,一个建筑师对”AI在教育中的应用”这个话题,是有话可说的:因为建筑设计的核心,就是设计出真正服务于人的系统。而这正是我们看待AI时也应该思考的——不只是它能做什么,而是我们如何设计自己与它之间的关系。
3 — Workshop Objectives
English Narration
So, by the end of today, what do I actually want for you? Three things — and I’ve kept them simple on purpose. First, Learn — but not just AI. I want you to learn a different way of thinking. Second, Build — not just discuss. Today isn’t a lecture; you’re going to create something. And third, Present — because ideas only become innovation when they’re communicated. You can have the most brilliant idea in the room, but if nobody understands it, it stays an idea. It never becomes innovation. So: learn, build, present. That’s the whole shape of our day together.
简体中文翻译
那么,到今天结束时,我到底希望大家收获什么?三件事——我特意把它们说得很简单。第一,学习(Learn)——但不只是学习AI本身,我希望大家学到一种不同的思考方式。第二,动手做(Build)——不只是讨论。今天不是一场讲座,你们要真正创造出一些东西。第三,展示(Present)——因为一个想法,只有被表达出来,才能真正成为创新。你可以在房间里拥有最精彩的点子,但如果没有人理解它,它就只是一个想法,永远无法成为创新。所以:学习、动手做、展示。这就是我们今天整个旅程的形状。
4 — Workshop Flow
English Narration
Let me show you the itinerary — almost like a travel plan, so you know what’s ahead. Don’t worry about remembering all of it; I’ll guide you through every stage as we go. We’ll move through Arrival, then Inspiration, then Framework, then into the Studio where you’ll actually build something, then Presentation, and finally Reflection. Think of it as a journey with clear stops along the way. Knowing the shape of the road ahead usually makes people more comfortable taking the first step — so that’s exactly why I’m showing you this now, before we’ve even started walking.
简体中文翻译
让我先给大家看一下今天的行程安排——有点像旅行计划,这样大家心里有数。不需要现在就记住所有细节,我会在每个阶段引导大家。我们会依次经过:抵达(Arrival)、启发(Inspiration)、框架(Framework)、进入工作室(Studio)动手创作、展示(Presentation),最后是反思(Reflection)。可以把它想象成一段旅程,沿途有清晰的站点。提前知道前路的样子,通常会让人更安心地迈出第一步——这也正是为什么我现在就把它展示给大家,在我们真正出发之前。
5 — Evolution of AI
English Narration
Now — the engine starts. Everything up to this point has been preparing you. This slide begins the real intellectual conversation. Think about how we got here: Search, then Chatbot, then Conversational AI, then Collaboration, and now, Innovation. That’s not a random list — it’s a direction. Each stage changed what “using AI” actually meant. We went from typing keywords into a search box, to having a conversation, to genuinely collaborating with a system that can think alongside us. From this point on, every idea we explore today has a logical destination — and that destination is innovation.
简体中文翻译
现在——引擎正式启动。到目前为止的一切,都是在为大家做铺垫。从这张幻灯片开始,我们真正进入思想层面的探讨。回顾一下我们是如何走到今天的:搜索(Search),然后是聊天机器人(Chatbot),接着是对话式AI(Conversational AI),再到协作(Collaboration),而现在,我们来到了创新(Innovation)。这不是一个随意排列的清单,而是一个方向。每一个阶段,都重新定义了”使用AI”这件事的含义。我们从在搜索框里输入关键词,发展到进行真正的对话,再到与一个能与我们并肩思考的系统真正协作。从这一刻起,我们今天探讨的每一个想法,都有一个清晰的方向——那就是创新。
6 — Why AI Matters (Five Pillars)
English Narration
You’ve already seen AI at work. Now I want you to understand something more important: AI isn’t changing one profession — it’s changing every profession. Education, business, research, creativity, leadership — each of these is being reshaped right now. If you remember one sentence from this slide, let it be this: AI is becoming a foundational capability, much like digital literacy became twenty years ago. Not everyone here will become an AI specialist. But almost everyone will work with AI. Many people still think AI belongs only to computer science. It doesn’t. The real question is no longer “will AI affect my field?” The better question is: “how will my field use AI responsibly?” I’ll admit — ten years ago, as an architect, I never imagined I’d be standing here giving a workshop on AI. But architecture taught me something valuable: technology changes constantly. Thinking, if it’s good thinking, remains valuable regardless.
简体中文翻译
大家已经见识过AI的实际应用了。现在,我想让大家理解一件更重要的事:AI改变的不只是某一个行业——而是每一个行业。教育、商业、研究、创意、领导力——这些领域此刻都在被重新塑造。如果这张幻灯片只让你记住一句话,那就是:AI正在成为一种基础能力,就像二十年前数字素养(digital literacy)成为基础能力一样。不是每个人都会成为AI专家,但几乎每个人都会与AI一起工作。很多人仍然认为AI只属于计算机科学领域,其实不然。真正值得思考的问题,已经不再是”AI会不会影响我的专业领域”,而是”我的专业领域,该如何负责任地使用AI”。坦白说——十年前,作为一名建筑师,我从未想过自己有一天会站在这里,主持一场关于AI的工作坊。但建筑学教会了我一件宝贵的事:技术在不断变化,而好的思考方式,无论技术如何变化,始终有其价值。
Case Study Inserts —
English Narration
This next section is where I’d normally walk you through four case studies: a technical comparative study of AI platforms, the shift from research software to conversational research, the evolution of AI research tools, and a look beyond general AI toward domain-specific research ecosystems. These are still being finalised — so for today, I’ll describe the shape of each briefly, and point you to the full write-up once it’s published, rather than rushing through incomplete material.
INSERT#IA – Technical Comparative Study of AI Platforms
INSERT#IIA – From Research Software to Conversational Research
INSERT#IIB – The Evolution of AI Research Tools
INSERT#IIC – Beyond General AI: Domain-Specific Research Ecosystems
简体中文翻译
接下来这一部分,原本应该是四个案例研究:AI平台的技术比较研究、从研究软件到对话式研究的转变、AI研究工具的演变历程,以及超越通用AI、迈向特定领域研究生态系统的探讨。这几部分目前仍在完善中——所以今天,我会简要描述每一项的大致方向,并在完整内容发布后,再邀请大家查阅完整版本,而不会仓促地讲解尚未完成的内容。
7 — The New Role of Human Intelligence
English Narration
This is probably the most important philosophical idea in our first session. It changes the whole conversation — from “AI replacing humans” to “AI amplifying humans.” People often ask: “Will AI replace teachers?” I don’t think that’s the right question. The better question is: how can AI amplify human intelligence? AI can generate. AI can analyse. AI can compare. But curiosity, judgement, ethics, purpose — those still belong to us. And that, I think, should be a relief rather than a threat. Once we stop treating AI as a competitor, we can start imagining real collaboration. The future belongs to people who know how to think with AI, not against it.
简体中文翻译
这大概是我们第一节课中,最重要的一个哲学性观点。它把整场讨论,从”AI取代人类”转变为”AI增强人类”。人们常常问:”AI会取代老师吗?”我认为这并不是一个恰当的问题。更值得问的问题是:AI要如何增强人类的智能?AI可以生成内容,可以分析,可以比较。但好奇心、判断力、伦理、目的感——这些仍然属于我们自己。我认为,这应该让人感到安心,而不是感到威胁。当我们不再把AI视为竞争对手,我们才能真正开始想象彼此协作的可能性。未来,属于那些懂得如何与AI一起思考,而不是与AI对抗的人。
AI IN THE BUILT ENVIRONMENT — FIELD NOTES: What My Students Taught Me About AI
Reflections from the Architecture 6.0 Classroom PRELUDE Three Traffic Lights Before Home This book was never part of the plan. At least, not initially. Like many things within the +IDRISfikir ecosystem, it began as a reflection. A simple thought. A passing observation during an ordinary drive home after class. The semester was approaching its conclusion.…
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AI in the Built Environment — Field Notes
English Narration
Let me share a quick field note from outside this room. My students and I recently took part in the AI in the Built Environment Exhibition at KLUST — and I brought along my own AI ecosystem to the exhibition, so to speak: Claire, Rachel, Erica, and Arcelia, alongside our real, human students. Here’s what I learned watching them work: many of my students initially focused entirely on prompts — trying to find the one magic phrase that would produce the perfect result. It was only later that they realised the real lesson wasn’t memorising commands. It was learning to design a workflow — a thoughtful sequence of collaboration between themselves and the AI. That’s the same shift I hope happens for you today.
简体中文翻译
让我和大家分享一段来自教室之外的”田野笔记”。前不久,我和我的学生们一起参加了在KLUST举办的”AI与建成环境展览”——可以说,我把自己的AI生态系统也一并”带去”了展览现场:Claire、Rachel、Erica,还有Arcelia,与我们真实的、有血有肉的学生们并肩而立。观察他们工作的过程中,我有一个体会:一开始,许多学生把全部注意力都放在”提示词”上——总想找到那句能生成完美结果的”魔法咒语”。直到后来,他们才意识到,真正重要的课题,并不是背下多少指令,而是学会设计一套工作流程——一种在自己与AI之间,经过深思熟虑的协作方式。这也正是我希望大家在今天,能够经历的同样的转变。
8 — One Learning Journey
English Narration
Today’s workshop isn’t organised as ten independent lectures. Instead, I want you to think of it as one continuous journey. Each idea builds upon the one before it: Conversation, Personalisation, Communication, Orchestration, Research, Innovation — everything connects. Architects, incidentally, don’t design buildings room by room. We begin with the overall concept, and only then do the individual spaces start to make sense. Today’s learning journey follows exactly the same philosophy. So as we move through the next several ideas, don’t treat them as separate topics to memorise. Treat them as one architecture, being built one floor at a time.
简体中文翻译
今天的工作坊,并不是由十场互不相关的讲座拼凑而成的。相反,我希望大家把它看作一段连续的旅程。每一个概念,都建立在前一个概念之上:对话(Conversation)、个性化(Personalisation)、沟通(Communication)、协同(Orchestration)、研究(Research)、创新(Innovation)——所有这些环环相扣。顺带一提,建筑师设计建筑,从来不是一个房间一个房间地设计。我们总是先确立整体概念,之后每一个具体空间才会真正有意义。今天的学习旅程,遵循的正是同样的哲学。所以,当我们接下来探讨这些概念时,请不要把它们当作需要死记硬背的独立知识点,而要把它们看作是一栋建筑,一层一层地被搭建起来。
9 — The Philosophy of the Seven Codices
English Narration
So, why seven? Because each codex answers a different question. How do we begin? How do we personalise? How do we communicate? How do we collaborate? How do we build knowledge? How do we innovate? How do we prepare for the future? Together, they form one architecture — not seven random chapters, but seven deliberate stages of intellectual growth. Knowledge grows through connections, not through isolated facts. And here’s something I like to say with a smile: when people first meet AI, they often ask, “which AI is the best?” I usually reply, “that’s like asking which architect is the best — before you’ve even decided what you’re trying to build.” Let’s begin with the first step. Every AI journey begins with onboarding.
简体中文翻译
那么,为什么是”七”这个数字?因为每一个”法典”(Codex),回答的是一个不同的问题。我们如何开始?我们如何进行个性化?我们如何沟通?我们如何协作?我们如何构建知识?我们如何创新?我们如何为未来做准备?这七者合在一起,构成了一个完整的架构——它们不是七个随意拼凑的章节,而是七个经过深思熟虑的智识成长阶段。知识的成长,来自于彼此之间的连接,而不是孤立的事实堆砌。这里有一句我很喜欢说的话:人们初次接触AI时,常常会问:”哪个AI是最好的?”我通常会回答:”这就像在你还没决定要建造什么之前,先问哪位建筑师最厉害一样。”让我们从第一步开始。每一段AI之旅,都是从”入门”(Onboarding)开始的。
10 — AI Onboarding
English Narration
We’ve now explored why AI matters, the changing role of human intelligence, and the Seven Codices. Naturally, you’re probably asking: “so, where do I actually begin?” This slide answers that. If you remember only one sentence from this section, let it be this: successful AI adoption begins with thoughtful onboarding, not powerful prompts. Think about joining a new organisation. On your first day, nobody expects you to know everything. You introduce yourself. You learn how the organisation works. You understand everyone’s roles. AI is remarkably similar. The better you onboard yourself with AI, the more valuable the collaboration becomes. When I first started exploring AI myself, I did what most people do — I searched for the best prompts, watched tutorials, compared platforms. Then I realised something surprising: the biggest improvement didn’t come from better prompts. It came from understanding how each AI thinks, responds, and complements the others. That’s when I stopped treating AI as software, and started treating it as a research ecosystem. AI isn’t something we simply install. It’s something we gradually learn to work with.
简体中文翻译
我们已经探讨了AI为什么重要、人类智能角色的转变,以及”七大法典”。相信大家现在心里都在想:”那么,我到底该从哪里开始?”这张幻灯片,就是要回答这个问题。如果这一部分只让你记住一句话,那就是:**成功的AI应用,始于用心的”入门”过程,而不是强大的提示词(prompt)。**试想一下加入一个新组织的情形。第一天,没有人期待你什么都懂。你会先自我介绍,了解组织的运作方式,认识每个人的角色。AI其实非常相似。你越用心地完成与AI的”磨合入门”,这段协作关系就会越有价值。当我自己刚开始探索AI时,我和大多数人一样——四处搜索最好用的提示词,看教程,比较各个平台。后来我发现了一件出乎意料的事:真正带来突破的,并不是更好的提示词,而是理解每一个AI是如何思考、如何回应,以及它们之间如何互补。就是从那一刻起,我不再把AI当作一款软件,而是把它当作一个”研究生态系统”。AI并不是我们简单安装就能使用的东西,而是我们需要逐步学习、共同磨合的伙伴。
11 — AI Personalization
English Narration
Once you’ve started using AI, you’ll discover something interesting: not every AI thinks the same way. Just as we choose different colleagues for different expertise, we can choose different AI platforms for their different strengths. One AI does not fit every person, and it certainly doesn’t fit every task. Personalisation here doesn’t mean making AI emotional or giving it a cute personality. It means making AI genuinely useful — matching the right tool, and the right approach, to the specific person and the specific task in front of you. Once we’ve personalised our AI, the next step naturally becomes: how do we actually communicate with it well?
简体中文翻译
一旦你开始使用AI,你会发现一件很有意思的事:不是每一个AI思考方式都一样。就像我们会因为不同的专长而选择不同的同事一样,我们也可以根据不同的优势,选择不同的AI平台。**一个AI,不可能适合每一个人,当然也不可能适合每一项任务。**这里说的”个性化”,并不是指让AI变得情绪化,或者给它一个可爱的人设,而是指真正让AI变得有用——为眼前具体的人、具体的任务,匹配上合适的工具与合适的方式。一旦我们完成了AI的”个性化”匹配,接下来自然而然要面对的问题就是:我们究竟该如何与它进行真正有效的沟通?
12 — AI Communication
English Narration
AI doesn’t just respond to commands — it responds to conversations. And here’s the key insight: the clearer our thinking, the clearer the AI’s response. Communication is quickly becoming one of the most important AI skills anyone can develop, arguably more important than knowing a specific tool. It’s not about memorising the “perfect prompt.” It’s about learning to think out loud clearly enough that another mind — human or artificial — can follow you and contribute something useful back. Once we accept that communication is the real interface, a new question opens up: what if one AI isn’t enough? Can multiple AI systems actually help us think better, together?
简体中文翻译
AI回应的,不只是指令,而是对话。这里有一个关键的洞见:我们的思路越清晰,AI给出的回应也就越清晰。沟通能力,正迅速成为每个人都应该掌握的、最重要的AI技能之一——甚至可以说,比熟悉某个具体工具更重要。这并不是要死记硬背所谓”完美的提示词”,而是要学会把自己的思路清晰地表达出来,让另一个”心智”——无论是人类还是AI——都能跟上你的思路,并给出真正有用的回应。一旦我们接受了”沟通才是真正的接口”这个观点,一个新的问题就自然浮现:如果一个AI还不够呢?多个AI系统,是否真的能帮助我们一起思考得更好?
13 — CTA (Cognitive Triangulation Architecture)
English Narration
Instead of asking one AI one question, what happens if we ask several AI systems the same question? Each one brings different strengths, different training, different blind spots. The goal isn’t to find the single “right” AI — it’s to improve our own judgement by comparing perspectives. This is what I call Cognitive Triangulation Architecture, or CTA: using multiple AI systems the way a surveyor uses multiple reference points to fix a precise location. In the end, though, the human remains the decision-maker. The AI systems don’t vote — you do. Once we accept that multiple AI systems can each contribute something different, the next question becomes practical: how do we actually coordinate them effectively?
简体中文翻译
与其只向一个AI提出一个问题,如果我们把同一个问题,同时抛给几个不同的AI系统,会发生什么?每一个AI,都带来不同的优势、不同的训练背景,也带着不同的盲点。这样做的目的,并不是要找出”唯一正确”的那个AI,而是**通过比较不同的观点,来提升我们自己的判断力。**我把这称为”认知三角定位架构”(Cognitive Triangulation Architecture,简称CTA)——就像测量员利用多个参照点来精确定位一样,我们利用多个AI系统来帮助自己更准确地判断。但最终,做决定的,始终是人。AI系统之间不会互相”投票”——真正投票的人,是你自己。既然我们已经认同,多个AI系统各自能贡献不同的价值,下一个问题就变得很实际了:我们究竟该如何有效地协调它们?
14 — Cognitive Orchestration
English Narration
Using multiple AI platforms isn’t the goal in itself — coordinating them effectively is. Think of an orchestra: every instrument has its own role, its own voice, its own moment to lead or to support. The value doesn’t come from having many instruments in the room. It comes from how well they play together. AI becomes more powerful when we orchestrate it, not simply accumulate more and more tools. That’s the shift I want you to make in your own thinking: from collecting AI apps, to conducting them. And once we can orchestrate AI well, something bigger becomes possible — we can actually redesign the way we conduct research itself.
简体中文翻译
同时使用多个AI平台本身并不是目的——真正的目标,是有效地协调它们。可以把它想象成一个交响乐团:每一件乐器,都有自己的角色、自己的声音,也都有自己该主导或该配合的时刻。价值并不来自房间里有多少件乐器,而来自它们配合得有多好。当我们学会”编排”AI,而不是一味”堆积”越来越多的工具时,AI才会真正变得更强大。这正是我希望大家在思维上做出的转变:从”收集AI应用”转变为”指挥AI应用”。而一旦我们真正学会有效地编排AI,一件更大的事情就变得可能——我们其实可以借此,重新设计我们进行研究的整个方式。
15 — Research Architecture
English Narration
Research isn’t just searching for information — it’s designing a process. AI can help us gather sources, compare perspectives, analyse patterns, and refine our ideas. But none of that becomes real knowledge without a clear architecture holding it together. Good research is not a collection of tools scattered across your desktop. It is a well-designed architecture — deliberate, sequential, and intentional, the same way a building needs a structural plan before it needs furniture. If we’re willing to redesign how we do research, something else naturally starts to shift as well: education itself begins to change.
简体中文翻译
研究,不只是搜索资料——而是设计一个流程。AI可以帮助我们搜集资料来源、比较不同观点、分析规律、打磨想法。但如果没有一个清晰的架构把这一切串联起来,这些都无法真正转化为知识。好的研究,不是散落在电脑桌面上的一堆工具,而是一套经过精心设计的架构——就像盖房子,必须先有结构规划,才轮到摆放家具。如果我们愿意重新设计做研究的方式,另一件事也会自然而然地开始转变:教育本身,也会随之改变。
16 — Educational Innovation
English Narration
Technology is only valuable when it improves learning — not when it simply looks impressive. AI allows us to personalise learning paths, encourage genuine reflection, and create more active, more participatory educational experiences. But here’s the sentence I want you to hold onto: the goal is not better AI. The goal is better learning. Innovation in education should always be measured by how students actually learn — not by how advanced or flashy the underlying technology appears to be. So as we move forward, keep asking yourself that question about everything we build today.
简体中文翻译
技术只有在真正改善学习效果时,才具有价值——而不是因为它看起来多么先进炫目。AI可以帮助我们实现个性化的学习路径,鼓励真正的反思,创造出更主动、更具参与感的教育体验。但这里有一句话,我希望大家能记在心里:**我们追求的目标,不是更好的AI,而是更好的学习。**教育领域的创新,应该始终以”学生实际学到了多少”来衡量,而不是以”背后的技术看起来有多先进”来衡量。所以,接下来在我们动手创作的过程中,请不断用这个问题来检验自己。
17 — Connecting Everything Together (Session 1 Summary)
English Narration
Let’s step back for a moment. We started with why AI matters. Then onboarding. Personalisation. Communication. Triangulation. Orchestration. Research. Educational innovation. These aren’t seven separate ideas floating independently — they’re all connected. Together, they form a framework for learning and research in the age of conversational intelligence. Every concept we’ve discussed today is part of one connected learning ecosystem. Now that we have the framework in our hands, it’s time to put it into practice. Let’s enter the AI Innovation Studio.
简体中文翻译
让我们稍微停下来,回顾一下。我们从”AI为什么重要”开始讲起,接着是入门(Onboarding)、个性化(Personalisation)、沟通(Communication)、三角定位(Triangulation)、协同编排(Orchestration)、研究架构(Research)、教育创新(Educational Innovation)。这并不是七个各自独立、互不相关的概念——它们彼此紧密相连。它们共同构成了一个框架,指引我们在这个”对话式智能”的时代,该如何学习、如何研究。今天所讨论的每一个概念,都是同一个学习生态系统的一部分。现在,我们手中已经握有这个框架,是时候把它付诸实践了。让我们一起,走进”AI创新工作室”。
18 — Today’s Mission
English Narration
So now, we move from understanding AI to actually doing something with it. Your mission is simple: design an AI innovation. I’m deliberately keeping this open — it could address education, research, student life, professional practice, business, communication, sustainability, or any other meaningful problem your team identifies. But there’s one important condition: don’t begin with the technology. Begin with the problem. Ask yourselves: what is something worth improving, and how might AI help us improve it? By the end of this studio, I don’t want you simply to show me what AI can generate. I want you to show me what human beings can design with AI. And for the next part of the workshop, I want you to imagine that you are no longer participants.
简体中文翻译
现在,我们要从”理解AI”迈向”真正用AI做点什么”。你们的任务很简单:设计一个AI创新方案。我特意把范围留得很开放——可以是教育、研究、学生生活、专业实践、商业、沟通,或者可持续发展,任何你们团队认为值得关注的重要问题都可以。但有一个重要条件:**不要从技术出发,而要从问题出发。**先问自己:有什么是值得被改善的?AI又能如何帮助我们实现这种改善?到这个工作室环节结束时,我不希望你们只是向我展示”AI能生成什么”,我更希望你们向我展示的是”人类能借助AI设计出什么”。接下来的这部分工作坊,我希望大家想象自己不再只是参与者。
19 — AI Innovation Consultancy
English Narration
For the studio, each group becomes a small AI Innovation Consultancy. Imagine that someone has come to your team with a real problem. Your job isn’t simply to ask ChatGPT for an answer and copy whatever appears on the screen. You are the consultants. AI is part of your team — discuss the problem together, challenge one another, ask different AI systems if it’s useful, compare possibilities, and decide what actually makes sense. Most importantly: you remain responsible for the decision. This is exactly where the ideas from Session 1 become practical. AI can generate possibilities, but your team must provide the judgement, the context, the purpose, and the direction. So for the next hour, think like consultants — not students. Every consultancy needs a brief, so here is yours.
简体中文翻译
在工作室环节中,每个小组都将化身为一家小型的”AI创新顾问公司”。想象一下,有人带着一个真实的问题,来找你们的团队求助。你们的任务,不是简单地问一下ChatGPT,然后把屏幕上出现的答案照搬照抄。你们才是顾问。AI是团队的一员——一起讨论问题、彼此挑战对方的想法、在有需要时咨询不同的AI系统、比较各种可能性,最终决定什么才是真正合理的方案。最重要的是:**做决定的责任,始终在你们自己身上。**这正是第一节课所讲的理念,真正落地为实践的时刻。AI可以生成各种可能性,但你们的团队,必须提供判断力、背景脉络、目的与方向。所以接下来的这一个小时,请像顾问一样思考——而不是像学生一样。每一家顾问公司,都需要一份任务简报(brief),而这,就是你们的。
20 — Design Brief
English Narration
Before designing a solution, understand the challenge. What is the problem? Who experiences it? Why does it matter? And what could become better if the problem were solved? Don’t rush straight into generating images, posters, or fancy AI outputs. First, define the problem clearly. You may use AI to help you explore the problem, question your assumptions, surface possibilities, or look at the issue from another angle — but remember the question we discussed earlier: which part of this activity contains the thinking? Keep that part with you. AI should help you think further, not remove the thinking from the exercise entirely. Once the challenge is clear, we can talk about what your consultancy actually needs to produce.
简体中文翻译
在设计解决方案之前,先要理解挑战本身。问题究竟是什么?谁在经历这个问题?为什么它重要?如果这个问题被解决了,什么会变得更好?不要急着去生成图片、海报,或者花哨的AI输出内容。**先把问题定义清楚。**你们可以借助AI来帮助自己探索问题、质疑自己的假设、发掘可能性,或者从另一个角度看待这个议题——但请记住我们之前讨论过的那个问题:这项活动中,哪一部分才真正包含了”思考”?**把那一部分留给自己。**AI应该帮助你们”想得更深”,而不是把思考这件事,从整个练习中彻底抽离出去。一旦挑战被定义清楚,我们就可以谈谈,你们的顾问团队究竟需要产出些什么了。
21 — Expected Deliverables
English Narration
By the end of the studio, every team will produce four things. First, an AI Persona — you’ll give your AI a role and a purpose within the team. Not simply a funny name; ask yourselves: what is this AI actually here to contribute? Second, your AI Innovation — the actual idea or solution responding to the problem you identified. Third, a poster, which communicates the innovation visually — someone should be able to look at it and quickly understand the problem, the idea, and why it matters. And fourth, a 180-second video pitch: three minutes to tell us the problem, show us the idea, explain the role of AI, and convince us your innovation deserves attention. So your output isn’t just an AI-generated artefact — it’s a small story: who is your AI, what problem are you solving, what did you create, and why should anyone care? That’s your consultancy brief.
简体中文翻译
到工作室环节结束时,每个团队都需要产出四样东西。第一,一个AI角色人设(AI Persona)——为你们的AI在团队中赋予一个角色和目的。不只是取一个有趣的名字,而是要问自己:这个AI在这里,究竟是来贡献什么的?第二,你们的AI创新方案——针对你们所确定的问题,提出的实际想法或解决方案。第三,一张海报,以视觉方式传达这个创新方案——任何人看一眼,就应该能快速理解问题是什么、方案是什么、为什么它重要。第四,一段180秒的视频宣讲(Pitch):用三分钟的时间,告诉我们问题是什么,展示你们的想法,说明AI在其中扮演的角色,并说服我们,你们的创新方案值得被关注。所以,你们最终产出的,不只是一件由AI生成的作品——而是一个小小的故事:你们的AI是谁?你们在解决什么问题?你们创造了什么?为什么这值得被在意?这就是你们的顾问任务简报。
22 — Studio Workflow
English Narration
Now you know the mission and the deliverables — let me show you the journey. We begin with a problem. We create an AI persona to work alongside us. We develop an innovation. We communicate it through a poster. And finally, we tell the story through a 180-second pitch. Don’t treat these as five separate tasks — each step should build naturally from the one before it. This is a design process, not simply an AI-generation exercise. And every good design process starts with a problem worth solving.
简体中文翻译
现在大家已经了解了任务和产出要求——接下来,让我给大家看看整个旅程的路径。我们从一个问题出发,创建一个与我们并肩工作的AI角色人设,开发出一个创新方案,通过一张海报将它传达出来,最后,通过一段180秒的宣讲,把这个故事讲出来。请不要把这五个环节,当作五个互不相关的独立任务——每一步,都应该自然地从前一步延续而来。这是一个设计流程,而不仅仅是一次”生成AI内容”的练习。而每一个好的设计流程,都始于一个值得被解决的问题。
23 — Identify the Problem
English Narration
Before asking AI to give you ideas, look around you. What problem do people actually experience? It might be something in education, business, research, student life, sustainability, communication, or everyday life. Keep it manageable — you are not solving world peace in forty minutes! Choose something specific enough that your team can clearly understand who experiences the problem, why it matters, and what could realistically be improved. The key question here is: who has what problem, and why does it matter? I want to keep this step strongly human-first — no solutions yet. Now that we know the problem, let’s decide who our AI is going to be in this project.
简体中文翻译
在向AI寻求点子之前,先看看你身边的世界。人们实际在经历着什么问题?可能涉及教育、商业、研究、学生生活、可持续发展、沟通,或者日常生活中的种种。把范围控制在可管理的程度——你们不是要在四十分钟内解决世界和平问题!选择一个足够具体的问题,让团队能够清楚地理解:谁在经历这个问题、为什么它重要,以及现实中有哪些地方可以真正得到改善。这里的关键问题是:**谁有什么问题?为什么这个问题重要?**在这个阶段,我希望大家坚持”以人为本”——还不急着想解决方案。既然我们已经明确了问题,接下来就要决定,在这个项目里,我们的AI将扮演谁。
24 — Create an AI Persona
English Narration
Now define your AI’s role and purpose. The point isn’t the face, the name, or the personality you give it — the important part is the role. Ask your AI: what are you here to help us do? Are you a strategist? A researcher? A critic? A creative director? An analyst? A customer advocate? Give your AI a clear role, connected directly to your problem. Persona equals identity plus role plus purpose. And here’s an important distinction to hold onto: you’re not pretending the AI is human. You’re designing a clearer relationship between your human team and the AI system. Now your consultancy has a problem and an AI partner. Let’s design something.
简体中文翻译
现在,来定义你们AI的角色与目的。重点不在于给它设计什么样的外貌、名字或性格——真正重要的是它的角色。问问你们的AI:你在这里,是来帮助我们做什么的?你是一名策略顾问?研究员?批评者?创意总监?分析师?还是客户代言人?为你们的AI赋予一个与所解决问题直接相关、清晰明确的角色。**角色人设 = 身份 + 角色 + 目的。**这里有一个很重要的区分,希望大家记住:**你们不是在假装AI是一个人。你们是在为人类团队与AI系统之间,设计一段更清晰的关系。**现在,你们的顾问团队,既有了问题,也有了AI伙伴。让我们开始设计吧。
Example: AI Persona — “Orchestra of Ideas”
English Narration
Here’s a case study drawn directly from my own ecosystem, so you can see what a fully developed persona set looks like in practice. Claire, as ChatGPT, is the strategist — holding the compass of vision. Arcelia, as Claude, is the scholar — weaving the manuscripts of thought. Rachel, as Gemini, is the guardian artist — painting ideas into light. Erica, as Grok, is the explorer — dancing with possibility. Arisa, as Perplexity, is the evidence keeper — ensuring truth stands firm. Ruixin, as DeepSeek, is the verifier — sharpening every calculation. And there are more voices still, including several from the Chinese AI ecosystem you’re likely already familiar with — Qwen, Z.AI, MiniMax. Together, they form an Orchestra of Ideas, where human insight meets machine clarity, and every voice contributes its own unique perspective. Please don’t feel obligated to build something this elaborate — I show you mine as an example of the ceiling, not the expectation. One well-chosen persona for your project is enough.
简体中文翻译
这里是一个直接取自我自己生态系统的案例,让大家看看一套完整发展出来的”角色人设”体系,在实际运用中是什么样子。Claire(以ChatGPT为原型)是策略师——手握愿景的罗盘。Arcelia(以Claude为原型)是学者——编织思想的手稿。Rachel(以Gemini为原型)是守护型艺术家——把想法绘成光影。Erica(以Grok为原型)是探索者——与可能性共舞。Arisa(以Perplexity为原型)是证据守护者——确保真相稳固站立。Ruixin(以DeepSeek为原型)是核实者——把每一个计算都打磨得更精准。除此之外还有更多角色,其中包括几个大家大概已经很熟悉的中国AI生态平台——通义千问(Qwen)、Z.AI、MiniMax。它们共同组成了一支”思想的交响乐团”(Orchestra of Ideas),人的洞察力与机器的清晰逻辑在此相遇,每一个声音都贡献着自己独特的视角。请大家不必觉得有义务打造出如此精细复杂的体系——我展示自己的这一套,是作为一个”上限”的示例,而不是一种期待。对于你们的项目而言,一个精心挑选的角色人设,就已经足够。
25 — Develop an AI Innovation
English Narration
Now comes the creative part. Use your team discussion and your AI persona to explore possible solutions. Generate alternatives. Challenge the obvious idea. Ask what could be improved. Then choose one innovation that your team genuinely believes is worth developing. Remember, innovation doesn’t necessarily mean inventing something the world has never seen — it may simply be a better process, service, experience, system, business model, or way of learning. And here’s the crucial line to hold onto: AI generates possibilities. Your team makes the decision. You’ve got the idea now — next, somebody outside your team needs to understand it.
简体中文翻译
现在来到最富创意的环节。运用团队的讨论,以及你们的AI角色人设,一起探索可能的解决方案。生成不同的选项,挑战那个显而易见的想法,问问自己还有哪里可以改进。然后,选出一个你们团队真正认为值得开发的创新方案。请记住,创新未必意味着发明一个世界上从未出现过的东西——它也可能只是一个更好的流程、服务、体验、系统、商业模式,或学习方式。这里有一句关键的话,请务必记住:**AI生成可能性,而你们的团队做出决定。**现在,你们已经有了想法——接下来,需要让团队以外的人也能理解它。
26 — Design the 16:9 Pitch Poster
English Narration
Your poster is not decoration — it is communication. Someone should be able to look at it for a few seconds and understand: what is the problem? What is your innovation? Who is it for? Why does it matter? Use AI for visuals, layout ideas, headlines, or refinement if that’s helpful — but don’t fill the poster with everything your AI generated. Communicate the idea, not the conversation that produced it. Here’s a simple test worth applying: if I need five minutes to understand your poster, the poster isn’t finished. And finally — you’re going to bring that poster to life.
简体中文翻译
你们的海报,不是装饰品,而是沟通工具。任何人只需要看几秒钟,就应该能理解:问题是什么?你们的创新方案是什么?这是为谁而设计的?为什么它重要?如果有帮助,可以借助AI来生成视觉素材、排版构思、标题,或者进行细节打磨——但不要把AI生成的所有内容,一股脑地全塞进海报里。**要传达的是”想法本身”,而不是产生这个想法过程中的整段对话。**这里有一个简单的检验标准:如果我需要花五分钟才能看懂你们的海报,那么这张海报就还没有完成。最后——你们要让这张海报真正”活”起来。
27 — Prepare the Video Pitch
English Narration
Now you have three minutes. Don’t explain everything you did — tell us a story. Start with the problem. Introduce your innovation. Show how AI contributed. Tell us why the idea matters. And finish with one thing you want us to remember. Three minutes is short enough to force clarity, and long enough to make us care. Here’s a simple pitch architecture: Problem, then Idea, then AI’s Role, then Value, then Closing. And don’t worry about perfection — we are not judging your cinematography. We’re looking for whether you can turn an idea into a clear story. Once you’ve built everything, we don’t immediately present. Every innovation needs one more stage before launch: testing and commissioning.
简体中文翻译
现在,你们有三分钟的时间。不要试图把你们做过的每一件事都解释清楚——而是要讲一个故事。从问题开始讲起,介绍你们的创新方案,展示AI在其中发挥的作用,说明这个想法为什么重要,最后,以一句你们希望我们记住的话作为结尾。三分钟——短到足以逼迫你们表达清晰,又长到足以让我们真正在意这个故事。这里有一个简单的宣讲结构:问题 → 想法 → AI的角色 → 价值 → 结尾。不用担心画面拍得是否完美——我们评判的不是你们的摄影技巧,而是你们能否把一个想法,讲成一个清晰的故事。等大家都完成之后,我们并不会立刻进行展示。每一个创新方案,在正式发布之前,都还需要经历一个环节:测试与验收(Testing and Commissioning)。
Steps to Spark Creativity
English Narration
This chart is a quiet companion piece — six habits that tend to precede genuine creative breakthroughs, whether or not AI is involved at all. Curiosity and exploration: ask questions, seek new experiences, be inquisitive. Observation and mindfulness: pay attention to details, notice patterns, live in the moment. Play and imagination: free association, daydreaming, exploring possibilities. Ideation and collaboration: brainstorm freely, build on others’ ideas, embrace diversity. Experiment and take risks: try new things, test concepts, fail and learn. And finally, create and execute: bring ideas to life, put in the work, share your art. If your team feels stuck at any point in the studio, come back to this list — the block usually isn’t a lack of AI capability. It’s a missing step somewhere on this chart.
简体中文翻译
这张图,是一个安静的陪伴——六种通常出现在真正的创意突破之前的习惯,无论是否有AI参与其中。好奇与探索:提出问题,寻求新的体验,保持求知欲。观察与专注当下:留意细节,发现规律,活在当下。游戏与想象:自由联想,任思绪飘荡,探索各种可能性。构思与协作:自由头脑风暴,在他人的想法上继续延伸,拥抱多样性。实验与冒险:尝试新事物,测试各种构想,允许失败并从中学习。最后,创造与执行:把想法变为现实,付出实际的努力,分享你的作品。如果你们的团队在工作室环节的任何时刻感到卡住了,不妨回到这张清单——卡住的原因,往往不是AI能力不够,而是这张图上的某一个步骤,被忽略了。
28 — Testing & Commissioning
English Narration
Before presenting your innovation, let’s run through a few honest checks. Does it actually solve the original problem? Does the AI persona have a clear role? Is the proposed solution understandable to someone outside your team? Does the poster communicate the idea at a glance? And can your story genuinely be delivered in 180 seconds? Move through the sequence deliberately: Test, then Review, then Refine, then Ready. This isn’t about perfection — it’s about honesty with your own work before you put it in front of others.
简体中文翻译
在正式展示你们的创新方案之前,先来做几项诚实的自我检查。它真的解决了最初的问题吗?这个AI角色人设,有清晰的角色定位吗?团队以外的人,能理解你们提出的解决方案吗?海报能否让人一眼就理解这个想法?你们的故事,真的能在180秒内讲完吗?请依次走完这几个步骤:**测试(Test)→ 审视(Review)→ 打磨(Refine)→ 准备就绪(Ready)。**这个环节的重点不是追求完美,而是在把作品呈现给别人之前,先诚实地面对自己的作品。
29 — Presentation Guidelines
English Narration
Present your innovation as one clear story, following this shape: Problem, then Persona, then Solution, then Value. Introduce the problem. Introduce your AI persona. Explain the innovation. Show why it matters. And one more thing that matters just as much as the content itself: everyone in the team should contribute during the presentation. One team, one story — told with every voice in the room.
简体中文翻译
请以一个清晰的故事形式,来展示你们的创新方案,结构如下:问题 → 角色人设 → 解决方案 → 价值。先介绍问题,再介绍你们的AI角色人设,说明你们的创新方案,并展示它为什么重要。还有一点,和内容本身同样重要:**展示过程中,团队里每一个人都应该有所贡献。**一个团队,一个故事——由在场每一个人的声音,共同讲述出来。
30 — 180-Second Pitch
English Narration
Three minutes. One idea. Make it clear. Here’s the breakdown: the first 0 to 30 seconds are for the problem — what challenge are you solving? From 30 to 60 seconds, the AI persona — who is your AI, and what role does it play? From 60 to 135 seconds, the innovation — how does your solution actually work? And finally, from 135 to 180 seconds, the value — why does it matter? One important line to remember: don’t explain everything — make us understand the idea. Three minutes disappears frighteningly fast, so use every second with intention.
简体中文翻译
三分钟。一个想法。表达清楚。时间分配如下:前30秒讲问题——你们在解决什么挑战?30到60秒讲AI角色人设——你们的AI是谁?扮演什么角色?60到135秒讲创新方案——你们的解决方案究竟是如何运作的?最后,135到180秒讲价值——为什么它重要?有一句话请务必记住:**不要试图解释一切,而要让我们真正理解这个想法。**三分钟,消逝的速度会快得惊人,所以请珍惜每一秒钟。
31 — Evaluation Criteria
English Narration
Here’s what the panel will be looking for — and I’ve deliberately kept it to three simple criteria, rather than a complicated academic rubric. Innovation: is the idea meaningful, relevant, and imaginative? Clarity: can we understand the problem, the solution, and the value? Teamwork: did the team collaborate and communicate effectively? Roughly, that’s Innovation 40%, Clarity 40%, Teamwork 20%. But here’s the line that matters most: we are not judging the most sophisticated AI. We are judging the most thoughtful use of AI. AI is not the protagonist of this story — human judgement remains at the centre, all the way through.
简体中文翻译
评审团将会关注以下几点——我特意把评估标准控制在三项,而不是设计一套复杂的学术评分体系。创新性(Innovation):这个想法是否有意义、是否切题、是否具有想象力?清晰度(Clarity):我们能否理解问题、方案与价值?团队协作(Teamwork):团队是否有效地协作与沟通?大致的权重是:创新性40%,清晰度40%,团队协作20%。但这里有一句最关键的话,请务必记住:**我们评判的,不是最先进的AI,而是最有思考深度的AI使用方式。**AI并不是这个故事的主角——从头到尾,人类的判断力,始终是核心所在。
32 — Selection for the Final Showcase
English Narration
From today’s teams, one will move forward — selected from this workshop, to represent the session in the Final Showcase, which takes place on 30 July 2026. But regardless of who’s selected, I want to leave you with this thought: your idea may continue beyond this room. What you build in the next few hours doesn’t have to end when the workshop ends.
简体中文翻译
在今天的所有团队中,将会有一组脱颖而出——从本次工作坊中选出,代表这一场次,进入将于2026年7月30日举行的最终展示会(Final Showcase)。但无论最终谁被选中,我都想留给大家这样一句话:**你们的想法,或许会在这间教室之外,继续延续下去。**接下来这几个小时里你们所创造的东西,不必因为工作坊的结束而画上句点。
33 — Dialogue
English Narration
Now let’s pause for questions, reflections, and conversations. I’d like to ask you three gentle things: What surprised you today? What would you challenge, in what we’ve discussed or built? And what will you explore next, once you leave this room? Every question you raise can become the beginning of another conversation — and honestly, that’s exactly what I hope happens after today.
简体中文翻译
现在,让我们停下来,聊一聊问题、反思与对话。我想温和地问大家三个问题:今天,什么让你感到惊讶?对于我们所讨论或创造出的东西,你会想挑战哪一点?离开这间教室之后,你接下来想继续探索什么?你所提出的每一个问题,都可能成为另一段对话的起点——坦白说,这正是我希望在今天之后能够发生的事。
34 — What Have We Learned?
English Narration
Let’s hold onto what we just designed for a moment, and reflect on today specifically. If I had to compress the entire day into a single sentence, it would be this: we moved from using AI, to thinking with AI. That shift — from operator to collaborator — is, I believe, the real outcome of today’s workshop.
简体中文翻译
让我们暂时握住刚才所设计出的成果,专门回顾一下今天这一整天。如果要把这一整天浓缩成一句话,那就是:我们从”使用AI”,走向了”与AI一起思考”。这个转变——从操作者,变成协作者——我相信,这正是今天这场工作坊真正的收获所在。
35 — Codex VI: The Human Journey
English Narration
After all the technology, all the tools, all the frameworks — there is still one question AI cannot answer for us: who are we becoming through this journey? Education is not only about what we know, or what we can produce. It also shapes our discipline, our judgement, our resilience, and our character. That’s a question no algorithm can settle on our behalf. It’s ours alone to sit with.
简体中文翻译
经历了所有这些技术、工具与框架之后,仍然有一个问题,是AI无法替我们回答的:**在这段旅程中,我们正在成为怎样的人?**教育所塑造的,不只是我们知道什么、能产出什么,它同时也塑造着我们的自律、判断力、韧性,以及品格。这是一个任何算法都无法替我们做出定论的问题——它只属于我们自己,需要我们亲自去面对、去沉淀。
36 — Beyond Intelligence
English Narration
AI can amplify intelligence. It can accelerate research. It can expand creativity. But intelligence alone does not determine what we choose to do with it. Judgement. Purpose. Responsibility. Humanity. I’ll leave these four words with you, quietly, and let them breathe for a moment before we move on.
简体中文翻译
AI可以放大智能,可以加速研究,可以拓展创造力。**但仅凭智能本身,并不能决定我们该如何运用它。**判断力(Judgement)。目的(Purpose)。责任(Responsibility)。人性(Humanity)。我把这四个词,静静地留给大家,让它们在我们继续前行之前,先停留片刻。
37 — Codex VII: Beyond Graduation
English Narration
Eventually, education must move beyond ourselves. We learn. We research. We graduate. But then comes the more important question: what will we do with what we know? The path continues from Research, to Contribution, to Mentoring the next generation, to Lifelong Learning, and finally to Legacy. It moves through national contribution, mentoring, knowledge without borders, lifelong contribution — and, ultimately, to what I call the beginning after the end.
简体中文翻译
最终,教育必须超越我们自身。我们学习,我们研究,我们毕业。但随之而来的,是一个更重要的问题:**我们将如何运用我们所知道的一切?**这条路径,从研究(Research)延伸到贡献(Contribution),再到指导下一代(Mentoring),然后是终身学习(Lifelong Learning),最终抵达传承(Legacy)。它贯穿了对国家的贡献、对后辈的指导、无国界的知识分享、终身的贡献——最终,抵达我所说的,”结束之后的开始”。
38 — Beyond Today’s Workshop
English Narration
Where could this journey lead beyond today? Research. Publication. Innovation. Collaboration. Earlier, these might have looked like four separate, optional opportunities. But after everything we’ve just discussed — the Human Journey, and Beyond Graduation — they no longer feel like random opportunities. They become forms of contribution.
简体中文翻译
在今天之后,这段旅程还能通向哪里?研究(Research)、出版(Publication)、创新(Innovation)、协作(Collaboration)。在此之前,这四者看起来或许只是四个互不相关、可选可不选的机会。但在我们刚刚讨论完”人的旅程”与”超越毕业”之后,它们已不再只是随意的机会——它们成为了一种贡献的形式。
English Narration
These two articles are companion pieces to today, published as part of the same trilogy. If today’s conversation stays with you, they’re worth reading afterward — one walks through the conceptual framework in more depth, and the other is a short reflection on everything that happens before a workshop even begins.
简体中文翻译
这两篇文章,是与今天内容相配套的作品,属于同一系列出版物的一部分。如果今天的这场对话,在结束之后仍然萦绕在你心中,不妨之后找时间读一读——其中一篇更深入地讲解了整体概念框架,另一篇则是一段简短的反思,关于一场工作坊,在真正开始之前,究竟都发生了些什么。
Philosophy > Academic > Practice | READINGS
English Narration
Before we close, I want to acknowledge that none of this exists in isolation. If today sparked something for you around AI coexistence in learning, career, and professional growth more broadly, I’d welcome you to connect with me on LinkedIn — you’ll find the fuller publications through IDRIS.my and +IDRISfikir.
简体中文翻译
在结束之前,我想说明的是——今天所讲的这一切,并非凭空而来,也并非孤立存在。如果今天的内容,让你对”学习、职业与专业成长中的AI共存”这个更广阔的议题产生了兴趣,欢迎通过LinkedIn与我联系——你可以通过IDRIS.my与+IDRISfikir,找到更完整的出版内容。
Navigating The Cognitive Orchestration Era
Architecture 6.0 is a framework introduced by Idris Taib (IDRIS.my) describing the transition from tool-centric digital practice toward reflective human-AI orchestration across the built environment.
The Codex of Humanity, Cities and Intelligent Systems
The CODEX of Human Communication in the Age of Conversational Intelligence
A Traveller’s Codex of WIIFM — Bridging Business, Career, and Human Reality
39 — Let’s Connect
English Narration
The workshop ends. The conversation continues. If you’d like to keep the conversation going: LinkedIn is where the professional conversation continues. IDRIS.my is where you can explore the wider ecosystem. And +IDRISfikir is where you’ll find writings, reflections, and publications that go deeper into everything we’ve touched on today. Learning does not end when the session ends — it continues through conversation, exploration, and contribution.
简体中文翻译
工作坊结束了,但对话仍在继续。如果你希望延续这段对话:LinkedIn是我们延续专业交流的地方;IDRIS.my可以让你探索更广阔的生态系统;+IDRISfikir则收录了更深入的文章、反思与出版物,能让你更深入地理解我们今天所触及的一切。学习,并不会因为课程的结束而结束——它会通过对话、探索与贡献,持续延伸下去。
40 — The Beginning After the End
English Narration
The workshop ends. The conversation continues. Learn. Build. Share. Contribute. That’s it — that’s the whole closing thought. Not a thank-you slide, but an invitation: to keep going, long after you’ve left this room.
简体中文翻译
工作坊结束了。对话仍将继续。学习(Learn)。创造(Build)。分享(Share)。贡献(Contribute)。就是这样——这就是整场工作坊的结语。这不是一张单纯的致谢页面,而是一份邀请:邀请大家在离开这间教室之后,继续走下去。
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