Hello, this is Hamamoto from TIMEWELL. As AI redefines every structure in society, how should university education change? Today I want to share my honest thinking about what that future looks like.
We Are Standing at an Educational Crossroads
- We are in the middle of a historic turning point. The evolution of generative AI carries an impact comparable to the steam engine or the internet — powerful enough to rewrite the operating system of society at its foundations. A future where white-collar work is replaced by AI, or where collaboration with AI is simply the norm, is no longer science fiction. In such a time, there is no reason for university education — the institution that develops future leaders — to remain unchanged.
An educational model that only transmits knowledge is rapidly losing its value. Information is instantly available by asking AI. What today's students need isn't the ability to accept information at face value — it's the practical wisdom to use that information as a tool and work toward solutions for the complex problems of the real world.
I am involved in AI education as a specially appointed associate professor at Shinshu University, while also supporting many companies' AI adoption as the CEO of TIMEWELL. From both vantage points, I've come to see an enormous gap between the educational environment and the business environment. The kind of people companies need and the kind of people universities are producing are far too distant from each other. Unless this gap is closed, Japan risks falling entirely behind in the global AI competition.
The new model of university education for the AI era that I believe in: one where students immerse themselves deeply in problem-solving and emerge as leaders who can independently command AI agents and create value.
Looking globally, MIT incorporated AI literacy as a required course for all departments in 2024. The National University of Singapore requires all students to complete hands-on AI projects. If Japanese universities fall behind this wave, they will definitively lose their competitive position in the global talent market. The urgency demands action now.
Deep Immersion in Problem-Solving — Thorough Project-Based Learning
The core of education going forward is simply this: dramatically increase the time students spend grappling with real problems. Boldly reduce time spent passively listening to lectures, and place project-based learning — PBL — squarely at the center of the curriculum, with students working in teams on raw, real-world challenges.
At TIMEWELL, which I lead, our vision is to build the world's #1 infrastructure for human ambition. We support many companies in creating new businesses and developing internal entrepreneurs. During my time at Panasonic, I ran an internal entrepreneur development program called "CHANGE by ONE JAPAN" for five years, supporting over 450 internal entrepreneur candidates from companies including Toyota, NTT, Honda, and SoftBank. The lesson was always the same: innovation doesn't emerge from clean conference rooms. It only germinates when you're in the field, engaging with the actual thing, listening to what customers are really struggling with.
University education is exactly the same, I believe. Depopulated communities facing decline, startups struggling with cash flow, global environmental challenges. In front of these kinds of problems, students should throw themselves fully into the field — conducting repeated interviews with stakeholders, getting their hands on the real substance of the problem. What they submit as solutions shouldn't be a polished report. It should be a working prototype of a product that customers can actually interact with, or a concrete service model. That's what they should be evaluated on — the quality and effectiveness of what they built.
On a personal note: when I was on a short-term assignment in Silicon Valley, I had the opportunity to visit Stanford's d.school. Students were working on real challenges brought in from companies, producing prototypes in a matter of weeks. The professors weren't "teaching" in any conventional sense. They posed questions, gave feedback, stimulated student thinking. That scene remains the foundation of my educational philosophy today.
| Traditional Education Model | PBL-Centered Education Model |
|---|---|
| Core is knowledge input | Core is output through problem-solving |
| Teachers transmit knowledge | Teachers accompany as coaches and facilitators |
| Evaluated by test scores and report content | Evaluated by prototype quality and customer feedback |
| Classrooms and libraries as learning spaces | Company sites, communities, and NPOs as learning spaces |
Assignments and coursework need to be redesigned from the ground up. Not "read Chapter X of the textbook and write a report" — but "interview five actual customers and design a product concept from those insights," or "build a prototype, run user testing, and report what you learned." Field-grounded, practical challenges should be central. Evaluation criteria should reflect what the business world actually measures: quality of customer interviews, prototype completion, user feedback. I think it's time to retire the era of ranking students by exam scores.
When I actually introduced PBL into my courses at Shinshu University, many students were confused at first. "Tell me the right answer." "I don't know what I'm supposed to do." Those reactions came up. But as they went into the field and had conversations with real people, you could watch their eyes change. Thinking for themselves, building with their own hands, communicating in their own words — that experience transforms students at a fundamental level.
Mastering AI-Driven Development — From Vibe Coding to Agentic Engineering
Creating value in the AI era requires more than knowledge of existing academic disciplines. A new kind of expertise is needed to wield AI freely — not just operating tools, but a deep understanding and practical capability to use AI as a thinking partner that extends your own ability.
In the software development world, "vibe coding" — proposed by OpenAI co-founder Andrej Karpathy in 2025 — spread rapidly. Applications can be built by giving AI instructions in natural language. A democratization of development. That same year, AI agents like Claude Code and Devin emerged one after another, making AI-driven development environments accessible to anyone for roughly $20/month in subscription costs.
In 2026, this has evolved into "agentic engineering." It's no longer about giving AI instructions. AI agents autonomously write code, run tests, and deploy. The human role is shifting toward deciding what to build and evaluating the quality of what AI generates.
The ability to clearly define the specification of what you want to achieve, give accurate instructions to AI agents, and evaluate and refine their output — this now determines an engineer's value more than the ability to write code line by line. University programming education needs to be redesigned from scratch to match this reality. Rather than teaching coding syntax, teach problem-solving through collaboration with AI. I want to see this transition happen as quickly as possible.
In practice, when I introduce AI-driven development in my Shinshu University courses, students with no programming background — including students from humanities programs — sometimes complete a working web application prototype in a matter of hours. Every time I see their eyes light up, I'm confirmed that this is the right direction.
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GTM Engineering — Running Sales and Marketing Solo
A revolution is underway on the business side as well. A new concept called GTM — Go-To-Market engineering — is overturning the established norms of sales and marketing.
GTM refers to the full process of bringing a product or service to market. Traditionally, this process was divided among specialists: SDRs (Sales Development Representatives), AEs (Account Executives), marketers, and customer success teams. AI agents are upending this structure.
OpenAI has a specialized "GTM Innovation" team pushing the automation of sales processes. Vercel replaced what had required a team of ten people in sales development with a single person working alongside AI agents. Lead qualification, outbound email drafting, initial customer response — AI agents handle these at speed, while humans focus on decision-making and relationship building. The number of leads handled stays the same; the labor cost drops dramatically. This is a significant development.
What does this change mean for university education? Every student — regardless of whether they're studying humanities or sciences — should learn to design and operate business processes using AI agents. Business school students building AI agents to automate marketing campaigns. Law students using AI to streamline contract review processes. Medical students using AI to search across literature and instantly grasp the latest treatment evidence. This kind of interdisciplinary practice should become standard in universities going forward.
AI-Driven Design — The Democratization of Creativity
AI is bringing significant transformation to the design space as well. The evolution of image-generation AI — Midjourney, DALL-E, Stable Diffusion — has enabled people without formal design training to produce high-quality visuals. AI features embedded in Figma have dramatically streamlined the conversion from wireframes to UI design.
But the essence of AI-driven design isn't simply being able to generate images. It's the ability to deeply understand user problems and express solutions visually — the aesthetic judgment to select the best option from multiple AI-generated variations — and the sensitivity to make final adjustments capturing nuances that AI cannot perceive. Training all of these capabilities together is what AI-era design education requires.
Universities should introduce practical curricula that combine design thinking with AI tools. Students using AI to rapidly generate prototype UIs, putting them through user testing, and iterating based on feedback — cycling through this dozens of times is what produces genuine AI-driven designers. Personally, I believe design is the domain where AI democratization offers the greatest benefit. Students who previously couldn't bring their ideas to life because they lacked design skills can now produce prototypes with AI. The potential is immeasurable.
Become a Commander of Multi-AI Agents
Project management going forward isn't just about managing people. It's about simultaneously running multiple AI agents — each with distinct specializations in research, coding, design, marketing — and having the capability to lead them as their commander.
A Deloitte report released at the end of 2025 predicts that AI agent orchestration — coordinating multiple agents in collaborative operation — will deliver exponential value to enterprises. At Microsoft's Build 2025, multi-agent orchestration capabilities were announced, showing a future where multiple AI agents combine skills to tackle complex tasks.
Developing this capability in university students is urgent. Within a single project, students should be setting up a research AI agent, a coding AI agent, and a document preparation AI agent, then giving them instructions as project leader and integrating the outputs. Understanding each agent's strengths and weaknesses, assigning appropriate tasks, checking output quality, and making final decisions — this orchestration capability is, I believe, at the core of leadership in the AI era.
Honestly, this capability cannot be taught from a textbook. It can only be acquired by actually running AI agents, failing, and iterating. That's precisely why universities should provide this space for trial and error generously. I run multiple AI agents in parallel daily in TIMEWELL's operations — and at the beginning, poor instructions consistently produced completely useless outputs. The accumulation of those failures is what led to operational capability today. I want students to fail a lot too, in a safe environment.
The Role of Teachers Is Changing — From Teaching to Coaching, Then to Architects of Education
If education changes this much, the role of teachers naturally changes too. AI will take over the position of the teacher who simply transmits knowledge one-directionally. Going forward, educators must be coaches and mentors who walk alongside each individual student and help their talents flourish.
I believe the teaching portion can largely be delegated to AI. In 2025, cases were already reported of universities introducing AI instructors to achieve both individualized language learning and large-scale course delivery simultaneously. AI functioning as a second instructor — providing explanations and feedback tailored to each student's level of understanding. Educators can then focus on the areas only humans can handle: deep discussions that AI cannot facilitate, student motivation management, and career counseling.
A new role is also emerging: architect of education. Using AI to design optimized learning plans tailored to each student's interests, progress, and strengths and weaknesses. One student gets a genome analysis project; another gets a startup marketing support assignment. Rather than a uniform curriculum, AI-supported design of individually optimized learning paths.
The assessment system needs bold reform as well. Rather than paper exam scores, the evaluation framework should be built around multidimensional indicators that reflect what the professional world actually measures: depth of customer interviews, prototype completion, contribution to the team, and skill in leveraging AI agents. This is a significant challenge for educators too, but it's not a road we can avoid.
Advanced Research Capability — Using AI Deeply to Generate Real Insight
AI has made information gathering dramatically easier. But precisely because of that, surface-level research that merely skims available information no longer generates any value. In an era where everyone can ask AI and get the same answer, what differentiates people is the quality of their questions and the depth of their insight.
Universities need to teach professional research methods — using AI as a research partner to see through to the essence of vast information and derive unique insights. Assigning different personas to multiple AI instances, having them debate the same topic, and surfacing the contradictions and blind spots. Analyzing academic literature databases across disciplines with AI to find research gaps that nobody has noticed yet. Analyzing voice data from customer interviews with AI to extract latent needs that haven't been put into words. A curriculum that teaches these methods systematically is needed.
When I conduct market research for new business exploration at TIMEWELL, I sometimes have AI agents complete in a few hours what a human researcher would take a week to do. But I never use that output directly. I use what AI produces as a starting point, then ask the questions that only a human can ask, go into the field, and verify. The real value of AI research is in accelerating human thinking — not in replacing it. Internalizing this distinction in students is, I believe, the core of advanced research education.
Hallucination and Injection — Seeing Through the Traps AI Sets
Working with AI effectively means confronting two unavoidable problems: hallucination — where AI confidently fabricates plausible-sounding false information — and prompt injection — where malicious instructions manipulate AI systems.
What makes hallucination dangerous is its sophistication. AI confidently cites papers that don't exist and presents fabricated statistical data. In 2025, Google's search AI feature was reported to have accepted a satirical article as fact and displayed misinformation. Students citing AI-generated fictitious references in their graduation theses is already a problem at universities around the world.
Prompt injection is a serious threat to AI system security. Malicious users send carefully crafted instructions to AI systems, extracting information that should not be disclosed or improperly manipulating AI behavior. As more companies integrate AI into their operations, demand for people who understand this threat and can counter it is surging.
University education must rigorously train the ability to recognize these AI traps and avoid them. The habit of never accepting generated information at face value — always going back to primary sources. The stance of continuously asking "is this really true?" about AI output. This isn't merely a skill — it's a fundamental attitude for living in the AI era.
In the 2025 Youth Fact-Checking Championship, a student team from Hokkaido took first place in the world. The growing enthusiasm for this competition — which tests the ability to distinguish truth in the AI era — among students worldwide is a very encouraging development. Japanese universities should integrate this kind of fact-checking practice into regular curricula.
Solving Field-Level Suffering Through Robotics
The power of AI is too valuable to confine to the digital world. Its true value is realized when applied to the challenges of the physical world.
In Japan, where population aging is accelerating, fields like construction, logistics, agriculture, and elder care are struggling acutely. During my time at Panasonic, I was responsible for factory automation sales, so I know firsthand how hard the conditions are in those environments. Workers sweating through summer heat in factories. Staff quietly sorting packages in logistics warehouses in the middle of the night. The desire to solve the suffering in these environments through technology has not changed.
Universities should greatly expand practical education programs that combine robotics with AI. Students going into the field, listening to the people who work there, then designing and developing robots and AI systems to solve those problems. Teaming up with farmers to develop an automated harvest robot using AI image recognition. Partnering with care facilities to build an AI monitoring system. Automating construction site safety management with AI cameras. This kind of practice-grounded learning develops the ability to bring technology to social implementation.
In 2025, a project between Tokyo Institute of Technology and an agricultural corporation — using AI-equipped autonomous drones to optimize pesticide application — attracted significant attention. Students going into actual rice fields, sitting down with farmers to identify problems, and presenting technical solutions. I hope to see this kind of industry-academia collaboration spread to universities across Japan.
What matters in robotics education, I believe, is not just technical skill but bringing genuine respect for the people in the field. Even the most sophisticated robot is worthless if the people who work there don't want to use it. Starting from the voices of the field rather than the ego of the engineer — conveying that stance to students is what I consider the foundation of robotics education.
AI for Science in Practice — Education That Produces the Next Nobel Prize
In the world of science, a new movement called "AI for Science" is fundamentally transforming how research is conducted. Its symbol is AlphaFold, developed by Google DeepMind — predicting the three-dimensional structure of proteins with remarkable accuracy, contributing to the 2024 Nobel Prize in Chemistry. What previously took months or years to analyze for a single protein structure can now be completed in hours using AI. This revolutionary change is accelerating drug discovery and new materials development at an extraordinary pace.
Japan's Ministry of Education also established a foundational strategic policy for promoting AI for Science in January 2026, making clear the country's commitment to this field. The University of Tokyo has reported research results combining whole-genome analysis with AI protein structure prediction to elucidate the causes of diseases that conventional methods couldn't identify. At Hiroshima University, software was developed where AI reads DNA and prevents errors in genome editing.
University science education cannot simply introduce these cutting-edge examples in textbooks and stop there. Students themselves must be provided with opportunities to actually run AI — on research projects involving genome analysis, discovering new protein binding patterns, and searching for new materials. Learning the theory one day and running AI in the lab the next. Building this kind of educational environment is the key to developing the next generation of scientists.
Personally, I believe AI for Science is the area where Japan has the greatest opportunity to lead the world. Japan has a deep research foundation in life sciences — including iPS cell research. In materials science and chemistry as well, many world-class researchers are active. Combining this with AI creates genuine potential for breakthroughs that will surprise the world. To make that happen, we need to develop as many young researchers who can work fluently with AI as possible. From the undergraduate stage, actually working with tools like AlphaFold and genome analysis, building experience integrating AI into their own research themes — that experience will generate significant advantages in graduate research and beyond.
Transforming Organizations for the AI Era — Together
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Closing — Beyond the Democratization of Challenge
Allow me a brief personal note. I spent 12 years at Panasonic covering a wide range of experience — from the front lines of sales to technology strategy to internal entrepreneur support. I was stationed in Silicon Valley, exhibited at SXSW and SLUSH. Through "CHANGE by ONE JAPAN," I supported over 450 internal entrepreneurs, and now at TIMEWELL I'm working to build the infrastructure for ambition. Through EMC GLOBAL with Yoichi Ito and Tatsuya Tsubuki, I work with students from over six countries including India, Indonesia, and the Philippines, developing challengers who will change the world from Asia. I've also performed as a DJ on the main stage at ageHa in Shin-Kiba.
These activities may seem unrelated at a glance, but the underlying desire is one: to create moments where people's eyes come alive.
AI tears down walls called expertise and dramatically lowers the threshold to taking on challenges. What once only a handful of exceptional people could do is now achievable by anyone with a strong enough vision. The era of democratizing ambition has finally arrived.
In this great swell, what should universities do? Provide the space and opportunities for students to use their own passion as a compass, take AI as their most powerful tool, and boldly engage with the challenges facing society. The shift from education that fills people with knowledge to education that creates value — it is beyond that transformation that the true leaders who will carry Japan and the world forward will emerge. I firmly believe this.
Finally, to those involved in university leadership and educational administration: this transformation cannot be realized through the efforts of a handful of progressive educators alone. A fundamental overhaul of curricula, reform of assessment systems, strengthening of industry-academia partnerships, investment in AI infrastructure — the entire organization must be committed to tackling this structurally. Rather than fearing change, embracing change. That is the message that students will feel most deeply.
Educational reform for the AI era cannot wait. For the future of students, and for the future of Japan, let's move from this very moment. I remain committed to standing at the forefront of that — as TIMEWELL, and from the classroom at Shinshu University. Let's build the future together.