hi!
My name is Hudson. I’m 16, left high school this spring, and moved across the country alone to pursue learning science research.
I received $20,000 from Emergent Ventures earlier this year and moved to Virginia Tech shortly after to collaborate with Prof. David Smith.
Papert Lab
Papert Lab is an applied cognitive science research nonprofit investigating the role of computers on how humans learn, solve problems, and conduct scientific research.
We envision a world where every person feels empowered and excited to use computers to augment their cognition and learning.
Previous research blending cognitive and computer science yielded artifacts like the GUI and computer mouse, LOGO, Scratch and the Dynabook. A post-AGI world must focus on fostering a community of continual building, learning, and disseminating knowledge. Papert Lab is researching, building, and piloting the next generation of computational paradigms that augment human cognition and learning.
Moving Humanity From Consumption to Creation
A world where AI conducts scientific research, writes code, and solves complex problems usually invokes a fear-based response among “outsiders,” or people who lack control over AI progress. This can manifest as some false sense of human intellectual superiority over AI, but I doubt this will sustainably uphold a contributive society as it becomes clear that AI is far superior to us in essentially all domains. That is, we can not forever be delusional in valuing human cognition over machine intelligence.
I feel this is likely to lead to a transition from humans being active contributors in society to passive consumers who let a small group of powerful humans and AIs direct their lives. Similar to this thought is the idea of “relative disempowerment” (Kulveit et al.), whereby human quality of life stagnates or marginally improves despite the global GDP experiencing rapid growth and technological advancement. This would be due to a misalignment between human needs and technological advancement (i.e. technological advancement does not serve human needs).
This is not an inevitability, though. If humans deliberately decide to be creators and change makers, we may be able to maintain, and even increase, our sense of meaning and purpose. Moreover, increasing the quantity and impact of human-initiated artifacts may help to ensure alignment between humans and AI. This does not mean, however, that we should purely be creators without consuming anything; if we don’t consume, how will we learn? Instead, we must draw a distinction between consuming to be entertained and consuming as a means to achieving an objective.
I have experienced this dichotomy first hand as I transitioned from being a passive consumer of calculus to using AI to extend and apply my understanding of calculus to my research in learning science. I have repeatedly observed this phenomena among the 30 study participants I have interviewed, where many of them use the computer science vocabulary they are taught in university to design complicated program behavior. To make this transition, though, one must be able to imagine the idealistic world in which their invention exists and instantiate its implementation through specifications that define its behavior.
Flourishing Is Something You Make
Collaborating with AI to cure disease, eliminate child mortality, end hunger, and improve the climate are sure to increase the average person’s quality of life and have been written about extensively (Amodei). A more interesting question is what to do once this state is reached. It is unlikely that all human struggle will be eliminated at this point, and perhaps may even be a stretch to assume that it will be reduced; it is more likely to be shifted onto some other problems. Thus, the question is not how to create this idealistic utopia, but how to increase one’s flourishing while living in it, which we define as relentlessly pursuing a virtuous vision.
Then to promote flourishing, one must learn how to (1) identify a virtuous problem to solve; (2) describe and instantiate the idealistic world where the problem is solved. We can think of these two sub-problems as a self-evolving loop of scientific discovery, whereby one makes a hypothesis of a virtuous problem through searching the problem space and determines how to approach this problem through searching the solution space.
This transforms flourishing into a problem which AI can guide and augment. We can imagine innovators specifying the behavior of tools-for-thought, mnemonic mediums, and collaborative AI agents — all of which help the innovator to augment her cognition through our scientific loop of flourishing. Thus, to increase the amount of people who flourish, we should foster a culture that encourages learning and creation with computers.
We can increase human autonomy by enabling technical and non-technical people to design systems that augment how they solve problems and learn in their own domains by describing the system’s behavior. This would enable people to work on more meaningful problems and utilize AI to accelerate progress and discovery, thus aligning one’s vision and time. In practice, this would mean that the limiting factor to making change would be one’s ability to choose non-trivial problems instead of their proficiency at performing specific tasks, which increases autonomy by enabling people to create more useful tools.
Accessibility Doesn’t Mean Empowerment
Although the GUI made computing more accessible to more people, it also masked the divide between the haves (technical) and the have-nots (non-technical). What is particularly problematic now is that because non-technical people can still use computers and computer programs (thanks to the internet), non-technical people are not cognizant that they lack technical skills and are at a disadvantage; their deficiencies are unknown-unknowns.
The same phenomena that happened with the GUI (masking skill deficiencies) is happening now, whereby the foundation model companies are trying to make agentic coding more accessible (e.g. Claude Code → Claude Cowork), but by doing so, the technical divide will once again be masked and the less-technical people will be left behind, since using the Claude Code CLI and knowing how to design specifications will likely continue to outperform chatbot interfaces that utilize algorithmic prompt optimization. By educating the brightest people across domains (particularly non-computing ones) to describe the behavior of systems that extend their thought, we can avoid repeating this pattern and design tools that genuinely augment human cognition and learning.
This idea of developing computational fluency to augment thought traces back to Douglas Engelbart and Seymour Papert. Many have speculated about why we have failed (not cared about?) their vision of using computers to support learning and thinking that is situated in creating things. However, I feel that our failure to broadly acculturate computer programming is primarily due to our failure to demonstrate the benefits of computer programming for augmenting human cognition.
Indeed, I had no idea that the computer could be much more than a word processor and a means to completing a goal before I started frequently using AI. Thus, we must create environments that naturally motivate curiosity and exploration with computers and AI, which will naturally develop one’s thirst for computational knowledge.
What is an applied cognitive science research nonprofit?
Good question! To better illustrate our work, first look at our initiatives:
In summary: we use our understanding of how people think, solve problems, learn, and conduct scientific discovery to design tools that are generally applicable to science and education, which we pilot and use to improve our own research.
We’re funded by patrons
Over the past six months, I have conducted 30 two-hour long participant interviews, received a $20,000 Emergent Ventures, moved across the country alone for research, became a visiting scholar at Virginia Tech, submitted three papers (one under review), published Papert Lab’s first essay, and started a weekly discussion group on augmenting human cognition with computers.
I’ll be staying at Virginia Tech through January to pilot our pedagogy for AI-assisted programming in courses with thousands of students, conduct a RCT to empirically evaluate our pedagogy, publish in the largest and most prestigious HCI conference (CHI), and expand Papert Lab’s research to cognitive science journals. I am also solidifying a visiting appointment at CMU beginning after I leave Virginia Tech, which will be announced soon!!
I cannot afford to continue collaborating with professors and graduate students at Virginia Tech, hosting the discussion group, nor conducting pilot studies if I don’t fundraise again. Paying myself the wage of a Virginia Tech Ph.D. student with research expenses and travel costs, I need another $20,000 to continue my research through January.
If you’d like to contribute to developing pedagogy that enables humanity to create and innovate with AI coding agents, please consider supporting our work. You can see more information about the Season III research agenda and funding breakdown here.
In 1953, Jean Piaget received a small grant from John Marshall of the Rockefeller Foundation to fund genetic epistemology research at the University of Geneva. Shortly after, Seymour Papert joined Piaget as a mentee and mathematician to immerse himself in epistemological and cognitive science research. These five years in Switzerland were particularly germane to Papert’s thinking of learning science, acting as an accelerator for his later work with AI and LOGO at MIT. Looking back, it is clear that John Marshall’s patronage significantly contributed to Papert’s formative development and future work.
Similar to Piaget, we are funding Season III of research through philanthropic patrons. As a 501(c)(3) nonprofit organization, all donations are tax-deductible. The Season III round closes September 1. All tiers are cumulative (i.e. the Dynabook tier also gets LOGO benefits). If you’d like to donate, get in touch at hudson [at] papertlab [dot] org.
- Name listed on Season III page of Papert Lab
- Patron-only monthly letters and demo videos during Season III
- Invite to Season III meetup in San Francisco
- Pre-print access during Season III
- Name listed on Papert Lab homepage
- Named in acknowledgements section of one publication
- Sponsor a project
- One 30 min meeting
- Named in acknowledgements section of all publications
- Collaborate on or commission a spinoff project
- Monthly 30 min meetings during Season III
**Funding closes on September 1st, 2026
If you’ve enjoyed this, consider checking out our first essay!
