So, how do we navigate this change? That’s the question I chase and I bring some ideas from both economics, tech and education.

2 Levels of Tech Use

Chris Dixon , in his book ‘Read, Write, Own,’ notes that we use new tech in two ways.

Level 1: In early stages, we do our tasks faster, better and higher quality.

Level 2: We use it do things that we couldn’t do before.

For example, early internet was about reading and accessing more material to read. It was like print-on-steroid. By 2000s, people start to write on the net – ReadWriterWeb (Richard MacManus). That was something new to do…

It looks like level 1 use of AI reached mainstream: From summarizing material to translations, analyzing data to internet search – we do our ordinary tasks but in a faster, better way.

But, are we tapping into the full potential of this technology? In other words, do we use it at Level 2?

I borrowed ideas from multiple disciplines to craft a map for us, knowledge workers. In summary, we can interact with AI with 3 approaches:

• Competition

• Cooperation

• Co-construction

Let’s start with the first one, competition, shall we?

Competition: What if AI takes all the jobs?

You read it in the news, and the reports. This approach positions AI as a competitor. Hence, the goal here is to differentiate – we need to find skills and areas that we can outperform AI. Routine or procedural tasks like coding can be in the wheelhouse of AI. So, our focus should be on tasks that human touch is essential. In a quick sketch, here are some skills and areas to consider:

• Creativity: Creativity is more than art. As Seth Godin elegantly put it, creativity is about bringing original thoughts. This could happen in tweets, meetings, writings and more. There are many applications of it in our lives. We negotiate, share and build ideas, and learn from each other.

• Context-based skills: Research, program implementations and commerce – all requires an understanding of the context. And, context is not always visible. Also, it changes from where you look at it. One needs to have an understanding of the culture, relations and historical. These factors define the success of a product, a program or a strategy. Otherwise, like Peter Drucker famously said , “culture eats strategy for breakfast.”

• Caring: Historian Noval Harari notes that expanded lifespan would cause the rise of the elderly care market. People will want to be taken care of other people, instead of AI-powered robots. You can expand from this point – any professional role in the scope of caring has a leg up agains the machine.

While competition have a darker vision blended with automation, techno-optimists offer a brighter version, in which we can do things better, faster, and cheaper. That’s the cooperation.

Cooperation: How can I do this better?

Cooperation is what Dixon described as level 1 – we use tech to do our ordinary tasks in a better way. Today, most of my AI use falls into this category. And there’s a good reason for that. By outsourcing so time-consuming tasks, I open up more free time, increase my output and get faster feedback. My use, as an example of cooperation, are 5 buckets:

• Igniter: Sometimes you need some help to kickstart your work. GenAI can build an outline, or a broad framework.

• Explorer: It can help with searching journal articles on a certain topics. For example, Scholar AI saves me hours by helping you find scholarly work.

• Explainer: When researching a topic, especially in a different or new discipline, the chances are you end up with full of jargon. AI, has innate talent to explain it to you in a simple, jargon-free way. When I was TA’ing for Prof. Beane’s “Managing Tech Organizations” class, this use case was the most popular among students.

• Editor: Many people, including multilingual people like myself, ask for help to check my grammar, editing. Once done, I double check the work and make sure it reflects my voice. This article is an example for it.

• Debugger: AI is extremely well in reviewing code.  When I end up with an error, I paste the code and ask it to debug. It helps with learning a lot. If you deal procedural (e.g., step by step processes) or technical things, you, too, can benefit from this.

As you see in these examples, in the cooperation mode, you allocate tasks in between AI and yourself. This is the basic use case and you can build on it. You can train AI model for your personal needs to get more specific feedback. Currently, I’m training a GPT to help me with my Mixed Methods class. Since it has a background on where my skills at and where I am heading, I get more personalized guidance where to focus on. And,  I am just scratching the surface of possibilities. There are many things we can now do, but couldn’t do before – what I call as level 2 use- and that’s what takes us to the final step: Co-construction.

Co-construction: What can I do with AI that I do that I couldn’t do before?

Constructivism, in learning theory, positions the learner as the builder of learning. Instead of receiving knowledge from outside, we build our knowledge through our interactions with our environment, things and people.  And, our existing ideas are important because we construct our new learnings on them.  With this approach, we can work with AI by building new things upon our combined (AI and ourselves) knowledge and capacities. This way, we can explore into new horizons that we haven’t gone before.

Refik Anadol’s “Machine Hallucinations” is a good example. This dynamic artwork is created by unsupervised machine learning – a form of AI that continuously creating itself -.  It is a blend of artist’s vision and AI’s unique contribution. Before this collaboration, neither AI model nor the artist could create something like this. Now, both of them has some new skills, and knowledge. It’s a good example of Level 2 tech use.

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Similarly, we can find ways to use AI models to enhance our work’s limits, explore new destinations.

• Analysis: AI can analyze the data for a large dataset and humans can provide context and depth with qualitative research and they can create Mixed Method studies. On another field, AI can do diagnosis from medical images and human doctors can provide the case and make customized (nuanced) judgements.

• Creative collaborations: You can bring in creativity by using AI’s capacity to generate ideas quickly. If you ask for a list of ideas, ask that question again but request a longer, better or weirder list. Once you have set of ideas, you can embed your vision, and add the depth and cultural relevance into it.

• Novel problem-solving/Decision making: When solving problems, AI can offer multiple perspectives to solve it. Then, humans can decide which ones to pursue and practically applying the solution. Later on, they can feedback the AI and improve the solution capacities.

In Summary

All these three options (Competition, Cooperation and Co-construction) are not distinctive. You can pick and choose one of them depending on your current task. Or, you can combine them with each other. Perhaps you can start with a competitive approach to clarify what would be your unique input. Then, you would think of which tasks to be delegated to AI (cooperation). After you clarify both your and AI’s parts, it might be good to take a step further and create something new.

Here’re some guiding questions to work through these 3 approaches:

1. What you can thrive on?

2. What you can do better (e.g. , faster, cheaper, easier, or higher quality)?

3. What else can you do (e.g., something you couldn’t do before)?

And some more…

As I get to finish this article, ChatGPT suggested add a few more thoughts and I turned them into questions as well:

• What are the ethical implications of my use?

• Can there be any privacy and security breaches (e.g., mine or others) in this use?

• Are there implicit biases in this use? If so, how can I make them explicit, or counter them?

I hope this helps your adventures with AI.

Course Description

This course explores how knowledge work —essential to creative, analytical, and strategic roles— can engage with generative AI (genAI) across three paradigms: competition, cooperation, and co-construction.

Drawing from disciplines such as economics, technology and education, this course offers a framework to engage with AI in three forms. Each form is centered around a core practice: (1) competition through differentiation, (2) cooperation through delegation, and (3) co-construction through exploration. Learners will practice these paradigms by creating individual and group projects. The course begins with an introduction to the theoretical backgrounds and socio-historical contexts, then each week will focus on one form: its theoretical foundations, history, real-world examples, and hands-on practice. Students will use a variety of AI tools, including GPTs, Gemini, Midjourney, Stable Diffusion, and Copilots. Finally, they will exhibit their project portfolio on a dedicated website for future reference.

The Rationale

Historically, technology (e.g., automation), has been viewed as a threat to blue-collar and routine administrative work (Acemoglu & Johnson, 2023). GenAI now extends the discussion into non-routine work—encompassing creative, analytical, and strategic ones. The prevailing discourse often positions AI as either a competitor or, at best, a collaborator. This course positions genAI as a creative partner —the co-construction path—, facilitating explorations previously not possible. One example of such exploration is Refik Anadol’s “Unsupervised”—where the artifact dynamically evolves within the artist’s vision (Anadol, n.d.). However, this course broadens the definition of creative work to include analytical, strategic, and academic pursuits.

Instead of approaching genAI as an isolated topic, this course situates it within a socio-historical context to encourage a critical understanding of this technology.  By putting AI within the evolution of internet—such as web1, web2, and web3— students will gain insights into the broader social power dynamics. As a natural consequence, this dialogue will encompass AI’s interaction with other contemporary technologies like blockchain and XR, positioning AI both in historical and current contexts.

In the next phase of the course, learners equipped with critical, creative, and technical capabilities of genAI, will delve into speculative pedagogies. This theoretical framework allows students to imagine, create, and build a future where they can engage in a joyful social dreaming to a more sustainable and equitable living (Garcia & Mirra, 2023), with genAI as an instrument in their group projects.

Instructor: Dogukan Ozgen

I am a PhD student in Education department, with research interests in blockchains, literacies and economies. My MA thesis, “Power, Platforms and Tensions of Writer Ownership in Web2 and Web3” (2023), investigated the internet’s evolution and the shifting power dynamics between platforms and creators. I also hold an MA degree in Political Economy. Drawing insights from political economy, I analyzed digital creative production from a value ownership perspective in web2 and web3 contexts. This background informs the course’s socio-historical context and speculative literacies aspect.

As an instructor, I have 14 years of teaching experience ranging from K-12 to college and workforce learning across various cultures. In the last three years at UCSB, I have supported management, entrepreneurship, and marketing classes at Technology Management Department as a teaching assistant. Currently, as a learning technologies coordinator at GGSE, I have run a digital literacies program (2021), facilitated an AI Literacies workshop (2023) and led the Virtual Reality Program at a local elementary school. My teaching background will help me contextualize these emerging technologies for the MAD class.

Course Logistics

The ideal class size is 20 to 30 students to foster individual attention and facilitate group work. Students will engage in weekly hands-on projects and a final group project, which will be showcased in a dedicated website. While free versions of genAI tools (ChatGPT, Gemini, Midjourney, Stable Diffusion) are sufficient, I recommend at least one paid subscription (approximately 20 USD per month) to enhance their experience.

By the end of this class, students will have developed AI skills and perspectives for immediate and future application, supported by a creative portfolio.

References

Acemoglu, D., & Johnson, S. (2023). Power and Progress Our Thousand-Year Struggle Over Technology and Prosperity. PublicAffairs.

Anadol, R. (n.d.). Unsupervised—Machine Hallucinations—MoMA [Personal]. Refik Anadol. Retrieved February 12, 2024, from https://refikanadol.com/works/unsupervised/

Garcia, A., & Mirra, N. (2023). Speculative Pedagogies: Designing Equitable Educational Futures. Multicultural Education Series. Teachers College Press.

Education is a key industry for tech innovation

Because education produces an immense level of data. Inherently... And it has a structure. In social media and elsewhere, collected data requires a lot of work to organize. However, educational data is mostly organized from the start.

Also, education as a business model is second-to-none. Most of the time, the payments are upfront. Before the service or products are provided. This helps organizations plan ahead and weather the storms... And, payments are regular. Nothing comes close to this level lucrative market other than healthcare insurance and real estate financing.

Finally, education is a necessity. This was particularly true for k-12 level, but in last 5 years, we have seen a similar trend in workplace and lifelong learning. This trend is understandable because developed countries switched from hard industries to service economies in the last 2 decades. And, in a service economy, knowledge is the main raw material and production tool. So, education embraces a new role: supporting service economies most important ingredients - talent.

Web2-based education

When we put together these 3 pillars, it’s easy to understand why education is and will be as a top priority for tech. But here’s the question:

“If and how do tech platforms’ priorities align with learning? If and how does data processing take place? Is it invasive or consented by all participants?” (Park, 2021)

For example, these platforms help learning persist during the pandemic but they also extended the surveillance on the intimate corners of people’ lives, and shaped the instruction and communication (Nichols & Garcia, 2022).

When considering platforms in education, we need to go beyond big tech because this industry has its own specialized giants. They operate in classroom management, school device monitoring, assessment, parent communication and curriculum support packages (Nichols & LeBlanc, 2020).

To give you more context, let me explain what I mean by '“a platform” in the educational context. A platform refers to two things:
(1) It is the hardware that apps are built on.
(2) It’s also digital spaces on which social and economic transaction take place (Nichols & LeBlanc, 2020).

Platforms work as stacks. Each layer of stack has its own social, economic, and technological dimensions. Understanding these stacks are critical. Then, we can uncover how their design and decisions effect the learning ecosystems.

Let’s work with an example.

An iPad is a platform that houses App store. And, the app store is another platform that holds apps. So, when a teacher brings in a cartoon making app inside the classroom, they also invite other platforms like the App store, and iPads. And, the priorities of these tools are not necessarily align with learning goals all the time.

Here are some examples what happens when platform and learning priorities (or you can say “incentives”) do not align:

• In Ohio district, teachers had to restructure their units because the software they relied on increased its fees.

• In a Texas elementary school, art teachers had to deal with a recent software update that removed some features they use in vocabulary studies.

• Seesaw, a parent communication platform, started to define the form and frequency of communication between parents and teachers. Teachers felt the pressure of increased communication in a Seesaw-friendly way.

• In other examples, teachers start to choose tools based on their compatibility to the platforms rather than their pedagogic value in the first place (Nichols & LeBlanc, 2020).

In summary, tech platforms’ design and development decisions directly affected teaching and learning. However, teachers and learners didn’t have a chance to take part in this decision-making processes.

Web3-native education

How do we address this issue?

In a more clear way, how do we create learning technologies that takes teachers’ and learners’ inputs into account when developing and designing products?

Well, web3 has a answer.
All of the topics mentioned above, namely data ownership, privacy, and governance are at the focus web3 ethos. They are fundamental for blockchain.

Hence, web3 offers unique data processing capacities (e.g. decentralized, encrypted, and smart). These capacities were not available before. So now, the floor is open for new experiments. DAOs are experimenting what it means to:

• build participatory communities,

• produce creative work

• take decisions as organizations.

We see examples in media, art and commerce. How about education?

Most of the current blockchain and education is about 3 broad categories:

1. Learning records; evaluation and assessment, verification, accreditation, certification

2. Administrative operations, such as hiring via smart contracts, continuing professional development, performance and payments.

3. Social contracts as in “learn-to-earn” framework in which learners receive crypto rewards for their learning progress (Capetillo et al., 2022; Park, 2021; Wolfson, 2021; Hernandez-de-Menendez et al., 2020).

However, web3 can offer more fundamental shifts (think of DeSci movement). And these shifts can affect the fundamentals of how we create educational technology, roles of teachers and learners and new organizational structures (less hierarchical) for educational institutions.

Since Education field is tied to many bureaucratic processes, it’s possible most of these new experiments will happen in non-traditional learning experiences. Online communities are good examples to observe innovation. In particular , learning DAOs try to create a web3-native education.

They question, experiment and create around topics like co-learning, co-ownership, and collaborative-governance.

Crypto Culture and Society (CCS) is a DAO focused on liberal arts education for crypto society. CCS allows learners to construct their classes together with instructors. They vote on how CCS DAO distributes its funding. In doing so, they invite stakeholders that are traditionally excluded from the decision-making process in education.

This initial step kickstarts a change in the power dynamics for the favor of traditionally excluded stakeholders. And, at the same time, it prompts these groups to gain literacies around these layers so they can make informed decisions.

Ed3 DAO is another community that solely focuses on how web3 is applied in education with emergent examples of teachers using NFTs, “learn to earn” models. From its DAO governance to work groups (“nodes”), Ed3 DAO tries new ways of collaborating and building education (D. Meyer & Saraf, 2022b; Patel, 2021; Vermaak, 2022).

In essence, learning DAOs look for alternatives to web2-based educational applications and they propose a potentially more equitable learning ecosystems (D. Meyer & Saraf, 2022a).

It’s time to build web3-native education by learning from these experiences. To do so, we will need to acquire blockchain literacies. So, we can make better decisions on how we learn, use and direct platforms in a way that’s aligned with learning priorities.

References

Acemoglu, D., Anderson, G., Beede, D., Buffington, C., Childress, E., Dinlersoz, E., Foster, L., Goldschlag, N., Haltiwanger, J., & Kroff, Z. (2022). Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey.

Acemoglu, D., Autor, D., Hazell, J., & Restrepo, P. (2022). Artificial Intelligence and Jobs: Evidence from Online Vacancies. Journal of Labor Economics, 40(S1), S293–S340. https://doi.org/10.1086/718327

Cacioli, L. (2020). Exclusive: Access, Connectivity and Inclusion—How UNICEF Leverages Blockchain to Close the Digital Divide. https://blockchain.news/interview/exclusive-access-connectivity-and-inclusion-unicef-leverages-blockchain-close-digital-divide

Capetillo, A., Camacho, D., & Alanis, M. (2022). Blockchained education: Challenging the long-standing model of academic institutions. International Journal on Interactive Design and Manufacturing (IJIDeM), 16(2), 791–802. https://doi.org/10.1007/s12008-022-00886-1

D. Meyer, S., & Saraf, V. (2022a). DAO.edu—The Future of Decentralized Learning. https://ed3.mirror.xyz/VJUCPEMKKvh5Fyh6Gb1_YEZrl2FCAP6jgmsByuCCkmA

D. Meyer, S., & Saraf, V. (2022b). From Web3 to Ed3—Reimagining Education in a Decentralized Worl…. https://ed3.mirror.xyz/0U3QG8-4K6CD_ltU6SJyKN3-uBD3x6nEFs-YeShzYmk

Gillespie, T. (2021). : Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media. Yale University Press. https://yalebooks.yale.edu/9780300261431/custodians-of-the-internet

Hernandez-de-Menendez, M., Escobar Díaz, C., & Morales-Menendez, R. (2020).Technologies for the future of learning: State of the art. International Journal on Interactive Design and Manufacturing (IJIDeM), 14(2), 683–695. https://doi.org/10.1007/s12008-019-00640-0

Nichols, T. P., & Garcia, A. (2022). Platform Studies in Education. Harvard Educational Review, 92(2), 209–230. https://doi.org/10.17763/1943-5045-92.2.209

Nichols, T. P., & LeBlanc, R. J. (2020). Beyond Apps: Digital Literacies in a Platform Society. The Reading Teacher, 74(1), 103–109. https://doi.org/10.1002/trtr.1926Nichols, T. P., Smith, A., Bulfin, S., & Stornaiuolo, A. (2021). Critical Literacy, Digital Platforms, and Datafication. In The Handbook of Critical Literacies (pp. 345–353). Routledge.

Park, J. (2021). Promises and challenges of Blockchain in education. Smart Learning Environments, 8(1), 33. https://doi.org/10.1186/s40561-021-00179-2

Patel, B. (2021). Building Liberal Arts for Crypto. https://society.mirror.xyz/sfgXhqtwwMkhHLnAM1jVr16MdSJ4RGSb1Y6CAKpslgc

Reynolds, C. (2021). Announcing: World Bank Web3 Community of Practice. https://www.learningeconomy.io/post/lef-and-the-world-bank-announce-the-blockchain-for-education-community-of-practice

Vermaak, W. (2022). A Deep Dive Into OdysseyDAO | CoinMarketCap. CoinMarketCap Alexandria. https://coinmarketcap.com/alexandria/article/deep-dive-into-odysseydao Voshmgir, S. (2020). Token Economy: How the Web3 reinvents the Internet. Token Kitchen.

Image credits: Photo by Pixabay: https://www.pexels.com/photo/lantern-lantern-festival-seoul-cheonggyecheon-stream-52547/

So I propose a different, a more comprehensive understanding of literacies. In this perspective, we consider not only the “use” of something but also the “make” of it.

Let me explain this in a different context: Today when we buy food, we have the option of buying “organic”, “fair trade” and “sustainable.” We recognize not just the “use” (nutritional label) and but also the “make” of that particular item.

We are free to buy whatever we want, but now we have the awareness. By purchasing that one item, we support one side or another.

This is the same with clothing brands as well.

How did this happen?

1. It started with awareness campaigns: “Look, something is wrong with the food industry.”

2. Then, awareness met with action: “Hey, we did something that’s fair, healthy and sustainable.”

3. Finally, us: “Ok. Now I know. I can still do whatever I want and I am aware of the consequences."

Now imagine how this can translate to digital landscapes:

1. “Look, something is funky with all these digital economies.”

2. Satoshi comes up: “Yo! I made something. It might be useful.”

3. Then other folks chimed in: “Yea. We did this, too, for similar reasons.”

4. Finally, us: “Ok, now we know.”

We are in somewhere between step 3 and 4 at the moment. My goal, at least, with this tiny essay is to crack open that “Ok, now we know” part a little bit more.

And, I am not gonna say something totally original.

Literacy scholars have already been poking things for decades. In the beginning, people (like me) thought literacy is a solely cognitive process that has nothing to do with context (Some people still do: see “growth mindset theory”).

But in the late 90s, some researchers said “wait a minute. This so-called ‘independent (autonomous) literacy’ model doesn’t explain what’s really happening.” They claimed literacies depend on contexts. And, they are social practices.

Does the late 90s ring a bell? Yea, Web 1.0 is coming full force. And, it’s changing what we know as literacies as well.

As web2 advanced, we entered a highly social, interactive and affinity based digital world. Now we are talking literacies in a world that is multimodal, hybrid, informal, polylingual and transcultural... It’s like matrix in a matrix in a matrix (you got it).

Web2 brought all these communication and user-facing connection innovations to us. So, it’s called “frontend revolution.” And, many of us thought of literacies reflecting these changes: digital literacies.

“How do we use these new tech-based connection tools effectively?”

“How do they affect the social world like economy, politics, science (...) ?”

We have seen the way people live, work, learn (and also uprise) changed.

If we look at what current digital literacies, it’s more about finding info, critically analyzing it, sharing and communication (1). It’s more focused on “use”, even the critical perspective is about the “use.”

The case is similar in financial literacies - budgeting, credit score, saving and investing...

None of these cover the “make” of tech.

But we started to notice how these technologies “make” things. When I say things, we can think “monies $$”, data (processing), business models, influence, competition, marketing, ownership, decision-making and so on... business-y stuff. And, we also saw how these affect our life.

Now, if we make a quick search, we will also see most blockchain/web3-related educational content (and initiatives) are about the use:

• “What is DeFi, DAO, NFT, DID?”

• “How to create a wallet?”

• “Intro to blockchain,”

Or if you go more technical:

• “Solidity development,”

I am not saying these are “not cool”, they are. But, they are also incomplete.They are not addressing one of the core proposal web3 brings:

• “What does it mean to own your digital life?”

• “How do we govern organizations in a decentralized ways?”

• “How can we use various resources (such as VC, grants, communities, talent) to create a something better and bolder?”

There are 3-levels of web3 literacies (I use blockchain/web3 in this essay):

Level 1: Ability to navigate through these technologiesLevel 2: Ability to use these environments for value creation in short to medium termsLevel 3: Ability to make informed decisions for sustainable value in the long term

The questions I pose above hits the level 3. Just like web2 was about level 2 by nature, blockchain is inherently about Level 3.

This is one of my first stabs at literacies levels. This essay is not about finding answers, but more questions. And building initial frameworks. Good questions - Questions that inspire, prompt action and kick our curiosity.

Feel free to question what I wrote. Challenge them if you will. Let’s start the conversation.

I also have a challenge for you this week. You know Chat GPT is all the rage now: “How AI will change [insert here any topic you want]”

In fact, it’s the fastest growing app in the history. Now, I challenge you to ask some “make” questions while holding off all “use” questions for a bit:

Who owns and governs it?

What happens to data?What’s the business model?

How does this new app sit with web3 ethos, and/or web2?

Come up with more questions or even answers for us.

PS: Just for clarification, I do not propose questions to color something black or white. The proposal here is to ask different questions.

PPS: These ideas don’t come from thin air. Here are some outstanding scholars I benefited when writing this piece:Nichols, T. P., & Garcia, A. (2022). Platform Studies in Education. Van Dijck, J. (2021). Seeing the forest for the trees: Visualizing platformization and its governance. Voshmgir, S. (2020). Token Economy: How the Web3 reinvents the Internet. Street, B. V. (1997). Social Literacies.

PPPS: Photo by Jezael Melgoza on Unsplash

• Platformization

• Web2 vs Web3

• How and why blockchain differs from other emerging tech like AI, XR and so on.

• What Blockchain Literacies needs to be.

Platformization

Platforms allow peer-to-peer interactions on a global scale. They act as “trusted intermediaries:” They manage the interaction between two parties who don’t know each other. You can see this in e-commerce, social media, education, and other forms of digital relationships.

As intermediaries, they access immense amount of data about both sides of transactions. Think of banks.They function the same way: Banks know who the sender and recipients are, the time, location and interaction. Imagine this type of data but it’s way more rich: psychographics, consumption patterns, daily habits and more… Through this level of depth and width, platforms make our lives way more easier than ever: one-click purchase, travel, always-on access to information and friends. Work and business also benefit these opportunities: remote work, freelancing, SAAS.

Web2’s ultimate contribution was to build a social internet. So, we can interact with more people through different mediums (i.e. videos, audio, text and more) in more efficient and scalable ways. Hence, it’s called the “front-end (user-facing) revolution.”

Web3, other hand, is called a “back end (server-facing) revolution.” Because the ultimate change it brings is not in the user-facing realms (yet) but more in the data structures. (Voshmgir, 2020).

Blockchain

Blockchain is the underlying technology of web3. It offers distributed ledgers (logbooks). We can record data of the interaction between peers and record the details in the nodes (participating computers) not in a central server (Yumna et al., 2019).

It has six major features:

1. Decentralized.

2. Traceable so each transaction can be seen.

3. It works with consensus mechanism, so each activity is approved by all nodes (participants), therefore there is no need for a trusted intermediary, like a digital platform or a bank.

4. It is immutable. Any attempt at modification will be visible.

5. It is based on smart contracts - these are self-executing computer programs.

6. Each blockchain has a currency or token that guarantees the transactions are protected and trustworthy (Yumna et al., 2019).

Web2 vs. Web3

To put this in a perspective, a contrast would be helpful:

• Web2 is associated with data monopoly, whereas Web3 is data sovereignty.

• Web2 is centralized as Web3 is decentralized,

• Web2 relies on dedicated servers; Web3 relies on user computers, also known as clients (Voshmgir, 2020).

Let’s dig deep for a bit:

Instead of housing all of it in one central unit (“data center”), blockchain distributes data to the nodes (participants) of a system. This changes who owns the data.

In the traditional sense of platforms, central units manage digital interactions (commerce, social, education). Blockchain has smart contracts to do this job. They are software code representing social agreements in between system participants. And, these agreements govern digital relationships on the platform. This changes who governs the data.

Why blockchain is unique?

Because of these 2 concepts: data ownership AND governance of digital interactions and data.

Other emerging technologies like AI, XR or else do not have alternative offers about these 2 concepts as web3 (yet).

Everything else we know about web3 grows on these 2 concepts: crypto currencies, NFTs, DAOs, DIDs and more.

“Ok, but why do these two concepts matter?”

Accessing information at this scale and controlling it single-handedly created some of the largest entities in human history. Today, these entities are dominant for long enough so they can spot and absorb challengers and insulate themselves from any competition (Moore & Tambini, 2021). Through this level of domination, they leverage unprecedented economic, social, and political influence.

Web2’s example clarifies one thing: The way we own and govern things defines everything else: work, life and learning.

Blockchain enables all participants (users, developers, and other stakeholders) to take part in ownership and decision making. So, it unlocks opportunities around new ways to come together, create and work together.

Therefore, blockchain literacies are NOT just knowing how to create a wallet, do digital transactions, or write code on solidity. This is the first version.

The next version of blockchain literacies involves thinking about how this technology would influence government, health, science, literacy, culture, and art (Yumna et al., 2019).

Here’s what my folks at Ed3 DAO say about learning and teaching AI:

“We’re not only teaching students the right way to use AI, but also the ethical considerations for when and how to use it. We’ll explore:

• Bias

• Copywright & ownership

• Environmental costs

• Accountability

• Malicious intent

https://twitter.com/Ed3DAO/status/1618640078015791104?s=20&t=B9y6dyyZ-AFPQcb_FdaD-A

Following their lead, here’s what next-level blockchain literacies need to include:

• New ownership and governance practices

• Bias

• Sustainability (environmental, economic and social)

• Speculations and malicious intent

And more:

Thinking about what’s possible:

How do we build on this infrastructure to create something bigger and better for more?

How can we leverage different resources (e.g. VC capital, community contributors, and so on) to leverage this?

DeFi works to build a financial structure without banks and other financial institutions in the middle.

DAOs promote non-hierarchical social organizations by introducing more distributed decision-making, voting, and funding mechanisms empowering the peripheries.

NFTs enable new ways to create digital scarcity through art, certifications, and other types of artifacts.

These applications and cultural effects and will affect how we design education and its future path. To better understand the depth and breadth of this impact, it is crucial where education and platforms are today and where this relationship can head to with web3.

In part 2 of this series, we will look at how web2 made its way into education, and how web3 ignites new ideas around learning. We will also continue to explore next-level blockchain literacies in this landscape.

Image credits: Alexis Antoine (Unsplash)