And oftentimes, they were doing this work with little cognitive and emotional support.

Dr. Carmen Garcia, president of CCSS, Superintendent of Morgan Hill Unified School District and an incredibly thoughtful and kind leader, welcomed the group with one sentiment; “being a superintendent is lonely”. Because no matter how big your team is, the high-pressure, highly-public, and highly responsible role of superintendent has little room for mistakes. 

In the education world, we’ve seen ad nauseam the ways educators can use AI to produce lesson plans, quizzes, and report cards. But I would argue, the most important potential of AI isn’t to enhance human productivity. It’s to enhance and support human thinking. 

So at CCSS, I chose to prepare our Superintendents to use AI as the thought partner they’ve always wanted, in a world where leading is a lonely job. 

This 2-part article is about AI’s cognitive abilities as a thought partner.

In my last piece of this series, I mapped AI’s capabilities to Bloom’s taxonomy, differentiating the competencies of AI from humans. My hope is that readers will see what humans can double down on as their unique advantage, while also identifying a new standard for quality of thought.

The second part provides ideas for how leaders can train an AI thought partner to represent whoever they want – a critic, a twin, a mentor, a philosopher, or a guide. 

In my last piece of this series, I mapped AI’s capabilities to Bloom’s taxonomy where we learned that AI’s splotchy cognitive competencies can help us: 

• explain the human advantage over AI 
• depict AI as a cognitive partner
• identify ways learners might use AI and be duped by AI
• narrate how AI will elevate our standards in education for the production of content, ideas, and discourse

Now, we’ll identify how leaders can finally have the thought partner they’ve always wanted. 

Leaders are often faced with complex decision-making. It isn’t easy to expect others in their ecosystem to be able to provide a full evaluation of the situation or the final decision, because the leader often has more information. Collaborative decision making is always an excellent strategy to involve more stakeholders, but that can also fail if the stakeholders are uninformed or the decision needs to be made quickly. 

So in the moments when a leader needs to make a decision, help her collaborators make a decision, or evaluate a decision she made, who does she turn to?

Imagine if every leader had a personal coach who was critical when she needed feedback, a twin when she needed efficiency, and a philosopher when she needed inspiration. Imagine that this guide knew everything about the leader, her ecosystem, her stakeholders, and her problems.

During my keynote at CCSS, the thoughtful Dr. César Morales, Ventura County Superintendent, said he had a lightbulb moment at this point. Although he didn’t feel comfortable producing content on ChatGPT, he realized he could have it critique his work. And that completely changed his perspective on AI. 

Breaking Down Complex Decision Making

So how do we do this? There are ways to literally create a digital twin using AI. In fact, my friend Bodo built two with his kids using my friend Dima’s AI platform. But let’s consider ChatGPT as our main tool.

Let’s start by breaking down complex decision making. 

To make a difficult decision (or write a letter to the board, advocate for a staff member, produce a business report, etc. etc.), leaders have to gather and analyze the appropriate information from various sources first. We can equate this to the “empathy” stage of design thinking. Without analyzing information from all sides, it’s impossible to conceive a wise decision or prioritize the components of the decision. 

As leaders brainstorm a solution to their problem, they should explore alternative perspectives and generate scenarios that assess the risk, trade-offs, and predict the response. If leaders are not considering what could happen if this decision were made, they may run into bigger problems. 

These components work much like Bloom’s in that they’re more of a spiral that volley back and forth between each other. In sum, complex decision making is made up of gathering information, clarifying complex concepts, exploring alternative perspectives, facilitating brainstorming, analyzing data, and generating scenarios and predictions.

But the reality is that leaders don’t always have time or the skill to make these levels of assessments before they execute.

Enter, AI. 

In addition to asking AI to brainstorm the decision for us, we can ask AI to analyze the decision we may want to make. Remember that AI cannot make meaning so humans must always make their own judgments. Here are my go-to questions for complex decisions.

These questions allow teams to quickly iterate and adapt their decisions before executing. They allow us to simulate outcomes and consider alternatives we may never have thought of. And most importantly, they equip us with strategies to improve our thinking that we can potentially learn from for future decisions. 

This, of course, is my main thesis across these articles: AI can help us become better thinkers.

Context Setting 

To set up a cognitive friend on ChatGPT, we first need to set clear context for our ecosystem using the four Ps, before you even ask my go-to questions. 

Place: Tell AI what makes up your ecosystem from the size of the organization to the history it’s had. 

People: Describe who your stakeholders are and be as detailed as possible. Try introducing a few personas that your decision impacts.

Purpose: Identify the goals and objectives of your organization, your own professional goals in your leadership role, and any KPIs that might be relevant to the short or long term.

Problems: Explain the obstacles your organization has had over the last few years. Explain what your team has been struggling with. 

By asking ChatGPT to remember these things, every new piece of information will build upon the last. 

To set up a critic, add the prompt: “You are an expert in complex systems thinking, conflict-resolution, and design thinking. You are also my critical yet supportive thought partner who helps me see beyond my blindspots.” 

To set up a philosopher: “You are an expert in philosophy, regenerative ecosystems, and moral theory. You are also my critical yet supportive thought partner who helps me see beyond my blindspots.” 

…you get the idea. Following this, present your draft solution to AI and then ask the aforementioned go-to questions.

There are oodles of prompt engineering resources out there that will show you how to increase the reliability of responses. Our Ed3 DAO community member Brian Piper recently identified prompts he’s used. Please choose your own adventure.

The main goal with setting up a cognitive thought partner is to improve your thinking, not just the production of content. If used correctly, leaders having a thought partner who knows them can be game changing. 

Grasping our Self-Governance

Technology will outpace our ability to keep up with it. Expanding datasets and neural links will likely help AI get “smarter”. But if we want to stand a chance against the machine, we must retain our self-governance, AKA our ability to own our decisions and data. We need to continually evolve our cognitive abilities and explicitly recognize the nuances only humans know, from politics to pedagogy.

I’m grateful to the folks at CCSS for inviting me to share my ideas with them and commend their continued leadership across their school districts, despite how lonely leading can be.

Check out my newsletter for more thoughts on AI + Web3 and my website, www.vritisaraf.com. Join our community at Ed3 DAO to continue the conversation and to access AI courses for educators. 

The post AI is the Cognitive Friend We’ve Always Wanted appeared first on Getting Smart.

Often, they were doing this work with little cognitive and emotional support.

Dr. Carmen Garcia, president of CCSS, Superintendent of Morgan Hill Unified School District and an incredibly thoughtful and kind leader, welcomed the group with one sentiment; “being a superintendent is lonely.” No matter how big your team is, the high-pressure, highly-public, and highly responsible role of superintendent has little room for mistakes. 

In the education world, we’ve seen the ways educators can use AI to produce lesson plans, quizzes, and report cards. But I would argue the most important potential of AI isn’t to enhance human productivity. It’s to enhance and support human thinking. 

So at CCSS, I chose to prepare our Superintendents to use AI as the thought partner they’ve always wanted, in a world where leading is a lonely job. 

This 2-part article is about AI’s cognitive abilities as a thought partner.

The first part differentiates the competencies of AI from humans. It identifies what humans can double down on as their unique advantage, while also identifying a new standard for quality of thought using AI.

The second part (coming next week) provides ideas for how leaders can train an AI thought partner to represent whoever they want – a critic, a twin, a mentor, a philosopher, or a guide. 

The Cognitive Dance of AI

In the last year, we’ve seen a rapid improvement in the abilities of generative AI. It can take millions of pieces of data and reconfigure them into billions of pieces of content. However, shortcomings with data validity, misinformation, and algorithmic bias have deterred some educators from considering it a reliable tool.

When writing my keynote, I wondered if understanding AI’s cognitive abilities could help advocate for its utility. A familiar framework came to mind: Bloom’s Taxonomy. 

When I was a teacher, Bloom’s played an important role in lesson planning and assessing the competencies of my learners. Recent critics have appropriately recognized that these cognitive levels shouldn’t be stacked linearly, but should be more of a spiral that volleys between levels as learning is happening. Either way, it’s been the most accessible representation of learning in the last 70 years.

I thought that mapping AI’s abilities to Bloom’s Taxonomy would group at the top, bottom, or even perhaps swallow all of Bloom’s. In reality, it was much more spotty and varied, revealing a keen representation of human and robot capabilities.

Mapping AI to Bloom’s

Here’s my evaluation. Remember that the purpose was to set our superintendents up for understanding when and how AI is most powerful. As you read this, keep in mind how you’ve been thinking about AI.

Remembering: The Relentless Recaller 

• Bloom’s Level: Remembering
• AI’s abilities: Highly competent. 
• Key actions: Retrieving information such as facts, dates, definitions, or answers.

How well does AI recall data or information?

This first one is obvious. AI can simultaneously access millions of pieces of information across large databases. It will always be able to retrieve data more quickly, accurately, and with more abundance, than humans ever will. 

Understanding: The Illusionist 

• Bloom’s Level: Understanding 
• AI’s abilities: Not competent. 
• Key actions: Recognizing, discussing, or explaining the meaning behind information.

How well does AI make meaning of information? 

When I evaluated this level, I didn’t expect AI to fail so soon on Bloom’s. AI can recognize patterns, categorize data, and extract pattern-based meaning from large datasets, but it doesn’t truly “understand” in the human sense. Its comprehension is based on patterns and data, not on consciousness or intuition.

During my keynote at CCSS, the very thoughtful leader Dr. Tom McCoy, Superintendent at Oxnard Union HS District, chimed in with an incredible example. He explained how his son, when completing a homework assignment that asked him to write a goodbye letter to racism, used ChatGPT for ideas. ChatGPT replied with an opening line to the letter: “Dear Racism, We’ve had such great times in the past…”. AI used pattern recognition to identify how great letters hook the reader but didn’t make meaning of the purpose of the letter and the weight of racism. AI did not understand the assignment. 

AI possesses an uncanny ability to generate responses that, at face value, seem informed and profound. This is because it excels in pattern-matching, recognizing and mimicking structures, sequences, and commonalities within data. But it’s not making meaning.

Applying: The Patterned Practitioner

• Bloom’s Level: Applying 
• AI’s abilities: Somewhat competent.
• Key actions: Using information in new contexts to predict, interpret, solve for, execute, or implement. 

How well does AI use information in new situations? 

AI, especially machine learning models, excels in applying learned patterns to new data. At the heart of AI’s application skills is a concept called “transfer learning”, which enables an AI model trained on one task to be repurposed for a second related task without starting from scratch. This is akin to a human leveraging their knowledge of cycling to quickly learn motorcycle riding.

However, humans possess an innate ability to make intuitive leaps. If faced with an unfamiliar problem, we draw from our varied experiences, even if they seem unrelated, to find solutions. AI, on the other hand, relies heavily on patterns it has seen. It struggles in scenarios where data is sparse or where intuitive, out-of-the-box thinking is required.

So the effectiveness of AI at this bloom’s level is somewhat competent and really depends on the data it has along with the complexity of the problem.

Analyzing: The Connection King

• Bloom’s Level: Analyzing
• AI’s abilities: Highly competent.
• Key actions: Identifying trends, differentiating, comparing, relating, and questioning. 

How well does AI draw connections among ideas?

Traditionally, Bloom’s illustrates that if a student isn’t able to remember, understand or apply, they probably won’t be able to move up on the taxonomy. But seeing AI fail at the lower levels and excel at this one further helps to make the case for Bloom’s Taxonomy as a spiral construct, not a linear progression. 

AI can analyze vast and multidimensional datasets with superhuman speed, identifying subtle patterns and relationships. For instance, in genetics, AI tools can sift through enormous genomic data to spot potential markers or mutations linked to diseases. AI can predict potential future patterns based on historical data, which makes it highly competent at this level.

Evaluating: The Emotionless Evaluator

• Bloom’s Level: Evaluating
• AI’s abilities: Minimally competent
• Key actions: Making a judgment, critiquing, depending, or providing an informed opinion.

How well does AI make judgments?

The act of evaluation is not merely about decision-making based on data; it is a complex cognitive process that often demands judgment, ethics, and contextual understanding. AI falls apart at this level. It does not operate with ethical judgment, it does not have cultural nuance, and it certainly does not have emotions. It over-relies on quantifiable metrics and although this perspective is important and can be used to evaluate our own blindspots, it is not the full picture.

We know that the instinct-based decisions leaders need to make in difficult situations are sometimes the best decisions. Steve Jobs is famously known for using his instinct to launch the iPad when tablets were failing in the market. 

This level is where humans can shine and have a serious advantage over the machine. I gave this one a “minimally competent” because although AI cannot make judgments, it can provide us with the right information and recommendations so we can make judgments.

Creating: The Copy-Cat Composer

• Bloom’s Level: Creating 
• AI’s abilities: Somewhat competent
• Key actions: Producing, designing, assembling, constructing, formulating.

How well does AI produce new or original work?

AI can create new content by merging patterns it has observed, but it isn’t original. It doesn’t have original thoughts, emotions, or consciousness. Even when AI creates music, artwork, or narratives, it does so by identifying and combining patterns in its training data. The result may sound or look unique to our ears or eyes, especially when the AI blends seemingly disparate styles. But at its core, AI is not inventing; it’s remixing.

And because of this, AI’s creative capacity is tethered to data. It cannot make the cognitive leaps across variable experiences even if the sheer vastness of combinations it generates seems groundbreaking. The permutations are just regurgitations in many forms. 

Human creativity often springs from emotions, personal experiences, cultural contexts, and epiphanies. It’s organic, nuanced, risky, and sometimes serendipitous and unintuitive. These elements are currently beyond AI’s grasp. So although AI is highly competent at creating remixed content, it is not competent at creating original content. 

How Learning Blooms

Mapping AI on Bloom’s taxonomy opened several cognitive and presentation pathways for me. 

• It helped me explain the human advantage over AI 
• It depicted AI as a cognitive partner
• It identified the ways learners might use AI and be duped by AI
• It allowed me to narrate how AI will elevate our standards in education for the production of content, ideas, and discourse

This last point is particularly important. One of the superintendents mentioned that using AI feels like cheating. She didn’t want people to think her thoughts and her work were not her own. That made perfect sense to me and it was difficult to justify AI’s IP leaching algorithm. 

Instead, I shared that the calculator gave us the shortcuts we needed for quick and generic mathematics, but what we put in the calculator — how we used and contextualized the answer, and how we reasoned through the validity of the response — is what made the output our own. The use of the calculator also enabled educators to level up their expectations for students. Getting an answer was no longer the sole outcome. Now, students had to show their work and reason through more difficult questions. 

Although a simplistic analogy, AI will similarly create new standards of productivity for us. The more ubiquitous AI is, the more we will use it to produce higher-quality content. When everyone is using it, we’ll think of new ways to assess student competencies.

The next article in this two-part series will dive into how AI can be a cognitive partner to leaders. In the meantime, check out my newsletter for more thoughts on AI + Web3. Join our community at Ed3 DAO to continue the conversation and to access AI courses for educators.

The post What Bloom’s Taxonomy Can Teach Us About AI appeared first on Getting Smart.

For too long, schooling has remained largely unchanged. Our education systems were created before our digital world was formed, during the industrial era. New discoveries in learning theory, emotional and moral development, technology, socialization, and pedagogy, have not been reflected in most learning institutes. Despite technological and pedagogical advancements, our schools, educators, and learners remain locked into outdated learning models and narrow forms of assessment.

In today’s world, learning can happen anywhere and anytime.

The evolution of the internet has allowed individuals to access content independently, find opportunities for advancement in professional and academic careers, and socialize on a global level. Those who have learned to be autonomous, resilient, social, and critical thinkers have been able to create unparalleled opportunities for themselves. But as learners evolve, the education system seems to be stuck.

Education is built around systems and standards, not the individuals.

For ages, political demands, broken funding models, and poor teacher training using standardized curricula prevented schools from focusing on students. Misalignment of incentives and priorities between parents, students, teachers, administrators, and policymakers created an education system focused on credentialing, and not learning.

Money unevenly distributed across schools and within schools.

In the United States, public funding forces standardization. At the very top, public schools receive funds mostly through local taxes, meaning that there is a funding disparity between neighborhoods from the get-go. Decisions about where the funding goes is often made to maximize wholesale deals for textbooks and facilities that align to local, State, and Federal guidelines meaning most schools end up standardizing to increase efficiency. Schools aren’t able to make decisions about where most of their money goes.

We need to find better ways to align incentives in education to help reduce competing priorities.

Democratizing Learning Organizations

While we don’t have all the answers, we see tremendous potential in an emerging (but not entirely new) organizational model that may provide better solutions to the systems we have today. DAOs.

A decentralized autonomous organization (DAO) is organized as a flat structure where members have democratic voting rights. It aims to decrease bureaucracy and increase member ownership, much like a cooperative.

DAOs operate through smart contracts so that transactions and rules are encoded on a blockchain. What does this mean? An action with precise rules enables a reaction without the need for a central governing authority or middle man. This automation shifts the organization from being corruptible or the hope that it “won’t be evil” to trustless, or “can’t be evil”.

DAOs align incentives for their members. The more successful the DAO is in meeting its goals, the more the members benefit. And, when each member benefits from the success of the institution, they become incentivized to contribute in more substantive ways and for the good of the whole.

Now imagine if we applied this concept to a school, a union, or even a hospital.

If a school or a university were to become a DAO, teachers and students would have stake in the school itself. They would be able to vote on critical decisions like curriculum, assessments, and allocation of budget. Voters would need to think equally about the success of the school and the happiness of it’s constituents in order to continue operating. If the voters weighed anything disproportionately (i.e. if students chose “easy curriculum” or didn’t choose), the outcomes of the school would organically falter and cause the institution to fail. The school would also need to iterate and evolve constantly to keep attracting more DAO members. And because the DAO governance systems are transparent and publicly viewable on blockchain, it would be difficult to corrupt or spend funding unfairly. Imagine if a student were able to have a voice in determining where their tuition funds are allocated!

“Imagine if a student were able to have a voice in determining where their tuition funds are allocated!”

DAOs could also solve the problem of undervaluing teachers. When we think about poor teacher training, teacher burnout, low pay, and teacher shortages, they all point back to how our schools and communities undervalue the role of the teacher. Studies show that the quality of your Kindergarten teacher can determine how much you earn after college. And yet, our teachers (who make up the second largest employment group in the US) are shortchanged on proper professional development and are expected to take on roles they are not prepared for including social worker, para-professional, administrator, entertainer, and guard. It’s like asking a doctor to be the surgeon, nurse, security, and administrator. We are seeing the impacts of increased demands on classroom teachers as many great educators leave the profession. By bringing educators into the decision-making processes, advocacy for their profession would be easier, transparent, and more effective.

DAOs can also decentralize the learning institution itself. Imagine if professors at MIT, Stanford, and the Indian Institute of Technology got together to create a suite of content that students could take from anywhere in the world. Students would receive a digital credential that lives in their own wallet and a membership token that could provide a return every time more students took those courses. Everyone would be incentivized to ensure the content is of high quality and accessible on a global level.

Although certified DAO schools haven’t yet been experimented with, DAOs are currently gaining momentum as decentralized learning communities and social clubs, because learning is increasingly happening outside of the school system.

New Models for Living, Earning, and Learning

City DAO is the first DAO to buy land cooperatively. Its mission is to build an on-chain, community-governed, crypto city of the future. They imagine things like permits, budgets, laws, deeds, and records to be transparent, efficient, and free of bureaucracy. Members mostly contribute to the DAO’s mission using bounties, or micro-tasks that are automatically paid through blockchain contracts. They’re also working on a brand new schooling model in their city, one that is decentralized and self-organized.

Dream DAO is the first DAO for high school students, activating GenZ to take control of their learning and careers. Members create opportunities for each other to learn about web3 and contribute to projects that are regenerating the world. This includes climate impact, sustainability, education, and gender equality.

Ed3 DAO is the first DAO empowering global educators to catalyze innovation, at scale. Incubated by an education startup k20 Educators, Ed3 DAO is providing educators the tools, skills, and resources needed to change the way education operates.

A Case Study: Ed3 DAO

As a non-profit, Ed3 DAO is building toward a holacracy model, where governance structure is distributed among self-organized yet symbiotic groups, rather than top-down authority. Each contributing member has an evolving role associated with a purpose bound by the mission, a domain or node, and accountabilities.

“As a non-profit, Ed3 DAO is building toward a holacracy model, where governance structure is distributed among self-organized yet symbiotic groups, rather than top-down authority.”

The activities of the DAO are developed in three phases:

Phase 1: Education

First, E3 DAO is aiming to educate 1 million educators in web3 principles and practices. Web3 was chosen not just because of the technology, but because it underscores what DAO members believe the optimal state of education should be.

This includes self-organized and unbundled learning, human-centered models, regenerative systems, critical thinking, and immersive, place-based learning…because learning can happen anywhere. Web3 is optimizing tools that will support these practices in education and if educators can learn about them, they will be able to catalyze grassroots change.

In October, Ed3 DAO is launching a suite of courses with verified credentials on[c] blockchain to teach educators about web3. These courses help educators understand different technologies like blockchain, non-fungible tokens, smart contracts and virtual reality, along with underlying principles like game theory, financial literacy, cryptography, student-centered learning, and decentralized systems. Each course contextualizes the content for education and encourages participants to enter the web3 ecosystem as they’re learning about it.

Ed3 DAO is also hosting a virtual conference for web3 and education, in November. The goal of this event is to bring educators together with web3 builders to have real conversations about what web3 could be for education. The event has both introductory and deep content about web3 and allows learners to “choose their own adventure” in a fun, avatar-based environment.

Phase 2: Acceleration

As global educators learn about web3, Ed3 DAO predicts a portion of those educators will want to take action. Educators will be encouraged to identify pain points and obstacles in education and prototype solutions. Too often, edtech products and new school models are created by those who have never taught, which produces products and services that aren’t actually impacting education positively.

Ed3 DAO is building an accelerator for educators to prototype micro-schools, edtech products, and education services to solve the greatest problems in education, using web3 tools and principles. Educators will gain business and management skills while leveraging their understanding of web3 to ideate and prototype new models in cohorts.

Phase 3: Research & Iteration

Since the goal is to catalyze innovation at scale, these efforts must actually make an impact. In phase 3, Ed3 DAO will conduct an in-depth study to review the effect of the first two phases. We’ll dig into what was accomplished, what changes need to be made to continue scaling, and what other communities can learn from our mistakes and successes.

Human-Centered Systems are the Goal

Decentralizing organizations is easier said than done, of course. Creating infrastructure to operate these organizations autonomously has not been perfected. But, the goals are independence, human-centered models, and moving away from our current systems which were built on principles of compliance and standardization. It made sense for the needs of the Industrial era, but in the Innovation era, these systems need an upgrade.

It’s time for education to explore new models that elevate learning experiences, promote agency, and place the needs of individuals over compliance and efficiency. While the answers to how we solve these problems aren’t totally clear, we can begin to look to web3 to see what might be possible.

Vriti Saraf (@vritisaraf) is the CEO & founder of k20 Educators, a metaverse space for learning, which has been recognized as the world’s top 200 most innovative edtech companies by GSV. She is also a co-founder of the first community organization on blockchain for educators, by educators, called Ed3 DAO. 

Dr. Mike Peck (@edtechpeck) is a co-founder of Ed3 DAO, the first DAO for educators, by educators. He is also a technology leader at a public school where he works with administrators, teachers, students, and community members to leverage digital learning. 

This post is part of our New Pathways campaign sponsored by ASA, Stand Together and the Walton Family Foundation.

The post What Can Decentralized Organizations Accomplish in Education? appeared first on Getting Smart.

Are you a coffee drinker? Well, Starbucks just announced the launch of their web3 rewards program. Maybe you’re a student… Pearson is selling their textbooks as NFTs now. Or perhaps you’re into fashion. Gucci is accepting a cryptocurrency token called “apecoin” for its products.

Today, every facet of the world market is exploring or building on something called web3. If you’re not deeply immersed in this new technology, you might be thinking one of three things:

• What is web3?
• Why should I care?
• Is web3 a fad?

Let’s cut through the jargon and get to the core of it: Web3 is the evolution of the world wide web. The web has gone through three iterations as a result of emerging technologies.

Web1 (Read)

The first generation of the web allowed users to consume information from largely static web pages. Most people didn’t know how to code, many didn’t have access to high speed internet, and some didn’t have access to computers at all. This meant modest adoption of the web.

Web2 (Read, Write)

As access to the web became easier with the adoption of mobile devices and nearly ubiquitous high speed internet, we saw the emergence of social media, which changed how we engaged with the web and with one another. Without knowing code, we were able to build virtual identities, unveil our inner thoughts to the world, build global relationships, and find resources for our learning, earning, and living.

Web2 also came with trade offs. It allowed big corporations to poach and store our personal identifying information and user data into centralized databases. Free products afterall, had a price. As sculptor Richard Serra said in 1973: “if something is free, you’re the product”.

Clickbait, doom scrolling, fake news, and misinformation became key ways for advertisers to monetize off of our engagement.

Web3 (Read, Write, Own)

Today, there is a beacon of hope for reversing the culture of monopolies and ads through the next iteration of the web. You’ve probably heard about blockchain, NFTs, metaverse, cryptocurrency, and other concepts that are often described with complex jargon. These technologies have actually been around for decades. Blockchain was invented in 2008, metaverse was coined in 1992, and virtual gamified environments have been around since 1962. We are finally ready for this confluence of tech because today, most of our activity lives on the web, and because COVID helped accelerate our virtual interactivity.

Web3 is grounded more in ethos than it is in tech. Here are the most important principles in web3:

• Web3 is about decentralization and self-organization.
• Web3 is about digital ownership
• Web3 is about transparency.
• Web3 is about human connectivity.

New Pathways for Learning, Earning, & Living

So let’s get more practical. Why is web3 so important for the future of education? Here’s what the future could look like:

1. On-chain Credentials

“The unbundling of learning has triggered the decentralization of credentials.”

The great unbundling of education started when the web emerged. Open access to the world’s knowledge gave rise to alternative schooling models, supplemental boot camps, online universities, and post-secondary learning by big corporations.  The web has empowered us  to choose how, when, and where to learn.

COVID accelerated this movement and brought greater numbers to online learning. But this rapid expansion of learning opportunities also instigated a challenge: how do we verify and present our learning from numerous providers?

Before web3, when we wanted access to transcripts, credentials, and diplomas, we called our bursars. We verified our personal identifying information on the phone or in person, we paid a fee, and were given a physical document with the institute’s watermark. With multiple institutes came multiple pieces of paper.

Today, that system is moot.

The unbundling of learning has triggered the decentralization of credentials. web3 enables credentials to live in a wallet that is owned by us. Blockchain technology, which is a decentralized and secure way of recording transactions and data online, is producing solutions for verification of credentials (the watermark) that live on a distributed ledger (decentralized access), and is attached to your identity.

On-chain credentials allow us to streamline the certification process for learning. It also enables employers to seek out talent based on competencies and metadata. You can think of it as a verified and reliable version of ‘LinkedIn endorsements’.

In early August, Maharashtra, the third largest state in India (with a population of half the size of the U.S.) eliminated the inefficiencies of transcript processing by 1,000 personnel, by issuing 100,000 verifiable diplomas on the Polygon blockchain.

The rapid adoption of strong infrastructure to support decentralized credentialing is both a signal and an important aid to the future of unbundled learning.

2. Decentralized Autonomous Organizations:

“Today, there are DAOs buying land to start new cities, DAOs as recreational and social clubs, DAOs saving the environment, DAOs as universities, and even a DAO for educators.”

Post-industrial era, we’ve had several attempts at decentralization. Think co-ops, communes, free-lancing, and home-schooling. Web2 elevated these attempts through self-organized groups on Meetup and Facebook, the creation of online businesses, and access to learning resources through Khan Academy, Coursera, and other MOOCs.

So when we talk about decentralization in web3, we’re not talking about something new. But this is the first time we’re creating decentralized systems with technical infrastructure to support global engagement and community ownership.

Enter, DAOs, or decentralized autonomous organizations.

The goal of a DAO is to resemble a scaled co-op. It uses blockchain to automate voting processes and fund disbursements, runs business models using cryptocurrency, publicly exposes the treasury so corruption is immediately evident, and through tokens, gives ownership rights to the community.

DAOs can be solutions for schools (where students and teachers have voting power), non-profits (where crowd-funding through NFTs is the main funding mechanism), and unions (where transparency and automation of voting systems reduce interference and corruption).

Ed3 DAO is the first DAO for educators, by educators. Its ultimate goal is to help educators become the catalysts for evolving education. As a non-profit, Ed3 DAO offers verified micro-credentialing courses for web3 topics, an annual web3 in education unconference, an active and curious community, and eventually, an accelerator program for educators to solve massive problems in education. The magic of Ed3 DAO, and other communities like it, is that the community benefits as the DAO grows through shared contribution and shared ownership that is often reflected in the use of social tokens.

The power of DAOs lay in the ethos of operation. Transparency of transactions, focus on human connectivity, and community ownership of decisions and actions make it the ideal model for learning organizations.

In the case of Ed3DAO, every member is incentivized to help catalyze change in education and grow the DAO because they benefit both intellectually and fiscally as the DAO succeeds. In the case of a DAO school, every student, teacher, and parent is able to contribute toward school outcomes because they are responsible for the school’s success, and in turn, their own success in a very tangible and immediate way.

3. Metaverse Environments

“The future of education in the metaverse is personalized, dynamic, human-centered, problem-based, immersive, and self-driven.”

Do you remember The Magic School Bus? Ms. Frizzle’s stellar sense of fashion wasn’t the only reason she became an iconic figure. She modeled the most optimal form of emergent, student centered learning, to ever exist.

Think back… The daily adventures of The Magic School Bus always emerged from a student question or curiosity. Ms. Frizzle brought students to a dynamic place to answer that question. She then let her students choose their own paths and solve their own problems. At the end, several academic and social emotional lessons were learned, reflected upon by the students themselves.

This is the goal of the metaverse. The future of education in the metaverse is personalized, dynamic, human-centered, problem-based, immersive, and self-driven.  

Today, most students learn from static texts inside the walls of their classroom, under didactic curricular models. It’s a one-size fits all strategy created during the industrial era. The use of virtual environments can change that. Imagine simulating spelunking, the eradication of mosquitos, the beating of a human heart, or even simply, practicing language with an avatar from another world.

Integration with blockchain and tokens in the metaverse has the potential to simulate practical life skills, too. Students can experiment with micro-economies, build small businesses, create micro-cities and infrastructure, explore financial literacy, and more. And with decentralized access, anything students or teachers build will be their own IP for resale.

The future of learning will emerge from web3

In the past year, billions have been invested in web3 infrastructure across finance, manufacturing, fashion, environmental solutions and more. Considering the impact of the workforce on education, we are not far from web3 infiltrating into teaching and learning.

The more informed educators and parents are, the better the impact of these technologies will be. And for our learners, especially for those who need more equitable access to workforce preparation, it is essential that we begin to have conversations about web3 in our schools.

Vriti Saraf (@vritisaraf) is the CEO & founder of k20 Educators, a metaverse space for learning, which has been recognized as the world’s top 200 most innovative edtech companies by GSV. She is also a co-founder of the first community organization on blockchain for educators, by educators, called Ed3 DAO. 

Dr. Mike Peck (@edtechpeck) is a co-founder of Ed3 DAO, the first DAO for educators, by educators. He is also a technology leader at a public school where he works with administrators, teachers, students, and community members to leverage digital learning. 

This post is part of our New Pathways campaign sponsored by ASA, Stand Together and the Walton Family Foundation.

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