Visiting One Stone in Boise taught me that school could be about leading and difference making. I learned that young people could be invited into work and roles that matter (to them and their community) and that consequential experiences turbocharge leadership and problem solving (perhaps the two most important skill sets). 

Teresa Poppen launched One Stone 15 years ago as a free afterschool program for Boise high school students. She explained the origin of the studio model: 

Our innovative initiatives grew out of the desire to help students use their voice to change the world. At the starting line for One Stone is Project Good, an experiential service program that mobilizes the power of passionate students to bring real-world solutions to complex issues. Next came Two Birds, our student-led and directed creative services studio. Then we doubled down on entrepreneurship through the launch of Solution Lab, a business incubator for high school students. In each of these platforms we teach and use design thinking—ensuring we are relevant, innovative, and focused on our end user for real results.

Students are “active participants in the governance and direction of the organization” and make up two-thirds of the board of directors. (The featured image is of a One Stone board meeting where Teresa is sitting in back listening to student leaders.)

In the fall of 2016, with support from Albertson Foundation, One Stone opened Lab51, a high school based on the unique student-led culture, rooted in empathy and powered by design thinking.

Lab51 students co-author lab and studio experiences inspired by a beautiful outcome framework. They demonstrate and track progress on a Growth Transcript. Students explore their passions and develop a sense of purpose through Living in Beta, a personal wayfinding program.

One Stone studio learning experiences invite student leadership and value creation. 

Poppen explains, “We are forging an army of good, for good.”

This Boise program and school is a place alive with possibility. It inspired my book Difference Making at the Heart of Learning. It convinced me that inviting learners into work that matters could more than a capstone experience, it could be central to the mission of education.

In November, Teresa Poppen retired as the Executive Director and Ultimate Difference Maker at One Stone. She’s made a big difference in Boise and created a model that inspires educators nationally. I’ve learned something on every visit. 

For more on One Stone see: 

• Not Your Typical Back-to-School Days at Learner-Centered Sites (8/23)
• One Stone Students Serve Up the Glass Half Full (6/21)
• On Building a Network of Schools Using the One Stone Growth Transcript (4/21) 
• The Power of the Pivot (3/20)
• Why Difference Making is the New Superpower (4/20)
• Living in Beta: One Stone’s Pilot Results Are In (8/21) 
• Living in Beta: New Advisory Program Helps Teens Learn With Purpose (10/20)

The post One Stone: Forging An Army of Good  appeared first on Getting Smart.

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.

Despite current debates and stark policy adoption for additional resources in the ecosystem (i.e., American Rescue Plan Act of 2021), outputs merely reflect micro-improvements to the factory model of education. We must be intentional and bold to design future-focused learning organizations that prioritize skills like entrepreneurship as a core aspect of the PreK-12 pathway. Designing “Schools of the Future” in alignment to the entrepreneurial demand of society is a necessity for 2030 and beyond. How do we achieve this serial transformation with a business model anchored in factory rudiments? Below I highlight and define an entrepreneurial education model that will guarantee agency, voice, and coherence for Generation Z and Generation Alpha. 

Defining the Entrepreneurial Education Model for Generation Z and Generation Alpha

Because the education model is static by design (i.e., industrialization), creating operational structures and instructional systems rooted in entrepreneurship will accelerate readiness for the demand of Delta 2030. It will not be easy. Creating learning experiences that prominently feature opportunity and problem-solving is the antithesis of our existing education model realms – labor development. On the contrary, an entrepreneurial education model encourages a diverse agency set with students. Voice and agency empower students to understand their perspectives in-depth and exercise intrapersonal strengths to solve complexities in the global economy. Agency in the context of social and societal equity is building confidence among students to address emergent issues in our communities.

The Design and Strategy Outline to Create an Entrepreneurial Education Model in the AC-Stage of Education

An entrepreneurial education model can be contextualized as a “short cut” to synthetically develop competencies for the 22^nd century. In alignment with this evolutionary transformation of learning experiences for Generation Z and Generation Alpha, we must underpin strategic approaches to form a shared mental model in the learning organization (i.e., intentional to culture changes). In essence, the ecosystem must learn, unlearn, and relearn roles, responsibilities, and new cultural norms to implement strategies of a disruptive model rooted in entrepreneurship ideologies. Leaders must become iterative architects that create organizational constructs where adaptive learning through problem-based, problem-seeking, project-based sequences are the core threads in all integrated systems. Classroom teachers must become a hybrid of practitioners and facilitators where the universal acceptance of “we do not know” is the learning experience in lieu of binary answers rooted in static pedagogy. Core programs and curriculum will include investigation and inquiry activities where instructional practices support cycles of new learning. Yes, these shifts will enable Generation Z and Generation Alpha to be co-authors of their learning and personalize rigor for ALL. It will facilitate student competencies that do not focus on the linear business development aspects of entrepreneurship but reimagines a future that impacts society holistically. 

Agency, choice, and elevating the voice of the next two generations require innovation and creativity. Designing a new education model requires the multiple perspectives of our families and students. We have to accept being comfortably uncomfortable in the AC-Stage of Education. As a moral imperative, education must respond with vitality to leverage excellence and equity for ALL.

Michael Conner, Ed.D., is the CEO/Founder of Agile Evolutionary Group, Corp., Pathways Fellow for Getting Smart, and former Superintendent of Schools. He is the creator of the Disruptive Effect Model and author of Intentional, Bold, and Unapologetic: A Guide in Transforming Schools in the AC-Stage of Education.

The post Schools of the Future: Creating the Entrepreneurial Education Model appeared first on Getting Smart.

As we look toward the horizon of innovation and the subsequent rise in diverse school models, we are excited to announce the Learning Innovation Fund and our first grant program A Big Push for Small Schools, an initiative that taps into the rich legacy of microschools and reimagines them as the pillars of modern-day learning. These small school communities are redefining the way students learn.

The Vision Behind the Fund

At the heart of the Learning Innovation Fund lies a shared vision – to unlock the true potential of education by creating a place for all learners. This initiative envisions a future where every learner’s unique journey is nurtured, and where innovative, impactful education is accessible to all. The fund’s mission is to amplify the impact of innovative models, that are diverse and sustainable and empower learners for life.

With a rich history of leading educational advocacy strategy and fostering innovation, our team at Getting Smart stands uniquely prepared to spearhead this transformative initiative. Our wide range of experience and deep understanding of education dynamics, combined with a network of thought leaders and visionaries, allows us to curate an environment where innovation flourishes. We’ve witnessed the power and success of many different microschool models and believe in their potential to revolutionize education. Over the next few years, the Learning Innovation Fund will feature many more grantmaking partnerships all focused on creating new equitable opportunities for all learners. 

A Collaborative Endeavor

The Learning Innovation Fund is not just an initiative; it’s a testament to the power of collaboration. The funding for this first grant program is led by the Walton Family Foundation. As we continue to expand our impact, we enthusiastically welcome and are actively seeking additional funding partners who share our vision and want to join us in propelling education into a brighter future. We are also seeking evaluation partners, technical assistance providers and leaders interested in joining a small-school community of practice. 

Grant Opportunity

Imagine the potential when vision meets resources. The Learning Innovation Fund presents a golden opportunity for educators, innovators, and administrators of small schools to bring their innovative dreams to life. With up to 20 grants ranging from $75,000 to $250,000, this initiative aims to accelerate the impact and scalability of microschool models, igniting a ripple effect of positive change. 

Grantees will benefit from a comprehensive ecosystem of support. Collaborate with evaluators, work alongside technical assistance and coaching providers, and join a grantee network that nurtures innovation. This fund isn’t just about financial support; it’s about cultivating a community of change-makers. Beyond grants, this initiative will also include a platform for storytelling, case studies, and community conversations. By amplifying awareness of small school potential, we’re not just shaping education; we’re changing perceptions and inspiring new paths of learning.

Who Should Apply

We welcome innovative small-school leaders with transformative ideas, poised to make waves. 

We’re looking for diverse national models across the K-12 spectrum that utilize various funding methods, be it private, public, or ESA-funded models. Our selection criteria emphasize geographic diversity, ensuring that urban, suburban, and rural areas are all represented. We are interested in high-engagement learning models ready to scale or replicate. Above all, our priority is to support models that cater to historically underserved communities. If this aligns with your expertise or that of someone you know, find more details and the application process here.

A Call to Action

Join us in this transformative journey of education. If you know passionate small school leaders who could benefit from this grant, we urge you to share this exciting opportunity with them. Let’s collectively amplify the reach and impact of small schools, ensuring that every learner gets the education they truly deserve. Sometimes, the smallest shifts lead to the most significant transformations. Let’s make a big push, together!

The post Big Push for Small Schools Grants appeared first on Getting Smart.

I’ve been in education for over 35 years now. I have almost seen it all having worked in over 100 districts, 500 schools, and with over 1,000 educators and ten state departments of education. I’ve been in Seattle, Hartford and Waterbury, Yonkers, New York City, Worcester and Fall River, etc., as well as state education departments up and down the Eastern seaboard.  From this work, I have seen how the mindsets, expectations, paradigms and federal and state policies have handcuffed the opportunity to serve our youth better than we do now.

Recognizing that the current design of school – in how we divide the disciplines, cover content, and push students through an assembly line of class after class in groups of 20-30 – was not working, I began my quest to seek out new school designs that far better serve youth and their future.  The first “aha” I had was in coming across the Big Picture schools, then just getting underway out of Providence RI.  To think that this school design was not organized to shepherd youth through a game-board of compliance (think chutes and ladders) but with a focus on the interests of students and the opportunity for them to explore their interests through internships and projects blew my mind.

I left Seattle after three years to work at Brown University where from I worked with school districts throughout New England and beyond to assist them in the creation of smaller learning communities, only to find that most of these systems really only wanted the money and didn’t want to really change how they did school very much.  Yes, some of them created SLCs and career academies, but these shifts were minor in relationship to what was truly needed to serve youth better.  Then I ended up working with several state departments of education with colleagues on how they could better assist and support school and district improvement in their respective states.  Unfortunately, the bottom line – as a result of NCLB and later the misguided federal Turnaround School efforts of the Obama administration – was raising the ELA and math proficiency levels in their lowest-performing schools.

Seeing that the current policy environment did little to truly incentivize and support the growth of innovative, learner-centered schools, and now grounded in the power of experiential learning – a focus of Northeastern University where I have been teaching since 2009 – I sought out to find other schools pushing the envelope in the design of school, designs that foregrounded student agency, relationships, and the possibilities of their future lives.  While working in Seattle, I came to know Jon Ketler and his colleagues and how they created their SOTA, SAMI, and IDEA schools, which have been highlighted here and here in Getting Smart.  One Stone, a tuition free private school in Boise ID, directed by students.  The well-regarded High Tech High in San Diego.  The quickly growing and awesome CAPS Network.  The innovative Design 39.  And Iowa BIG, amongst many others.

The problem as I see it and as I attempt to detail in A Revolution in Education, is that each of these endeavors were the result of creative educators who through serendipity were able to manifest their visions from idea to reality.  These educators by way of serendipitous events, relationships, and access to resources were able to materialize their visions with the support of others and a fortuitous context that supported their taking root.  This is incredible and wonderful, but this means that an educator with a vision has to have the courage, commitment, and savvy and be afforded a similar alignment of stakeholder interest and welcoming context to get their vision off the ground.  But our current public school ecosystem – with its current policies and funding streams – is very much designed to support the current status quo and hence very good at sustaining school as we now know it.

It continues to be increasingly clear that our current designs and aims of schooling are doing a gross disservice to our youth, their families, our communities, and the world.  Student apathy remains high.  Outcomes remain low – for most.  It is for all these reasons that I push for a revolution today.  Our youth remain disengaged and are graduating without the skills desired by employers.  And perhaps most important, students continue to vocalize the lack of personal relevance and development of agency and opportunity they want, left abysmally unsure of what they want to do upon graduating and how they can pursue a desired future.

While the stories of High Tech High, the Tacoma Schools, Iowa BIG, and numerous others in the book can inspire us to rethink what school could be, these origin stories foreground how the start-up of such schools relied on the courage, persistence, creativity, and serendipitous events, relationships, community advocacy, and resources that led to their taking root and growing over time.  Educators should not have to hope for or fight for the same fortuitous circumstances as afforded the other schools named in this book.  Our public school system should be proactively incentivizing and supporting district and school leaders, classroom educators, and community partners to break the mold and build new designs of learning that serve their youth better.  When educators are empowered and supported in pursuing creative visions, transformative school models can grow.

So, the call to action is clear:  To school and district leaders, classroom educators, policy actors, parents, and community members, the future of education is yours to pursue.  We can use many of the stories presented in the book and as evident elsewhere to help us pursue new designs.  The revolution has been and continues to be underway here and there, but we need it to grow everywhere.  Not slowly, and in only some communities.  Rather, we need the number to grow exponentially, and in all communities.  To do this, district and state policy actors need to be brave and figure out ways to incentivize and support the significant growth of agency-oriented schools.  We can’t keep hoping that a few will break the mold despite the circumstances.  We need the system to actively incentivize and support the development and growth of new designs.  Then we need school and district leaders to embrace the challenge, community members to advocate for the new designs, and then district and state policy actors to create the greenfield for such schools to take root and grow.  Fast.  

How many more generations of students should we lose to apathy, compliance, and the lack of skills and knowledge that can benefit them moving forward in their lives?  How much longer can we wait not taking advantage of the creativity of our educators and the possibilities of what we can do for our youth, our communities, and the world?  Let’s stop tinkering inside the current model – something our current schools are very good at – and re-vision the aims of our schools and how we can achieve those ends – toward agency and opportunity for all.

Learn more about A Revolution in Education: Scaling Agency and Opportunity for All.

Chris Unger is a Teaching Professor in the Graduate School of Education at Northeastern University and supports the Graduate School of Education’s Network for Experiential Teaching and Learning (NExT) with a number of his colleagues at the University.

The post Viva la Revolución appeared first on Getting Smart.

Opportunities and experiences at all stages of students’ lives are integral in shaping the adults they become. For today’s students, though, the pandemic stole valuable time, resulting in steep academic and social-emotional declines that will impact them for years to come. 

As COVID-related disruptions disappear into the rearview mirror, the educational community must take time to reflect on the purpose of education and reevaluate strategies currently in place to prepare our next generation for their future. Instead of focusing solely on passing state assessments, remediating to address gaps, or identifying students for advanced academic programming, schools must become a place where all children can develop their talents.

Nurturing Talent from the Start

Nurturing talent must be an ongoing process rather than a single event. Programs must be re-envisioned with a lens toward adulthood, rather than doing only what it takes for students to get the best score on tomorrow’s test. And these programs must help all students – even the youngest ones – develop the skills they need to succeed in their lives and careers beyond graduation.

Talent development research emphasizes that ability is malleable and domain-specific, rather than fixed and global in nature, and developed by specialized educational and training opportunities. The goal of talent development is to transform potential and recognized abilities into domain-specific competencies, expertise, and creative productivity or eminence.

Looking at education from this perspective, there are two components that foster the critical and creative thinking, as well as problem-solving, skills required for talent development:

1. Domain knowledge and skills – The ability, competence, expertise and detailed understanding of core topics.
2. Psycho-social skills – Mental skills and social-emotional learning (SEL), such as a student’s ability to engage with others, regulate emotions and see themselves as capable.

Addressing a Mismatch in Learning and Student Needs

While these programs are seeing success, in many schools, students are unable to effectively develop their talents because there is a mismatch between the learning environment and student needs. Since achievement only tells one chapter of a student’s story, schools also must consider student abilities and social-emotional competencies. To truly identify opportunities to personalize learning based on student needs, schools must triangulate ability, social-emotional skills and achievement measures.

By looking through different lenses and evaluating these multiple measures, schools can hone in on student’s individual talents and engage them in ways they learn best in the classroom. Many districts are interested in this strength-based approach to learning and have to reimagine what learning looks like by starting pilot programs at individual schools or with certain groups to refine processes and leverage lessons learned as they scale to a wider population.

Richardson ISD in North Texas believes that everyone deserves an enriching learning environment, regardless if they are gifted learners or struggling students. The district developed a robust accelerated learning program and a creative approach to using ability data in every classroom to address this goal. They use a center-based classroom to provide opportunities for an educator to adjust their practices, ensuring constant student engagement and monitoring, while providing immediate feedback as the students work with collaborative peer groups.  

Reinvented Programs Deliver Results

To help all students achieve at the highest levels, many school systems are leveraging student strengths and focusing on teaching them to apply skills and experiences in ways that help them grow. This is the approach taken by talent development-based gifted education services, such as the one in Elgin, Illinois. Elgin U-46 is a large suburban K-12 district northwest of Chicago that serves a socioeconomically, racially, linguistically and culturally diverse population of 36,000 students. 

Over the last several years, Elgin U-46 redesigned its programs to support equity and access to advanced learning services. The district implemented several evidence-based practices – including the use of multiple assessments for identifying students for advanced programming, providing early-grade enrichment, and using a combination of universal screening and local norms for placement – with demonstrated results. During this time the number of Hispanic students in the program has doubled, better reflecting the population of the district’s student body. 

Another example of an innovative talent development program is Project OCCAMS, a partnership between the Center for Talent Development at Northwestern University, the Center for Gifted Education at the College of William and Mary, and the Columbus (Ohio) City Schools. The cutting-edge program helps prepare students from low-income backgrounds for advanced high school courses. 

Project OCCAMS develops and delivers an accelerated online English language arts (ELA) curriculum that compacts ELA standards for grades 7-9 into a two-year course sequence in grades 7-8. This allows students to earn a high school credit in middle school and enter high school one year accelerated. Coursework is facilitated through the Center for Talent Development’s online platform and uses a “hybrid” delivery model combining elements of online and in-class learning. Pilot teachers are engaged as co-developers of the curriculum using a design-based research model. All project partners also participate in an active online community where feedback, coaching and technical support are provided in near real-time.

As schools reimagine their roles in the post-pandemic world, a high priority must be given to developing the talents of all students, not just a select group that may do well on achievement tests. Educators and administrators must use a combination of measures – ability, achievement and SEL – to better understand each individual student’s style of learning and how to best engage them in the classroom. Personalized instruction that enables students to self-direct their learning in creative ways will be imperative as we prepare students for success beyond graduation.

Susan Corwith is the associate director at Northwestern University’s Center for Talent Development.

Anna Houseman is the director at Riverside Insights.

The post From Classroom to Adulthood: The Importance of Talent Development from an Early Age appeared first on Getting Smart.

I reached out to several educators and some students to find out what they thought about the changes we are experiencing in education, especially with these emerging technologies. There are, of course, concerns, ongoing conversations, and definitely excitement, although sometimes hesitant excitement, about what these technologies might bring to the world of education and work. 

Educator predictions

Nicole Biscotti @BiscottiNicole, Educator 

“AI has exploded on the scene and everyone is trying to figure it out at the same time which is exciting. A lot of educators are afraid that kids will use technologies like ChatGPT to cheat and have even banned its use. The reality is that kids have been cheating since the beginning of school and we have an exciting opportunity as educators to shape the narrative around this new technology. Biscotti plans to continue to learn as much as she can about how to use AI to better support her students. She says as an educator, “I feel that I am obligated to prepare my students for their future, not my past. These tools will only improve and they are here to stay. It’s imperative that kids are familiar with them and know how to use them or we risk sending graduates out into the workforce at a competitive disadvantage.”  

Dr. Michael Harvey @doctor_harves, Pedagogical Prognosticator from Blenheim New Zealand

“AI/emerging tech has yet to really shape this past school year. Questions are being asked of what learning is and the role of assessment, but the actions to this change are yet to be realized.” He is thinking about /planning how to transition assessment tasks to a process rather than a product model. As for the future, Harvey thinks that “the next school year will bring pushback from the establishment and for those who want to progress in education a movement away from assessment of learning to assessment for learning and the role of AI to support student independence in learning.”

Alex Isaacs @mr_isaacs_math, Edtech Specialist from Long Branch Public Schools 

“Emerging tech has helped teachers gain time while assisting with lesson planning, quick questions, and providing feedback to students in real-time. It has also helped increase data analytic capabilities by providing insights into student performance trends and instructional practice.” He plans to delve more into emerging technologies and how they can benefit the teachers and students in his school district. By participating in multiple coaching cohorts, including GoGuardian and Pear Deck, he hopes to gain more insight into helping his colleagues with these platforms. As for the future, “the discussions surrounding the ethical use of emerging tech will continue next school year.” He believes that more edtech platforms will increase their integrations of AI-related tech to provide differentiated instruction and personalized pathways to every learner.” Check out his AI-themed Wakelet collection that he and colleague Lois Alston created.

Vicki Van Matre @MsVanMatreArtRm, Teacher at Bluffton High School and  ISTE Certified Instructor 

Next year, Vicki will be teaching a class called the” 3rd class” for their new Education Professions pathway. One basis for this class is introducing technology’s role in the modern classroom. She was planning on discussing AI and how it could help the teacher but also discuss how to use it appropriately as a student. Her plans focus on involving students more in learning about ChatGPT by writing a lesson for them and having them make modifications where necessary. She also wants her students to experiment with a variety of technology to see “what THEY think would work best in the classroom.”  While she uses technology in her art room, she has not yet experimented a lot with DALL E to see if I want to use AI for any of my art classes. However, she believes that if she does choose to use it, most likely it would be with the senior independent study students to have them experiment with it. 

Student reflections

Student, grade 10

“As soon as I was introduced to AI, I knew that it would change the world. The more I interacted with it, the more I understood its vast potential.” He shared that he “may or may not have used it to complete a few small assignments (Never in Spanish).” His uses were more as a study tool, in particular for reviewing for Chemistry. He would ask it to give a simple definition and an example and it worked perfectly to help him process the information and content better. He believes that “AI by itself is mediocre at best. However, when collaborating with humans, it can do amazing things.” As for benefits, he has an interest in starting an online store but has never really had the time and he hopes that with the help of AI to get a jumpstart this summer. “The school will look right through all the great things that AI is capable of and try to prevent it from being used completely.”

Lola, grade 11

“I believe that emerging technology has provided and created new opportunities for students. The new ideas and aspects of technology accessible to students now provide new experiences and can help to improve a student’s capabilities and knowledge about the subject they are studying.” Looking ahead, “I believe that by next school year, technology will continue to grow and emerge in new ways that will allow students to continue to have opportunities that will enhance their learning environment.”

Dimitri, grade 8

“AI and a lot of new tech has helped me with coming up with ideas for projects or providing me with a summary of a book, if I forgot something that I had read or I didn’t understand it.” As for the next school year, “I hope we will be allowed to use ChatGPT in class to help us understand books and literature more.”

With the rapid advancements in AI and emerging technologies, there are now incredible opportunities available to us and as we reflect on the past year, it becomes evident that AI and emerging tech have revolutionized education in ways we never imagined.

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We are a few months into a new age of human-computer interaction, an era that will change how we work, our aspirations and even our identity. The rise of artificial intelligence, especially generative AI, boosts productivity in content creation–text, code, images and increasingly video.  

An OpenAI/Penn study suggests around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of large language models (LLM), while 19% of workers may see at least 50% of their tasks impacted. Unlike prior waves of automation, higher-income jobs face greater exposure to LLM capabilities.

With tools built on top of LLM, including sector-specific models such as BloombergGPT in finance and MedPalM2 in healthcare, about half of all worker tasks in the US could be completed significantly faster at the same level of quality.

With the caveat that none of us knows how AI will influence life and work, the following are six preliminary conclusions about the nature of work and learning.

1. AI narrows the skill gap.

Generative AI augments content creation and boosts response capabilities, especially for low-skill workers and learners. A new Stanford/MIT study of customer support agents showed that generative AI increased productivity by 14% on average with the greatest impact on novice and low-skilled workers. AI also narrowed the productivity gap between lower-skilled workers and workers with more skills.

Next generation AI tutors will accelerate skill-building (even better than current adaptive systems i-Ready, DreamBox, i-Station, ALEKS) by modifying path as well as level.

MIT’s David Autor optimistically told NPR “that we could use AI to expand job opportunities, lower barriers to entry to a whole range of occupations, and reduce inequality.”

Accelerated skill building and real-time skill augmentation are promising developments and may begin to level the skills playing field but the experience gap (below) is likely to widen along with the wealth gap.

2. AI augmented workforce values experience.

New productivity and quality expectations may raise employment experience requirements. “Employers might reasonably expect entry-level workers to be conversant with AI and something like 50% more productive. And this means while the skills gap may narrow, the experience gap could become a chasm,” said Ryan Craig, Achieve Partners (a private equity firm that invests in hiring intermediaries).

“The bar for good entry-level jobs will be higher, meaning fewer jobs that look entry level, and rendering career launch even more difficult,” said Craig. He continued, “Employers will only want to onboard entry-level workers who’ve already proven they can do the job…The problem will become even more acute with the emergence of industry-specific large language models”

“In the ChatGPT era, the future of career launch and socioeconomic mobility will depend on scaling pathways that not only teach, but also provide relevant work experience. CTE and youth apprenticeships will become priorities for every high school,” said Craig.

“As generative AI transforms entry-level jobs and puts a premium on experience, these earn-and-learn models are likely to be the best bet for helping millions of young people launch careers,” added Craig.

Author of the forthcoming book Apprentice Nation, Craig thinks work-based learning (client projects, internships and apprenticeships) can narrow the experience gap for high school and college students.  

3. AI makes Super T professionals.

 For three decades, talent consultants have referred to T shaped professionals as having the breadth to collaborate across disciplines with the depth of expertise in a specific field. For individuals and teams, generative AI extends breadth and, with sector specific tools built on LLMs, extends expert-level depth.

BloombergGPT answers financial questions, conducts analysis, and builds projections.  Google’s Med-PaLM2 answers medical questions and supports diagnosis (and passes medical licensing exams). GitHub Copilot and Replit Ghostwriter generate, complete, and explain code. All of these, to Craig’s point, are most useful to workers with some sector knowledge and experience.

AI changes the vocational identity of augmented professionals. It affords a new sense of purpose and agency–expanding the scope of the change you can make in the world. Suddenly, you can be a 5x coder, maybe a 10x coder, doing more than a skilled team could accomplish in the same period of time.

AI may not take your job, but a clever, sector-knowledgable augmented (Super T) human will.  

4. Augmented work requires new skills.

 In a May research brief, Microsoft said “As AI reshapes work, human-AI collaboration will be the next transformational work pattern—and the ability to work iteratively with AI will be a key skill for every employee.”

“ChatGPT and the underlying technology of large language models is likely to change nearly every job that involves writing, analysis or otherwise processing information…someone with prompt engineering skills will find themselves in demand across a growing range of professions, from coding and engineering, to marketing, management, law, research, product development, administration and many others,” said ASU’s Andrew Maynard.

Prompt engineering requires analytical judgment, intellectual curiosity, and creative evaluation–three of the top skills identified by Microsoft. Leaders they surveyed said “it’s essential that employees learn when to leverage AI, how to write great prompts, how to evaluate creative work, and how to check for bias.”

Source: Will AI Fix Work? Microsoft 

Problem finding, a precursor to prompt engineering, is spotting work worth doing–work that matters to you and your community. KEEN, a network of 55 engineering schools committed to developing an entrepreneurial mindset, calls it opportunity recognition which leads to solution design and impact delivery. It takes curiosity, willingness to explore contrarian views, connections for many sources, and a commitment to creating value for communities.

5. Be a better you.

With the widespread use of generative AI, we will all be producing less and editing and curating more. Editing requires Models of Excellence–you have to know what good looks like. Editing also requires a personal narrative. To be a better communicator than ChatGPT, you have to be a great storyteller.

Kevin Kelly told Steve Levitt that in the age of big prediction models, there is value in being unpredictable. “A really worthy goal is to arrange your life or become something where you’re not predictable by A.I. Again, A.I. is a prediction thing. It’s going to try and guess what the next average human would say, and you don’t want to be the average human if at all possible. You want to be you.”

And, while you’re being your best creative self, get the facts right. In the early models of generative AI we’re co-creating with tools that hallucinate convincingly. Add an explosion of deepfakes and editing for creativity AND accuracy is the new challenge.  

6. Augmented work changes pathways bets.

The dynamic velocity of the employment market makes postsecondary learning more important than ever. However, it is more important than ever to pursue learning with a sense of purpose and a plan for employment. Debt without a degree and related employment is the new worst case scenario. If you can get a full scholarship to a selective college take it and start building work experience. If not, consider an earn and learn ladder, find an employer willing to pay for training and development.  

Liberal arts might be more valuable than ever–they build the knowledge, skills, and mindsets that make us most human–but don’t pay too much for a degree without building experience and employability.  

Next Steps for EdLeaders

Start the conversation with staff and community about the age of human-computer interaction. They need to hear from you. You don’t need all the answers, you just need to show up and acknowledge that we’re living through something new and important.

Banning generative AI isn’t an option. After an initial ban, NYC Chancellor David Banks said, “New York City Public Schools will encourage and support our educators and students as they learn about and explore this game-changing technology while also creating a repository and community to share their findings across our schools.” They launched a Day of AI to explore new possibilities.  

Encourage exploration and use with disclosure. Take a human-centered, teacher-in-the-loop approach (see US ED OET Recommendations for more). Update your code of conduct (and your acceptable use policy as needed) and give instructional staff the ability to establish parameters around when and how students should engage in AI to complete an assignment or project.

Update your digital citizenship training and introduce the ethics of AI beginning not later than middle school.

Start iterating assessment and grading practices. Assume that any work done outside of school is augmented.

Expand access to Real World Learning including internships, client projects, and entrepreneurial experiences. Expand access to accelerated pathways including college credit and credentials.  

This change won’t be easy but this new era means young people can do more than ever–more than we dreamed possible even a few months ago. It’s time to invite them, especially learners furthest from opportunity, into a future of possibility, into work that matters. Their potential just got bigger and better.

The post The Rise of AI: New Rules for Super T Professionals and Next Steps for EdLeaders  appeared first on Getting Smart.

One can choose to go back toward safety or forward toward growth. Growth must be chosen again and again; fear must be overcome again and again.

-Abraham Maslow

Schools and organizations are human systems, filled with opportunities. And yet there is a profound difference between building from a foundation of schooling and building from a foundation of learning.

Rather than simply replicate and scale the arrangements of schooling, we must seize the possibility to shift from a schooling system to a learning ecosystem to truly empower learners, educators, and parents to create a virtuous future for themselves and their communities.

Clearly, we live in this moment of increasing complexity with generative AI (ChatGPT), geopolitical crises (Ukraine), financial instability (inflation), pandemics (Covid), and ecological precariousness (climate change, biodiversity loss) impacting all aspects of living in the modern world. With this as the backdrop, we get to move away from the rigidity of schooling models we’ve inherited (and unconsciously reinforce) and venture into the emerging learning paradigm.

Technology, if used well, might allow us to return to our deep humanity

The future will be what we make it and therefore certainty is an impossibility. But if I can be so bold, it’s clear our future will be one filled with life-long, life-deep, and life-wide learning that must unlock both human and planetary flourishing. This means leveraging exponential technologies to become more human and solve wicked problems as we create new possibilities. To cultivate first-class human beings, rather than second-class robots, as the saying goes.

As an education futurist and learning strategist, and CEO of The Learning Future, my days are filled with all kinds of wonderful conversations and work in, on, and about learning (plus all the other prepositions). I am lucky to attend a range of global fora relevant to this, including an Education Futures Fellowship at Salzburg Global Seminar last year and the UNGA, and reflecting on this I want to share a list of themes that I see are emerging and exciting me and other rather brilliant educators and innovators with whom I work and learn.

So below is an incomplete and constantly evolving list of key concepts for our collective Learning Future.

A-Zs that can unlock learning

This new alphabet (terribly English-centric), from A-Z, seeks to help learners, educators, leaders, and parents discover some of the key trends and themes that are taking hold. Our challenge will be how we choose to redesign and remake our experiences, environments, and ecosystems so that we unlock our true creative potential and thrive in the future.

Agency — The feeling of conscious control over our actions and their consequences. Last year, we released a podcast with the brilliant Charlie Leadbeater and a network of Australian schools that unpacks 10 lessons for placing agency at the heart of schools. [Listen]

Belonging — We have a need to belong and yet 1 in 5 people suffer from chronic loneliness*. Cultivating environments of belonging that create a social fabric is key to psychological safety and any optimal learning or work experience. (*Campaign to End Loneliness)

Creativity — A fundamentally human skill, to create something that wasn’t there before, great words from a mentor of mine Larry Rosenstock. In this creativity crisis, it’s essential we develop our unique creativity at all levels of education and work to best contribute to the economy and society.

Discernment — In a world awash with information, navigating misinformation and developing the ability to think critically is a key pillar of education. How do we decide what is right and challenge bias and mental models? We develop our capacity to challenge, question, take perspective and unearth bias.

Embodiment- We live our lives in our bodies and practices that help us connect the brain and body are the baseplace for optimal well-being through learning. Mind-body interrelationships are so clear as the extended mind research is now telling us.

Flourishing — The ultimate goal of education and all social progress. Collective flourishing — where we have social prosperity in planetary limits set by biocollective.

Generative AI Generative artificial intelligence describes algorithms (such as the now infamous ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos by giving it tasks in fairly usual language. It has huge ramifications for schooling, especially assessment, and also for the workforce. (McKinsey)

Humanistic psychology — (thanks Maslow!) As a whole person, we are greater than the sum of our parts.

Inquiry-based learning — A focus on investigation and problem-solving. Growing a learner’s capacity for curiosity, critical thinking, and creativity through real-life questions, problems, and scenarios to explore.

Joy — An under-appreciated emotion, it is more than happiness. Joy can co-exist with sadness and it is often a by-product of the core modality of play.

Kindness — “No act of kindness, no matter how small, is ever wasted.” (Aesop). Above all, be kind. Famous last words from Aldous Huxley. Schools and workplaces that cultivate kindness create psychological safety and more virtuous cultures that support optimal learning and growth.

Learning intentions — Provide clarity of the knowledge and understanding, to support student success and guide improvement strategies.

Meta-learning — Learning about learning, especially mental models. We become great learners only when we can rise about our learning and look at how we learn best, and what models we are using in our life to learn quicker. Great work from FS blog.

Neuroscience — Brain research highlights how complex structures and processes of the brain impact human functioning and behavior (affective and cognitive). See the excellent work of Mary Helen Immordino-Yang

OCEAN: Who are you? OCEAN is an acronym that seeks to deconstruct personality and increase self-awareness by considering your: Open-mindedness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Great article here.

Pedagogical core: This refers to the content (the what?), the resources (with what?), and the teachers (with whom?). It is the core business of schools and when we focus on all these elements it offers many ways of innovating learning environments and transforming education (OECD, 2013).

Questioning: “My mother made me a scientist,” recalled physicist Isidor Rabi, Nobel Prize laureate. “Izzy,’ she would say, did you ask a good question today?” (Forbes, 2020)

Relationships: quality relationships are shown to be the strongest predictive factor for our health. And increasingly I consider all learning as understanding relationships, between concepts, emotions, memories, possibilities, and perspectives.

Self-determination theory: One of the most important ways of understanding how we can help ourselves and others live a purposeful life. (Deci & Ryan, 2000)

Trans-disciplinary learning — Making connections across subjects that allows for deeper learning and grows learner capacity to tackle complex global problems we face now and in the future.

Understanding — “Seek first to understand, then to be understood” (Covey, 1989). So much of our learning is not about knowledge, but about understanding, which is connecting knowledge

Vitality — Rather than thinking of just the outputs of school. Even the well-known PERMA framework added vitality after the fact.

Wellbeing — Evident in our sense of satisfaction, a connection to meaning and purpose. It is the sense of feeling well, linked to our thoughts, emotions, actions, and experiences. This is a core goal of modern learning organizations and systems, as the renewed global focus tells us.

X — The unknown variable. In our complex world, not all can be known, not all can be understood. Yet we must still make progress, and factor in the unknowns.

Yes (as a default) — So often we default to no in our schools and workplaces, or as leaders. If we start with yes it opens possibilities that can then be explored through ideation and prototyping.

Zest — remember that this is your life. Nothing is achieved without enthusiasm and ultimately we should all ask ourselves this key question of ourselves, our colleagues, and our students: what is yours to do today?

I’m aiming to create a short video of each of these terms across the rest of 2023. If you want to follow along, check out The Learning Future YouTube Channel and the podcast that we release.

Whatever learning you’re currently engaged in, I hope it brings you new discoveries, and new self-awareness and reminds you of the difference you make to the world around you.

Here’s to a full year of fully human learning.

This post was originally published here.

Louka Parry is the CEO and Founder of The Learning Future, an organisation that supports schools, systems and companies to thrive in tomorrow’s world. A former teacher, he became a school principal at 27 years old and was named Inspirational Public Secondary Teacher of the Year for South Australia.

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