Special Webinar: Future‑Ready Alignment

The following transcript has been edited for readability. Timestamps have been removed and minor transcription issues corrected. Speaker comments have not been altered.

Opening Remarks

Rich Portelance

Today, we're going to be talking about Future-Ready Alignment: Redefining Curriculum for What Comes Next. Welcome to the EdGate Powers webinar. I'm your host, Rich Portelance, and today we have an outstanding panel of industry experts to discuss future-ready alignment.

And a few housekeeping items before we get going. All attendees' mics will be muted throughout the program, but if there's a question that you have, we're going to do a Q&A portion at the end of the session. So, please put your questions in using the button at the bottom of the screen. We will address it either in line and answer it through the interface, or we'll answer it live at the end of the session. 

Over the past several months, one theme has become increasingly clear. The industry is moving beyond the excitement of AI and into the reality of implementation. And that shift is exposing something very critical; AI is only as effective as the systems it operates within. Without strong alignment across standards, curriculum, and data, AI does not create coherence. It accelerates fragmentation, inconsistency, and risk. 

So, how do we avoid those things? And what's become increasingly clear is alignment isn't just a technical requirement. It directly shapes what students are able to learn and do. So, today's conversation sits at the intersection of systems that scale to meet market requirements and systems that support meaningful student outcomes. So it's really critical for what we do because without alignment as infrastructure neither is really possible. 

So, without further ado, I'm going to ask each of our panelists to provide a brief introduction and then we're going to kick into the webinar. 

Hillary, can you start?

Hillary Rinaldi

Thanks so much, Rich. I’m so excited to be here and join in for another EdGate webinar. 

My name is Hillary Rinaldi. I'm a vice president at Whiteboard Advisors focused on K-12 research and policy. We're an education policy firm based in DC. While I do a lot of work across the K-12 landscape, I also have a unique purview into the implementation work happening in districts in mathematics and high quality instructional materials, as I lead the national math improvement project, which is the community practice of six largest districts in the country.

So, I’m really excited to dig in today and think about the connectivity to math in particular, as well as just how we support all students in building their skills necessary for success in the future. 

So, thanks for having me. 

Rich Portelance

Thank you, Hillary. 

Next, Larry Johnson from EdGate. And you're muted, Larry.

Larry Johnson

Yeah, I just noticed that. Thank you.

Hi, I'm Larry Johnson. I have worked in the classroom in the education arena most of my life. I was primarily a science teacher for 30 years, mostly physics and a lot of geology along with that at the college level. And I've worked for EdGate for… This is my 22nd year, I think, here after retiring from teaching. I’ve worked on a variety of projects and heavily based lately in the third-party review section for EdGate of helping publishers be better aligned to the state specific standards in a variety of states that are now requiring it.

Rich Portelance

Excellent. Thank you, Larry. I appreciate that. We're looking forward to having some conversation around that. 

Next, Zarek Drozda.

Zarek Drozda

Thanks, Rich, and thanks to the full group for assembling for this great conversation. 

I’m Zarek Drozda, the executive director of Data Science For Everyone (DSFE). DSFE is a national initiative based at the University of Chicago and we're associated with the Freakonomics books and podcasts if you all know that. That series came out of the lab that  economist Steve Levit had started at the university. And our mission really is to make sure that students are future-ready for a world of increasing data and AI tools.

We've been really focused historically in the K-12 mathematics realm and trying to modernize and update student learning targets to make sure that their quantitative preparation is relevant for the many digital and, you know, kind of complex information needs of today. And we've also been doing some increasing work in the science and social studies realms within the K-12 subject world. And I think more broadly we're just trying to make sure that school feels relevant for kids as they go through a world of many emerging tools and as we have some of our, you know, first AI natives. 

So, very excited to dip into this and talk about the world of standards and updating learning targets, etc.

Rich Portelance

Thank you, Zarek, and we love what you're doing up there at the University of Chicago with Data Science For Everyone. So, thank you for all the great work. 

And last but not least is Peter Coe. Peter? 

Peter Coe

Thanks, Rich. It's great to be with you all. 

I'm here with Student Achievement Partners (SAP), a national nonprofit focused on standards-based education. We began as a nonprofit, really as stewards of the Common Core State Standards (CCSS). And now through our work, especially in high school math, we’re seeking to understand the landscape, build consensus around the math that matters most, and provide prioritized, modernized guidance for what students should learn in the first three years of high school. 

So, I think it's really relevant to the conversation we're having today. I'm really happy to be here.

Rich Portelance

Fantastic. 

Discussion

Rich Portelance

So, let's jump into some questions. The first thing I want to talk about is the AI hype versus system reality. So, my question is, where is AI delivering real value versus surface-level adoption? So, Peter, can I start with you to talk through that a little bit and how it works in the classroom, math, and real-world use?

Peter Coe

Sure. I think the headline would be that, you know, we see real value in cases where AI is making effective instruction more possible for more students. So, in other words, not as a sideshow, not as a workaround to replace effective instruction, but to actually enhance and make more possible. The really challenging work that it takes for educators to lead and orchestrate strong learning experiences. I think we see that in, you know, a couple of use cases, like helping a teacher to make sense of and adapt the curriculum for the particular needs of the students that they have. We see that in tools that help teachers quickly gather student responses, make sense of them, and enable the teacher to quickly act. You know, the challenges that come with effective teaching are many. And so, the more that we can organize our tools around the specific uses that make that difficult work more possible for educators, I think the more value we'll see.

Rich Portelance

And Zarek, you know, behind Peter's head is a sign that says, “Math that matters is future ready”. You know, as we expand this conversation into data literacy and broader systems, you know, how does that sit? Can you respond to that?

Zarek Drozda

Yeah, I think just, like, on your broader question, it's, you know, part of me thinks it's, like, too early to tell the impact in education at large of AI tools. But I want to… I want to make a division between using AI tools for existing learning targets versus updating our learning goals for students so that they're future-ready. I think in that first camp around, you know, take our existing curriculum and our existing school structures, where can AI be useful? I think the most impact is going to be… The most impact and value will be generated where you have the most precise, specific, targeted use cases. And so, as an example here, I always love to call out the work that Quill.org does in helping students with one very precise learning objective, which is how to write sentences better, right? And sentence structure, grammar, and argument pros. They're focused on, like, one thing, which an LLM or a large language model is already quite good at, right? Because it's text-based. And they've built a really, you know, long track record of getting an automated tutoring tool that helps students with that one precise skill. I worry that some of the broader claims in edtech right now that “AI is going to transform X” are a little bit overstated. 

Now, back in the student learning target realm, my concern and optimism here is that all the policy energy around AI and student-facing tools to date has focused on risk mitigation and containment of the student-facing applications, right? But I worry we aren't concurrently building the student muscle around how to leverage the tools for productive academic purposes, or helping them to know how to identify the risks of those tools, and, you know, the hallucinations, the misinformation, etc., and be able to do that effectively as independent adults and citizens after they graduate.

Because the more we remove the tools from the classroom, the further we drive the use of AI tools underground toward unregulated, you know, social purposes, adolescent personal identity, romantic relationships, being a social media influencer, right? And I think as AI capabilities and AI tools proliferated in just the past few years, and kids began to play with them, they're applying those tools in the context of the digital social media culture we've built up over the past 10, 20 years, and it's reinforcing all the things around student anxiety, social comparison, you know,  I think that's the unintended consequences of well-intended, but simplistic policy. And I think there could be a ton of value if we position students as producers, not consumers of emerging technology tools widely defined, including AI, and then we apply that, and we help them apply it to academic career life pursuits, right. So, it can allow students to, like, realize the promise of project-based learning and inquiry-based instruction if we're able to guide it in the appropriate way. 

Rich Portelance

Hillary, any thoughts on that?

Hillary Rinaldi

Absolutely. I agree with Zarek there.

I think the, you know, how we support students in building their own digital literacy competencies, and certainly that includes of data science, quantitative reasoning, and the critical thinking necessary to engage in the digital-first world, which Gen Alpha is very much
that. I think students are already testing how and when to use AI. I think we also should be giving students more credit. There was a recent survey that Securly did about student use in AI across several large districts, and the data shows that most students are using AI within the guardrails of what their district is saying to use it for. Yes, there are a handful of students, or, you know, a small percentage that are just trying to get answers, but there is a way to use it, to engage, to actually build on that student’s ability to learn, right, and truly use it as a learning partner. 

I think how we support students in advancing those skills also means we have to support teachers in how they are using tools. And this doesn't mean that the future of learning and the realization of AI's influence in education is that, you know, we don't need teachers. It's not that at all. We need teachers to be able to do what they are best at, and be strong practitioners that can leverage these tools to, you know, remove some of the administrative burden of those jobs, or to Peter's point, be able to do other things faster. That you can capture exit tickets in real time, and use that data to inform your next lesson in a way that used to happen for hours and hours after school. So, I think it's both, you know, to Zarek's, like, the two camps of where we are right now in that first camp, there's a lot of opportunity for both students and teachers, and I think we need to give more benefit to students' ability and capacity to engage in these spaces too.

Rich Portelance

And Peter, I'm curious, are we seeing more activity in certain areas than actual impact?

Peter Coe

Sorry, trying to find my mute button. I apologize. 

I think, sometimes, yeah. I think that, you know, in the same way that one-to-one laptop instruction, students learning from a video, or that we replace that video with a chatbot, right, I think that can be really sound, really exciting, and really compelling, and I think there are great, you know, there's great technology and there's great potential there. And at the same time, I think we know that in classrooms, a lot of students just don't engage with those interfaces to the degree that they need to in order to be successful. So I think the kinds of actions and use cases that are really going to strengthen and reinforce the great teaching will happen with an individual educator or a team of educators in a classroom. That's the place where I think there's even more potential and room for further exploration.

Rich Portelance

So, let's go backwards a little bit to, you know, from the end use to at the beginning. AI is framing and providing a lot of value in the classroom, but there's, you know, extraneous stuff. If we go back to systems, and Larry, talking about alignment and what you do, when is AI layered into systems that aren't well aligned? What are you seeing? What breaks first when people are using these systems, and how might it affect the classroom?

Larry Johnson

Well, I'm going to tie that in with comments both from Missouri and Hillary that they just made. So, and I'll do that first, one of the things I found, because I've worked directly with AI in a couple of projects now at EdGate, and one of the things that I see as a risk and I'm concerned with on just a public, you know, national level… Worldwide level for that matter, is the tendency of people to, you know, buy in or believe what they hear. 

So, my concern is, in AI, it isn't always right, and it doesn't always interpret the data that you provided it. It doesn't always interpret it correctly, and I've found that in projects I've done, you know, we've asked AI to graph out a certain set of data, for example, and what the graph
showed was totally wrong. It misinterpreted the data and then plotted it out incorrectly. So, my point is that I'm concerned that with AI tools, there still needs to be a top layer of help, evaluation, scrutiny, for students so that they don't just believe whatever AI tells them because that is a concern for me. 

And as far as the first thing that breaks, coupled with that, that's what I see that breaks first is that the AI… What AI couples with your own human-driven stuff has to be scrutinized because it isn't always correct, or it misdirects, you know, it misinterprets the data to repeat that again, and then it misdirects the path in which, you know, a teacher or a student is my bigger concern. The student then takes the results based on that. So, you know, there has to be, in my opinion, there has to be a human connection with whatever AI generates because otherwise, there's no third-party review of the AI.

Rich Portelance

Yeah. No, that makes a lot of sense. 

Hillary, you look like you have something to say.

Hillary Rinaldi

I just... We're still in the middle of the emergence of this technology, right? What if you use ChatGPT or Claude today, it looks remarkably different from what it did two months ago. And I don't even know if you could say it's the same as what it was two years ago. So I think there's both, maybe, you know, for those of us that have been early adopters or users, we see those shifts and how quickly it's moving, but it also doesn't… It's not already ubiquitous either, right? This isn't something that's happening for everyone everywhere. But if we anticipate that the future will mean you are interacting with AI in one way or another, and I think we still paint it as like a big bucket of just like, what is AI, what do our students need to know and understand? 

And I think to Larry's point, and I think they're excited too, like, the hallucinations of AI and data is very much still true. The current role that it's playing is projecting back to you what can appear correct, right? And so, if you don't know what kind of rules, direction, and, like, explicit instruction you have to give your AI, then you're not going to get those same results. I think this is very much true in how we support students in understanding, you know, quantitative reasoning and data analysis, so that when they get something, like Larry's point, right, is the typical student right now going to be able to look at something that has been AI generated and one, know, that it's been AI generated, and two, be able to assess whether or not that is accurate, right? How are we looking at it that way? So, I think that's part of the throughline and connective tissue of why, you know, as many of us focus a lot of our time on K12 math, like why is this where we wanted to talk about the role of AI? And so I wanted to draw that triangle. 

I suspect Zarek has things to add to that, too.

Zarek Drozda

The thing I just added really quickly is, I think Hillary makes a good point here, like, the gut-checking the output of an AI tool, and for a student to have a gut sense of whether it feels right, or it's far off, or it's like close to the ballpark. That's like, in a nutshell, why we don't just throw away all of our existing school subjects, right? It's like, you still need that knowledge base in order to just to make sure we're absolutely clear on… And we thought a lot internally about, like, what is the requisite change needed to update, for instance, the K-12 math progression for the new world that we're living in, right? And I think it's something around like a 30% change of content. It's not 100%. It's also not 0%. And I think if we're in that spectrum of imagining, okay, there's a substantial shift, but it's not, you know, the whole progression of content and skills that we currently learn. 

I also think there's a shift away from really honing procedural fluency, to appreciating procedural fluency, and being able to have a much deeper sense of the conceptual understanding, especially the high school mathematics that students should be equipped with as they take those skills and apply them in a variety of problems post graduation. And we can dig way more into that. But, yeah, agree. 

Rich Portelance

You know, one of my business partners calls it amplified human intelligence. And I think that's a great way to frame it up. It's, you know, if you don't have the human intelligence, then there's nothing really to amplify, and you can't trust the results. 

So, I want to define high-quality digital learning versus low-value usage. So, Hillary, can you define quality engagement and outcomes in that scenario?

Hillary Rinaldi

Just a simple, straightforward question, Rich. I appreciate it. 

No, well, I wrote a piece, I think, two weeks ago. I put something on the Whiteboard Advisors Blog about, like, the trends in youth tech, right? So, there is an active debate happening in state legislatures right now around screen time. How much screen time is too much? What does that look like at varied age groups, right? Does it look different for our youngest learners in K-2 versus high school? And how does that look? 

I had a conversation with Dr. Francis Baez, who's the chief academic officer in LAUSD (Los Angeles Unified School District), and they're putting out district guidance around those screen time allotments and what that should look like, right? I think it's very important, and Zarek mentioned this, of, like, well-intentioned policy that has unintended negative consequences. Legislating screen time is a very challenging business to get into, because that creates so many downstream challenges at the classroom level, right? You can then be sitting in a classroom, and you have a teacher who's trying to figure out, well, “Today we're doing our exit ticket digitally, or we're doing all of those things”. I think what really… The root of that, of what, you know, many folks are concerned about around screen time comes into that not all screen time is created equal, right?

If you're that, like Peter said, if you're watching, if you're engaging with a video, or you're off task watching a video that's not actually what your teacher thought you would be doing, that's very different than using an adaptive curriculum and doing a module that is part of your core content, right? And so, I also think there's a little bit more nuance that needs to be applied in this screen time debate to ensure that we're not creating, like, the pendulum doesn't swing the other way and we've reduced students back to, like, solely using paper and pencil when, like, I don't very often write things by hand at this point anymore, right? Like, what does the current job market seek and need? And how do we ensure students are not being disserviced by an unnuanced policy conversation?

Rich Portelance

Okay. So, there's a quality versus quantity component to that. 

And Peter, any thoughts to add?

Peter Coe

Yeah, I mean, I think, you know, when I see a question like, “Define high-quality digital learning”, I'm always going to start with defining high-quality learning, right? And then, what's the digital aspect that's going to really buttress and make that vision possible? 

You know, when I think about math, I think about probably a lot of the aspects that other folks on this panel would say, and folks in the audience as well. This idea of a really focused and coherent experience. You know, you'll probably hear me say this a lot, and I'm sure folks are familiar with the idea, but you know, our high school math landscape is really cluttered. There's a lot of different content. And so, thinking about high-quality learning and high-quality digital learning that's really focused on a core set of fundamental ideas that students can come back to. They don't feel like they're learning something brand new every day, and I think I'll connect to that. 

I think a little bit of where you were going, Hillary, just with this idea of durable skills, and how we can actually use digital tools to build the skills that are necessary in the modern workplace, right? So, you know, how can a digital tool that's providing this focused learning experience also engage students in collaboration, right? In creativity, in some communication, in ways that they're going to need as they proceed through K-12 and beyond. So, I would say really it's these
two ideas of a really focused and coherent experience for students, and one with an eye to the future, where they are building some of those durable skills. We've got a great report out where we looked at different “Portraits of a Graduate” across the country, and we found, you know, there are a few bright spots, but really this is an opportunity that states are naming. We want these durable skills to happen in classrooms. But the connection to math isn't always clear. So I think there's a real opportunity there for folks to build those experiences and those tools that are going to help kids engage in that way.

Larry Johnson

Yeah, I'd like to also just kind of add onto that, that, you know, one of the challenges of any school is that, in any given classroom, the potential of at least one of the students not having the ability of screen time at home is then to help that student be able to transition from screen time in the classroom to the absence of it at home. And being able to somehow apply those and leverage those. The lack of at-home learning with the presence of it at school can be challenging for a student.

Rich Portelance

I think that's a great point, Larry. I want to segue a little bit into why alignment matters, because it affects what students should actually be able to do versus being AI-ready, right? And to leverage the things that we talked about, and it's not just about the AI, it's about the students having these durable skills. 

So, Peter, you talked about durable skills, math pathways, and quantitative reasoning. Can you elaborate on that a little bit? And then I want to kind of kick back to Zarek and what he thinks and how that's applied to data literacy?

Peter Coe

Sure. And there have been some great points made already about just our own experience with  AI. And I think it's worth reflecting on our own experiences with digital tools, right? We've all used a calculator, and we've all used Google Maps. I think we've all had experiences where Google Maps or a calculator has given us the wrong answer, and we recognized it. I think we've also had experiences, perhaps where one of those tools gave us the wrong answer, and we didn't recognize it.

And so, I think that distinction is really, really important. Because I think that, you know, to be AI-ready or to be fluent with AI, you know, part of it means, I think, a real enhancement and emphasis on students’ conceptual understanding, their ability to quickly estimate answers, to assess the reasonableness of an output or a response. I think those things are only going to become more important. And so, in the same way that, you know, the invention of the calculator did not reduce my need to mentally multiply, I think that to be AI-ready, it largely does mean that the the conceptual foundations, right, I think, you know, Zarek was talking about, you know, that 70% or that there's a sizable chunk that's really important, and is perhaps only going to become more important as AI becomes more prevalent. So, I think part of it is definitely an enhancement, and an increased emphasis on conceptual understanding of the most important math, K-8, and certainly many aspects of high school math as well. 

And I think on the other side of it, right, is understanding all the ways that AI can be a useful tool to you as a mathematician, or an engineer, or a social scientist, right? So, I think that is
beginning to find ways for AI and AI-enhanced tools to find their way into student experiences. So, being ready to use those to analyze a function or quickly synthesize a data set. I think using it as a tool in those ways is going to be equally important.

Rich Portelance

Zarek, how do you feel about that and the real-world applications of what we're talking about?

Zarek Drozda

Yeah. Well, I think Peter hit it really well. 

And I think maybe just as a framing comment, also, like, on the alignment issue, right, that Rich you were trying to I think tease out. I think first is a guardrail, you know, and again speaking strictly in the world of, like, student learning targets and, like, what we're expecting of our high school graduates, right? So, throwing out all the stuff around like edtech and like changing the classroom experience just for a moment. 

And then I think also a principle that we're really invested in, which is making sure that students are empowered, confident producers of solutions against, you know, complex challenges in the real world and not just passive consumers of AI tools and like digital tech. So, you know, we believe that data skills are fundamental to that, right, that's in our organization’s name. We think data literacy gives you both the toolkit to question and decipher the output of AI tools really well. It also gives you the technical skills to customize AI models as a student for a variety of use cases, right?

If you're able to manipulate and confidently load training data into your Claude Code space, or if you're able to, you know, make sure that you're able to work with customized AI tools in your future career, whether you're in law, or nursing, or whatever the sector is, that's going to be critical. And I think there's a whole broader set of conceptual thinking skills that are also math adjacent, but fall in science and social studies as well, around, like, design thinking and quantitative critical thinking, where you're looking for correlation causation, you're looking for reasonable estimations on the fly, probabilistic thinking, there's a deciphering skill, right, I think around, like, pattern recognition, abstraction, simplification frameworks from all the abundance of information we have out there. There's a bucket around validation, so error detection, you know, counterfactual thinking, sort of like meta-analysis, and, like, you know, those could go on and on. We have a pretty defined set of those learning targets that we've wrapped up into what we call the data science and data literacy learning progressions, which we are trying to create as a model to get some of those, like consensus learning targets, down on paper. And then we're going to be splitting those up, and then sending them, you know, half will go into math, a quarter will go into science, another quarter will go into social studies. For, like, deep integration with the existing core academic subjects that we have, blending some of those, like, newer age, you know, AI-relevant, data-relevant skills, which are really things that I think a lot of educators and school leaders have been excited about for a while. Like, these aren't brand new ideas; they're just that the AI is undergirding the importance of them.

The last thing I would say on alignment is that I think the challenge I see is that many people are building these frameworks, and many people are building an answer to what are the learning targets that matter most for an age of AI. And you're going to have states use different frameworks over time. And you could have a situation in which, five years from now, we have state education systems with wildly different standards as they have adapted their curriculum to, you know, AI skills, whatever we call that umbrella, in really different ways. And so, I think it'll be critical to bring some commonality to. 

One resource I want to highlight just on that coherence question, the National Academies recently released, like, two weeks ago, a consensus study on the foundations of data and computing.  And, you know, data and computing was the title. There were several AI foundations undergirding the text of the 300-page report. But, they put a stake in the ground from, you know, consensus built amongst the research community of, like, what are the foundational principles of some of the most important student skills to hone as we march into this world. And I think that that's one resource in addition to our framework that I, like, really point folks towards because they try to build some coherence around all these kinds of emerging frameworks.

Rich Portelance

So where are we falling short? Hillary's like, I think she's chomping at the bit to jump in. 

Hillary Rinaldi

Aren't I always? Yes. Rich knows me too well. 

I think, like, I also would introduce a companion piece to this, of like, how are we assessing these skills? Then it's one thing to say, like, "These are the learning targets and what we want to see students be able to do”. Then it's how are we measuring that progress and being able to report back on student outcomes? And so, I think there's a whole component of this of how assessment can also catch up in this space, right? Of, like, when I was working for the Senate Committee on Health, Education, Labor and Pensions (HELP), and we were writing the Every Student Succeeds Act (ESSA), that's where the Innovative Assessment Demonstration Authority (IADA) was created. States have not fully taken advantage of that, partially because there were, you know, there's a lot of regulation around it. But, we also couldn't have imagined in 2015 what it would look like in 2026, around how far psychometrics have come, and how we could use things in real-time data. 

So, I think there's also, like, complementary to what Zarek is saying of there's additional opportunity for how we measure and support students and teachers in understanding whether or not these learning targets have been met. But that opens a whole different can of worms around alignment of how our assessments are aligned to those things there, which I'm sure Larry has thoughts on too.

Rich Portelance

You know, Larry, I'm gonna ask you a question. I liken alignment to, you know, to having the cars that run on the road. They need the gas, maybe that's the publishers, but the highways to get there is alignment, right? And with some federal regulations and local regulations, we can make them super highways and make them better, or we can make them worse in some cases.

So, Larry, you know, as we look at the lack of alignment in those outcomes, what are you seeing? And is it fair to say that outcomes are only achievable when alignment systems are working at the level that they need to be working?

Larry Johnson

Well, that's a great question. 

First of all, I want to add something to what Zarek was saying about AI as a tool. And what I see that needs to happen is that culturally, we embrace AI as a tool, not an endpoint. And, you know, in our culture, we tend to grab on to the easy fix. So, the tendency is to, “Oh, this is what AI said, okay”, and that cannot be the culture. The culture has to be the perception that AI is a tool that I can leverage, but it isn't the endgame. And so that, I think, is the most important critical crossroads that we are in right now, the tendency to want it to be an endpoint, therefore, less work for me. But that, you know, that's going to be a challenge, I think, throughout the next 10 years in particular, that's really going to be a challenge to establish that culture. It's a tool. 

But then, to your question, Rich, yeah, so states are varying, and they're becoming much more attentive to the descriptors of a standard. So there, you know, it used to be if the lesson was on photosynthesis and it was taught, and the lesson was about photosynthesis, for example, then yeah, we're good. But now states are, and they all vary, as has already been pointed out, they're all different. They all have their nuances, and yeah, this is what I want, and another state will go “Well, no, I want this”, and so that's a big challenge. So, the lesson on photosynthesis nowadays might look like “Students need to write an explanation as to how photosynthesis occurs in this scenario”. Well, now you have “Write an explanation in the lesson. In other words, a student has to be directed to actually write an explanation. Or, it could be to make a presentation using graphics that describe the process. So, it isn't like it used to be where, yeah, this lesson is on photosynthesis, cool. It isn't the case anymore. And that's where I think the tendency is, you know, Virginia, Texas, Indiana, there are other states, Utah, jumping on the bandwagon to hold publishers more accountable to these standard descriptors. Some people call them action verbs, but they're not actually verbs. They don't or aren't always action verbs. They're just action descriptors that are specific to what students have to do around the concept. 

So that's the biggest challenge I see now.

Rich Portelance

And Peter, are you seeing that, you know, some of those nuances that Larry's talking about in the world of math as you're, kind of, developing some curriculum?

Peter Coe

Sure. Yeah. I mean, the fragmented nature of our ecosystem is well known, and I think that's really the role that Student Achievement Partners is trying to play right now is to really build consensus around, you know, a smaller number of competencies that we can really anchor high school math around. But, absolutely, I think this is one of the things I think with math in particular can be a real challenge. You know, we want a curriculum about functions. Well, there are a lot of different things you could do with functions. A lot of different tools and representations.  So, I really do think those details absolutely do matter as you're thinking about building a coherent system. Right? If we're going to focus on functions, what about functions in particular?

Just to quickly respond, you had asked, you know, where do we see us falling short right now? I wanted to just quickly say, I think a big place I see us falling short is just too many learning targets, right? Too many standards. The cluttered nature of high school math, in particular, I think, is really important to name. And you know, Zarek was doing a great job explaining, you know, what some of these really rich learning experiences might look like in the context of working with data. And those are really only possible when we have a more manageable number of conceptual targets that we're trying to hit. So, I really just want to say, you'll probably hear me say this a lot, but just the more that we can bring focus to this enterprise and say, “Here's a small set of ideas that we're really after”, that just makes so much more possible. And before I'm criticized for, you know, watering things down and taking things out of the curriculum, I just want to be really clear that, you know, I'm talking about, maybe a smaller number of standards, but really at a deep level, where students have the kind of rich learning experiences and develop those lasting understandings.

Rich Portelance

Well, the skills are going to be the coherent piece that goes along. 

So, Hillary, I apologize. I was going to ask the next question, but-

Hillary Rinaldi

No, I wanted to jump in because I think this is important for the relevance and rigor, especially in math high school pathways, right? What we don't want is to get back to a place where kids are being tracked in different math pathways based on preconceived notions, or implicit biases, or not, you know, getting them where they need to go, right. The goal should be that every student has options after high school that they're excited about. So, I think the relevance and rigor piece is really important, and also doesn't mean that everyone should be ending their high school career at calculus right there. But it's hard in the current discourse when calculus translates to being like the most challenging option, and a good litmus test as you're entering college admissions of, like, what is the ultimate?

I think Zarek and his organization have done incredible work to think about what other options there should be in high school, and I agree with Peter that maybe there isn't an overindexing on what standards are covered in the traditional math pathways. At the same time, we still have more students who are failing Algebra 1 on the first try, so certainly we should be. So, there is also that, of, like, we have to find the happy medium where we're supporting students on the pathway that sets them up for success, and that we're acknowledging that we can do a lot of this better, right? That not every kid is sitting in a math classroom with high-quality instructional materials and high-quality instruction. I think we're getting closer to that, and there's a lot of effort underway to do it. But we also know it can't just be that we're, like, taking away standards and not making sure that those students have mastered what they need to. 

I think the question in the chat around, like, “Why does everything need to be assessed?” I think we're also using assessment in, like, a wide variety of ways, like, there's, you know, exit tickets, there's formative assessment, there's state summative, etc. I think I'll speak on behalf of all of us to say we're using assessment to mean a lot of different things and not just a state summative assessment, which we can talk more about assessing durable skills in that way. But just for the audience, we're using assessment to mean any of those points in time at which a teacher can get more information about a student's mastery of a concept.

Rich Portelance

And I want to get back to Zarek. 

How does the lack of alignment limit those outcomes that we're talking about, and what are the gaps in data literacy that you're seeing in these systems that we're building?

Zarek Drozda

Yeah, I think… And I'll also try to react to what Peter and Hillary said. 

You know, we've been tracking student achievement in the basics of data and probabilistic thinking, right? So, if you look at the subdomain of math scores, and if you look at just the slice that's around data, stats, ability to apply math functions to like real-world situations, those scores have not been doing great over the past decade, even pre-pandemic. So, there's just a large gap there that even though some of the data and statistics probability standards show up in math as a required expectation, they don't actually get taught in practice, because, to Peter's point, there's already too many concepts in a given year of high school math. 

Teachers often run out of time by the time they, like, reach, you know, finals. And the first thing they cut is some of the skill areas that are, you know, maybe not assessed enough. Or they just were not part of the teacher's own math experience when they were going through school. And yet those are some of the skills that are the most critical to survive and adapt in a world of AI, especially as it impacts every sector. So, there's like a huge gap there. I think more broadly speaking, the overpacked and sort of cluttered nature of high school math expectations, which does not have as much coherence as it could, means that you have situations in which educators are sort of forced to run through a mile-wide, inch-deep of the concepts in math that do really matter a lot. You know, linear functions and solving those with automaticity, and exponential functions. Imagine if the whole country were literate in exponential functions pre-COVID. We would have had much better policy discussions on that topic, right? And you could apply that to a variety of different scenarios. So, I think, you know, if we can get to a world in which students are able to slow down and go through some of… A smaller number of concepts in the curriculum, but then spend a lot of time authentically with them, and they're able to apply them in the context of projects in the context of problems they care about. 

I think the broader gripe I have, and this is departing from your original question, Rich, I'm now just ranting. You know, AI forces us into a situation in which students know that a good chunk of the things they were learning in school could be Googled and could be put into an AI tool and solved with a button. And so, in that context, we have to do a much better job of justifying to students why we're spending their time on teaching something. And why they're having to sit through a 60-minute class period learning about, you know, fill in the blank. If we can do… I think it's an opportunity to make that way sharper, and prove why, like, you know, some stuff in Algebra 2 actually does matter a lot, and why, you know, the foundations of physics matter quite a bit. But if we can do that on a more targeted, prioritized set of topics and then allow the time for exploration and depth, I think we'll be in a much better spot.

Rich Portelance

So, as always, we're having an incredibly vibrant conversation. I'm going to jump ahead a little bit because we're running tight on time. I want to talk about scaling across states, and really what changes when organizations move from one state to a national scale.

Larry, I want to start with you to talk a little bit about the complexity and fragmentation that happens or occurs. You know, from an alignment standpoint on the back end, what do you see moving from one state to multi-state, and then I want to talk to Peter about impact. 

Larry Johnson

So, at EdGate, for example, we have a system approach versus a project approach. So, a system approach means that we have, available to us in our own database, we have all the US standards, all the different subjects, and we have… And then publishers send us their content, we correlate that, put it in the databases at the same time, and so publishers can then extract a report using the ExACT reporting tool. They can extract a report which then tells them, “Okay, these lessons in this particular course of ours match these standards in math, science, social studies, whatever they have. And it also shows them the gaps, so then they can focus on, “Okay, let's edit for Utah, let's edit this series of lessons that we see we have gaps. We're kind of in the ballpark, but let's edit the lessons so that we can better fit that particular state's needs. 

Whereas if it's a project, then it's just simply, okay here here are the standards, and here are the lessons, and it becomes a manual project of looking at the lesson and then seeing if it fits the standard, and that's very laborious, very time consuming, and that is pretty much basically all human-driven, so the AI is not at a level yet which could actually do that on a detail level. So, that's really the advantage of a system approach, as it has just so many more options and scalability for large projects. To the point of your state versus national, state versus all states, is that is a huge challenge, and the only way that it can work is if you have a system set up like ours. So, what we do with publishers' content is that we assign the concepts that the lessons are meeting. That allows us then to… They can run a report on any state they want, because the concepts are on the standards, and the concepts are on their lessons. We let the reporting tool then associate them. And that's not specifically AI, but it is where a reporting tool allows publishers to then visually see where they are and where they aren't meeting a certain set of standards in a state. And so, it's a global alignment.

Rich Portelance

You're going from a one-to-one to a one-to-many component using the system to do that. And I can see in a world today where we have, you know, fragmented standards, you know, Common Core no longer exists, that this is really kind of a critical component, right? 

So, Peter, talk about that impact on learning across states.

Peter Coe

Yeah, we really are seeing, you know, states move in different directions when it comes to high school math. We got a memo that came out this week, just noting some trends. You know, there are some places where states are moving closer together, and some places where states are moving farther apart. So I think the kind of careful alignment work that Larry is describing really is essential. Because, you know, one state may have a set of expectations around functions that looks one way, another state will have, you know, some nuance difference there, and it really is important to get that right. I think if you don't, you know, you run into these challenges where a student has been experiencing a topic one way with one product, and then they experience it a different way, it gets assessed in a third way, and none of it is really working together. So, I think, you know, having that shared set of clear expectations that are driving learning experiences, extra practice, assessment, right, having that all really be aligned to the same set of expectations is the only way to ensure real coherence.

Rich Portelance

So, from a practical standpoint, Peter, what should organizations do first? Let's talk about real, actionable steps.

Peter Coe

Sure. I mean I think that this isn't going to surprise you, but I'll just say find some focus, right? I think, you know, all of us are trying to decide what's the right framework, what's the one place I can look. And I think SAP does have some good resources out and is going to continue to put more out. But I do think, you know, thinking about those core topics that are most essential for success in algebra, in data science, right? Focusing on a small number of topics that you want to give students extra exposure to, with increased depth, is really the way to go. I think as you explore expansion, really consider each state's individual expectations as they, you know, as relevant to that set of core ideas. I do think that's going to be really important as well.

Larry Johnson

Yeah, I'd like to jump on that. I know you're more cognizant of time, Rich, than I am, but this is especially what Peter was just saying about focus for an individual state. This is especially
becoming apparent in the realm of career and technical education, where, for example, like a farming state, a state whose economy is based mostly on agriculture, they'll have a large number of agricultural sets of standards, and they may not have any IT standards at all. And so as you go across different states, this idea of a state's focus on what they need, per se, spans all subjects. This isn't relegated just to the academic arena; it's career technical ed as well. Yeah, it's a much bigger issue for publishers because it does demand that they have individual… They have state specific, that was what I should say, state-specific courses to meet those specific states.

Rich Portelance

Yeah. No, that's a really good point. And we know that there's, I think, something like, seven times as many CTE standards as education standards and yeah, that's an issue. 

Zarek, data and literacy. Where are you seeing… What should people do first?

Zarek Drozda

Yeah, I think, like, something particularly concrete that a school or district leader can do is pull… Start a working group with, you know, some of your most entrepreneurial, passionate teachers around these topics, and begin to wrestle at a cross-subject level. So, you know, get teachers from math, get a couple from science, a couple from social studies, a couple from CS (computer science), and what is our collective plan for thinking about, you know, learning targets in a world of emerging tech, and can we start having some early conversations around those, and to integrate data skills into the curriculum. 

We've been doing this with a cohort of school districts where we'll come in as facilitators, and we'll say, "Hey, let's like, you know, grab some teachers across those subject areas where it touches most directly. Because that brings in the opportunity to have a coherent conversation around how to adapt the curriculum for the world that we're running into, versus having it be a siloed one where math makes a change, science makes a change, social studies makes a change, no one talks to each other, and then, you know, you have, like, territorial problems. We want to bring some kind of coherence to that, and there are really exciting opportunities to finally create interdisciplinary learning experiences in high school as a result. And we've already seen some districts using, you know, data literacy, data science kind of activities as a bridge between the school subjects, because you can do that so much more easily with a really concrete topic-aligned data set. That's one strategy on the implementation side. But, like, I think a cross-subject working group is where I push people first.

Rich Portelance

And Hillary? 

Hillary Rinaldi

I'm sure in working with some of the largest districts in the country that are grappling with how to improve student outcomes, there are several pilots underway within these districts to test new tools. Whether it be for teachers, or for students, or for both, right. And I think the desire to test and assess the success of those tools and how they're building them alongside is really important so that teachers are supported and coached on both their pedagogical practices, but also how they can and should be using these tools. 

This is a new foray for our teachers just as much as it is for our students, and having that support. So, I don't want to lose the need to support teachers on all of this work. I couldn't agree more with what Zarek shared, and there are ways that we can include teacher and student voices as we pursue these new ventures across education and across subjects.

Closing Thoughts

Rich Portelance

So, I want to, because we're tight on time, I want to do a quick round here, one sentence. What's the most important shift education leaders need to make in the next 12-to-24 months? 

Larry, I'll start with you.

Larry Johnson

Okay. 

Well, I think more focus on making sure that their content actually meets the descriptors of the standard. That's the biggest piece I see in our third-party reviews is that, you know, yeah, this lesson is on the quadratic, but it's missing the part in the standard where the students have to write an explanation of how they got there. So, that's the biggest challenge I see for publishers.

Rich Portelance

Hillary? 

Hillary Rinaldi

Support teachers in these instructional shifts, right? We say the first step was adopting challenging academic standards. The second is adopting high-quality instructional materials. The third is implementation, and that has so many steps therein. So, how are we supporting teachers and students in using the resources that they already have, as well as how do we work with these new tools and engage to support a fully, you know, literate, fluent, digital, math across subjects for students as they move through K-12. It's a run-on sentence, right?

Rich Portelance

I wasn't judging. 

Peter Coe?  

Peter Coe

I'm going to say do more with less. Find ways to increase depth and rigor, and, you know, all the rich learning experiences therein, with a smaller number of content objectives.

Rich Portelance

I like it. 

Zarek? 

Zarek Drozda

I'll second Peter’s. I think the other one I'll add is, you know, I think I'm going to come back to that edtech versus tech ed thing. I think there's a rush to buy edtech tools right now to do AI stuff, and I think instead we should not sidestep the question which I think is much more important, which is how do we adjust the curriculum to ensure students are durably prepared for a changing world? And so I think, continue to think about the tech ed side.

Rich Portelance

Ah, thank you for that. So, as everybody can tell, an amazing panel and panelists. This conversation was incredibly fluid. I would love to have you all on again because there's a lot we didn't cover. But one thing that feels clear from the conversation is that AI is not going to fix weak systems; it's going to expose them. And the organizations that succeed will be the ones that treat alignment not as a step but as infrastructure, and take care of their students' needs.

Thank you all very much for joining us, and if you have any questions, you can reach out. We're going to send out all the information via a slideshow, so anybody who did attend, we'll get this
to you. It'll include the links and the mentions today that Hillary and the others brought up. 

But again, thank you to all our panelists. We appreciate your time today, and thanks for joining us. Have a great day