Beyond Output: Teaching Discernment and Human Reasoning in the Age of AI using UnBlooms™

Thank you for coming to today's session beyond output teaching discernment and human reasoning in the age of AI using blooms on blooms. Excuse me.

Got to retrain my tongue there.

I'm going to go ahead and move us forward into framing the session, but before I do that I just want to talk about logistics for those of you that may be newer to our webinars. We like to

make these as engaging as possible. We

like to make this about content sure, but also about your experience about you sharing what you learn what you have to offer and how you engage with us and

with each other as well. Um So we've propped that up and and we want to make this as resourceful as possible for you.

So continue to use the chat.

One thing we're doing that's new is we're also going to use the Q&A feature.

So if you have a comment or something to share that goes best in the chat. If you

have something that's a question geared toward us myself or a presenter I'll just introduce them in a few moments then that goes in the Q&A. If

you get it wrong, that's fine. No big

deal, but we're trying to create a pocket or a channel for questions now because there's so many of you attending and then we can try and track those and get to as many of those as possible when when we get to the open discussion

toward the end.

Okay?

So thank you again for being here and let's go ahead and move forward.

So the framing for the context of the session today is thinking about how polished output since November of 2022 and increasingly since is no longer

reliable evidence of student thinking.

AI can do that very very well. So what

are we doing differently as we think about our teaching and our students learning experiences?

What what counts as visible visible evidence of learning?

Correct answers or correct product doesn't necessarily reveal reasoning or judgment.

And then how can we redesign things that could surface the decisions, the revisions, the justifications that students are creating as they go through their learning experiences. So,

you've heard probably people talk about the shift from product to process.

That's great. It starts a conversation, but how do we go deeper into what process means?

What are the telling signs within the process that show us what students are doing and what they're learning? And

what they're owning and what they're creating. All of those things and you

creating. All of those things and you can start thinking about blooms a little bit, but we're going to approach it differently through a new lens today.

Okay. So, I just wanted to frame that for you going into the session and then hear a little bit from you in the form of Oh, Q&A is not showing potentially.

So, we're going to go back to I guess just using chat then and we'll try and grab your questions.

Okay.

Let's do a quick poll here. So, how has or excuse me, where has GenAI most changed how you think about assessment?

And you'll see the poll pop up momentarily.

Yes, we'll be sharing the recording.

Absolutely. You'll get a recap with the recording link.

Okay, there's our poll for you.

So, you can see the response options and for this one you can choose more than one.

And we'll let that populate for a bit.

We'll show you show you the results after we've got a majority of responses here.

And I see a contribution in the chat.

That's great. Thank you.

We're at about 2/3. I'm going to give it a another moment. Probably go to the 1-minute mark on this.

Okay. We're over 70%. Let's go ahead and look at the results briefly.

And more comments in the chat. I love

that. More We'll get more contextual uh feedback there as well.

Okay, and then Kelly, if you can post the results for us.

We can see them here pretty well. We've

got that locked. Okay, we're good.

So, the number one response, I'm rethinking assignment design and prompts. Great. Then what are some you

prompts. Great. Then what are some you know, we'll think about what are some good models there for sure. How are you rethinking the design aspect specifically and what prompts are you using and and by prompts, that's the

instructions you can be giving to students.

Are you rethinking what counts as evidence of student thinking? That's

essential. That's great. So, that

evidence, how do we go from these traditional assessments that we've always used that we think tell us what students know to those that actually involve more evidence. And by evidence,

how can we link that back to their learning process? Great.

learning process? Great.

I'm rethinking when and how AI should be allowed. You've probably seen our prior

allowed. You've probably seen our prior sessions related to a couple of different AI assessment scales.

That's a good guide to look at and you can think about where they might be able to use AI and how and for which assignments they would do so and for which assignments they might not do so.

So, that can be variable by assignment, not just a course level policy.

Great. So, that gives us a gives us a sense of what you're thinking there.

Let's go ahead and move on to the next one.

I'll put share results. You've already

got that. So, let me go back and jump to the next question here and we'll get that pulled up to pop up briefly. And again, we'll just spend

briefly. And again, we'll just spend about a minute getting your responses.

This is a single response. Which

statement best describes your current approach?

And there may be no one that perfectly fits your approach, but which which one most closely describes your approach?

And while that's happening, thank you for sharing your experiences in the chat, Christine and others.

I'm seeing substantial redesigns that folks have experienced, and maybe that's happened over time. You know, we've had a a few

over time. You know, we've had a a few years now to adapt.

Going on going on 4 years to adapt, and so hopefully that's been something you've been able to scale over time, not try to do all at once.

And let's go ahead and share the results.

Okay. So, now you can see these this time.

Um so, majority, slight majority, substantially redesigning assignments to make reasoning more visible. Great.

Um making small adjustments to assignments or instructions.

Um still figuring out what changes are needed. Yeah, absolutely. Um this is a

needed. Yeah, absolutely. Um this is a process. This is evolving still. It's

process. This is evolving still. It's

going to continue to evolve.

Um so, just getting on the path and moving forward and and staying aware and absorbing what you can and then applying it accordingly. So, that's great. Um so,

it accordingly. So, that's great. Um so,

definitely the majority of you are in motion on this and making good efforts.

Uh let's see on the margins here. I'm

mostly keeping existing assignments and expectations in place. Okay. Maybe there

aren't issues or weren't issues. Great.

And not yet addressing AI in a deliberate way.

Yeah. Well, there's opportunity for sure.

Okay. I'll stop share on that.

And let's move us forward.

So, I have the pleasure of Well, first I'll introduce myself since I didn't do that at the beginning. I waited till now to do so. Brett Christie, VP for Educational Innovations at Alchemer.

I've been in this position for about 6 years now. Before that, 25 years in

years now. Before that, 25 years in higher education at faculty positions, faculty development positions at the campus level, and then system level for the Cal State system.

Um but it's my pleasure today to introduce Tina Austin. Um Tina's someone who I've seen as one of the most active and productive in terms of critically analyzing our

reality, um what it means for education. I've

followed her work as closely as I can keep up with it because there's a lot of it. Uh she's

extremely active, one of the most active I see. I mean, her her screen time on my

I see. I mean, her her screen time on my devices is incredible. Um and I really enjoy reading her work, following what she's doing, and trying to keep up with it. Um as a more formal introduction,

it. Um as a more formal introduction, Tina's an educator, researcher, consultant, and author whose work focuses on AI, teaching, and the future of human reasoning in education.

She's recognized internationally as one of ASU GSV's top leading women in AI, a Microsoft Innovative Educator Expert, and one of a handful of global faculty invited to speak at OpenAI headquarters

for its higher education summit in 2025.

Uh she's the author of the Un-Blooms workbook, how to design, teach, and assess human reasoning in the AI era, and the creator of the Un-Blooms framework, which helps students rethink how they design learning experiences and

assess reasoning in AI-enhanced environments.

She's taught at UCLA, USC, CSU, and Caltech, and in 2022 she launched the University of California AI Initiative, a statewide community of practice focused on AI in teaching and research.

Uh it's my pleasure to now turn it over to Tina, and I'll stop share.

Thank you so much, Brett, for that generous introduction and for inviting me here today to be here amongst your uh wonderful participants. All right, so

wonderful participants. All right, so I'm going to go ahead and share my screen.

And this is also so we can stay in touch at the afterwards because I I only have about half hour to talk here today, and usually these are like week-long workshops, so I'm going to try and

condense it as much as I can, but I wanted to make sure that we stay uh in touch.

Fantastic.

Okay. So, was everyone able to open that? Hopefully.

that? Hopefully.

Just get one thumbs up and I think it should be good.

Or anyone.

Does Yes, perfect.

So, my question today is if AI can already do most of the assignments, why are we even still sending students to school? I noticed most of the

to school? I noticed most of the participants here today are from higher ed, and I'd love to know, if you want to drop in the chat,

what we think.

Because AI can already complete the output.

As we all know, in 2-3 seconds, we can get ChatGPT or Claude to answer the question for us.

Output is no longer the goal. Thank you.

Co-intelligence.

Facts. Teaching process.

Teaching how to learn control.

Critical thinking. That's true. They

can't learn that just by working with AI.

To be able to teach students.

Nice. I like that one, discernment.

This is how we learn in AI-run world.

The brain tends to be use it or lose it.

That's right.

Doing your own thinking has intrinsic value. Yes, and I love that because that

value. Yes, and I love that because that kind of ties into the conversation about um the IKEA effect and the value of doing things by yourself. I see a lot

about process-based learning. I haven't

seen yet anyone to comment on the social, like how learning is fundamentally still a social act. I saw

that most people here today talked about preventing misuse. Um, redesign

preventing misuse. Um, redesign assessment was the big one and how do we show uh evidence of learning? So, as

Brett mentioned earlier in the introduction, I undergrads, but also grads, not just online, but also in person, not just small classes, but also

large large classes. And that kind of made me have to shift my redesigning assessment every single time. And that

also had made me change everything I was doing when the pandemic came along and I had to think about how can we still keep this engaging and useful for the students that are uh attending these

classes.

So, as some people are mentioning in the chat, experience, wisdom, and learning how to work with each other, that shifted every single time that we moved classes, whether to online or in person

or um big classes or large classes. And

then AI came along and I just asked, "What is it that we want students to still learn?" And I think all of the

still learn?" And I think all of the answers in the chat were very valuable.

Another thing that, you know, when AI came along was, "Well, what about cheating?" And, "You know, what about

cheating?" And, "You know, what about academic integrity?" And so, we see all

academic integrity?" And so, we see all the papers that are coming out every week about um whether AI can detect whether AI writing can

actually be detected and whether um those are actually useful. I want to see the chat if anyone is still um wants me to talk about AI uh detection tools because we know more and more

evidence is increasing the showing that those are not entirely accurate. Just

want to see before I move on because this is another problem with output-based grading. Thank you. Is that

output-based grading. Thank you. Is that

is that it will ultimately come back to um detecting. We don't want to do that. We

detecting. We don't want to do that. We

want to move from product to process and find out um, what is it that we need? I

mean, are we just looking at drafts or we just looking at reflections? Um, and

then how do we actually get those visible moments of judgment where the thinking actually happens.

So, AI is actually making that harder to see if you're still grading the output.

Me, this happened with AI coming along and having to find out where where does this um, what do I need to change? And then

the other part of my journey was getting the opportunity to now explain it in different disciplines. So, when I brought in AI to my my regular classes,

um, most people said can we like borrow your syllabus and then how do we translate that across different disciplines? How are you redesigning it

disciplines? How are you redesigning it not just in your STEM classes, but how can we do this in now humanities and communication?

So, every single time I was giving them my syllabus, um, in the beginning, you know, we have this uh, Bloom's taxonomy in there and the question was, okay, how do we how are we rethinking pedagogy now

in the age of AI when uh, the output is going to instantly become available with AI.

How are we really grading the process?

So, that became obvious to me that we this can no longer um, at this at least not in this particular model, this particular pyramid, work so well because uh, AI is already doing so

great at creating the output. AI can actually not just help with create, but let me see in the chat if folks uh, have any ideas of where where else AI can and can help with these

the um, Bloom's levels. Some people say and I love reading this by the way. I

feel like I want to just read out the chat.

Have students earn chat GPT says.

Yes.

Yes, turn it in is flagging ridiculously. Thank you, I know.

ridiculously. Thank you, I know.

Yes, it can do all of them. Well,

um AI it can definitely come in here and apply better. It can analyze better. It

apply better. It can analyze better. It

can evaluate sometimes and it can create. So, what are we really doing?

create. So, what are we really doing?

And we can't really ask them to I mean in the past you would have your student come in and maybe memorize a textbook, come back and take an exam, maybe do some quizzes and then finally they would

hand in some dissertation at the end.

But now the dissertation grading um is not enough to show that the student did the thinking.

So, we thought about the evolution of Bloom's taxonomy, how Bloom's himself never said it was supposed to be a pyramid. And in fact, at the very top he

pyramid. And in fact, at the very top he had evaluate. And then later on um

had evaluate. And then later on um a lot of people came in and and decided to modify it. And then we have create at the top and now it's this uh logo

pyramid that a lot of people use. But um

but he never really said it was a pyramid and he never the famous pyramid that ended with create never really existed. There's lots of different I

existed. There's lots of different I mean, I can go into the whole philosophy of this and and what I'm not going to.

Others now in the age of AI uh including my incredible colleague Michelle um proposed this idea of flipped Bloom's.

And it it works really well as in in writing classes. But um what I noticed

writing classes. But um what I noticed in our STEM classes and working with faculty in physics and and other departments in STEM, this uh model doesn't really hold

up.

And so, the other issues that came up is about changing pedagogy. Uh I was collecting surveys from different faculty in different workshops. And the

The was um breaking apart blooms in this in this shape, and the idea was how can we move from an arc hierarchical, uh,

um, framework to something that's more recursive. So, the initial outline for

recursive. So, the initial outline for Blooms, which I drew, was more of like, um, from Blooms was a circle.

And the idea was to think about where, um, what creating actually means in the age of generative AI and what that looks like in a biology class versus a literature class, and allowing faculty

to come in and tell me how they would redesign it. So, eventually the model

redesign it. So, eventually the model kind of evolved a little bit, and it looks more like something like this when we're critiquing the output, refining the output, questioning the output, and then

regenerate generating and then comparing them. So, um, along the way we developed

them. So, um, along the way we developed these metacognitive checkpoints where students would have to stop for start with, you know, without AI, then bring

in AI, then compare it. And so, there's a whole process of, um, how this works.

And I as you'll notice, AI is actually in the sideline. So, we are not focusing on students becoming copywriters, we're not focusing on them just working with AI in every

single class. In fact, there's a

single class. In fact, there's a decision tree on when to use AI, when to skip AI, um, where would it be best to

have AI, um, in there to amplify the students' work, recognizing that learning isn't linear, problem-solving is important. So, that's why in the

is important. So, that's why in the middle of it I put problem-solving because I thought back to how, um, I was doing it in my classes and how it was working for my colleagues as well, cuz

when, as you mentioned, Brett, in 2023, when I brought in the group of, um, different UC lecturers and, uh, in our AI, um, task force and our AI

initiatives group, I was initially asking everyone about how they're redesigning assessment. And so one thing

redesigning assessment. And so one thing that became initial immediately obvious was that now we have to really focus on solving a problem instead of just focusing on what used to

be hierarchical higher order or lower order skills.

Context here now determines the entry point. So we're not saying you're going

point. So we're not saying you're going to start at remember, you're going to start at create, or you're going to start at evaluate. It looks different for a different class and AI if we use it, it's there to be critically

evaluated.

And we are using it as a way to really rethink everything. So again, in

rethink everything. So again, in traditional Blooms, a professor wants to have their students learn about enzyme structure and function. They would start from the very bottom,

move all the way up. Now you might say, well, in every single class we're not starting from the bottom. That's true. In every

given day you're not using all six levels of Blooms. Um and that's that's fair, but along the way along the course, you are doing that. Like you have your day when they

that. Like you have your day when they come in and take the test, and then you have the day where everybody hands something in.

Um now with unBlooms, that's also true.

You could start at any point. Maybe for

a history class, you could start analyze, apply, and then evaluate, and then at the end you could go back to analyze. It's the process is kind of

analyze. It's the process is kind of um circular, but it's also a spiral. So

you're not going back to the same point.

It's not a not a repeat loop where it's an infinite loop. Um and in a science class that looks different. For a coding class that I taught, we could um start with create and then analyze, apply. And

so the goal is to really think outside the box. And And this is just the

the box. And And this is just the general framework. Thank you. Someone

general framework. Thank you. Someone

says, yes, I've got a link to the presentation on um I don't know if that's the same one or I'm just glancing at the thing on the side.

And so when you compare the two traditional blooms, the idea at the end is getting it right. And this is where the academic

it right. And this is where the academic integrity issue comes up. Whereas for

un-blooms, because it's so process-based, the goal is to understand why you are wrong and what that reveals.

In traditional blooms, the evidence of learning is the correct final answer point. In un-blooms, the the evidence of

point. In un-blooms, the the evidence of learning is to have that visible reasoning process and metacognitive awareness.

And so again, in traditional blooms, the student thinking is linear. It goes from memorized to apply to create.

In un-blooms, it's recursive. So they

can go back. They can predict what the AI output's going to be. They can

compare it. They can revise it. And then

they can interrogate it.

In traditional blooms, AI could be a threat to academic integrity or ignored entirely.

Whereas in un-blooms, it's a partner for thinking with and against. But I'll give you an example. In my class, we had students critique papers. And then we had

critique papers. And then we had students that didn't that used AI to critique papers. These there's papers

critique papers. These there's papers that were retracted in 2023 because they had some clear AI use.

And so I asked my students, what do you think are wrong? What do you think might be wrong with these papers?

So I'm I think maybe one or two people got that right.

So only the students that actually used their eyes could see that this rat was not anatomically correct. And the cell

was not anatomically correct either.

But the students that used AI were not able to see that.

So, we don't use AI to detect AI, and and that's part of the principles of one of the principles of Un Bloom's is, um, we really focus on student thinking, and we

start with having them think about, um, the output or how it would look like before they even touch AI. I had to correct this because it was think first,

and then use AI as a critic, and then compare, and then decide, um, what to avoid, and what are the metacognitive checkpoints. So, part of

metacognitive checkpoints. So, part of the Un Bloom's framework was just the first part that I explained to you, uh, of having that freedom to enter that spiral at any point depending on the

class. But, the second part is the

class. But, the second part is the metacognitive checkpoints, which was recently peer-reviewed, and final step has create or resist AI. Uh, students

need to be able to, after just going through these steps, be able to decide if, uh, when to push back, when to accept, and there's a whole decision tree, which I can't go over in like 20 minutes, but it is for the something

that I've gotten a lot of great feedback from.

And, over time, they have been really become really good at finding AI's errors. They've become very good at

errors. They've become very good at finding confidence into their own their own work. And, they've also been able to

own work. And, they've also been able to know when to use, when to drop, and what model to use. So, there was a Some of you know me from my uh, talk last year

at Educause, where we went over what types of tools to use, whether it's AI agents, um, custom bots, um,

um, podcasts, and when to drop those.

So, I'm happy to share those links later.

Yes, the call for pre-thinking needs to be the first message, and that's exactly what we try and focus on. So, um,

again, a lot of people take these the talks and then turn them into a an infographic and sometimes those infographics are incorrect and I see them online and I have to come and correct them.

But I was lucky to have a lot of this work featured in many places and another aspect is when to skip AI and it's not as simple as what you see on this slide because if I were to put all of the tiny

details on the slide, you wouldn't be able to see it. So

there's a whole decision tree and the book which I'll share with you in a minute.

Let me just go over to my other slides.

Which is really I find very interesting because over time someone that attended my talk last year at Open AI and then attended two other of my workshops said, "Well, I really see this is like evolved

so much." and it has. So I'm only trying

so much." and it has. So I'm only trying to give you like the trailer of it and the short time that we have and then I hope that just based off the trailer, we're not uh

making um um very Someone wrote a 30-page paper based off of just the trailer which I thought was fascinating, but but then I had to say, "Well, there's at least this

little tiny detail that was missed." and

so the idea is and I thought this was funny about when to use and when to shuffle. So and I also saw today, I

shuffle. So and I also saw today, I don't know if anyone saw Jason's post.

He He took some of the process-based learning questions and he put them all into Claude and asked whether the framework is going to look

more like a circle or is it going to look like a pyramid?

And he found that it actually did end up looking like just like un blooms. Except the difference was um he didn't have a context entry point. It

just gave him basically a loop and it wasn't like you had to start at a particular spot. Whereas with un blooms,

particular spot. Whereas with un blooms, we say, "No, you have you Depending on your class, you may enter at one point or another." And so, over time,

or another." And so, over time, um you're not just in in indefinitely looping back to the same point. Um it

can look more like an improvement spiral here, but I'm not going to get into the details because people want to know more of like what can this actually do instead of what is the shape of this model?

So, um there's three moves. There's the

uh when we use AI before, after, and then some people say, "Oh, is this just the sandwich method? Or is this just the create method?" No, it's different, uh

create method?" No, it's different, uh and it's con- context-driven. There is

uh uh uh aspect of it where you protect the productive struggle, where we're building intentional friction, where judgment is forming. And so, I'm glad you mentioned um the topic of

discernment because initially we were talking about judgment, and then over time, the word that we chose for this was the discern- discernment spiral.

And so, we're looking at when students make those decisions, when they build confidence, and then when do they refi- revise.

So, the framework goes deeper in terms of like where where there's no AI, where there's where you use AI, and then when do you actually resist AI? Cuz that's

very, very important because uh I saw someone yesterday as well on on LinkedIn asking, "So, are we not able to do the same things that AI used to do? I mean,

at what point are we going to decide when we're going to use AI? And at what point are we going to decide can we just not never do what AI used to do at all?"

So, that's where the resis- framework uh comes in and level five.

And so, it's it's funny cuz some people initially when they hear about Unbloom's, they think it's the inverted Bloom's. But the inverted Bloom's, like

Bloom's. But the inverted Bloom's, like I said, is still a triangle, it's still hierarchical. Whereas here, it's not

hierarchical. Whereas here, it's not hierarchical, and it's not starting necessarily with create. It can start with create, so we're not rejecting Michelle's model. We We There is It is

Michelle's model. We We There is It is kind of like a choose your own journey, if you will. So, you can in that journey um go through that path. Where

discernment shows up is again when they decide to when they come to that fork in the road, and what the evidence of judgment looks like again kind of just just depends on

the activity. So, I did have like a

the activity. So, I did have like a little mini activity here for you today.

So, for example, for a clinical session, it would look different. Um and then I pointed out how

different. Um and then I pointed out how um Entropics guidelines also include this uh discernment spiral, which I thought was cool because they all

actually tested this out on 50,000 people, and they noticed that you just by using the tool alone, you're not going to develop discernment. You

it you must re-ed at every single step revisit discernment, and that's what that uh five-level framework that I just showed you is about. And again, there's another infographic here about

questioning generating refining and critiquing. At every single step, we

critiquing. At every single step, we stop, and we check for those um those metacognitive checkpoints. And so,

like I said, one of the tools that we we used initially was those um podcasts that I don't know if anyone here has used Notebook LM podcast. Let me see in the chat.

A couple of us. Yes. And so, has anyone really gone into the issues with those podcasts?

[laughter] Yes. Yes. Thank you. Yes, some people

Yes. Yes. Thank you. Yes, some people are noticing uh They're saying it's sexist, and they're saying yes.

Copy. Yeah. So, we do go into all of those details um in the workshops and and how students actually go from level one to level five and decide uh what to do next. Yes, you can have students that

do next. Yes, you can have students that come in and interact with the podcasters.

The resistance level is very important and I really haven't talked enough about that. That is where, you know, as the

that. That is where, you know, as the tools get better, we want our students to be able to know when to push back.

And um if you're interested in more, there's the the workbook that you can order.

It's We're actually moving on to the second edition of this where we have examples and it's going to be paperback. And if

you're interested in collaborating or working together down the line, feel free to scan the One thing I want to emphasize is when we say grade the thinking, it's a lot more nuanced

than just having process because we don't want to have process for process sake or friction for friction's sake.

It's about again knowing when to use and when to skip. But learning is recursive and it's not linear. And here's my email if you're interested in or workshops for

your department. I'm really happy to

your department. I'm really happy to discuss more. If you If you're not

discuss more. If you If you're not connected with me, feel free to connect with me. And I'm happy to open the floor

with me. And I'm happy to open the floor to questions.

Um thank you.

Great. Thank you, Tina. And we'll have just a couple minutes for any other questions you want to put in the chat here.

And as we're pausing for any of those, a reminder, yes, we'll provide the recap with the recording.

You have the links we're giving you now, but we'll also be providing those in the recap. And then we'll also be providing

recap. And then we'll also be providing a filtered transcript of the of the chat.

Names redacted and all of that, but we'll be providing the resources. Any

questions?

Oh, someone said this model seems to be a to assume the inevitability of including AI. Actually, no, it doesn't.

including AI. Actually, no, it doesn't.

So, I this is the problem when I'm going over things really fast and I thought I still went over time because I was only supposed to talk for a half hour and the outlines here and it's the goal is problem solving and metacognitive

reflection. So, it's not at all about

reflection. So, it's not at all about having AI in every step of the process at all. So, if I when I do the the

at all. So, if I when I do the the custom workshops for faculty, we have assignments where like there's days where there's just completely no AI and

it's a lot better actually without AI.

Someone's asking, do they trust in engaging this going towards mastery goal orientation?

Um, do they trust that exploration process is an important grade worthy as the final paper or yes, because um, we've had student panels come and explore this and they've come and talked

about how building the project was was very rewarding for them and they come to school to be able to problem-solve and learn how to problem-solve and that's and I didn't get to talk today about the

IKEA effect is is kind of like when you build something that you made you enjoy it more and and it's kind of like also seen in animals where they do

something the task becomes rewarding because it's part of something they do.

So, they do enjoy the process and help us we have tons and tons of examples that we bring to workshops where it's just the final output and we compare that and so yes, the answer

to your question is yes. Yeah, we may have had a hand raise for an audio question, but at this point if Heather, if you have it that you can put into the chat, please do so. I'm going to move us

along here.

Appreciate that.

Uh, just a couple quick moments to wrap up. Hopefully, I picked the correct jump-in point. Great.

So yeah, just wrapping up. Thank you

Tina so much for your time and sharing all of your resources, all the work you've been doing yourself with your students and your research. Appreciate

that very much and I know people got a lot out of it, a lot of stimulating conversation happening during questions and appreciate your interaction as well.

Let me go to a couple of announcements.

We have a webinar already next week scaling course quality, a faculty-led affordable and compliant approach that we'll have Casey Coburn from Metropolitan State University of Denver joining us for that

session. So we've got the link going

session. So we've got the link going into the chat.

I'll go pretty quickly so don't rely on the QR potentially.

So next Wednesday 12:00 p.m. Eastern.

And then the one after that June 17th excited to have Flower Darby from University of Missouri joyful teaching in challenging times practical ways to renew energy and support student success.

Flower also has a great new book out and so I know that will be part of her sharing as well and there's a lot of dialogue around that happening.

And then another opportunity if you're going into summer thinking of doing some light professional learning we have our free AI literacy micro-credential. It's

four modules each takes about one hour so you could chip away at that an hour a week have it done [snorts] in four weeks 30 hours or 30 minutes a week and have it done in eight weeks.

You could approach it however you want it's asynchronous independent so the links there for you and you do receive a a micro-credential at completion.

And that wraps it up for today. Oh we

have one more session coming up this Friday a curious spotlight session. So

every few weeks we do a spotlight session just 30 minutes where we're featuring different aspects of our Curriki platform our educator platform, all the resources we have and the tools we have available. Um so we'll be

talking about our review and revise tool for analyzing course quality and suggesting enhancements and giving you resources for those.

Um so the link should be going into the chat for you for that one as well.

Hopefully you're keeping track of all those different links and picking the events you might be interested in and we hope to see you at some of those in the near future. And again, thank you so

near future. And again, thank you so much, Tina. Yes, go ahead.

much, Tina. Yes, go ahead.

Thank you so much. I just dropped in my my LinkedIn contact cuz some people were saying they couldn't um scan it. Um so I just dropped that in there and thank you again for having me

and um I'd love to uh continue the conversation um with them and anyone who's interested. Thanks again for

who's interested. Thanks again for having me.

Thank you. And if you haven't already received it, I put in Tina's Substack.

Um I would highly suggest following that as well.

A lot of good information and a lot of what you saw today is nested in the different articles that she writes there.

So. Thank you again, Tina. Thank you,

everyone. Have a great day.