Reimagining Bloom’s Taxonomy for AI-Driven Learning With Michelle Kassorla

In this episode of Teach & Learn, Dr. Cristi Ford speaks with Michelle Kassorla, a Georgia State University professor and AI education thought leader who believes it’s time to flip the traditional learning model that is Bloom's Taxonomy. 

From AI-generated essays to student-created content that precedes understanding, Kassorla shares how today’s learners are starting with creation—and why educators must rethink scaffolding, assessment and agency. You’ll hear how friction fuels critical thinking, how reflective footnotes deepen metacognition, and how transparency around AI use is reshaping classroom practice. 

Dr. Ford and Kassorla discuss: 

☑️ Why Bloom’s Taxonomy needs reimagining in an AI-driven world 

☑️ How students create before they understand and what that means for teaching

☑️ The role of friction in authentic learning

☑️ Reflective footnotes and transparency statements as new assessment tools

☑️ How educators can design for agency, voice and deeper engagement

Michelle Kassorla, Ph.D., is an Associate Professor at Georgia State University, Perimeter College and AI in education thought leader. She’s on the AI Expert Panel for EDUCAUSE, where she leads the AI Literacy committee for Higher Education. A recipient of a CETLOE fellowship, she published “Teaching with GAI in Mind” and co-authored a textbook and papers with Eugenia Novokshanova. She shares her insights on LinkedIn and her blog, “The Academic Platypus.” 

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Transcript

Dr. Cristi Ford (00:00):

In today's episode, we're exploring a bold idea that sparked a lot of conversation online. It comes from a newsletter by our guest where she challenges the traditional Bloom's Taxonomy and suggests it's time to flip it in the age of AI. Join me for the conversation.

Speaker 2 (00:18):

Welcome to Teach and Learn, a podcast for curious educators, brought to you by D2L.

Dr. Cristi Ford (00:23):

Each week we'll meet some of the sharpest minds in the K to 20 space. Sharpen your pencils. Class is about to begin.

(00:30):

Listeners, welcome back to another episode. I'm Dr. Cristi Ford. In today's episode, I am thrilled to welcome Michelle Kassorla. Michelle is an English professor at Georgia State University Perimeter College. I love Michelle that you co-teach this with and against AI in composition one and two courses, so we'll have to talk about that. But also she's an author, a thought leader, a keynote speaker on AI and education. Michelle, I'm so happy to have you here.

Michelle Kassorla (00:58):

Thank you so much. That was so nice of you to say.

Dr. Cristi Ford (01:01):

Yeah, I want to jump right in. I will say to you, Michelle, as you do, I spend a lot of time looking and reading and learning and spending time with people who are really having authentic conversations as it relates to AI emerging pedagogical approaches. And so I was really struck by something that you posted not too long ago, and I thought that I would want to share this with our listeners.

(01:25):

So listeners, our focus of today's episode comes from a post that Michelle wrote for her newsletter, Academic Platypus, and listeners will link to everything that we're going to mention today in our show notes so you can take a look at and start to follow with Michelle as well. But what I loved about the post and what really caught my eye was the title. She titled it Inverted Bloom's in the Age of AI.

(01:49):

And one of the things I will say to the listeners before we start unpacking this is you really made a case for why we should be rethinking the traditional Bloom-Saxony hierarchy, specifically in this age of LLM's and AI's. And it really piqued my interest. And as I started to read your post, I realized I wasn't alone. There were a lot of comments and discussions about this after you published it. And so I guess I want to have you just kick us off. One, foundationally for those who are listening, let's talk about what is the traditional Bloom's model, and then what do you mean when you say it's been inverted in the age of AI? And I want you to also maybe specifically talk about what's changed in terms of how students learn today and why this shift is necessary.

Michelle Kassorla (02:38):

We've always been told that Bloom's Taxonomy is the way that we move towards critical thinking skills. And a lot of, for example, designers in the educational learning space have been relying on Bloom's to make sure that we're scaffolding the things that we teach our students in a specific way. So we start with remembering and then understanding, applying, analyzing, evaluating, and finally we get to the uber peak, which is creating, and that is what has really pushed education since the fifties, really when Bloom's Taxonomy kind of came out.

(03:28):

And so lots of people have made different kind of pictures and diagrams and all kinds of tries trying to understand Bloom's better, understand it more deeply, try to, people say this isn't a representation of each level of a student's knowledge base, but we kind of used it that way, even if you say that. So there's what we say and what we do.

Dr. Cristi Ford (03:58):

What we do.

Michelle Kassorla (03:59):

So what happened was I was having a conversation with my colleague, Eugenia Novokshonova, and we were talking about Bloom's, and she was discussing it with me. And then I got off the phone with her and I thought, "Oh my goodness, oh my goodness. We aren't creating last. We're creating first." And so what's happening is that the students are creating with AI, so they're creating an essay or they're creating a picture, or they're creating a graph. They're doing that creative process without having gone through all those different layers of Bloom's to get there.

(04:43):

So in other words, they don't have the skill or the knowledge for what they've created. They just created it. And I'll give you a very good example of how this happened because I made this inverted Bloom's Taxonomy and I didn't really know what I was saying. I mean, it took me a while to wrap my head around it because I've had a whole lifetime of Bloom's, right?

(05:05):

So when I inverted it-

Dr. Cristi Ford (05:07):

So have I.

Michelle Kassorla (05:08):

And I thought, "Okay, what does this really mean?" And started to think about it and started to deconstruct it really, and to understand all the things that go into the creation that you have sitting in front of you. And then I thought, "Well, this makes a lot of sense because in the age of AI, we need to let our students do that creating, okay, because they're going to make that essay, they're going to make that picture, and they are using AI in that way." But then let's stop and say, "Okay, now let's unpack what you made." So then is the time when you move backwards through the Bloom's Taxonomy and you start with create, then you evaluate what you've created. Is this good? Is this bad? Why is it good or bad? Is it the quality you wanted? Does it say the things you want it to say?

(06:09):

Those are important distinctions. Okay, then you move up to analysis. Can you take that apart? Do you understand all of its constituent parts? Do you understand why they were organized in that way? Okay, now can you apply that to a different situation? So can you take what you did, for example, with an essay and put it into a website? Or can you then take it from a website and put it into a song? Can you apply these constituent parts to understanding how something else is done? Then understanding, so can you make the connections to truly understand what you've done and to be able to explain it to someone else?

(06:59):

So I asked my students, for example, "Can you explain this to me as if I were in sixth grade? What if I was in a graduate student? So can you explain this back to me?" And anyone who's in the education sphere knows that you don't really know something until you explain it. So that's the understanding level. And then finally that remembering level. Now without looking back at that diagram, that essay, whatever you created, can you recreate it? Do you understand enough about it that you could make it without looking at what you did with the creation you made with AI?

(07:37):

So each one of these stages is a higher and higher personal agency and a lower and version of AI agency. So when you created, it was all AI, but when you get up to understanding and then remembering, that's all personal agency. So we have to flip it and say, "We understand that you want to create with this, but you still have to understand what it meant when you created and what kind of thing you have made." And so it's a problem-based solution, but it's something that I think really helps us to understand how to teach with AI.

Dr. Cristi Ford (08:19):

So I love that you are sharing this, and I will say probably for folks that are listening, this comes across as a little provocation in terms of how you're challenging the way in which we teach, the way in which we learn. As I'm listening to you describe that inversion, I'm thinking about the importance of understanding photosynthesis and the steps of understanding photosynthesis. You need to know those steps before you can understand the role that chlorophyll plays in photosynthesis. But what I hear you saying is that since students are creating first and understanding later, how are you helping your students to distinguish between authentic creation and maybe mere production where AI is involved?

Michelle Kassorla (09:06):

Okay, so that was a really good question actually I got on the discussion we had about the inverted Bloom's. I had somebody challenge me on that very same point. And the thing is that when I create something, so the inverted Bloom's, when I write an essay. I am pulling from knowledge that I have previously had anyway. We're not building ex nihilo, which is the Latin for out of nothing. Basically, only God could do that because you always create from the materials and you have materials that you have always. So I inverted Bloom's. Yes, I didn't really understand what it meant, but then again, I've been using Bloom's all my life and I understood a little bit about it. I understand what inversion is, I understand what the theory of it is. So when I do it, even if I don't understand the creation, I still do have an understanding of some of the materials that went into it. So when I create, I am actually just, what did you call it?

Dr. Cristi Ford (10:24):

Oh, I was talking about a difference between authentic creation and mere production.

Michelle Kassorla (10:29):

So really is there a difference? Because when I put things together that I understand in my own creative process, okay, I'm using all the materials that I've understood to make that. Okay, what is AI doing? It's also taking all of the information and understanding of all the materials around it to create something.

Dr. Cristi Ford (10:51):

So let me maybe move us forward a little bit. You talk about the importance around friction and that friction is a key concept in critical thinking. Can you really maybe elaborate on how you frame friction and why is that especially critical in the context of AI?

Michelle Kassorla (11:11):

Well, first of all, I'm going to connect that a little bit actually to the idea of creation. Because in order to have critical thought, you have to have friction. So what is it? What does that mean? When I was a graduate student, I had a very wise dissertation advisor. Her name was Dr. Bonnie Tue Smith. And she said to me, "If you're comfortable, you're not learning. Go find something to make you uncomfortable." And that was something that she always said to me. And I thought about the fact that you can't be comfortable. So you have to be in search of friction in order to be learning. And friction is anytime you have to overcome something, you have to solve something, you have to do something that maybe you don't really want to do. If you're just gliding through it, that's not friction.

(12:08):

So doing, for example, a worksheet, sometimes it's just not friction. And sometimes you'll get friction for one student, but you won't get friction for another. One student may be able to pull together a quadratic equation like that. The other student may not understand it, and that may be a lot of friction for them. So we have to understand that critical thinking comes with friction. So if we understand that, then we can understand what it means to cheat. Because look, everyone's saying, "Oh, you're cheating with AI. AI is terrible. It's just a cheating machine." But you have to understand, it's only cheating if you're trying to use AI to avoid friction.

(12:58):

If you're using AI to instill friction or to understand friction or to help you research to get through the friction, those are really good ways to use AI. So I try to teach my students to be extremely troublesome with AI. AI is very obsequious. It likes to agree with everything you're doing. It smiles and tries to assist you in everything. It tells you you're fantastic, your ideas are great, and here's some more stuff. And I've always imagined AI to be like a super intelligent Labrador retriever. So you ask it to go get something, it fetches it. Sometimes what it brings back is a stinky fish, okay. It's not what you asked for. And you're like, "This isn't what I asked for." And it's like, "Oh yes, it's you'll love this. It's wonderful."

(13:58):

Okay, so I tell my students, "Your ideas, your intention, your voice are all things that you have to protect." And AI a lot of times will try to, I don't know, make those less important, less apparent by agreeing with everything you have to say. So your job is to search out friction.

Dr. Cristi Ford (14:24):

Let me ask you a follow up to that. I think what I am hearing you talk about, and there's a dichotomy here in terms of the importance of friction as a key concept for critical thinking. I'm channeling the listeners who are saying, "Yes, but my students aren't intrinsically interested in being engaged in X or Y subject area." So I guess for me, as they're listening, how can you as a faculty member, help them think about how can they intentionally design their learning experiences to promote that deeper cognitive engagement in the advent and the ages of AI?

Michelle Kassorla (15:04):

Well, that is the question. What we have to do is we have to, first of all, we have to break down everything that we're doing. So look, I'm an English professor. I didn't get any education in how to teach. Really. I just learned about my subject.

(15:27):

And when I wanted to be a better teacher, I went to people in the education department. I said, "How can I be a better teacher?" And they said, "Okay, you have to have formative assessment instead of summative assessment. You have to scaffold your stuff. You have to have meta cognitive reflection. You have to make sure that you're following through and understanding what the students know." I didn't do it. It was too much work, right?

Dr. Cristi Ford (15:54):

It's a lot of work.

Michelle Kassorla (15:56):

But AI, yeah, AI does something that we aren't expecting, which is to uncover where we need to go back to those very basic and very clear ideas that our education department, our educators have always been telling us to do. We have to have, you can't have a summative assessment. You can't have some big assessment that's all the points at the end of the class. You have to take that summative assessment, you have to break it down.

(16:26):

You have to look at every step of the learning process, make sure that students are learning each thing. And look, you have to also make sure that they're giving you some meta cognitive reflection. So my colleague, Eugenia Novokshonova came up with a fabulous idea, and I have used it ever since. And we have told everybody about it, and I don't even think she knew how special it was until AI came along.

(16:56):

And that is that we have reflective footnotes. So we ask our students to footnote what they do for their process for a meta cognitive reflection of the process. So let's say you're teaching calculus and a student uses a specific formula, ask them to footnote that formula and explain to you why they used that formula and why it was better than other formulas they could have used. Ask them to tell you why they organized the equation in the way that they organized it. Don't ask them to just reproduce an equation you put on the board or do it in a specific way, but start asking those questions. Why did you solve the problem this way instead of that way? How come you have an understanding of it because of this and not that?

(17:53):

And that's when we start to understand what our students are thinking and how they're thinking. And that allows us to get involved and have them better engage with the material because we're better engaging with our students.

Dr. Cristi Ford (18:05):

Yeah, I wholeheartedly agree there. We actually in D2L, released an openly available masterclass on a reflective teaching practices to that point that you talk about it in terms of the power of that opportunity for reflection for our students. And so as I go back maybe to some of the points and commentary, I think you're honing in, as I was reading the post and intrigued by this approach, some of the readers suggested that the model felt like a poor justification of AI usage rather than a roadmap for ideal learning.

(18:40):

And so I'd love to hear how you respond to that tension as we've been talking about describing current student behavior versus the prescription of better pedagogical approaches. So when you talk about reflection, you talk about these other practices, how do we continue as educators to balance that tension to be vulnerable and open to utilizing AI, but making sure that we're really thinking about the ways in which we want to produce these learners that can go out and be productive citizens in the future of work that we don't even know what's going to exist and those jobs are going to exist yet.

(19:16):

So I said a lot there, but would love to hear what your thoughts are.

Michelle Kassorla (19:21):

Well, I think the point that you make that is so important is the idea of vulnerability. Look, all of us are new with this technology. And I even stood in front of my class today and I said, "Look, I'm introducing a new tool today, and I'm not really sure how it will work. I'm hoping it'll work well, but if it doesn't, I want you to have some suggestions for how it might work better or if it's just completely useless to you."

(19:47):

So these are the ways that we get our students to have buy-in is when we show a vulnerability. And the advent of AI is a really great time for us to start showing that with our students. Now, I teach higher education, so I can hear the elementary school teachers going, "I can't do that," but actually you can. A lot of times elementary school students really want to share what they know and they want to show that they are smart, and sometimes the easiest solutions are the best solutions, and they might suggest them to us. So I think that that vulnerability is super important.

(20:33):

I think that we also need to take a moment to just take off the policeman's cap and put down the whistle and teach, because we're not policemen, we're teachers. So if our students are using AI, then we need to figure out why they're using it, how they're using it, for what purpose they're using it. And we need to understand how we can fit that into our teaching practices to encourage them to have more critical thinking, more friction in what they're doing, and we need to let them be learners.

(21:15):

And AI is this amazing tool that we can start to really use a lot of differentiation. Our students can start to understand things better. I have students who told me that in middle school they started writing paragraphs and they kept going through the circle, circle, circle, circle, middle school, high school, and then into college. Let me have you write a paragraph. They said they never got past the paragraph. And those students couldn't dive into and sink their teeth into real research and critical thinking and all of the great things that make academics so interesting to those of us who are academics.

(21:58):

So AI allows us to bring those students up and to present them with the opportunity to start using that. And the way that it does it, is at least in higher education, and I think also probably in the upper levels of high school, you can say, "Look, there's mechanics and they're called mechanics because they're mechanical. Let's give those mechanics to the machine and let's talk about what is truly human about communication or what is truly human about this topic or what is important about it." And then focus on that and stop worrying about where the period is.

Dr. Cristi Ford (22:37):

We have been having conversations over the last six to nine months about the ways in which we evaluate and assess learners. And so it is probably not lost on you, or this is not the first time that you've heard it, that traditional artifacts like essays. What are some new forms of assessment do you think that are better suited to evaluate student understanding as well as agency in an AI-infused educational landscape of today?

Michelle Kassorla (23:03):

Okay, so we do some traditional stuff with our essays, like having our students read their essays aloud in order to discover their voice. And we tell them, "If you can't say a word, change that word. If the phrasing doesn't work in your mouth, change that phrasing. It's not you. That's not your voice." And so that's one way, just a simple way that we're teaching them to use voice and to understand what their own voice is. But look, the whole idea of agency and intention is super important with assessment. So what we're doing is, first of all, we redid all of our rubrics, we took mechanics off of them because everyone should have a perfect essay. That should be expected at this point.

Dr. Cristi Ford (24:00):

So your expectations are higher around mechanics now, right? Is that what you're saying?

Michelle Kassorla (24:06):

Oh yeah. The mechanics better be perfect because they have no excuses for not having perfect mechanics unless they're prompting the AI to put in some random errors, which they do do to get past the teachers who think that they might've used AI. So it's a very fake thing. So when we started creating these courses, we wanted to look at what the employers would want from our students in five years. So the first thing is that they can follow directions. So our directions are not easy. They're very specific, they're very diffuse. We use a lot of linking from our directions for writing an essay back to the content pages. And D2L is fabulous for this, by the way, just throwing that out there.

(24:55):

The second thing is we want our students to be transparent. So we tell them you have to do a transparency statement. You have to tell me what AI tools you used, how you used them, and why you used them. Okay? So they have to be very conscious of their AI tool use. They have to check their facts, because if I find a fact that isn't true or a source that doesn't exist, that's a violation of academic integrity. That's very serious. I do not need an AI detector for that. No, I do not. By the way, I never use an AI detector. That's a whole another conversation, but don't use them. Okay?

(25:35):

They have to sound human. So I tell my students, "You have to read your work aloud. It has to sound like you, and you need to make sure that it sounds very human, that you have some different kind of expressions in there. It has to be interesting."

(25:56):

And the last thing is to be efficient. So our students have to understand how to use their time wisely. So that is what an employer will want in the future. So these are the things that we're teaching our students to do with AI, and we're teaching them to learn with in AI. And hopefully when they walk out of my classroom, they're doing this and they are bringing it with them, and when they go into another situation.

Dr. Cristi Ford (26:25):

That's fantastic. I really appreciate how tangible those examples were for our listeners to be able to really think about getting back to Bloom's, the application of the things that you shared and how they may use them in their own classrooms. And so I thank you for first of all just the work that you're doing and sharing your voice and really providing a space to be able to have these conversations around AI. And I really want to thank you for coming on the podcast today for Teach and Learn. It's been such a pleasure to learn from you and hear more about your work.

Michelle Kassorla (26:58):

Thank you so much for the opportunity to share. I really just want to help everyone who's trying to do this because it's a lot of hard work and it's really scary. We don't know what we're doing. This is brand new territory for all of us.

Dr. Cristi Ford (27:12):

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(27:43):

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Speaker 2 (27:48):

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Dr. Cristi Ford (28:02):

And remember to hit that subscribe button and please take a moment to rate, review, and share the podcast. Thanks for joining us. Until next time, school's out.