by Jarek Janio, Ph.D.
In higher education, conversations about learning outcomes often orbit one central framework: Bloom’s Taxonomy. It’s ubiquitous in curriculum design, accreditation rubrics, and faculty workshops. But what if Bloom isn’t enough?
At a recent Friday SLO Talk, Dr. Gavin Henning encouraged educators to think beyond traditional cognitive frameworks. He introduced alternative taxonomies Fink’s Taxonomy of Significant Learning and LeFever’s Medicine Wheel and showed how generative AI can help us design outcomes that reflect learning as a whole human experience.
That idea resonated deeply. But what does it mean in practice to “design beyond Bloom”? And how do we do it without losing the clarity, rigor, and measurability that assessment requires?
To explore this, imagine a conversation between two colleagues: a transformative educator, who seeks depth and meaning in learning, and a radical behaviorist, who sees learning as observable behavior shaped by environmental conditions. Their perspectives may differ, but in dialogue, they illuminate how AI can support outcome design that’s both expansive and practical.
A Conversation Across Philosophies
Transformative Educator (TE):
I loved Henning’s talk. He invited us to go deeper. Instead of just focusing on what students remember or analyze, he asked: What if learning outcomes included things like identity, self-awareness, or empathy? That’s what significant learning really looks like.
Radical Behaviorist (RB):
That’s fine as long as you define what you mean by those terms in observable terms. If you can’t see or measure it, you can’t assess it. Learning isn’t a transformation of the soul. It’s a change in behavior.
TE:
But that’s where the Medicine Wheel and Fink’s Taxonomy come in. They give us frameworks for thinking about learning beyond cognition: emotional growth, values, the human dimension.
RB:
I have no problem with those domains as long as you can specify what behaviors go with them. For example, if you say “the student develops empathy,” what do you observe? Do they listen actively in discussion? Do they restate another person’s position before responding? That’s behavior. That’s assessable.
TE:
I see what you’re saying. And that’s why I think AI is so helpful. I’ve started asking ChatGPT to generate outcomes based on Fink’s taxonomy and it gives me things like, “Students will reflect on the impact of a leadership experience on their personal values.” That’s a starting point.
RB:
And what do they do to show that reflection? Write a journal entry? Create a video? Lead a group discussion? AI is a great stimulus, but we still need to build in the reinforcement and ensure that behavior is what’s being assessed not internal states.
TE:
So maybe the value of these alternative taxonomies is that they widen the scope of behaviors we consider legitimate learning outcomes. Bloom is great, but it doesn’t do much for reflection, connection, or purpose.
RB:
Agreed. Bloom’s verbs work because they’re behavioral “compare,” “construct,” “design.” But Fink and LeFever can work too if we treat them as domains of behavioral outcomes rather than as vague categories of personal growth.
Where AI Fits Into the Process
What both educators agree on is this: AI can help.
Not by replacing the instructor, and not by producing outcomes that are automatically valid but by providing a first draft, a stimulus for clearer thinking.
Henning showed how ChatGPT can generate outcomes, activities, case studies, and even rubrics based on Fink’s or LeFever’s models. With the right prompts, AI can do things like:
- Draft SLOs for a student leadership retreat that address emotional and spiritual domains
- Generate reflection prompts that cue students toward values articulation
- Create a rubric for assessing growth in self-directed learning or civic engagement
In short, AI can help translate holistic ideas into concrete tasks. It won’t ensure quality, but it accelerates the design process and reduces the barriers to experimentation.
A Radical Behaviorist’s View of “Transformation”
What a transformative educator might call identity development, a behaviorist would call a new repertoire of behavior.
For example:
- A student used to remain silent in group work. Now they regularly contribute and suggest action steps.
- A student once avoided feedback. Now they revise their work based on peer and instructor comments.
- A student who couldn’t name their values now articulates what matters to them and how it shapes their decision-making.
Is that transformation? Maybe. But from a Skinnerian view, it’s just new behavior under new contingencies. And that’s not a diminishment it’s a clarification.
If we can define those behaviors, create environments that elicit them, and reinforce their development, then we can design outcomes for them and yes, even use AI to help us do so.
Toward a Shared Vision
So, what happens when you pair a transformative educator’s passion for depth with a radical behaviorist’s insistence on evidence?
You get a powerful, practical framework for using AI in outcome design:
- Start with broader domains (Fink, LeFever, or your own institutional framework)
- Use AI to generate candidate outcomes and performance tasks
- Translate each into observable behaviors that reflect the intended learning
- Design environments that reinforce those behaviors
- Use assessment tools that track whether the behavior has changed as a result of instruction
In this way, AI becomes less about automation and more about amplification helping educators scale the work of designing for deeper, richer learning.
Final Thought: The Value of Dialogue
When educators from different traditions talk not to persuade, but to understand something valuable happens.
The transformative educator helps us remember that students are more than test scores.
The radical behaviorist reminds us that evidence still matters.
And together, they show that learning outcomes can be both expansive and accountable especially when supported by thoughtful use of AI.
So perhaps it’s not about abandoning Bloom, but about building beyond it.
With better tools.
Clearer language.
And a shared commitment to designing learning that students can actually demonstrate because in the end, what matters isn’t what students feel or believe.
It’s what they do.