AI & Forward-Thinking Pedagogies
Part Three
Bloom’s Taxonomy & AI
by Dr. Jodi L. Porter
& Dr. Terrance Weatherbee
This article is the third in a series from the Acadia Divinity College (ADC) Futuring Lab, “AI and Forward-Thinking Pedagogies.” If you haven’t read the other articles in this series, you can find them here: “The AI / Cognitive University” and “Comparative Gogies and AI.”
When we first began to wonder at ADC about the possible roles of generative AI in our teaching and learning, we chose to revisit the core outcomes that inform our curricula and syllabi.
We have program outcomes for each of our Master of Divinity (MDiv), Master of Arts in Theology (MA), Bachelor of Theology (BTh), and Doctor of Ministry (DMin) degrees. We use these program outcomes to build learning outcomes for each course. To do so, we utilize Bloom’s Taxonomy to help us imagine how to guide student engagement with content and process.
Four Domains of Bloom’s
Bloom’s Taxonomy first emerged in the 1950s, using nouns to name the stages of development through which an instructor can guide students. In the 2000s, active verbs replaced the nouns. Also, for the cognitive domain, “evaluate” (“evaluation”) shifted down one level, “synthesis” dropped out, and “create” took the top level.[1]
Though we may be most familiar with its cognitive domain, Bloom’s Taxonomy includes three total domains: cognitive (knowledge-based), affective (emotion-based), and psychomotor (action-based).
In a 2016 article, Marcella LaFever proposed a revision to the Taxonomy that supports Indigenous ways of being. She adds a spiritual domain.[2] We chose to add this domain to our work with Bloom’s, since we aim to include I/indigenous ways of knowing in our curriculum. We describe this domain as integrative, focusing on the individual-in-community and involving both self-reflection and community contributions that advance right relations, belonging, and empowerment.
At ADC, we try to help students reach the top of the “cognitive pyramid,” with skills in evaluation and creativity. We also try to help them develop robustly in the affective, psychomotor, and integrative domains.
As ADC faculty write their syllabi, they choose relevant active verbs (or synonyms of those verbs) from the Taxonomy to craft their learning outcomes. They are articulating their hoped-for levels of student development and beginning the process of imagining activities and assessments to cultivate that development.
For example, for our course on Interpreting the Old Testament (OT), Rev. Dr. Matthew L. Walsh crafted these learning outcomes:
- Discuss how an understanding of the OT’s ancient Near Eastern context is foundational for its interpretation. (discuss = affective domain, lower-level skill, synonym of respond)
- Apply the various types of analyses and tools used in the study of the Bible to interpret the OT. (apply = cognitive domain, lower-level skill)
- Articulate the value of different interpretations and approaches, recognizing that the social and cultural contexts of the interpreter impact one’s reading of a biblical text. (recognize = integrative domain, lower-level skill, synonym of value)
- Demonstrate an awareness of the relevance of the OT for Christian theology and practice. (demonstrate = performative domain, lower-level skill, synonym of apply)
On the one hand, it can seem tedious to structure learning outcomes so carefully. On the other hand, taking time to do so can ensure intentionality in the shape of your course and your assessment of its effectiveness. At ADC, we have designed a ChatGPT-powered Course Content Creator that knows our program outcomes and our parameters for using Bloom’s Taxonomy. Now, in a matter of minutes, our professors can draft revised or new learning outcomes for their courses just by crafting the precise and poignant prompts they would like the Content Creator to follow.
And we do not presume every course will help students develop in every domain. Ultimately, we work together to craft learning outcomes across our courses that support a full degree experience (achievement of the program outcomes) through the four Taxonomy domains.
Bloom’s Digital Taxonomy
We are also exploring how to leverage AI to help us imagine new learning strategies, in accordance with Bloom’s, to help our students achieve our learning outcomes. Andrew Churches designed “Bloom’s Digital Taxonomy” in 2008, reflecting on how digital capacities emerging at that time (such as Google, mind mapping software, blogs, and podcasts) can intersect with Bloom’s cognitive domain.[3]
We have consulted with our colleague Dr. Terry Weatherbee at the Acadia School of Business, imagining a similar overlay of AI technologies onto the cognitive domain of Bloom’s Taxonomy. Weatherbee suggested a continuum of agency between teacher and student, with AI mediating the agency between the two. We could sketch the overlay in this way (see image).
Here Weatherbee illustrates that perhaps AI could have more agency, compared to the teacher, in helping students achieve the lower-level skills on Bloom’s; and less agency, compared to the teacher, in helping students achieve the higher-level skills on Bloom’s.[4] For example, perhaps we are okay encouraging students to use AI to develop research questions and literature reviews, but we would encourage students not to leverage AI as significantly as they create learning assessments and portfolios.
Bloom’s Taxonomy and these illustrations can help faculty imagine how to leverage AI technologies for the work they do already. If they have not identified explicit learning outcomes, the Taxonomy offers a place to start doing so. And the digital versions can spark ideas for ways to integrate AI to achieve those outcomes in the classroom. AI can help our students with their content acquisition and review (a cognitive capacity), organization of learning (an affective capacity), and brainstorming processes (a cognitive capacity), to suggest a few more ideas.
Triple-Loop Learning
But at ADC, we are most interested to leverage AI to embody efforts in teaching and learning that are harder to fully achieve without such technologies. Can AI help us decolonize learning? Offer transformative education? We think so. And in our conversations with Dr. Weatherbee, we realized one significant way AI can help us do so: in our efforts toward triple-loop learning.
In triple-loop learning, instructors accompany students as they do their work, think about their work, and think about how they think. Triple-loop learning can help diverse learners reflect on how to apply their learning in new contexts, effectively cultivating lifelong learners and transformative change agents. The practice is essential for heutagogical approaches to learning. It also supports students to achieve higher levels of Bloom’s Taxonomy and, importantly, to grow in the integrative (self-reflective and community-focused) domain.
In Dr. Weatherbee’s sketch, we see that AI perhaps could have a larger role in helping students produce their learning outcomes and products, and it could (or should) offer more limited support as instructors and students reflect, at increasingly deeper levels, on that work.
In our REB-approved experimental course on AI and Ethics in Christian Ministry, a course entirely generated and delivered by AI, we are asking AI to utilize our typical rubrics to offer immediate, personalized feedback on student assignments and reflections. In this way, students may receive more feedback (or more individualized feedback) on their work than professors typically can offer. AI can be a tool that responds more often to students than professors may be able to do, especially in larger classes or amidst heavy course loads.
Intentional, Effective Learning
We believe it is essential, for our best teaching and learning efforts, to begin by making explicit the structures that guide our choices of learning outcomes and activities. In doing so, we then can begin to imagine how to welcome new technologies – from Wikipedia and Google to generative AI – into the purposes we already have, or to help us achieve purposes that may have been more difficult previously.
In leveraging AI, perhaps our students free up more time to reach the higher capacities on Bloom’s Taxonomy, capacities for cognitive creativity, emotional intelligence, perceptive and adaptive leadership, and presence in community. (And perhaps we as faculty free up more time for our creative work in research and individual mentoring!)
AI can support students through pedagogical, andragogical, and heutagogical approaches to learning, perhaps with the most agency for content-driven pedagogical pursuits and the least agency for student-driven creative and reflective pursuits.
Our students will need to know how to navigate AI in a world that will come to adopt its use across all domains and sectors. We can help prepare them not only with AI awareness but with practice in utilizing AI to develop skills for contextualized, lifelong learning and transformative, global change.
Connect with Us
For resources and ongoing conversations with colleagues about heutagogy, Bloom’s Taxonomy, and AI for learning, CLICK HERE.
[1] See “Bloom’s Taxonomy” at uwaterloo.ca and www.neovation.com.
[2] LaFever, M. (2016). Switching from Bloom to the Medicine Wheel: creating learning outcomes that support Indigenous ways of knowing in post-secondary education. Intercultural Education, 27(5): 409-424
[3] See “Bloom’s Digital Taxonomy” at www.neovation.com.
[4] Here I am citing personal conversations between Jodi L. Porter and Terrance Weatherbee, on the Acadia campus, Fall 2023.
