AI as the “More Knowledgeable Other”

Helping Students Bridge the Logic Gap in Business Writing
In business writing, students often make claims like:
“The company’s debt is healthy because its quick ratio is 2.1.”

​But what’s missing is the why. What connects the data point to the conclusion? The logical bridge—what argumentation theorist Stephen Toulmin calls the warrant—is often implied, assumed, or completely skipped.
Warrants are the invisible glue of business logic. Without them, even evidence-rich arguments fall flat.
What if we could use AI tools to help students see and strengthen that invisible glue?

Bridging the Zone of Proximal Development
This is where Vygotsky’s concept of the Zone of Proximal Development (ZPD) becomes especially relevant. The ZPD is the space between what students can do on their own and what they can do with guidance. To cross it, learners need support from a More Knowledgeable Other (MKO)—a teacher, peer, or scaffold.

Today, with the rise of conversational AI, that MKO does not have to be a person. AI tools like ChatGPT can act as digital MKOs, providing just-in-time support within a student's learning zone (Stojanov, 2023).
When students are learning how to build arguments in business contexts, AI can act as a scaffold, helping them spot where logic is implied but unspoken—and guiding them to make it explicit.
From Evidence to Explanation: AI as Reasoning Coach

Let’s return to our example:

“The company’s debt is healthy. The quick ratio is 2.1.”

A student using AI could be prompted to ask:

• “What does a quick ratio measure?”
• “What assumption am I making about financial health?”
• “Would this ratio still be healthy in a different industry?”

This turns AI into a dialogic partner—not giving answers, but modeling the type of Socratic questioning that reveals gaps in logic (Sraveu & Moore, 2017). It becomes a digital tool for guided inquiry, helping students move from recall to analysis.

This also reflects Rosenblatt’s (1978) reader-response theory, where meaning is not transmitted but constructed through interaction. When students ask AI to simulate the role of the reader, they begin anticipating audience needs and adjusting their reasoning accordingly.

Toulmin Meets Vygotsky: Structuring Thought with Support
Toulmin’s model asks students to clarify:

• Claim: What are you trying to prove?
• Evidence: What backs it up?
• Warrant: Why does the evidence support the claim?

But students do not always know how to generate a warrant on their own. That is where AI fits in.

​By guiding students through these steps, AI serves the constructivist function of scaffolding abstract reasoning into teachable, repeatable steps (Schunk, 2019). When prompted correctly, it pushes students beyond surface-level explanation and toward the metacognitive processes that characterize expert reasoning.

As noted in the research, “AI…can enhance writing by supporting students at different cognitive levels,” especially as they move from foundational knowledge toward higher-order thinking (Schunk, 2019; Smith, 2008).

Learning to Think Like Analysts, Not Just Writers
When students internalize the logic behind business metrics like the quick ratio, they move from writing to reasoning. They no longer just summarize—they analyze. They no longer just report—they persuade.

And with AI acting as a more knowledgeable other, they get to practice those moves in a low-stakes, high-feedback environment.

As Bruffee (1984) and Myers (1986) argue, tools that simulate collaborative dialogue—like AI—can extend students’ thinking and improve their capacity to reflect, revise, and clarify meaning.

Let’s Rethink What Writing Instruction Can Be
If we train AI to simulate not just grammar checkers but critical readers—to engage students in reasoning, not just revision—we can create writing classrooms where students learn how to think, not just what to write.
Toulmin gave us the model. Vygotsky gave us the pedagogy. AI can help us bring both to life.


References
Bruffee, K. A. (1984). Collaborative learning and the “conversation of mankind.” College English, 46(7), 635–652. https://doi.org/10.2307/376924
Myers, G. (1986). Reality, consensus, and reform in the rhetoric of composition teaching. College English, 48(2), 154–174. https://doi.org/10.2307/376397
Rosenblatt, L. M. (1978). The reader, the text, the poem: The transactional theory of the literary work. Southern Illinois University Press.
Schunk, D. H. (2019). Learning theories: An educational perspective (8th ed.). Pearson.
Smith, M. K. (2008). Bloom’s taxonomy. The encyclopedia of pedagogy and informal education. https://infed.org/mobi/blooms-taxonomy/
Sraveu, C., & Moore, K. (2017). The Socratic method and cognitive growth in learning communities. Oxford Academic Press.
Stojanov, G. (2023). The PAH continuum and digital learning: Pedagogy, andragogy, and heutagogy in the AI era. Journal of Educational Technology & Society, 26(1), 15–28.
Toulmin, S. (1958). The uses of argument. Cambridge University Press.