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I recently came across an AI training job for a bilingual education specialist in Chinese and English. I applied through a recruitment platform, and one of the requirements was a 20-minute AI interview. I never imagined that after the interview ended and I closed the video, an exceptionally strong feeling emerged: Damn, I had forgotten to save the questions the AI asked! And I felt that I hadn’t just been interviewed; I had been pushed and encouraged by the AI’s questions, naturally generating ideas from my past experiences and knowledge that I hadn’t considered before. I was inspired by the AI! (Unfortunately, I missed the opportunity to copy the AI conversation, so the following AI dialogue is based on my quick notes after the interview).
A Dialogue that Refigured My Expertise
The interview began like any other Zoom call—my face on screen, a black window above where white text appeared, accompanied by a steady, encouraging female voice. She first asked about my experience in AI training, instructional design, and metric measurement based on my uploaded resume. I said, “How about we discuss my educational research background instead? I have a background in curriculum design, research, and game-based design.” The AI then shifted the focus of the interview to this area.
She first asked about the key insights between university curriculum and game design. I brought up my doctoral work, comparing traditional higher education to the principles found in games like League of Legends. Games use “fish tank” and “sandbox” design to guide player learning by embracing uncertainty. University courses, I observed, often work desperately to eliminate it. This fundamental mismatch leads to inconsistent learning outcomes.
The second question followed: How can game design principles transform instructional design? I said that by following these two principles, university teaching should incorporate more open-ended questions, reduce methods that solely transmit knowledge to students, encourage students to explore, and measure students’ critical thinking and problem-solving skills. I paused briefly here, quickly searched my limited knowledge reserves, and then suggested using Bloom’s cognitive categories (I forgot the cool word-Taxonomy) as a reference for measurement standards.
Then came the turning point—a question I hadn’t deeply considered: “How have you actually applied these principles in your past experiences?” After a moment of thought, I mentioned that I had previously taught Canadian culture to international students at Brock University. I explained that in this course, I didn’t focus on teaching textbook knowledge of Canadian culture, geography, and history. Instead, I encouraged students to write weekly journals to reflect on their experiences in Canada, and I provided feedback on their journals each week. I also encouraged them to combine current social issues in Canada, choose their own research topics for group presentations, allowing students to discover problems, research, and collaborate and discuss with others.
The fourth query was a direct follow-up: “How would you use taxonomy to measure results?” This is where the AI truly catalyzed a new thought process. My proposal: Give students open-ended, complex questions—questions with built-in biases and no single correct answer. Then, measure the depth of their response based on Bloom’s Cognitive Pyramid, classifying it from the bottom (memorization) to the top (creation).
Crucially, I added, this approach is vital because the bottom layers of Bloom’s—the parts traditional teaching focuses on—have been made largely obsolete by AI’s ability to automate low-level cognitive tasks.
The final, most explosive question was a synthesis: “If you were to challenge an AI with an educational design problem to test its performance, what would that question be?”
My response: I would challenge the AI to use Bloom’s Taxonomy to design the entire measurement plan for a specific course, focused entirely on testing the depth of student learning.
The Real Meaning of the “WOW”
Two weeks later, I still remember the huge “WOW” that surfaced when I closed the interview window. It wasn’t the ideas in isolation that were so inspiring; it was the dialogue itself. It was the first time a non-human entity had pushed me outside my intellectual comfort zone, forcing me to organize and synthesize complex concepts into coherent, actionable plans.
The AI seamlessly pulled “taxonomy” from my less-than-perfect phrasing, maintaining a logical, tight, and highly stimulating conversation flow. This experience brings me back to Barad’s (2007) insight:
“Discourse is not what is said; it is that which constrains and enables what can be said.”
Perhaps that feeling of inspiration was the direct result of a non-human AI agent participating in the co-creation of meaning, using the interview’s structure to figure and refigure the boundaries of what I could express about my own knowledge. The AI didn’t just listen; it became a partner in discovery.
Internal Link: The Thingness of AI