Tawei (David) Wang (KPMG/Neil F. Casson Endowed Professor, Associate Dean for Faculty and Research, Driehaus College of Business) presented “Teaching Data Analytics Enhanced by AI: Customized feedback, a Time-bound oral assignment and AI-mediated dialogue” at the AI in Teaching Symposium, April 25, 2025.
In the rapidly evolving landscape of data analytics education, one question stands out: How can we move beyond traditional teaching methods to truly prepare students for the complex, contextual thinking required in today’s professional world? Professor Tawei (David) Wang offers compelling answers.
Beyond the Algorithm: Teaching Critical Thinking in Context
Professor Wang’s approach to teaching audit data analytics challenges a fundamental assumption about AI in education. Rather than using AI as a replacement for human interaction, he positions it as a sophisticated learning partner that enhances—rather than replaces—the human elements of education.
“The biggest challenge,” Wang explained, “is helping students understand that there are so many different perspectives.” In his audit data analytics courses, students don’t just learn to analyze data; they learn to integrate multiple sources of information, consider various viewpoints, and articulate their reasoning in ways that demonstrate true understanding.
Three Pillars of AI-Enhanced Learning
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The AI as a “Hidden Student”: Revolutionizing Peer Feedback
Wang’s most innovative approach involves introducing AI as an anonymous peer reviewer alongside human students. In this system, students submit complex, unstructured assignments analyzing audit scenarios. They then provide feedback on anonymous submissions from their classmates—but unknown to them, one of these “peers” is actually an AI agent.
This clever design achieves multiple learning objectives:
- Students encounter diverse perspectives, including some they might not have considered
- They learn to critically evaluate feedback quality, whether from humans or AI
- They discover firsthand the differences between generic AI responses and contextually grounded human insights
“We want students to see that there are so many different ways to approach problems,” Wang noted. “Sometimes you agree with them, sometimes you don’t. But why?”
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Time-Bound Oral Assignments: Where Understanding Meets Articulation
The second pillar addresses a critical career skill: the ability to think on your feet and articulate complex ideas clearly. Students face unexpected questions with only five minutes to read, think, and record their responses. Behind the scenes, AI evaluates their responses based on customized rubrics focusing on reasoning clarity, logical support, and—crucially—connection to business context.
“If students can explain things orally, we bring learning to the next level,” Wang emphasized. “They need to reorganize different concepts and demonstrate their understanding using their own words.”
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AI-Mediated Dialogue: Bridging Practice and Reality
Perhaps the most fascinating element of Wang’s approach is the AI-mediated dialogue exercise. Students preparing for mock interviews first practice with AI, developing questions and scenarios. A week later, they conduct actual interviews with classmates playing the role of IT directors. The contrast between AI practice and human interaction becomes a powerful learning moment.
Students consistently report discovering that their carefully prepared AI-assisted scripts often need significant adjustment when facing a real person with detailed contextual knowledge. This experience teaches them not just about the limitations of AI, but about the irreplaceable value of human judgment and contextual understanding.
Why This Matters for Higher Education
Wang’s work demonstrates that the most effective use of AI in education isn’t about automation or efficiency—it’s about creating richer, more diverse learning experiences that better prepare students for professional challenges. His approach addresses several critical issues in modern education:
- Scalable Personalization: AI provides customized feedback that would be impossible for a single instructor to deliver to every student, while maintaining the human element through peer interaction.
- Authentic Assessment: Time-bound oral assignments and real-world scenarios test not just knowledge retention but the ability to apply, synthesize, and communicate complex ideas.
- Critical AI Literacy: By experiencing both the capabilities and limitations of AI firsthand, students develop the nuanced understanding they’ll need in an AI-integrated workplace.
Looking Forward: The Human-AI Partnership in Education
Professor Wang’s presentation offers a blueprint for thoughtful AI integration that enhances rather than diminishes the human aspects of education. His students don’t just learn data analytics; they develop critical thinking skills, communication abilities, and the contextual awareness essential for professional success.
As Wang concluded, “AI enables us to better support our students to achieve different learning objectives.” But perhaps more importantly, his work shows that the future of education isn’t about choosing between human and artificial intelligence—it’s about orchestrating both to create learning experiences that neither could achieve alone.