Teaching with AI in Technical Courses with Jingjing Li
Nov 19, 2024
auto_awesome
Jingjing Li, Andersen Alumni Associate Professor of Commerce at the University of Virginia, dives into using generative AI in technical courses. She discusses innovative assignments that enhance student engagement with AI tools, uncovering the diverse metaphors her students use to express their experiences. Topics also include the impact of AI on learning outcomes and the importance of integrating AI literacy into curricula, addressing the different capabilities among students and how this shapes their interactions with AI.
Integrating generative AI in technical courses enhances student engagement and real-world application of concepts like machine learning.
Ongoing professional development and mentorship are crucial for educators to improve teaching effectiveness and student evaluations.
Deep dives
The Role of Generative AI in Teaching
Generative AI is being integrated into teaching methodologies to help educators like Jingjing Li enhance their students' learning experiences. She emphasizes the importance of using AI tools to facilitate understanding and engagement in complex subjects such as machine learning. By incorporating AI tools like ChatGPT and Copilot, she aims to prepare students for real-world applications of AI in business. This approach also encourages faculty to make informed decisions about their AI policies, whether they permit or restrict AI usage in classrooms.
Transformative Teaching Journey of Jingjing Li
Jingjing Li reflects on her path to becoming an educator, influenced by her family's teaching background and her own experiences in graduate school. Initially, she faced challenges maintaining authority while being only slightly older than her students, resulting in average teaching evaluations. Determined to improve, she participated in teaching excellence training and observed experienced professors, which ultimately led to significant improvement in her teaching ratings. Her reflection underscores the importance of ongoing professional development and mentorship in teaching.
Student Engagement and AI Integration
In her courses, Jingjing found innovative ways to incorporate generative AI to engage students in assignments involving predictive modeling. She structured assignments that required students to build predictive models, then analyze the cost-benefit of different outcomes using their AI-generated insights. By breaking down assignments into manageable tasks, her approach fosters critical thinking and practical application of machine learning concepts. This hands-on experience allows students to learn not only how to use generative AI but also the implications of their analytical decisions.
Evaluating the Effectiveness of AI in Learning
Jingjing analyzed students' experiences with generative AI and discovered varying levels of effectiveness based on their understanding of fundamental concepts. Those with stronger coding skills and conceptual knowledge effectively used AI to enhance their analysis and decision-making. Conversely, students lacking those foundational skills often struggled to leverage AI productively, leading to less impactful learning experiences. Her findings highlight the necessity for educators to ensure that students have a solid understanding of core concepts before integrating AI tools into the learning process.
In my new job at the University of Virginia, I recently met Jingjing Li, Andersen Alumni associate professor of commerce. Jingjing teaches business intelligence at both the undergraduate and Master’s levels, and her research interests include artificial intelligence and data analytics. She has conducted some very thoughtful experiments in her courses in using generative artificial intelligence to teach about machine learning in business analysis.
In our interview, we talk about her scaffolded assignments, the metaphors her students use to describe working with generative AI, and the relationships between conceptual understanding and AI literacy.