What We Learned about GPT and Engineering Education
As we integrated GPTs into the learning environment, we uncovered both the strengths and the challenges of using AI in engineering education. While the benefits of engagement and real-time feedback were clear, there were some limitations that need to be addressed as AI becomes more widely used.
KEY TAKEAWAYS
STRENGTHS
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Immediate Feedback Enhances Learning
One of the biggest advantages of GPTs is the real-time feedback students received from their questions, helping them understand the consequences of their decisions immediately. This continuous loop of learning and improvement led to deeper comprehension of risk management principles.
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GPTs Bring Real-World Complexity to the Classroom
GPTs can model the intricacies of real-world engineering challenges that are hard to capture in traditional classroom settings. Students were able to experience real-world engineering decision-making in a controlled environment. GPTs allowed them to interact with complex scenarios, making the learning process more practical and impactful.
Challenges
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AI Limitations in Understanding Nuanced Contexts
GPTs are powerful, but they may lack the ability to fully understand and respond to highly nuanced, complex situations. This limits their capacity to mimic certain real-world dynamics that require deeper contextual understanding. Especially when students asked questions that are out of context
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Infrastructure and Technology Costs
The implementation of GPTs requires technical infrastructure and investment, which may not be feasible for all institutions. Ensuring the necessary tools and support systems are in place is a key consideration.