Student and AI responses to physics problems examined through the lenses of sensemaking and mechanistic reasoning
arxiv(2023)
摘要
Several reports in education have called for transforming physics learning
environments by promoting sensemaking of real-world scenarios in light of
curricular ideas. Recent advancements in Generative-Artificial Intelligence has
garnered increasing traction in educators' community by virtue of its potential
in transforming STEM learning. In this exploratory study, we adopt a
mixed-methods approach in comparatively examining student- and AI-generated
responses to two different formats of a physics problem through the cognitive
lenses of sensemaking and mechanistic reasoning. The student data is derived
from think-aloud interviews of introductory students and the AI data comes from
ChatGPT's solutions collected using Zero shot approach. The results highlight
AI responses to evidence most features of the two processes through
well-structured solutions and student responses to effectively leverage
representations in their solutions through iterative refinement of arguments.
In other words, while AI responses reflect how physics is talked about, the
student responses reflect how physics is practiced. Implications of these
results in light of development and deployment of AI systems in physics
pedagogy are discussed.
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