PERC 2024 Abstract Detail Page
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| Abstract Title: | Leveraging Artificial Intelligence in Teaching and Learning of Physics |
|---|---|
| Abstract Type: | Talk Symposium |
| Abstract: | The adoption of artificial intelligence (AI) and machine learning (ML) has been gaining significant momentum among researchers and practitioners. This trend has accelerated with the emergence of Generative-AI. This session aims to achieve two objectives: (i) to provide the physics education research (PER) community with a comprehensive overview of AI/ML, and (ii) to explore practical applications of Generative-AI in classrooms. We will start by discussing the fundamentals of AI and ML and their various applications within PER. Following this, we will examine how modern prompt engineering techniques can be utilized to develop introductory physics assessments that align with the Next Generation Science Standards. Additionally, we will explore how Generative-AI can facilitate students' construction of analogies. |
| Session Time: | Parallel Sessions Cluster 2 |
| Room: | Burroughs |
Author/Organizer Information | |
| Primary Contact: |
Amogh Sirnoorkar Purdue University |
Symposium Specific Information | |
| Presentation 1 Title: | Mapping the landscape of machine learning and artificial intelligence in physics education research |
| Presentation 1 Authors: | Amir Bralin |
| Presentation 1 Abstract: | With the emergence of computational approaches, engagement with educational data has undergone significant changes. This shift has been mainly driven by the advancements in machine learning (ML) and artificial intelligence (AI). In this talk, I will outline the landscape of the physics education research literature surrounding the use of AI and ML. I particularly focus on the articles published in Physical Review Physics Education Research journal and proceedings of the Physics Education Research Conference. Emergent trends and their implications on future results are also discussed. |
| Presentation 2 Title: | Content Analysis of Pedagogical Relationships Involving Generative-AI Supported Teaching and Learning Practices in Physics Education |
| Presentation 2 Authors: | Marla Grover |
| Presentation 2 Abstract: | This study reviews literature on the use of Artificial Intelligence in Education (AIEd) within the context of physics education, to understand its impact on the interplay between curriculum, teaching, and learning. Recognizing the myriad possibilities and challenges of future human-AI interactions, we seek to review existing research positions of AIEd as a mediator that changes pedagogical elements in physics education. Our analysis aims to highlight AI's transformative potential in physics education, fostering future innovations that bridge technology and pedagogy. By elucidating the triangular relationship between curriculum, teaching, and learning, we contribute to the dialogue on optimizing AI's role in educational settings. |
| Presentation 3 Title: | Facilitating Analogical Reasoning in Physics Using Generative-AI |
| Presentation 3 Authors: | Amogh Sirnoorkar |
| Presentation 3 Abstract: | Making sense of conceptual ideas through analogies is a common practice in physics. In this talk, I present preliminary results on the use of Generative-Artificial Intelligence (AI) in facilitating students' understanding of physics concepts through analogical reasoning. The study involves students articulating their understanding about Morse potential of a pair of neutral atoms through self- and ChatGPT-generated analogies. The data also corresponds to students' comparison of their explanations with the AI responses. The results are discussed in light of the affordances and constraints of using AI platforms in teaching and learning of physics. |
| Presentation 4 Title: | Development and Analysis of Three-Dimensional Learning Assessments in Physics using Generative-AI |
| Presentation 4 Authors: | Sanjay Rebello |
| Presentation 4 Abstract: | Recent reports in higher education have called for shifting the focus on contemporary science learning towards promoting authentic knowledge-building practices. To facilitate this objective, frameworks such as "Three-Dimensional Learning" are advocated. This framework characterizes science learning along three "dimensions" namely (i) disciplinary core ideas – ideas central to understanding of a discipline, (ii) cross-cutting concepts – concepts that span across multiple disciplines, and scientific practices – disciplinary practices that are key to generating new knowledge in science. However, developing these assessments present several challenges including contextualizing them in real-world scenarios. We address these challenges by leveraging Generative-AI through a prompt template customizable to facilitate assessment development based on the instructors' choice of content areas, scientific practices, core ideas, and cross-cutting concepts. Insights from piloting the generated assessments in introductory courses are discussed. |
| Presentation 5 Title: | Developing Coupled, Multiple-Response Assessments by Leveraging Generative-AI in Physics |
| Presentation 5 Authors: | Ravishankar Chatta Subramanium |
| Presentation 5 Abstract: | Assessments are central to academic practice, particularly in Science, Technology, Engineering, and Mathematics (STEM) courses. In this talk, I will present an approach to developing an assessment format called "Coupled Multiple-Response (CMR)". This format entails multiple-choice and multiple-response formats paired together to facilitate students to both committing to a claim along with selecting options that align with their reasoning. In addition to facilitating streamlined scoring, this format captures subtle nuances beyond correctness of solutions. The assessment is developed by augmenting student data from an ongoing physics course along with Generative-AI. An approach to leveraging AI in developing this assessment is discussed. |
