arXiv subjects

Amogh Sirnoorkar

Publications and source records attributed to Amogh Sirnoorkar.

9 recordsLinked to original sources

arXiv2026

Students' Epistemological Beliefs and their Chatbot Preferences in AI-mediated Physics Learning

Evolving technologies have traditionally influenced pedagogical practices and Generative AI is one such technology that promises to transform higher education. In this study, we investigate the association between introductory students' preferences for chatbot behavior and their epistemological beliefs surrounding physics. Our context involves a custom built online module on waves containing simulations integrated with a chatbot. While students' chatbot preferences were captured through three provided options (guided-inquiry, direct answer, and a combination of inquiry and answer), their epistemological beliefs were captured through the standardized Epistemological Beliefs Assessment for Physical Sciences (EBAPS) survey. Results highlight that students who preferred chatbots that initially engage them in guided-inquiry but provide answers when explicitly sought (`Combination'), demonstrated sophisticated epistemological beliefs than those who preferred answer-providing chatbots. Notably, we did not observe any association between the EBAPS' total scores among students who preferred guided-inquiry and those who preferred answer-oriented chatbots. Furthermore, the observed differences did not remain statistically significant after applying a Bonferroni-adjusted significance level. Implications of these results for the design and instructional use of chatbots in physics education are discussed.

physics.ed-ph↗

arXiv2025

Examining Student and AI Generated Personalized Analogies in Introductory Physics

Comparing abstract concepts (such as electric circuits) with familiar ideas (plumbing systems) through analogies is central to practice and communication of physics. Contemporary research highlights self-generated analogies to better facilitate students' learning than the taught ones. "Spontaneous" and "self-generated" analogies represent the two ways through which students construct personalized analogies. However, facilitating them, particularly in large enrollment courses remains a challenge, and recent developments in generative artificial intelligence (AI) promise potential to address this issue. In this qualitative study, we analyze around 800 student responses in exploring the extent to which students spontaneously leverage analogies while explaining Morse potential curve in a language suitable for second graders and self-generate analogies in their preferred everyday contexts. We also compare the student-generated spontaneous analogies with AI-generated ones prompted by students. Lastly, we explore the themes associated with students' perceived ease and difficulty in generating analogies across both cases. Results highlight that unlike AI responses, student-generated spontaneous explanations seldom employ analogies. However, when explicitly asked to explain the behavior of the curve in terms of their everyday contexts, students employ diverse analogical contexts. A combination of disciplinary knowledge, agency to generate customized explanations, and personal attributes tend to influence students' perceived ease in generating explanations across the two cases. Implications of these results on the potential of AI to facilitate students' personalized analogical reasoning, and the role of analogies in making students notice gaps in their understanding are discussed.

physics.ed-ph↗

arXiv2025

Feedback That Clicks: Introductory Physics Students' Valued Features in AI Feedback Generated From Self-Crafted and Engineered Prompts

Since the advent of GPT-3.5 in 2022, Generative Artificial Intelligence (AI) has shown tremendous potential in STEM education, particularly in providing real-time, customized feedback to students in large-enrollment courses. A crucial skill that mediates effective use of AI is the systematic structuring of natural language instructions to AI models, commonly referred to as prompt engineering. This study has three objectives: (i) to investigate the sophistication of student-generated prompts when seeking feedback from AI on their arguments, (ii) to examine the features that students value in AI-generated feedback, and (iii) to analyze trends in student preferences for feedback generated from self-crafted prompts versus prompts incorporating prompt engineering techniques and principles of effective feedback. Results indicate that student-generated prompts typically reflect only a subset of foundational prompt engineering techniques. Despite this lack of sophistication, such as incomplete descriptions of task context, AI responses demonstrated contextual intuitiveness by accurately inferring context from the overall content of the prompt. We also identified 12 distinct features that students attribute the usefulness of AI-generated feedback, spanning four broader themes: Evaluation, Content, Presentation, and Depth. Finally, results show that students overwhelmingly prefer feedback generated from structured prompts, particularly those combining prompt engineering techniques with principles of effective feedback. Implications of these results such as integrating the principles of effective feedback in design and delivery of feedback through AI systems, and incorporating prompt engineering in introductory physics courses are discussed.

physics.ed-ph↗

arXiv2025

Dual-Role Dynamics in Prompting: Elementary Pre-service Teachers' AI Prompting Strategies for Representational Choices

Pre-service teachers play a unique dual role as they straddle between the roles of students and future teachers. This dual role requires them to adopt both the learner's and the instructor's perspectives while engaging with pedagogical and content knowledge. The current study investigates how pre-service elementary teachers taking a physical science course prompt AI to generate representations that effectively communicate conceptual ideas to two distinct audiences. The context involves participants interacting with AI to generate appropriate representations that explain the concepts of wave velocity to their elementary students (while casting themselves as teachers) and the Ideal Gas Law to their English teachers (while casting themselves as students). Emergent coding of the AI prompts highlight that, when acting as teachers, participants were more explicit in specifying the target audience, predetermining the type of representation, and producing a broader variety of representations compared to when they acted as students. Implications of the observed 'exploratory' and 'prescriptive' prompting trends across the two roles on pre-service teachers' education and their professional development are discussed.

physics.ed-ph↗

arXiv2025

From Self-Crafted to Engineered Prompts: Student Evaluations of AI-Generated Feedback in Introductory Physics

The abilities of Generative-Artificial Intelligence (AI) to produce real-time, sophisticated responses across diverse contexts has promised a huge potential in physics education, particularly in providing customized feedback. In this study, we investigate around 1200 introductory students' preferences about AI-feedback generated from three distinct prompt types: (a) self-crafted, (b) entailing foundational prompt-engineering techniques, and (c) entailing foundational prompt-engineering techniques along with principles of effective-feedback. The results highlight an overwhelming fraction of students preferring feedback generated using structured prompts, with those entailing combined features of prompt engineering and effective feedback to be favored most. However, the popular choice also elicited stronger preferences with students either liking or disliking the feedback. Students also ranked the feedback generated using their self-crafted prompts as the least preferred choice. Students' second preferences given their first choice and implications of the results such as the need to incorporate prompt engineering in introductory courses are discussed.

physics.ed-ph↗

arXiv2025

Applying a STEM Ways of Thinking Framework for Student-generated Engineering Design-based Physics Problems

This second paper in a multi-part series builds on the first, which introduced the Ways of Thinking for Engineering Design-based Physics (WoT4EDP) framework for STEM education in an introductory undergraduate physics course. Here, we apply the framework to analyze transcripts of group discussions and written reports from 14 student teams as they engaged in a self-generated engineering design (ED) problem in an introductory physics laboratory. We qualitatively examine: (i) the aspects students address in their problem statements; (ii) how they engage in design-based, science-based, and mathematics-based thinking, as well as metacognitive reflection, while developing solutions; and (iii) how they incorporate computational thinking through Python coding. Key findings highlight the need for: (i) increased guidance for iterative problem framing; (ii) structured support for assessing design limitations, engaging in a feasibility study, adopting a systematic approach to applying physics and mathematics in their iterations, and making specific metacognitive reflections; and (iii) integration of Python-based activities into laboratory tasks with appropriate scaffolding. We present our findings through a detailed qualitative analysis, drawing extensively from the qualitative methods literature. We outline our analytical approach, present coding charts, and employ qualitative methods such as thematic analysis and thick description to convey our findings. In doing so, we contribute to ongoing efforts to enhance the rigor of qualitative analysis in physics education research (PER). Based on our analysis, we provide valuable insight for educators and researchers in designing physics-based engineering design tasks and promoting interdisciplinary problem-solving in STEM education.

physics.ed-ph↗

arXiv2024

Student and AI responses to physics problems examined through the lenses of sensemaking and mechanistic reasoning

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.

physics.ed-ph↗

arXiv2023

Theoretical exploration of task features that facilitate student sensemaking in physics

Assessment tasks provide opportunities for students to make sense of novel contexts in light of their existing ideas. Consequently, investigations in physics education research have extensively developed and analyzed assessments that support students sensemaking of their surrounding world. In the current work, we complement contemporary efforts by theoretically exploring assessment task features that increase the likelihood of students sensemaking in physics. We identify the task features by first noting the salient characteristics of the sensemaking process as described in the science education literature. We then leverage existing theoretical ideas from cognitive psychology, education, and philosophy of science in unpacking the task features which elicit the characteristics of sensemaking. Furthermore, we leverage Conjecture Mapping -- a framework from design-based research -- to articulate how the proposed task features elicit the desired outcome of sensemaking. We argue that to promote sensemaking, tasks should cue students to unpack the underlying mechanism of a real-world phenomenon by coordinating multiple representations and by physically interpreting mathematical expressions. Major contributions of this work include: adopting an agent-based approach to explore task features; operationalizing conjecture mapping in the context of task design in physics; leveraging cross-disciplinary theoretical ideas to promote sensemaking in physics; and introducing a methodology extendable to unpack task features which can elicit other valued epistemic practices such as modeling and argumentation.

physics.ed-ph↗

arXiv2023

Sensemaking and Scientific Modeling: Intertwined processes analyzed in the context of physics problem solving

Researchers in physics education have advocated both for including modeling in science classrooms as well as promoting student engagement with sensemaking. These two processes facilitate the generation of new knowledge by connecting to one's existing ideas. Despite being two distinct processes, modeling is often described as sensemaking of the physical world. In the current work, we provide an explicit, framework-based analysis of the intertwining between modeling and sensemaking by analyzing think-aloud interviews of two students solving a physics problem. While one student completes the task, the other abandons their approach. The case studies reveal that particular aspects of modeling and sensemaking processes co-occur. For instance, the priming on the `given' information from the problem statement constituted the students' engagement with their mental models, and their attempts to resolve inconsistencies in understanding involved the use of external representations. We find that barriers experienced in modeling can inhibit students' sustained sensemaking. These results suggest ways for future research to support students' sensemaking in physics by promoting modeling practices.

physics.ed-ph↗