This exploratory field study investigates the integration of innovative forms of recitation tasks in a first-year introductory mechanics course, focusing on smartphone-based experimental tasks alongside programming and standard recitation tasks. Smartphones, combined with external sensor modules, serve as a gateway enabling students to conduct various low-cost and authentic physics experiments with first-hand data collection outside traditional lab settings. These tasks aim to enhance students’ agency in independent physics experimentation and enrich homework assignments by dissolving boundaries between lectures, recitation sessions, and traditional labs, and thereby linking theoretical and experimental aspects of undergraduate physics education. To explore this potential, we implemented and evaluated a sample set of nine smartphone-based experimental tasks and, for comparison, three programming tasks as weekly exercises in a first-year physics course at RWTH Aachen University. We investigated students’ perceptions of learning with these new tasks through 12 short surveys involving up to 188 participants, focusing on factors such as goal clarity, difficulty, or feasibility at home. In two additional surveys with 108 and 78 participants, students assessed affective responses to the smartphone-based experimental tasks relative to the programming and standard recitation tasks. Our findings indicate that the smartphone-based experimental tasks were generally well suited to the students and tended to outperform the programming tasks in terms of perceptions of learning with the tasks and affective responses. Overall, students responded positively to the new experimental tasks, with perceptions comparable to, or only partly below, those of long-established standard recitation tasks. These results suggest that smartphone-based experimental tasks can be successfully integrated into teaching and contribute to refining traditional recitation tasks. Students’ differentiated perceptions of the three task types investigated offer valuable insights into how students perceived technology-enhanced recitation tasks in terms of feasibility, engagement, and instructional value. This provides a meaningful basis for instructors and researchers aiming to design more effective and student-centered learning environments in undergraduate physics education.

This study investigates how undergraduate physics majors develop an interest in specific subfields. We examine interest formation through the lens of social cognitive career theory (SCCT) using four SCCT constructs: learning experiences, self-efficacy, outcome expectations, and proximal environmental influences. We conducted 27 interviews with physics majors across multiple years of study between 2020 and 2022. Our first research question used SCCT constructs to provide detailed insights into factors affecting interest formation in various subfields of physics. We identified many factors that positively impacted students’ interests, such as positive learning experiences (e.g., coursework, experimental projects, extracurricular clubs, and self-directed learning), high self-efficacy (e.g., encourages seeking new opportunities and developing positive outlooks on future careers), outcome expectations (e.g., wanting a stable job and work-life balance), and proximal environmental factors (e.g., influences from the COVID-19 pandemic and institutional factors). Our second research question involves a comparison between the two subfields that had the largest number of students expressing interest, which were astronomy and astrophysics and biomedical physics. Exposure to astronomy occurred prior to college, while biomedical physics was usually encountered during college. Additionally, outcome expectations related to biomedical physics revolved around practical or clinical applications that helped people, while astronomy was linked to fundamental, curiosity-driven research. By understanding these factors that affect how students develop their interests, we hope to provide valuable insights to physics departments and career guidance professionals who want to better support physics majors’ transitions beyond the bachelor’s degree.

Quantitative reasoning is an essential learning objective of physics instruction. The Physics Inventory for Quantitative Literacy (PIQL) is a published assessment tool that has been developed for calculus-based physics courses to help instructors evaluate whether their students learn to reason this way. However, the PIQL is not appropriate for the large population of students taking physics who are not enrolled in, or have not completed, calculus. To address this need, we have developed the General Equation-based Reasoning inventory of QuaNtity (GERQN). The GERQN is an algebra-based version of the PIQL and is appropriate for most physics students; the only requirement is that students have taken algebra, so they are familiar with the use of variables, negative quantities, and linear functions. In this paper, we present the development and validation of the GERQN, and a short discussion on how the GERQN can be used by instructors to help their students learn.

Despite its intellectual and philosophical significance, cosmology and general relativity (GR) remain largely inaccessible to high-school physics teaching because of the advanced conceptual and mathematical prerequisites to master these topics. Integrating them into upper secondary physics teaching, outside specialized courses, poses a significant challenge that remains unresolved. While the strong rationale for their inclusion in the curriculum is widely acknowledged, significant obstacles, both practical and conceptual, hinder implementation. Moreover, empirical evidence on successful classroom and curriculum integration of modern physics remains scarce. This contribution reports on an implementation study of a GR and cosmology course developed for upper secondary school students as part of an educational project launched during the centenary of GR in 2015 and tested ever since for several years. The course aimed to expand students’ knowledge to include current physics topics while highlighting their foundations in areas of classical physics such as Newtonian mechanics, electromagnetism, and waves. Targeted teaching and learning materials are focused on conceptual and qualitative understanding, while systematically combined with a mathematical treatment accessible at the upper secondary level, avoiding oversimplification. A key element is an active learning approach, incorporating activities and tasks such as engaging applications related to current research, reflective exercises, thought experiments, and hands-on tasks. The main research objective was to explore whether a conceptually deep and educationally effective GR and cosmology course could be successfully implemented for nonspecialist upper secondary students. A pre-post study assessed both conceptual learning and affective outcomes, including interest, curiosity, self-concept, and perceived relevance of science. Results showed encouraging gains in both learning and motivation, with large to very large effect sizes for conceptual learning of core principles. Additionally, no or small effects of predictors such as gender were observed. We conclude that the integration of GR and cosmology into upper secondary physics teaching, in the form of courses and materials that are engaging, comprehensible, and impactful, is feasible. This can enable learners in nonspecialist courses and with diverse backgrounds to engage with these intellectually and educationally rich and stimulating topics.

This study examined how students utilized metacognition while solving physics problems. A case study design was implemented, involving 30 11th-grade students. Data were collected by using open-ended physics problems, with a think-aloud strategy applied during the process. Both quantitative and qualitative analyses were conducted using the metacognition during problem-solving rubric, classifying students as either experts or novices. The findings revealed several key insights: First, the majority of high school students fall into the novice category, struggling with the application of metacognitive knowledge and skills while solving physics problems. Second, expert students demonstrate strong evaluation and planning skills but limited metacognitive knowledge and monitoring abilities. Third, novice students exhibit major deficiencies in metacognitive knowledge and monitoring. Fourth, assessment practices focusing heavily on final results may hinder the development of deeper metacognition and problem-solving skills. Fifth, metacognitive knowledge and monitoring are crucial for generating successful solutions for scientific problems, especially in high-complexity tasks. Last, familiarity with the content and the complexity level of the problem may influence students’ success in problem solving. The metacognition rubric developed for this research may serve as a valuable tool for future studies in the field.

Introductory physics is a gateway course that is required for many science, technology, engineering, and mathematics (STEM) majors. In introductory physics courses, women and racially minoritized students often have lower grades and higher attrition than white men. Inclusive teaching is widely recommended to ensure that all students have the opportunity to fully participate and succeed in the learning process. Thus, inclusive teaching practices may help increase the diversity of people in STEM majors and careers. However, these recommended inclusive teaching practices are not widely used in introductory physics courses. This study seeks to better understand the extent to which introductory college-level physics instructors use inclusive teaching strategies as well as their reasons for the use or nonuse of such practices. We conducted semi-structured interviews with 11 male instructors from a variety of higher education institutions in Michigan who taught introductory calculus-based physics courses in Spring 2022. The participants were asked to describe their use of and beliefs about 23 specific inclusive teaching strategies. Results show that few of these instructors are aware of the full range of inclusive teaching strategies. Most instructors are aware of inclusive teaching strategies that are related to active learning and frequently articulate valid reasons for implementing these strategies. However, when implementing inclusive teaching strategies, they often do not do so in ways that are known to be most effective. Instructors are less aware of and less willing to use inclusive teaching practices that require acknowledging differences in students’ identities. Their reasoning is that physics is an objective science that has nothing to do with race, gender, or other aspects of identity. Overall, this preliminary study suggests that significant work is needed to help introductory physics instructors understand, value, and implement the full range of inclusive teaching strategies.

In this paper, we describe student understanding of the “force concept” (basic concepts of Newtonian mechanics) by representing it as a vector-valued quantity, which we refer to as the “score-state vector,” in a (30-dimensional) Force Concept Inventory (FCI) score space. We use a large ensemble of FCI results collected throughout Japan to explore the global statistical structure of the vector. We used principal component analysis to project the score-state vector into a three-dimensional subspace and found that the student score states follow a non-normal distribution. The distribution exhibits two snakes and is also skewed like a screw. This implies that the correlation of the second order (such as the commonly used correlation coefficients) is insufficient to describe the global structure of student score states. We find the distribution to be roughly divided into two subdistributions corresponding to novice respondents and intermediate (transient) and expert respondents. This indicates that “novice” and/or “expert” are not additional qualities given to the ensemble of samples but are understood as a label distinguishing the characteristic areas of the entire distribution. In addition, we observe, as a result of the non-normal distribution, the deviation of the second-order correlation of the score-state vector from that of an artificial interitem-uncorrelated ensemble of samples. The observed deviations realize many previously obtained results and present us with many other new results. Although ours is only a first step, it is a necessary process toward constructing a more accurate model of the student’s reasoning about Newtonian mechanics.

Group work is commonly adopted in university science laboratories. However, student small-group discourse in university science labs is rarely investigated. We aim to bridge the gap in the literature by characterizing student discourse group roles in inquiry-based science labs. The instructional context for this study was a summer program hosted at a private research university in the eastern United States. The program was designed as a bridge program for matriculating students who were first generation and/or deaf or hard of hearing (DHH). Accommodations such as interpreters and technological tools were provided for DHH students. We analyzed 19 students’ discourse moves in five lab activities from the video recordings, resulting in a total of 48 student-lab units. We developed codes to describe student discourse moves: asking a question, proposing an idea, participating in discussion, chatting off-task, and talking with instructor. Through a cluster analysis using the 48 student-lab units on quantified discourse moves, we identified four discourse styles, high on-task high social, high on-task low social, low on-task high social, and low on-task low social. The results show that individual students tended to demonstrate varying discourse styles in different lab activities; students’ discourse styles within the same groups tended to be aligned with their group members. By examining group members’ discourse styles in mixed-gender groups, we did not observe a difference in engagement levels between female and male students. DHH students in mixed hearing ability groups, however, were observed to have a lower level of engagement compared to their non-DHH group members. We discuss possible factors that may have contributed to the observations for genders and students with different hearing abilities. We also provide suggestions for promoting equitable small-group discourse in university science labs.

We investigate the multilingual and multimodal performance of a large language model-based artificial intelligence (AI) system, GPT-4o, using a diverse set of physics concept inventories spanning multiple languages and subject categories. The inventories, sourced from the PhysPort website, cover classical physics topics such as mechanics, electromagnetism, optics, and thermodynamics, as well as relativity, quantum mechanics, astronomy, mathematics, and laboratory skills. Unlike previous text-only studies, we uploaded the inventories as images to reflect what a student would see on paper, thereby assessing the system’s multimodal functionality. Our results indicate variation in performance across subjects, with laboratory skills standing out as the weakest. We also observe differences across languages, with English and European languages showing the strongest performance. Notably, the relative difficulty of an inventory item is largely independent of the language of the test. When comparing AI results to existing literature on student performance, we find that the AI system outperforms average postinstruction undergraduate students in all subject categories except laboratory skills. Furthermore, the AI performs worse on items requiring visual interpretation of images than on those that are purely text-based. While our exploratory findings show GPT-4o’s potential usefulness in physics education, they highlight the critical need for instructors to foster students’ ability to critically evaluate AI outputs, adapt curricula thoughtfully in response to AI advancements, and address equity concerns associated with AI integration.

Achieving knowledge integration for deep learning requires students to construct well-connected knowledge structures by understanding and applying the central ideas of a concept. However, research on student learning in work and mechanical energy reveals persistent challenges in grasping fundamental concepts, particularly in connecting the work-energy theorem, potential energy, and the principle of conservation of mechanical energy. This study investigates the effectiveness of two instructional interventions based on the conceptual framework of work and mechanical energy: one employing lecture-based instruction and the other incorporating a cooperative learning approach. The findings provide strong evidence that the conceptual framework is an effective tool for guiding instruction to promote knowledge integration. Additionally, the use of cooperative learning instruction was shown to further enhance students’ ability to connect concepts and develop deep understanding, offering valuable insights for designing instructional strategies aimed at fostering knowledge integration.

Previous research has found gender inequitable equipment usage across various lab course contexts. Few studies, however, have tested possible remediation strategies. In this work, we use hierarchical linear modeling to compare men and women’s lab equipment usage in two group work structures across three course contexts. In one in-person course, students formed their own groups in class and rotated into new groups every unit. In the other two courses, one in person and one remote, students were assigned groups formed by the instructor and worked with the same group all semester. In line with former studies, we found gender inequitable equipment usage in the course with in-class formed, rotated groups. We did not observe gender inequitable equipment usage, however, in the course with instructor-assigned, fixed groups. Analyzing equipment usage across the semester within each course, our results suggest that this improvement comes from a combination of both instructor-assigned groups and keeping groups fixed for the semester. Our findings present many opportunities for subsequent controlled studies to probe these practices.

This paper presents a methodological approach based on the use of complex networks to analyze the structure and content of curricula. We analyze the concept network built from the final year of a particular high school physics curriculum, as well as that of mathematics. We examine the most central nodes in each case, the community structures (coherent units or groupings), and the changes that occurred when the network was considered in isolation or integrated with mathematics. The results show that the integrated physics and mathematics network has a higher average degree compared to the individual networks, driven by numerous interdisciplinary connections. The modularity analysis indicates similarities with the original curriculum layout, as well as notable differences that may suggest alternative ways of organizing the content. The differences between separated and integrated networks also highlight the prominence of certain key concepts.

Inequities in student access to trigonometry and calculus are often associated with racial and socioeconomic privilege, and often influence introductory physics course performance. To mitigate these disparities in student preparedness, we developed a two-pronged intervention consisting of (1) incentivized supplemental math assignments and (2) AI-generated learning support tools in the form of optional hints embedded in the physics homework assignments. Both interventions are grounded in the situated expectancy-value theory of achievement motivation, which posits that students are more likely to complete a task that they expect to do well in and whose outcomes they think are valuable. For the supplemental math assignments, the extra credit was scaled to make it worth more points for students with lower exam scores, thereby creating even greater value for students who might benefit most from the assignments. AI-generated hints were integrated into the homework assignments, thereby reducing or eliminating the cost to the student, in terms of time, energy, and social barriers or fear of judgment. Our findings indicate that both these interventions are associated with increased exam scores; in particular, the scaled extra credit reduced disparities in completion of supplemental math assignments. These interventions, which are relatively simple for any instructor to implement, are therefore very promising for creating more equitable undergraduate quantitative courses.

Science, technology, engineering, and mathematics (STEM) graduate and doctoral programs experience consistently high attrition rates. Moreover, persistent disparities exist in gender and racial representation. Students from historically excluded backgrounds have lower retention rates than white and Asian American students. To date, little work has been done to understand the student experience and how it contributes to attrition or persistence. There is also a lack of information regarding the causes of demographic disparities in graduate student attrition. Past studies in this context primarily focused on student academic preparation and mentoring relationships. Although student self-efficacy is found to be a contributing factor for undergraduate student retention, there is currently minimal research on the role of self-efficacy in STEM graduate student retention. This study uses a sociological approach, based on the notion that the surrounding environment influences student experiences and behavior, to understand student retention in physics graduate education. In particular, we examine the relationship between four departmental support structures (mentoring and research experience, professional development, social and academic integration, and financial support) and students’ intentions to persist. We also examine the mediating role of academic self-efficacy in this relationship. We use the construct of self-efficacy under the assumption that self-efficacy is a dynamic construct influenced by, in this case, the academic environment. We use a mixed-method convergent design approach. We collected 396 student responses from 19 physics graduate programs across the United States, 20 follow-up semistructured student interviews, and 9 department leaders’ interviews. This study presents a retention model for graduate education that shows the critical role of student socioacademic integration and self-efficacy on intention to persist. Students’ in-depth interview data corroborate this finding, indicating that a supportive social and academic environment (e.g., working with peers on coursework, socializing, and communicating with faculty members and mentors) helps them in completing the program degree. Moreover, we found that departmental leaders are unaware of the struggles students experience due to the departmental environment (burnout, lack of socioacademic integration, and discrimination) and attribute students’ decisions to leave the program to factors external to the program, such as desire for career change. Student interview data emphasized the importance of cultural change toward supporting student learning and growth, improving work-life balance, and developing and maintaining healthier student-faculty relationships. The results of our study motivate further research to help us better understand how changes in these areas can impact student retention.

Group configurations shape social skills and learning outcomes by fostering student interaction and collaboration. Yet, the factors influencing group formation, social cohesion, and group stability have remained understudied in physics education. Here, we examine these factors in a cohort of 90 students from ninth and tenth grades at a private school in Chile. For a semester-long physics project, students were assigned to groups either through random allocation (control group) or given the autonomy to form their own groups (experimental group). Our findings indicate that the groups formed by choice developed stronger friendship bonds throughout the semester, while the groups formed randomly demonstrated greater levels of cooperation and more frequent changes in membership, suggesting weaker social cohesion. Additionally, tenth-grade students were more likely to stay in their original groups, possibly indicating increased social maturity. These findings emphasize the ongoing challenge in education of balancing student choice in group formation with the need for diverse group compositions that enhance learning. This study provides practical recommendations for physics teachers and researchers.

This study presents the development and validation of a novel multiple-choice test designed to assess university students’ conceptual understanding of electric field, force, and flux. The test of understanding of electric field, force, and flux was constructed based on the results of previous studies using a phenomenographic approach to classify common errors in these concepts. The final 10-item test, designed with targeted distractors, was administered to 116 students enrolled in an introductory electricity and magnetism course. Statistical analyses, including item difficulty and discrimination indices, confirmed the reliability and discriminatory power of the test. Students’ performance revealed significant misconceptions in applying the superposition principle, Gauss’s law, and electric force interactions. The results of this study provide valuable insights into students’ learning challenges and offer physics educators and researchers a useful tool for assessing conceptual understanding and guiding instructional improvements.

[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] This study investigates physics instructors’ acceptance and implementation of generative AI (GenAI) in physics education, guided by Rogers’ diffusion of innovation (DOI) theory, focusing on its five-stage innovation-decision process: knowledge, persuasion, decision, implementation, and confirmation. Two survey versions were developed—one for GenAI users and another for nonusers. Data were collected through an online survey featuring five-point Likert scale items and open-ended questions, yielding 320 responses from high school and university physics instructors. The persuasion stage was explored in depth using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). A mixed-method approach combining quantitative structural equation modeling (SEM) and qualitative content analysis was employed. We found instructors distributed across different adoption stages, with key barriers including insufficient technical knowledge (51.75% of nonusers) and language processing limitations (50.5% of users). Hedonic motivation ($\beta =0.498$) was found to have a substantially stronger influence on adoption than performance expectancy ($\beta =0.121$), with an effect size approximately 4 times greater. We identified significant differences between paid and free GenAI users, with paid users more likely to request prompt-writing guidance (30.56% vs 6.47%) despite using more advanced models. Support needs also varied substantially, as half of paid users requested budget support for GenAI services compared to only 17.65% of free users. Most physics instructors used GenAI primarily for assessment tasks, particularly for generatingphysics problems with solutions. Their primary concerns included incorrect physics information in GenAI responses, potential negative impacts on students’ analytical thinking, language barriers, and challenges with prompt writing. These findings suggest that successful GenAI integration in physics education requires physics-specific training and differentiated support based on whether instructors use free or paid versions of GenAI tools.

[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] Artificial intelligence-based chatbots are increasingly influencing physics education because of their ability to interpret and respond to textual and visual inputs. This study evaluates the performance of two large multimodal model-based chatbots, ChatGPT-4 and ChatGPT-4o, on the brief electricity and magnetism assessment (BEMA), a conceptual physics inventory rich in visual representations such as vector fields, circuit diagrams, and graphs. Quantitative analysis shows that ChatGPT-4o outperforms both ChatGPT-4 and a large sample of university students, and demonstrates improvements in ChatGPT-4o’s vision interpretation ability over its predecessor ChatGPT-4. However, qualitative analysis of ChatGPT-4o’s responses reveals persistent challenges. We identified three types of difficulties in the chatbot’s responses to tasks on BEMA: (i) difficulties with visual interpretation, (ii) difficulties in providing correct physics laws or rules, and (iii) difficulties with spatial coordination and application of physics representations. Spatial reasoning tasks, particularly those requiring the use of the right-hand rule, proved especially problematic. These findings highlight that the most broadly used large multimodal model-based chatbot, ChatGPT-4o, still exhibits significant difficulties in engaging with physics tasks involving visual representations. While the chatbot shows potential for educational applications, including personalized tutoring and accessibility support for students who are blind or have low vision, its limitations necessitate caution. On the other hand, our findings can also be leveraged to design assessments that are difficult for chatbots to solve.

[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] We present a study in which a version of a common conservation of mechanical energy introductory physics problem, an object released on an inclined plane, is given to OpenAI’s GPT-4 large language model (LLM). We investigate how different permutations of object, action verb, and property of the incline impact the responses of the LLM. The problem setup and prompting was left purposefully minimal, requiring the LLM to state multiple assumptions to justify the final answer. We specifically studied how different keywords lead the LLM to analyze the system as rolling versus sliding and how this may be different from physics experts and novice learners. We found that domain-specific terminology may impact the LLM differently from students. Even for correct answers, it generally did not specify the necessary assumptions it needed to state to come to this solution, falling short of what would be expected from an expert instructor. When conflicting information was provided, the LLM generally did not indicate that was the case in its responses. Both issues are weaknesses that could be remedied by additional prompting; however, they remain shortcomings in the context of physics teaching. While specific to introductory physics, this study provides insight into how LLMs respond to variations of a problem within a specific topic area and how their strengths and weaknesses may differ from those of humans. Understanding these differences, and tracking them as LLMs change in their capabilities, is crucial for assessing the impact of artificial intelligence on education.