[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] Students struggle to acquire the needed energy understanding to meaningfully participate in the energy discourse about socially relevant topics, such as energy transformation or climate change. Identifying students on differing learning trajectories, as well as differences in knowledge used, is essential to help students achieve the needed energy understanding. Collecting and analyzing the longitudinal and fine-grained data necessary for this represents a substantial challenge. However, the use of a digital workbook, which captures all interaction data, has enabled us to collect such data from $N=548$ students (data from 172 students were analyzed after applying exclusion criteria). Using machine learning and natural language processing, we analyzed the data to identify productive and unproductive learning trajectories and their underlying reasons. The learning trajectories were classified according to the post-test score. To analyze the tasks from the digital workbook, machine learning methods, specifically random forest, and natural language processing, were employed to identify how students on different learning trajectories progress through the unit. The random forest analysis was accurate in distinguishing between productive and unproductive learning trajectories. Furthermore, natural language processing was employed to analyze open-ended responses, which revealed disparities in the knowledge elements that students on productive and unproductive trajectories utilized. The findings of this study indicate that machine learning techniques have the potential to provide valuable insights into student learning trajectories, which can inform the design of instructional units and the feedback provided to teachers and students.

Digital simulations are powerful instructional tools for physics education. They are often designed to visualize canonical physical phenomena, with adjustable parameters for influencing the system. While this is sufficient for developing conceptual and qualitative intuitions, it does little to help physics students build connections between physical systems and the mathematical models and equations that represent them. We present PhysMath, a suite of interactive physics simulations for use in upper-division courses. These simulations allow students to explore connections between mathematical equations and the phenomena they represent by inputting, modifying, and observing changes in system behavior. In this paper, we describe our first simulation—the Bead-On-Hoop for Classical Mechanics—and report findings from pilot interviews with intermediate physics students interacting with the simulation. Our findings validate the simulations’ design and highlight its potential for scaffolding students’ mathematical sensemaking.

In 2017, the University of Utah experienced a tragedy that accelerated already proposed changes to the physics and astronomy graduate program. This article outlines the changes made to the graduate program and discusses students’ experiences with the early implementation of the new policies. In-depth, open-ended interviews were conducted with students over 2 years to understand their experiences under or with the new graduate program. Findings suggest that student interviews and direct recruitment aided in students’ decisions to attend a graduate program, and a weeklong orientation helped build community among the cohorts. A centralized Advising Day, dictated by the department, facilitated enrolled students meeting with department members to ensure they were on track in their program and taking courses geared toward research interests favored by students. Findings suggest that qualifying or comprehensive exams need more instruction and study materials, and that students still face issues of financial insecurities, healthcare concerns, and sexism that need addressing. Further research should examine how these reforms have impacted students at the end of their time in the program. Content Warning: This paper discusses the death by suicide of a graduate student.

[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] Recent research in science education has largely focused on using ChatGPT to solve problems and evaluating its accuracy and problem-solving features. However, as artificial intelligence (AI) is becoming an important tool for human development, effective strategies for learning and education using human-AI collaboration (HAI) learning remain underexplored. HAI is emerging as a powerful tool for enhancing educational outcomes, especially with the integration of advanced natural language AI systems such as ChatGPT-4o. This study explores the role of HAI in solving scientific problems, comparing its efficacy with human-human collaboration (HHC) among high school students. We employed two different groups: one using HAI with ChatGPT-4o, and the other using HHC, focusing on their performance in scientific knowledge and scientific thinking tasks. Results showed that while both collaboration methods significantly improved students’ performance in solving scientific problems, HHC exhibited a greater effect size compared to HAI. The use of ChatGPT-4o was particularly noted for its ability to provide interactive learning experiences and guide students through problem solving; however, challenges such as inconsistencies in responses and image recognition hindered its effectiveness. Additionally, students tended to use ChatGPT-4o as a tool for obtaining answers rather than engaging in deeper collaborative explorations. This study underscores the importance of developing targeted training for students in effectively collaborating with AI tools. The results can also provide useful information for future exploration on methods to strengthen HHC by integrating AI tools, leveraging the strengths of both approaches to enhance education outcomes.

There is currently little physics education literature examining thinking and learning in graduate education and even less literature characterizing problem solving among physics graduate students despite this being an essential professional skill for physicists. Given reports of discrepancies between physics problem solving in the undergraduate classroom and “real-world” problem solving, we sought to investigate whether this discrepancy exists at the graduate level. We first investigate the problem-solving skills present in first-year graduate physics assignments. A recent framework that characterizes problem solving as decisions-to-be-made was used. Assignments were taken from the four core courses of one academic year at one research-intensive university and coded by two researchers. We found that only 4 of the 29 decisions in the framework were present in most of the assignments. We then interviewed 11 instructors from 3 universities and asked which decisions they expected of first-year graduate students. Eleven decisions were expected by eight or more of the participants, but only four of these decisions were commonly practiced on assignments. Therefore, there seems to be a mismatch between instructor expectations and practice of problem solving on assignments. This suggests that graduate physics courses may not be aligned with the problem-solving skills that physics graduate students will need in their research or future careers.

This work explores the integration of artificial intelligence (AI) custom chatbots in educational settings, with a particular focus on their applicability in the context of mathematics and physics. In view of the increasing deployment of AI tools such as ChatGPT in educational contexts, the present study explores their potential in generating topic-related learning material. The study assesses the impact of learning with AI-generated explanations as Supplemental Material on the learning experiences and performance of sixth-grade students, with a particular focus on proportional relationships in mathematical and physical contexts. The randomized controlled study with $N=214$ students compared supplementary learning material in the form of traditional textbook material with explanations previously generated by an AI custom chatbot. The results demonstrated that while the AI-generated materials had an indefinite impact on learning outcomes, they significantly enhanced positive-activating emotions, situational interest, and self-efficacy while reducing intrinsic and extrinsic cognitive load. These findings underscore the potential of AI to transform educational practices by fostering a superior learning experience. However, further research is required to clarify its impact on learning performance and long-term learning outcomes. The study highlights the importance of careful integration and customization of AI tools to maximize their benefits in physics education.

In this quantitative study, we explored learners’ intrinsic motivation towards three different experimental physics educational activities offered by our department to upper-grade high school students. The inspected activities were lecture demonstrations, a science show, and hands-on practical work. The respondents were Czech high school students ($N=9647$) who participated in these activities. From each of these activities, a highly comparable subset of 1128 respondents was selected for the comparative analysis. To assess their intrinsic motivation and its covariates, we used three dimensions of the Intrinsic Motivation Inventory: interest/enjoyment, effort/importance, and value/usefulness. The data were evaluated using a unidimensional IRT Graded Response model. Our study revealed that the science show was more appealing to high school students than traditional lecture demonstrations and, surprisingly, even more appealing than practical lab work. This increased interest was more pronounced among students who identified themselves as less diligent in physics. Paradoxically, the respondents perceived the science show as less useful than the other two activities. Delving deeper, gender differences in perceived intrinsic motivation were generally minor, with one exception: in a comparison of the interest generated by the science show and practical work, girls favored the science show more than boys did. These findings underscore the need for a nuanced understanding of pedagogical strategies and offer insights into different approaches to promoting student engagement and interest in physics. Our research could assist educators in distinguishing the activities that might be more beneficial in terms of generating interest among particular groups of students.

Teachers’ goals and orientations underlie their instructional decision making and can make the difference between “talking the talk” and “walking the walk” when trying to change lab instruction. Schoenfeld’s Resources, Orientations, and Goals (ROG) framework was employed to shed light on the challenges involved in a reform in lab instruction that aims to grant students more agency in experimental research practices. We focus on high school physics teachers engaged in a professional development process as members of a national network of Professional Learning Communities (PLCs, $N\approx 275$ participants). One of the annual themes in the PLCs addressed lab instruction. The teachers were involved in: (a) experiencing, as learners, Deliberation Labs designed to reform lab instruction while taking into consideration the widespread constraints faced by high school teachers; and (b) engaging in collaborative reflection on their classroom experiences to encourage them to rethink their ROGs. The data were gathered from two professional development sequences held during the 2020 and 2022 school years that differed in the structuring of collaborative reflection. To achieve a large-scale portrayal of teachers’ goals and orientations toward integrating experimental research practices in the instructional lab, we analyzed surveys administered in the PLCs. The survey results highlighted two main orientations that interfered when implementing the Deliberation Labs: the “lab serving theory” orientation and the “restricting agency” orientation. To better understand the processes of change in teachers’ ROGs, we present two case studies that were involved in the structured collaborative reflection on practice. They deal with experienced PLC teacher-leaders who were motivated to try out the Deliberation Labs in their classrooms. The analysis shows how they interpreted their experiences in ways that align with the lab serves theory orientation, thus undermining the goals that the Deliberation Labs were designed to achieve. These results highlight the winding road to change in teachers’ ROGs and emphasize the need to support them in resolving conflicting goals and orientations. They also raise dilemmas regarding the nature of feasible yet effective professional development programs that can lead to large-scale change.

Accuracy on assessments is commonly studied in education research but response time (RT) is relatively less investigated even though decades of research in cognitive sciences indicate that time can be an important dimension for understanding student learning. To better understand RT and the potentially important relations between accuracy and RT in physics education, we conducted an exploratory investigation by collecting and analyzing both accuracy and RT data on physics-relevant math skills on low-stakes pre and posttests as well as course exam scores in algebra-based and calculus-based introductory physics courses over two semesters for a total of $N=1936$ participants. Overall, we found a high level of variation in response times revealing weak but consistent patterns of associations between RT and accuracy on skills and exam scores. First, we found a nonlinear relationship between RT and accuracy on the pretest and on the post-test, which may indicate a variety of strategies and engagement among students on these participation-credit-only tests. Second, the results indicate that while RT alone does not predict course grade, when controlling for accuracy on pre or posttest math skills, students with lower RT on these skills are more likely to get better grades. Therefore, both pre or posttest accuracy and speed predicted course grades, though accuracy explained a substantial amount of variance ($\sim 35\%$) while pretest RT explained a much smaller amount of variance ($\sim 1\%$). Third, controlling for both pretest accuracy and pretest RT, we found that students who sped up from pre to posttest were likely to get higher exam scores; however, students who slowed down were on average likely to have a higher post-test score. Fourth, since systemic inequities in STEM education have been documented via measured mean differences between some demographic groups for exam scores and accuracy on math skills, we compared RTs by sex, race, first-generation status, and citizenship to potentially gain more insight into these inequities. We found no consistent or conclusive evidence of demographic differences, though in multiple comparisons, Black, Hispanic, Native American, and Pacific Islander students had larger RTs on average, and in one comparison they were slightly faster. We found that RT was not a mediator of demographic differences in physics grades, though, as expected, accuracy on math skills was a mediator. We briefly discuss how our results relate to various cognitive models such as cognitive ability, speed-accuracy trade-offs, fluency and cognitive load, dual-process theories, and student psychological factors like self-efficacy, anxiety, and motivation. We argue that, based on which (if any) of the above mechanisms are at play, valuing speed in physics may have benefits, such as improving fluency to reduce cognitive load and drawbacks, such as unintentionally using speed as a proxy for achievement or inducing excessive stress that may interfere with performance and student well-being.

Following the global COVID-19 pandemic, digital learning environments have gained in importance in education all around the world. A new digital learning format called a “Digital Learning Module” (DLM) was developed within the Physics Education Research (PER) group at CERN, consisting of a series of prerecorded explanatory and experiment videos, interactive elements, an interactive simulation experiment, and expert interviews. So far, one DLM has been created on the topic of positron emission tomography (PET). Aiming to establish a set of effective design principles that can be employed in the design of future DLMs or similar digital learning environments, the goal of this research is to evaluate the already existing PET DLM. In doing so, this research brings together different theoretical and empirical studies, in order to identify design principles fitting the format of the DLM. It also reviews key physics literature on PET to identify its disciplinary-relevant aspects. First, an analysis of the PET DLM was performed in terms of the identified design principles and disciplinary-relevant aspects. Then, nine semistructured interviews were conducted with high-school students from Greece and Austria as they worked through the PET DLM. A qualitative content analysis of the interview transcripts has determined the set of effective design principles that will constitute the foundation of future DLMs developed by the CERN PER group and can serve as a starting point and reference for researchers developing similar digital learning environments elsewhere.

Inequity is persistent in science, technology, engineering, and mathematics classrooms despite significant attention to upending it. In order to better understand how and why inequity persists, an analysis is presented of 27 self-identified white male physics faculty and graduate students responding to an interview prompt about the social exclusion of a Black woman by peers in a physics classroom. Three main research questions are addressed: (i) How familiar are these men with the alienation Black women frequently experience?, (ii) To what do they attribute this alienation?, and (iii) What do they think an instructor should do in a classroom where this alienation is occurring? Interviewees were found to have little understanding of the bias and discrimination Black women face, were hesitant to name the underlying cause as racism or sexism, and generally had limited insight into how to address racial and gender bias in their own classrooms. These findings indicate majority group instructors are ill equipped to recognize or disrupt oppressive structures in their own classrooms and may inadvertently exacerbate hostile interactions.

Dual-process theories of reasoning suggest that humans reason using two processes often referred to as process 1 (heuristic) and process 2 (analytic). When presented with a situation requiring any sort of reasoning or decision making, process 1 automatically engages and generates an initial mental model to address the situation. Process 2 may or may not be engaged to assess the initial model as a plausible solution. In a study by Kryjevskaia et al., a “screening” question regarding a pulse on a spring aimed to identify students with relevant content knowledge who nevertheless seemed to rely on process 1 when answering a subsequent “target” question. The study was offered as evidence that dual-process theories can explain some discrepancies in student responses to related questions. The study described here assesses the same pair of questions for their ability to distinguish between incorrect answers that stem from inadequate conceptual understanding and those that stem from reasoning approaches. We use Frederick’s cognitive reflection test as part of this analysis. Our results largely support a dual-process-theories perspective of student reasoning.

Although most teachers recognize the importance of taking investigative, open-ended approaches to students’ learning experiences, implementing them in high school classes can be challenging for teachers. In this work, we analyzed data from multiple sources from a teaching Community of Practice (CoP) to investigate (a) barriers to taking an open-ended approach in teaching labs in physics classes, (b) shifts in teachers’ beliefs about taking an open-ended approach during their engagement in a physics teaching CoP in a partnership program, and (c) a case study of one teacher whose shifts in perceptions about taking an open-ended approach in teaching labs led to her successful implementation in her class. The findings confirm the existence of well-known psychological and structural barriers that can prevent teachers from adopting investigative approaches in teaching physics labs. Moreover, we learned how the interaction of these barriers further complicates the adoption of open-ended approaches in physics classes. The study also revealed a significant gap between teachers’ current practices and their desired methods of conducting labs, particularly in terms of structured versus open-ended approaches. The case study offered deeper insights into how shifts in teaching practices occur through changes in perceptions within a supportive CoP.

[This paper is part of the Focused Collection in Investigating and Improving Quantum Education through Research.] The modern physics course is a crucial gateway for physics majors as it provides an introduction to concepts beyond the scope of the K-12 education. This study collected 167 modern physics syllabi from 127 U.S. research-intensive institutions using publicly available syllabi (51.5%) and private correspondence (48.5%). Due to the size of the corpus being large enough for natural language processing (NLP) to provide significant insight into the curricular content of a course, but still being small enough for human analysis to remain feasible, both NLP and human-analysis methods were utilized. The use of both methods provided a way to test the newly introduced method of NLP in physics education research and offered a novel tool for cross-validation. Public course catalogs were consulted to identify prerequisites and corequisites with 89.3% of students having completed a course in calculus II or higher. Foundational topics like Newtonian mechanics (94%), electricity and magnetism (84.4%), and waves or optics (77.2%) were frequently required. Most institutions consistently taught quantum physics (94%), atomic physics (83%), and relativity (70%), but there was variability in the inclusion of other topics alongside these three main topics. The study highlights the current modern physics curricula at U.S. research-intensive institutions, emphasizing the importance of a consistent and comprehensive education for physics majors across universities. This insight contributes to the ongoing discourse on optimizing physics education in higher education.

There is a growing need for more effective interdisciplinary science instruction across undergraduate degree programs. In addition to supporting students’ connections between disciplinary concepts, interdisciplinary learning can develop students’ critical thinking skills and allow them to evaluate scientific investigations and claims between diverse topics. Physics Education Research literature has particularly focused on introductory physics courses for life sciences students, in part because students majoring in life sciences represent one of the largest demographics enrolled in physics courses. This literature has primarily focused on students’ development of conceptual understanding, modeling skills, and perspectives of the two fields. In this study, we explored how biology and physics undergraduates approach and perceive critical thinking between the two disciplines. We conducted structured think-aloud interviews with biology and physics students, asking students to first complete portions of established biology and physics critical thinking assessments and then respond to several follow-up questions about critical thinking more generally. Using thematic analysis to inductively code interview responses into emergent themes, we found that most students, regardless of major, described different approaches to evaluating biology and physics experiments. However, physics students provided similar definitions of critical thinking in the two disciplines, while biology students provided similar and different definitions in almost equal numbers. The exception was related to the use of quantitative methods solely being associated with critical thinking in physics, despite both critical thinking assessments involving quantitative data analysis. When looking across constructs, we saw no clear trends or relationships between individual students’ responses to each of the interview questions. We also explored students’ broader perspectives on the two fields and found that physics students assume that physics is needed to understand biology but not vice versa, which did not align with their perspectives on critical thinking between disciplines. We use this complexity to motivate future work to understand the impact of biology and physics instruction, as well as other STEM disciplines, on developing students’ critical thinking skills and perceptions.

Research-based assessments (RBAs) allow researchers and practitioners to compare student performance across different contexts and institutions. In recent years, research attention has focused on the student populations these RBAs were initially developed with because much of that research was done with “samples of convenience” that were predominantly white men. Prior research has found that the Force Concept Inventory (FCI) behaved differently for men and women using differential item functioning (DIF) analysis. We extend this research in two ways. First, we test the FCI for DIF across the intersection of gender and race for Asian, Black, Hispanic, White, and White Hispanic men and women. Second, we apply the Eaton and Willoughby five-factor model of the FCI to interpret the results of the DIF analysis. We found large DIF on a large number of FCI items. The patterns of items with large DIF follows the five-factor model. The alignment of DIF with this factor structure, along with the measurement invariance of this factor structure across these ten social identities, indicates that the items on the FCI are likely not biased but are instead measuring real differences in physics knowledge among these groups. We frame these differences as educational debts that society owes to these marginalized groups that physics instruction needs to actively repay.

This study explores the use of artificial intelligence in grading high-stakes physics exams, emphasizing the application of psychometric methods, particularly item response theory, to evaluate the reliability of AI-assisted grading. We examine how grading rubrics can be iteratively refined and how threshold parameters can determine when AI-generated grades are reliable versus when human intervention is necessary. By adjusting thresholds for correctness measures and uncertainty, AI can grade with high precision, significantly reducing grading workloads while maintaining accuracy. Our findings show that AI can achieve a coefficient of determination of $R^2\approx 0.91$ when handling half of the grading load, and $R^2\approx 0.96$ for one-fifth of the load. These results demonstrate AI’s potential to assist in grading large-scale assessments, reducing both human effort and associated costs. However, the study underscores the importance of human oversight in cases of uncertainty or complex problem solving, ensuring the integrity of the grading process.

This article reports on a controlled study to investigate the efficacy of incorporating explicit discussions about the duality of human reasoning and its implications for learning in physics instruction. The central goal is to probe to what extent, if at all, such discussions improve student performance on tasks that tend to elicit intuitively appealing but incorrect responses. We hypothesized that explicitly discussing the reasoning pathways and hazards associated with the interactions between automatic process 1 and deliberate process 2 could boost students’ performance on a variety of topics in physics. In addition, we explored the relationships between student performance in physics, as measured by the Force and Motion Conceptual Evaluation (FMCE), and student tendency toward cognitive reflection, as measured by the cognitive reflection test (CRT). The results revealed a strong link between postinstruction performance on FMCE and the CRT score. However, no measurable effect of explicit discussions on the duality of reasoning on FMCE performance was observed. Possible explanations for the null result and future directions for research are explored.

Previous research has shown that in prehigher education stages, inquiry-based teaching is not sufficient for forming a mature understanding of the nature of science (NOS). However, there is relatively little research conducted on colleges. Inquiry-based teaching should not overlook cognitive frameworks, as students’ limited scientific reasoning abilities may become overwhelmed by the challenges of scientific inquiry. In the college physics laboratory, we manipulated underlying pedagogy (lecture, implicit inquiry, and explicit inquiry) and group compositions (homogeneous and heterogeneous), resulting in a $2\times 3$ teaching treatments randomly across six classes ($n=208$ students) to explore the effectiveness of different teaching methods and grouping strategies in enhancing college students’ understanding of the NOS, as well as the potential relationship between students’ views of the NOS and their scientific reasoning skills. This study found that students’ understanding of the NOS was influenced by the teaching mode, and the teaching mode had different effects on students with varying abilities in scientific reasoning. Explicit inquiry-based teaching was particularly important for enhancing the understanding of the NOS among students with low to medium scientific reasoning abilities.