Summary

GenGenAI creates opportunities to teach expert problem-solving skills in reasonably complex real-world contexts.
Students still need a foundation of basic physics knowledge to interact productively with GenGenAI.
A two-part structure appears most effective: concepts first, complex problem-solving later.

Background

I have been exploring the use of context-rich real-world problems in physics teaching for more than 17 years. These problems are open-ended and “ill-structured,” meaning that they lack complete information and do not have a predetermined final answer. Students must make assumptions, identify relevant concepts, and gather appropriate information. This kind of work mirrors how experts actually approach problems and has many benefits, but students also find it challenging and often intimidating.

Students’ difficulties typically arise from:
(a) limited context-specific knowledge,
(b) uncertainty about which concepts apply in unfamiliar situations,
(c) lack of strategy or not knowing how to begin, and
(d) gaps in basic physics knowledge.

Another issue is that context-rich problems do not fit well into most exam-based assessment structures. As a result, my past implementations relied on a few relatively straightforward scenarios where the context-richness was limited.

Why engage students in real-world problem questions?

The underlying goal is to help students develop adaptive expertise—the ability to use their learning beyond the narrow confines of textbook-style questions. Research from the Wieman group at Stanford has analyzed expert problem-solving across STEM fields and identified 29 decisions that experts consistently make (Price et al., 2021). Burkholder and colleagues (2022) showed that a subset of 15 of these decisions can be practiced meaningfully in first-year physics courses. These decisions involve finding and evaluating information, identifying useful concepts, planning solutions, and evaluating results.

This approach contrasts sharply with typical textbook problems, which are highly structured and provide all necessary information. Students’ only decisions then are to choose formulas and substitute numbers. Burkholder et al. (2020) instead emphasize the process: their problem-solving template gives significant credit for reasoning and decisions, and only a small fraction for the final numerical answer.

What are the barriers?

Although the decision-focused approach has clear instructional value, I had concerns about how well it could be implemented in my courses. My questions centered on familiar student difficulties:

• How do students acquire enough basic physics and math skills to work effectively with open-ended problems?
• How do they look up relevant information if the context is unfamiliar?
• How do they identify which physics concepts are applicable when the situation looks entirely new?

Indeed, Burkholder et al. found that students often struggle to construct an appropriate predictive framework—the set of models, choices, boundaries, and information that guides the solution.

There is also the practical issue of fitting such open-ended work into an exam-oriented assessment structure typical of first-year science courses.

How does GenAI help?

Consider a potential case study: an autocrane topples while lifting a car from a ditch. Students receive only a photo and must determine whether the operator used reasonable judgment. A simple web search will fail unless students already know technical terms like boom length, outrigger, or counterweight. Without that vocabulary, students stall out before any physics reasoning can begin.

This is where GenAI provides a major opportunity. Students can ask in natural language, “Why do autocranes topple?” and receive key terminology and conceptual scaffolding. ChatGPT-4 can generate an initial physics model, including assumptions. That model may be incomplete or partially incorrect, but this creates an ideal opening for students to evaluate, critique, and refine a model—core expert skills.

GenAI therefore lowers the barrier to getting started. Students can form initial ideas without needing extensive prior content knowledge, allowing them to focus on evaluating information, selecting relevant physics concepts, and engaging in sensemaking. This aligns with findings from Wang et al. (2024), who showed that ChatGPT-4 can help students identify relevant knowledge and construct the beginnings of a useful predictive framework.

With GenAI in place, I can imagine course modules in which students practice prompting the GenAI, evaluating models, deciding what information is needed, and checking whether results make sense. At the same time, these activities help students build adaptive expertise and confidence in tackling novel problems.

How could GenAI be implemented in class?

My current courses use online edX materials that include about 400 questions per course, supported by an open textbook. Roughly half of these questions are reading quizzes or homework; the rest are discussed in class. This provides structured practice with the basics: definitions, core concepts, representations, and the physics-related math needed for more advanced work.

I envision freeing up at least four lectures to introduce GenAI-supported case studies. The displaced material will move into tutorials or homework, and a small number of rarely assessed topics will be trimmed.

The skills practiced in case studies—evaluating information, making decisions, planning solutions, and sensemaking—are at the top of Bloom’s taxonomy. Vantage College’s small class sizes make it an ideal environment for a pilot. Effective implementation will require the instructor to guide motivation, facilitate small-group work, and experiment with suitable assessments (e.g., group presentations and reflective writing).

The course would initially maintain traditional exams to ensure alignment with other sections. Two smaller assessments should be enough to support and assess the new skills:
(1) a group component on the midterm, and
(2) a final mini-project where students analyze a real-world scenario and reflect individually on their learning.

Future Developments

GenAI continues to advance rapidly, and its capabilities are reshaping what it means to do physics in an educational context. This raises fundamental questions:

• Why should students practice traditional textbook-style problems?
• What role should GenAI play in our instruction?
• How do we assess students’ physics skills?

My recent experience implementing a concepts-first approach (Webb & Paul, 2023) informs my answers. In a 200-student PHYS 118 section, front-loading concepts made early lectures more fluid and reasoning-focused. However, when we revisited longer problems at the end of the term, it became clear that many were overly specialized or exam-driven rather than conceptually meaningful.

Thus:

1. Traditional textbook problems remain useful for building foundational math and physics skills—but they do not need to be overly complicated. Problems that enhance conceptual reasoning are the most valuable.
2. GenAI is well-suited for context-rich case studies. These require students to evaluate models, make assumptions, and engage in expert-like thinking. These skills represent genuine disciplinary expertise.
3. Assessment must reflect what students actually do in the course. A realistic structure blends traditional exams for fundamentals with project-based assessment of expert decision-making in real-world situations.

Assessment Ideas

To align with course goals, assessments should include:

1. Assessment of fundamentals, mainly via quizzes and exams.
2. Assessment of expert problem-solving skills, ideally during the final tutorial or in a dedicated session.

A structure with one midterm, one final exam, and several quizzes seems appropriate. Group quizzes work well in Vantage College but require class time.

Current Experience

Goal: explore whether context-rich problems supported by GenAI and peer review can be integrated smoothly into a first-year physics course.

I implemented eight examples of varying complexity. For three of them, I used UBC’s ComPair tool for assessment. Students worked in small groups (2–5 students) to solve a context-rich problem and uploaded a scanned solution. They then (individually) reviewed two solutions from other groups, comparing them and explaining which one they considered stronger and why. Each solution typically received four peer reviews. The intent was to help students recognize important features of physics models and to see how different groups organized and communicated their solutions.

What I learned:
The context-rich problems and ComPair activities fit naturally into the course structure. The total time investment amounted to about 10 lecture and tutorial hours (roughly 14% of class time), which did not disrupt the broader curriculum. The main trade-off was shifting some traditional problem-solving questions to homework and removing a few low-value items. All core physics topics remained in the course.

Students were able to construct simple models, but they often missed important elements in more complex scenarios. Even when they understood the situation visually, many needed additional scaffolding—especially when the key quantity required to solve the problem was not obvious (e.g., stopping distance in the “cell phones and traffic” example). The timing of these activities mattered: context-rich problems were most effective when they followed soon after the relevant concepts were introduced.

The ComPair tool itself worked very well, but the depth of students’ peer-review comments was mixed. Most comments were brief and somewhat superficial, and some students appeared hesitant to offer constructive criticism. Stronger incentives or clearer expectations may help encourage more thoughtful peer review. Monitoring or accountability measures might also be useful. The superficiality may partly reflect that this component contributed only a small, easily earned portion of the overall grade.

Overall, I consider the initial implementation a success. We demonstrated that these activities can be integrated into first-year courses without major disruption. Although much can be improved—both in scaffolding and in encouraging deeper engagement during peer review—the experience showed clear value in having students model real-world situations. The next steps will involve refining the activities and gathering more systematic evidence of student learning.

References:

Price, A. M., Kim, C. J., Burkholder, E. W., Fritz, A. V., & Wieman, C. E. (2021). A detailed characterization of the expert problem-solving process in science and engineering: Guidance for teaching and assessment. CBE Life Sciences Education, 20(3), ar43-ar43. https://doi.org/10.1187/cbe.20-12-0276

Burkholder, E., Miles, J., Layden, T., Wang, K., Fritz, A., Wieman, C., et al. (2020). Template for teaching and assessment of problem solving in introductory physics. Phys. Rev. Phys. Educ. Res. 16, 010123. doi: 10.1103/PhysRevPhysEducRes.16.010123

Burkholder, E., Salehi, S., Sackeyfio, S., Mohamed-Hinds, N., and Wieman, C. (2022). Equitable approach to introductory calculus-based physics courses focused on problem-solving. Phys. Rev. Phys. Educ. Res. 18, 020124. doi: 10.1103/PhysRevPhysEducRes.18.020124

Wang KD, Burkholder E, Wieman C, Salehi S and Haber N (2024) Examining the potential and pitfalls of ChatGPT in science and engineering problem-solving. Front. Educ. 8:1330486. doi: 10.3389/feduc.2023.133048

Webb DJ and Paul CA (2023) Attributing equity gaps to course structure in introductory physics. Phys. Rev. Phys. Educ. Res. 19, 020126.

A while ago, I looked for a way to integrate all course resources in one place. In particular, I was unhappy with how pre-class reading assignments, lecture materials and homework were presented to the students in different places. Long story short, UBC’s adoption of edge.edX allowed me to build a customized, fully integrated course resource, based on the openstax physics textbooks. These resources contain everything students need for my courses for free: reading, lecture materials, homework. I followed the design principle of a HybridEd course (per MIT’s definition), where I am doing interactive lectures in person that are supported by a standalone online course.

With the help of colleagues and grad students, I have built three OER lecture courses and one online lab course. I am currently working on a fourth course. All courses are publicly available here:

https://phas.ubc.ca/open-education-resources

The only additional things I am using in class are my lecture notes that contain introductions and follow-ups on the lecture questions on edX and a discussion forum (Piazza).

Overall:
My main focus is on teaching large first-year physics lecture courses, course design and the related pedagogy and approaches, as well as assessment. My teaching philosophy is informed by constructivism and cognitive psychology, in particular by R. Mayer’s theory of multimedia learning (Mayer 2014). Mayer’s theory and the principles derived from it explain the rationale behind my extensive use of worksheets even in large lecture courses. More detailed explanations and a description of Mayer’s model can be found in my teaching philosophy.

Current activities:
My recent research and development activities have evolved around four areas:
(a) Hybrid education and online learning
(b) Open-educational resources
(c) Self-regulated learning
(d) Assessment

(a) (b) New course developments
Ideas from all four areas listed above have flown into my most recent course designs and developments for Mechanics and Electricity & Magnetism (both calculus). Following a model that I have previously developed with Stefan Reinsberg for on introductory physics course (see Hendricks, C., Reinsberg, S., and Rieger, G., 2017), I have now transitioned both of these courses to open-educational resources with the following features:
– use of an edX.edge website to host all course materials, designed as a standalone online course
– close integration of all these materials into a weekly structures that include
• chapters from an open textbook customized as weekly reading assignments with integrated reading quizzes.
• all in-class worksheet and clicker questions with randomized numbers for extra practice and accountability.
• textbook problem questions and/or previous exam questions customized as weekly homework sets.
• highly-relevant extra materials such as video solutions, PhET simulations, practice exams and study tips.

These OER projects started just before the COVID-19 pandemic and the transformed course materials were available for the first time in 2020W. The edX website and the integrated course materials in the form of a standalone online course are designed to support students in their self-regulated learning outside of class. Especially during the pandemic, this continues to be a huge asset. Teaching PHYS 117 with these materials in the fall term of 2020W have further convinced me of this approach: Between the synchronous and recorded lectures and tutorials, the Piazza discussion page and the edX.edge website, there was enough redundancy and flexibility to provide students with choices and at the same time with a clear structure.

(c) Self-regulated learning
This will be a key area for me over the next few years. My initial focus was on supporting students’ in their self-regulated learning outside of class (see paragraph above). A presentation by Dr. Deborah L. Butler (Faculty of Education) on how instructors can support students in their self-regulated learning, especially during in class activities, led me to expand my focus.
Together with Dr. Jess McIver and with input from Silvia Mazabel (Faculty of Education) (and initially physics grad student Sean Cooper and Gillian Gerhardt from CTLT) we developed SRL-related items that support students in class. These fall into three categories:
• Task-interpretation and initial planning. Here we provided extra steps and extra prompts on worksheets to help students with getting started with difficult problem questions. These steps and prompts encourage students to think about all the cognitive resources and course materials at their disposal and annotate their worksheets with relevant items (equations, drawings, vectors, etc.).
• Resources approach to multiple-choice questions. We adopted a learner-centered view in which all student ideas are seen as valuable resources. No idea was dismissed as ‘incorrect’ – students have reasons that are worth exploring. The classroom discussion accordingly shifted toward how these ideas and student resources can be constructively combined with the course resources. The instructors emphasized the value of all student contributions, modeled strategies for evaluating ideas, double-checking and sensemaking. We also encouraged students to adopt this inclusive stance in their small-group discussions in class and on the Piazza discussion forum. Overall, this did not require much additional work, mostly a shift in our attitude, away from seeing misconceptions and towards seeing ideas and resources.
• Forward facing feedback from ABC grades.This idea goes back to discussions I had with Gerwald Lichtenberg (University of Applied Sciences – HAW Hamburg) during his sabbatical leave at UBC: in addition to the overall test score, we posted average scores for specific question types. These scores are meant to give specific feedback on what to improve on for the next test or exam. This grading scheme is an implementation of the forward-facing feedback idea that was presented in one of the SRL workshops at UBC. The question “types” are related to:
A Definition and units
B Concepts and Understanding
C Calculations for familiar problems
D Calculations for unfamiliar problems

The average score of all questions belonging to each of these types are calculated and posted on canvas. Depending on this average score, a student might choose to follow our recommendations, which is specific to each question type. Research evaluating our efforts in the area of self-regulated learning is currently underway.

(d) Assessment
I continue to be interested in assessment at scale, i.e. ways to automatically grade students fairly. This means that the assessment should target different levels of student knowledge in addition to covering the content area. A key area for STEM students to improve in is their ability to solve problems.
During my current sabbatical leave I have a chance to work with members of Carl Wieman’s group at Stanford University and think about problem-solving and the assessment of problem-solving skills more broadly. Their efforts have recently produced a template (Burkholder et al., 2020) that assesses students’ problem-solving skills in a much more meaningful way than what is commonly done in undergraduate courses, including my own. One of the barriers to implementing their approach at scale is the amount of expertise needed to grade the templates and to adjust classroom instruction and include appropriate context-rich problem questions. While I have some experience with the design of context-rich problems, I have so far not been able to assess them in a meaningful way. I am now working with Eric Burkholder and with input from Carl Wieman and others to come up with ideas to implement their enhanced problem-solving approach in my large courses. One of the key challenges will be to find out whether or not the template can be broken up into smaller sub-parts and practice and assess these parts separately without sending the wrong message to students. What I am concerned about is the fact that problem-solving is not a linear process. For example, students should critically evaluate the final result and consider underlying assumptions again. Authentic problem-solving is an iterative approach that I would like my students to adopt.

Mayer (2014) “The Cambridge Handbook of Multimedia Learning”, 2nd edition, edited by R. Mayer, 2014.
Burkholder, E. W., Miles, J. K., Layden,T. J., Wang, K. D., Fritz , A. V., and Wieman, C. E. (2020). “Template for teaching and assessment of problem solving in introductory physics”, PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH 16, 010123.

I haven’t used blogs before, but it seems to be a good way to share my thoughts on teaching and learning with anyone who is interested.  I am a tenure-track instructor at UBC and have been involved in using active learning approaches and evidence-based teaching practices for a while now, mainly in first-year undergraduate physics courses. You will find my current teaching philosophy on this site. I will also add a few things about myself and my current projects, but this will be for another day.