Breakout  Sessions 4:15PM - 5:10PM

Abstract

Many physics teachers want to create custom simulations to address specific student misconceptions but lack programming expertise. This workshop demonstrates how generative AI can serve as a creative partner, enabling teachers to build interactive physics simulations without traditional coding skills.

Through live demonstrations, I'll show how conversational AI Claude helped me create web-based simulations for challenging A-level concepts like center of mass conservation and vector subtraction. These simulations, easily embedded in Google Sites, directly target student visualization difficulties that standard textbook diagrams cannot address.

Participants will experience the collaborative design process firsthand, working to prompt AI in creating basic physics simulations. We'll explore how this approach transforms teachers from consumers to creators of educational technology, allowing for personalized learning tools that match specific pedagogical needs.

The workshop emphasizes AI as an amplifier of physics understanding rather than a replacement, showing how clear physics knowledge and effective AI communication can produce engaging, interactive learning experiences. Teachers will leave with practical skills to create their own simulations and a framework for ongoing collaboration with AI in physics education.

(Teachers who have yet to try AI to make simulations may find the session most useful. Participants must bring their own laptop.)

Abstract

In this workshop, participants will experience the lesson from a student’s perspective: a brief, hands-on orientation to set up the iOLab and explore its core tools, followed by inquiry activities on motion and forces. In these tasks, students collect their own data, create and interpret graphs, account for expected and unexpected results, look for patterns, and use evidence to explain phenomena. They connect their findings to key ideas in dynamics and forces, strengthening the link between practical work and theory.

The lesson design also promotes collaboration and adaptive thinking, while naturally building digital literacy. Classroom observations show higher engagement and a clearer grasp of Dynamics concepts. The lesson also incorporated a more open-ended format for the last task to foster students’ critical thinking and develop their creative problem-solving skills.

(Participants must bring their own laptop with iOLab software installed. A USB-A port is required for the receiver.)

Abstract

Why should we teach for epistemic agency? What does it look like when students actually create and own their knowledge? This presentation connects theory and practice through two stories. First, our argument in Learning to Become Ignorant (Koh & Tan, Science Education), where we propose ignorance as a stance for inquiry, with the quality of ignorance as the key to this approach. Second, students’ own accounts from the Singapore Young Physicists’ Tournament (SYPT), where such agency was lived, not taught. Together, these perspectives invite teachers to see discomfort, uncertainty, and curiosity not as obstacles, but as vital conditions for deep learning.

Abstract

Quantum physics can be one of the most fascinating and most challenging topics in the H2 Physics syllabus.  This talk takes a clear, practical look at key ideas, common misconceptions, and effective ways to help students grasp abstract concepts such as quantum states and wavefunctions, wave–particle duality and uncertainty, and energy quantisation.  We’ll unpack what these topics are really about, highlight common pitfalls, and explore teaching strategies that build both clarity and curiosity.  You’ll see how to use analogies that illuminate rather than confuse, visualisations that make the invisible visible, and simple experiments that connect theory with evidence.  Whether you’re new to teaching quantum physics or looking to refresh your approach, this talk offers ready-to-use ideas to make quantum principles more intuitive for your students.

Abstract

EJC introduced an AI-assisted marking approach during the Term 2 weighted assessment for JC1 students to promote fairer and more consistent evaluation of practical work. The assessment comprised 12 experimental tasks. Students worked in groups within their classes to complete a unique experiment and submitted a report demonstrating their competencies in designing experimental procedures, analyzing data, identifying sources of error, and reflecting on their work. Their performance was evaluated using a holistic rubric encompassing domains such as critical thinking (CAIT), data competency (DCCT), and reflection (RT). 

To enhance consistency in marking and feedback quality, an AI agent was developed on Pairs Chat, Lady Curie (LC), to review student reports alongside teachers. LC highlighted missed assessment points, suggested domain-level performance bands, and facilitated the consolidation of feedback. This collaborative process supported marking standardization, reduced anchoring bias, and provided students with more targeted and transparent feedback.

Findings indicated that LC enhanced fairness and comprehensiveness in marking, though challenges remained in handling visual data, lenient tendencies, and moderation time. While LC cannot replace teacher judgment, it served as an effective calibration and reflective tool. Future improvements will focus on refining LC’s evaluation parameters and exploring alternative AI-models to further enhance precision and adaptability.

Abstract

To foster student autonomy, we aim to use generative AI to replicate dynamic classroom questioning for self-directed science learning. Current static hints lack adaptability. AI can provide scaffolded, responsive support aligned to the curriculum, promoting deeper understanding without giving away answers if implemented thoughtfully and with pedagogical rigor.

Generative AI supports Physics learning through three modes: asynchronous learning logs student interactions for teacher insights; synchronous use offers real-time feedback, aiding differentiated instruction; and Study-Buddy mode enables self-quizzing and answer checking with AI-guided feedback. These approaches enhance independent study, identify misconceptions, and tailor support to diverse student needs.

Generative AI in Physics education enhances formative assessment, enables personalized support, and improves engagement tracking with minimal added workload for teachers. Students benefit from deeper cognitive activation, constructive feedback, and conceptual guidance. Both groups report positive experiences, with AI augmenting the teacher’s role, and supporting scalable, personalized learning.

Abstract

This Authentic Task aims to equip JC2 Physics students with the ability to think critically and evaluate scientific information. It encourages them to apply their knowledge in unfamiliar, real-world contexts, fostering adaptability and informed reasoning.

Aligned with the completion of the Nuclear Physics syllabus, the task unfolds over two 1-hour lessons. In the first session, based on the NUPEX resource, students explore dating methods in art and archaeology through group discussions. Between sessions, they engage in hands-on data collection using radioactivity sensors. The second session features a past A-Level planning question, requiring students to integrate their theoretical understanding and field data collaboratively. The teacher facilitates the learning process and guides students through critical evaluation using the Claim-Evidence-Reasoning framework.

Students strengthen their scientific reasoning by using evidence to support claims and adapting their knowledge to novel situations. The task fosters collaborative learning, data-driven argumentation, and a deeper understanding of how Physics applies to real-world issues.

Abstract

This project addresses the need to enhance student engagement and data literacy in H2 Physics through authentic investigations using accessible EdTech tools. With the new H2 Physics syllabus (first examined in 2026) emphasising data literacy, the team designed an HBL day activity using students' smartphones as the data-logging equipment. Our intervention investigates Simple Harmonic Motion and damping through the Phyphox app, transforming personal devices into laboratory instruments. Students collect real acceleration data, then process and visualise datasets using spreadsheets, developing digital literacy whilst exploring abstract physics concepts concretely. Results demonstrate appreciation for data driven investigation and enhanced engagement.  This accessible approach bridges theoretical physics with authentic inquiry, preparing students for the data-driven demands of the new syllabus whilst fostering meaningful learning through readily available technology.

Abstract

In a recent effort to digitize marking for a large class of Engineering Physics students where open-ended student responses are grouped by OCR/AI, the large variety of wrong answers even for simple questions vividly demonstrated the Anna Karenina Principle: there are many pathways to failure (wrong answers in this case) but limited pathways to success (correct answers). The wrong pathways contain rich information on how students think and learn, and also expose weaknesses in reading and mathematical skills. The presentation also explores how this pathways information, together with current developments in AI, can be used to personalize help for each student.