ANALYZING THE PHYSICAL MEANING OF COMPLEX GRAPHS USING ARTIFICIAL INTELLIGENCE TOOLS: THE CASE OF ELECTROMAGNETIC WAVES

Аннотация

Understanding the physical meaning behind complex graphs is a critical skill in physics education, particularly in topics such as electromagnetic waves where multiple parameters—such as electric field strength, magnetic flux, and frequency—interact dynamically. This study investigates the potential of artificial intelligence (AI) tools, including large language models and graph-interpreting algorithms, to assist students in interpreting and analyzing such complex visual data. By integrating AI into classroom instruction, learners are guided through the process of decoding waveforms, identifying physical properties, and making predictions based on graphical data. The research highlights improved comprehension, enhanced engagement, and the development of scientific reasoning skills when AI is used to support graph-based learning inphysics. Keywords: Electromagnetic waves, artificial intelligence, graph interpretation, physics education, data visualization, AI-assisted learning, waveforms, signal analysis, conceptual understanding, scientific reasoning Graphs are foundational tools in physics, offering visual representations of relationships between variables and enabling insight into physical systems. However, interpreting complex graphs— particularly those related to electromagnetic waves—remains a significant challenge for many students. Such graphs often include multiple axes, time-varying signals, phase relationships, and overlapping field representations that canoverwhelm novice learners. In recent years, artificial intelligence (AI) tools, particularly large language models (LLMs) and visual data processors, have shown promise in educational contexts. These tools can interpret graphical information, provide real-time explanations, and scaffold students’ understanding through guided questioning and feedback. In physics education, AI can serve not only as an automated tutor but also as a facilitator that helps bridge the gap between abstract graphical data andconcrete physical meaning. This study aims to explore how AI tools can be employed to support students in analyzing the physical content embedded in complex wave graphs. Focusing on electromagnetic waves as a case study, the research evaluates how students interact with AI when interpreting graphs of electric and magnetic field oscillations, energy propagation, and frequency-time relationships. The ultimate goal is to assess whether AI-assisted instruction leads to better comprehension, http://www.internationaljournal.co.in/index.php/jasass 97 Volume 15Issue 07, July 2025 Impact factor: 2019: 4.679 2020: 5.015 2021: 5.436, 2022: 5.242, 2023: 6.995, 2024 7.75 more accurate interpretations, and greater confidence when working with graph-based representations in physics. This study employed a mixed-methods approach to evaluate the impact of AI tools on students’ ability to interpret complex graphs related to electromagnetic waves. Participants included 48 upper-secondary and early undergraduate students enrolled inan introductory physics course. Two groups were formed:  The control group used conventional teaching methods (textbook illustrations, instructor-led explanations).  The experimental group used AI-based tools such as ChatGPT and graphical interpretation platforms (e.g., Desmos, GeoGebra, and Wolfram Alpha) integrated with prompts andtutorials focused onwavegraphs. Instructional activities focused on:  Graphs of electric and magnetic fields as functions of timeand space.  Frequency, amplitude, and phaseshift analysis.  Visualization ofenergy propagation inwaveforms.  Interpretation ofvector field diagrams and sinusoidal models. Studentscompleted apre-test and post-test consisting ofgraph comprehension tasks, followed by a guided AI interaction task for the experimental group. Qualitative data were collected through student reflections and teacher observations to assess engagement, clarity, and depth of understanding. The experimental group demonstrated significantly higher improvement in graph interpretation skillsthan thecontrol group. Key results included:  Quantitative Gains: Post-test scores showed a 32% average improvement in the experimentalgroup compared to a14%gain inthe control group.  Error Reduction: Misinterpretations of phase differences and amplitude labeling dropped by47% inthe AI-supported group.  Enhanced Conceptual Understanding: Students in the AI group correctly explained how the electric and magnetic field vectors oscillate perpendicularly in space and time, referencing AIfeedback during reflection. Qualitative feedback indicated that students appreciated AI’s ability to break down complex graphs step-by-step and explain terminology such as "wavelength," "wave vector," and "polarization." Several participants noted that the ability to ask follow-up questions in natural language increased theirconfidence. http://www.internationaljournal.co.in/index.php/jasass 98 Volume 15Issue 07, July 2025 Impact factor: 2019: 4.67