Using LLM to Identify Pillars of the Mind Within Physics Learning Materials | #Sciencefather #Researcherawards


Introduction

Artificial Intelligence (AI) has rapidly transformed multiple scientific disciplines, with learning sciences being a major beneficiary. In particular, Large Language Models (LLMs) are revolutionizing how researchers analyze and interpret educational materials. This research explores the integration of LLMs into physics education through the lens of the “Five Pillars of the Mind,” a neural network-based framework for understanding cognitive processes in learning. By applying AI tools to textbook analysis, the study investigates how different cognitive pillars are activated when students engage with physics concepts, particularly in the area of forces. The research highlights both the opportunities and challenges of using LLMs for cognitive analysis, showing their potential to accelerate and deepen our understanding of how learning occurs at the neural and conceptual levels.

Theoretical Framework: Five Pillars of the Mind

The Five Pillars of the Mind represent distinct neural networks that support various modes of thinking essential for learning. These include systems responsible for sensory integration, logical reasoning, abstract thought, emotional engagement, and social cognition. Applying this theory to physics learning provides a structured way to analyze which cognitive processes are activated by specific educational materials. By labeling learning content according to these pillars, educators and researchers can map how different types of mental activity contribute to the comprehension of scientific concepts.

Methodology and LLM Application

The study utilized three AI-based tools—GPT-4o, o4-mini, and MAXQDA AI Assist—to analyze eight pages of physics learning material about forces for students aged 12–14. These tools were tasked with identifying which of the Five Pillars were engaged by the text. The results from each model were compared with manual annotations made by expert researchers. Evaluation metrics such as precision, recall, and F1-score were applied to quantify model accuracy and reliability, ensuring a robust and data-driven analysis process.

Comparative Analysis and Results

Results demonstrated significant differences among the models. MAXQDA AI Assist achieved the highest performance, with a perfect precision score (1.00), a recall of 0.67, and an F1-score of 0.80. In contrast, both GPT-based models exhibited “hallucinations” by falsely identifying pillars or concepts not present in the text, leading to lower precision and F1-scores. Interestingly, ChatGPT o4-mini performed twice as well as ChatGPT 4o, suggesting that smaller, optimized models may outperform more complex ones in domain-specific educational analyses.

Discussion and Educational Implications

The findings underscore the growing potential of AI in supporting educational research. LLMs can drastically reduce the time needed to analyze learning materials and reveal patterns that might be overlooked by manual analysis. However, issues such as bias, model hallucination, and limited contextual understanding remain challenges. This study shows that AI can serve as a complementary tool for educators and researchers, enhancing our ability to map cognitive engagement in real-world learning contexts.

Future Directions and Conclusion

The successful use of AI for analyzing physics textbooks marks a promising step toward integrating LLMs into educational psychology and curriculum design. Future work could expand beyond written materials to include student essays, classroom interactions, and video-recorded experiments. This broader application could help teachers tailor instruction to specific cognitive needs, fostering deeper and more personalized learning experiences. Ultimately, refining AI tools for educational analysis will pave the way for a new era of data-informed, neuroscience-based teaching strategies.

 Global Particle Physics Excellence Awards


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Hashtags:

#Sciencefather, #Reseacherawards, #ArtificialIntelligence, #LearningSciences, #PhysicsEducation, #NeuralNetworks, #FivePillarsOfTheMind, #LargeLanguageModels, #EducationalAI, #CognitiveScience, #MachineLearning, #AIinEducation, #TextAnalysis, #PhysicsLearning, #LLMResearch, #ScienceEducation, #CognitiveFramework, #AIinResearch, #ChatGPT, #MAXQDA, #STEMEducation, #NeuroscienceLearning,

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