The Impact of AI in Physics Education: A Comprehensive Review from GCSE to University Levels

With the rapid evolution of Artificial Intelligence (AI), its potential implications for higher education have become a focal point of interest. This study delves into the capabilities of AI in Physics Education and offers actionable AI policy recommendations. Using a Large Language Model (LLM), we assessed its ability to answer 1337 Physics exam questions spanning GCSE, A-Level, and Introductory University curricula. We employed various AI prompting techniques: Zero Shot, In Context Learning, and Confirmatory Checking, which merges Chain of Thought reasoning with Reflection. The AI's proficiency varied across academic levels: it scored an average of 83.4% on GCSE, 63.8% on A-Level, and 37.4% on university-level questions, with an overall average of 59.9% using the most effective prompting technique. In a separate test, the LLM's accuracy on 5000 mathematical operations was found to decrease as the number of digits increased. Furthermore, when evaluated as a marking tool, the LLM's concordance with human markers averaged at 50.8%, with notable inaccuracies in marking straightforward questions, like multiple-choice. Given these results, our recommendations underscore caution: while current LLMs can consistently perform well on Physics questions at earlier educational stages, their efficacy diminishes with advanced content and complex calculations. LLM outputs often showcase novel methods not in the syllabus, excessive verbosity, and miscalculations in basic arithmetic. This suggests that at university, there's no substantial threat from LLMs for non-invigilated Physics questions. However, given the LLMs' considerable proficiency in writing Physics essays and coding abilities, non-invigilated examinations of these skills in Physics are highly vulnerable to automated completion by LLMs. This vulnerability also extends to Physics questions pitched at lower academic levels.Comment: 22 pages, 10 Figures, 2 Table

Autonomously Reconfigurable Artificial Neural Network on a Chip

Artificial neural network (ANN), an established bio-inspired computing paradigm, has proved very effective in a variety of real-world problems and particularly useful for various emerging biomedical applications using specialized ANN hardware. Unfortunately, these ANN-based systems are increasingly vulnerable to both transient and permanent faults due to unrelenting advances in CMOS technology scaling, which sometimes can be catastrophic. The considerable resource and energy consumption and the lack of dynamic adaptability make conventional fault-tolerant techniques unsuitable for future portable medical solutions. Inspired by the self-healing and self-recovery mechanisms of human nervous system, this research seeks to address reliability issues of ANN-based hardware by proposing an Autonomously Reconfigurable Artificial Neural Network (ARANN) architectural framework. Leveraging the homogeneous structural characteristics of neural networks, ARANN is capable of adapting its structures and operations, both algorithmically and microarchitecturally, to react to unexpected neuron failures. Specifically, we propose three key techniques --- Distributed ANN, Decoupled Virtual-to-Physical Neuron Mapping, and Dual-Layer Synchronization --- to achieve cost-effective structural adaptation and ensure accurate system recovery. Moreover, an ARANN-enabled self-optimizing workflow is presented to adaptively explore a "Pareto-optimal" neural network structure for a given application, on the fly. Implemented and demonstrated on a Virtex-5 FPGA, ARANN can cover and adapt 93% chip area (neurons) with less than 1% chip overhead and O(n) reconfiguration latency. A detailed performance analysis has been completed based on various recovery scenarios

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background

<p>Abstract</p> <p>Background</p> <p>Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis</p> <p>Results</p> <p>Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg.</p> <p>Conclusion</p> <p>This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.</p