That\u27s Some Honeymoon

https://digitalcommons.library.umaine.edu/mmb-vp/6244/thumbnail.jp

The School Where Lincoln Went

https://digitalcommons.library.umaine.edu/mmb-vp/6490/thumbnail.jp

Star-Land (I Envy You)

https://digitalcommons.library.umaine.edu/mmb-vp/6497/thumbnail.jp

A Moonlight Stroll

https://digitalcommons.library.umaine.edu/mmb-vp/4413/thumbnail.jp

I Love The U. S. A. : Flying Squadron Edition

https://digitalcommons.library.umaine.edu/mmb-vp/3569/thumbnail.jp

Dream On, Dreamy Eyes

https://digitalcommons.library.umaine.edu/mmb-vp/4596/thumbnail.jp

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

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 openAI’s flagship gpt-3.5-turbo large language model (LLM), we assessed its ability to answer 1337 physics exam questions spanning general certificate of secondary education (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 proficiency of gpt-3.5-turbo 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 be 45.2%. 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 pysics questions pitched at lower academic levels. It is thus recommended that educators be transparent about LLM capabilities with their students, while emphasizing caution against overreliance on their output due to its tendency to sound plausible but be incorrect

Shot noise detection in hBN-based tunnel junctions

High quality Au/hBN/Au tunnel devices are fabricated using transferred atomically thin hexagonal boron nitride as the tunneling barrier. All tunnel junctions show tunneling resistance on the order of several k$\Omega$/$\mu$m$^{2}$. Ohmic I-V curves at small bias with no signs of resonances indicate the sparsity of defects. Tunneling current shot noise is measured in these devices, and the excess shot noise shows consistency with theoretical expectations. These results show that atomically thin hBN is an excellent tunnel barrier, especially for the study of shot noise properties, and this can enable the study of tunneling density of states and shot noise spectroscopy in more complex systems.Comment: 20 pages, 4 figure