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As the importance of comprehensive evaluation in workshop courses increases, there is a growing demand for efficient and fair assessment methods that reduce the workload for faculty members. This paper presents an evaluation conducted with Large Language Models (LLMs) using actual student essays in three scenarios: 1) without providing guidance such as rubrics, 2) with pre-specified rubrics, and 3) through pairwise comparison of essays. Quantitative analysis of the results revealed a strong correlation between LLM and faculty member assessments in the pairwise comparison scenario with pre-specified rubrics, although concerns about the quality and stability of evaluations remained. Therefore, we conducted a qualitative analysis of LLM assessment comments, showing that: 1) LLMs can match the assessment capabilities of faculty members, 2) variations in LLM assessments should be interpreted as diversity rather than confusion, and 3) assessments by humans and LLMs can differ and complement each other. In conclusion, this paper suggests that LLMs should not be seen merely as assistants to faculty members but as partners in evaluation committees and outlines directions for further research.

Workshop courses designed to foster creativity are gaining popularity. However, even experienced faculty teams find it challenging to realize a holistic evaluation that accommodates diverse perspectives. Adequate deliberation is essential to integrate varied assessments, but faculty often lack the time for such exchanges. Deriving an average score without discussion undermines the purpose of a holistic evaluation. Therefore, this paper explores the use of a Large Language Model (LLM) as a facilitator to integrate diverse faculty assessments. Scenario-based experiments were conducted to determine if the LLM could integrate diverse evaluations and explain the underlying pedagogical theories to faculty. The results were noteworthy, showing that the LLM can effectively facilitate faculty discussions. Additionally, the LLM demonstrated the capability to create evaluation criteria by generalizing a single scenario-based experiment, leveraging its already acquired pedagogical domain knowledge., Comment: The final version appears in the proceedings of the 32nd International Conference on Computers in Education (ICCE 2024)

Spin orbit torque (SOT) has been considered as one of the promising technologies for the next-generation magnetic random access memory (MRAM). So far, SOT has been widely utilized for inducing various modes of magnetization switching. However, it is challenging to integrate multiple modes of magnetization switching together. In this work we propose a method for implementing both unipolar and bipolar switching of the perpendicular magnetization within a single SOT device. The mode of switching could be easily altered by tuning the amplitude of the applied current. We show that the field-like torque plays an important role in the switching process. The field-like torque induces the precession of the magnetization in the case of unipolar switching, whereas it helps to generate an effective z-component torque in the case of bipolar switching. In addition, the influence of key parameters on the mode of switching is discussed. Our proposal could be used to design novel reconfigurable logic circuits in the near future., Comment: 4 pages, 6 figures