The increasing complexity and scale of modern education have revealed the shortcomings of traditional grading methods in providing consistent and scalable assessments. Advancements in artificial intelligence have positioned Large Language Models (LLMs) as robust solutions for automating grading tasks. This study systematically compared the grading performance of an open-source LLM (LLaMA 3.2) and a premium LLM (OpenAI GPT-4o) against human evaluators across diverse question types in the context of a computer programming subject. Using detailed rubrics, the study assessed the alignment between LLM-generated and human-assigned grades. Results revealed that while both LLMs align closely with human grading, equivalence testing demonstrated that the premium LLM achieves statistically and practically similar grading patterns, particularly for code-based questions, suggesting its potential as a reliable tool for educational assessments. These findings underscore the ability of LLMs to enhance grading consistency, reduce educator workload, and address scalability challenges in programming-focused assessments.

This paper describes a tangible visualization that explores the link between the impact of energy feedback on household consumers and the resource demand impact on energy production. Specifically, it positions a novel perspective attempting to move beyond the known limitations of current eco-feedback systems and contributes to enhance our understanding of how consumers comprehend energy production. The work is informed by a comprehensive study of an installation that displays the ratio of current power generation sources and the percentage of grid renewables. The paper provides design insights for creating novel eco-feedback visualizations that leverage the balance between user lifestyles and the desire to influence consumption behaviors and practices. Evaluation results show an increase in energy literacy and awareness as well as identifies high consumer preferences towards simple, representative interfaces and ubiquitous immediate feedback. Our study shows potential in terms of future scenarios for eco-feedback in distributed energy micro-generation and other inevitable disruptive changes for the energy utility.