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University students have begun to use Artificial Intelligence (AI) in many different ways in their undergraduate education, some beneficial to their learning, and some simply expedient to completing assignments with as little work as possible. This exploratory qualitative study examines how undergraduate students used AI in a large General Education course on sustainability and technology at a research university in the United States in 2023. Thirty-nine students documented their use of AI in their final course project, which involved analyzing conceptual networks connecting core sustainability concepts. Through iterative qualitative coding, we identified key patterns in students’ AI use, including higher-order writing tasks (understanding complex topics, finding evidence), lower-order writing tasks (revising, editing, proofreading), and other learning activities (efficiency enhancement, independent research). Students primarily used AI to improve communication of their original ideas, though some leveraged it for more complex tasks like finding evidence and developing arguments. Many students expressed skepticism about AI-generated content and emphasized maintaining their intellectual independence. While some viewed AI as vital for improving their work, others explicitly distinguished between AI-assisted editing and their original thinking. This analysis provides insight into how students navigate AI use when it is explicitly permitted in coursework, with implications for effectively integrating AI into higher education to support student learning. 

Abstract As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and human individuals performing equivalent writing and illustrating tasks. Our findings reveal that AI systems emit between 130 and 1500 times less CO2e per page of text generated compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than their human counterparts. Emissions analyses do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans. 

Abstract The recent proliferation of large language models (LLMs) has led to divergent narratives about their environmental impacts. Some studies highlight the substantial carbon footprint of training and using LLMs, while others argue that LLMs can lead to more sustainable alternatives to current practices. We reconcile these narratives by presenting a comparative assessment of the environmental impact of LLMs vs. human labor, examining their relative efficiency across energy consumption, carbon emissions, water usage, and cost. Our findings reveal that, while LLMs have substantial environmental impacts, their relative impacts can be dramatically lower than human labor in the U.S. for the same output, with human-to-LLM ratios ranging from 40 to 150 for a typical LLM (Llama-3-70B) and from 1200 to 4400 for a lightweight LLM (Gemma-2B-it). While the human-to-LLM ratios are smaller with regard to human labor in India, these ratios are still between 3.4 and 16 for a typical LLM and between 130 and 1100 for a lightweight LLM. Despite the potential benefit of switching from humans to LLMs, economic factors may cause widespread adoption to lead to a new combination of human and LLM-driven work, rather than a simple substitution. Moreover, the growing size of LLMs may substantially increase their energy consumption and lower the human-to-LLM ratios, highlighting the need for further research to ensure the sustainability and efficiency of LLMs. 

Network Analysis has traditionally been applied to analyzing interactions among learners in online learning platforms such as discussion boards. However, there are opportunities to bring Network Analysis to bear on networks representing learners' mental models of course material, rather than learner interactions. This paper describes the construction and analysis of collaborative educational networks based on concept maps created by undergraduates. Concept mapping activities were deployed throughout two separate quarters of a large General Education (GE) course about sustainability and technology at a large university on the West Coast of the United States. A variety of Network Analysis metrics are evaluated on their ability to predict an individual learner's understanding based on that learner's contributions to a network representing the collective understanding of all learners in the course. Several of the metrics significantly correlated with learner performance, especially those that compare an individual learner's conformity to the larger group's consensus. The novel network metrics based on collective networks of learner concept maps are shown to produce stronger and more reproducible correlations with learner performance than metrics traditionally used in the literature to evaluate concept maps. This paper thus demonstrates that Network Analysis in conjunction with collective networks of concept maps can provide insights into learners' conceptual understanding of course material. 

Wikidata is a publicly available, crowdsourced knowledge base that contains interlinked concepts structured for use by intelligent systems. While Wikidata has experienced rapid growth, it is far from complete and faces challenges that prevent it from being used to its full potential. In this paper, we propose a novel method for improving Wikidata by engaging undergraduate students to contribute previously missing knowledge via concept mapping assignments. Rather than allow students to edit Wikidata directly, we describe a workflow in which knowledge is constructed by students and then reviewed by an expert. We present a case study in which we deployed a workflow in a large undergraduate course about sustainability, and find that it was able to contribute a substantial number of high quality statements that persisted in and contributed previously missing knowledge to Wikidata. This work provides a preliminary workflow for improving Wikidata based on classroom assignments, as well as recommendations for how future educational projects could continue to improve Wikidata or other public knowledge bases.