Background: ChatGPT became widespread in early 2023 and enabled the broader public to use powerful generative AI, creating a new means for students to complete course assessments.
Purpose: In this paper, we explored the degree to which generative AI impacted the frequency and nature of cheating in a large introductory programming course. We also estimate the learning impact of students choosing to submit plagiarized work rather than their own work.
Methods: We identified a collection of markers that we believe are indicative of plagiarism in this course. We compare the estimated prevalence of cheating in the semesters before and during which ChatGPT became widely available. We use linear regression to estimate the impact of students’ patterns of cheating on their final exam performance.
Findings: The patterns associated with these plagiarism markers suggest that the quantity of plagiarism increased with the advent of generative AI, and we see evidence of a shift from online plagiarism hubs (e.g., Chegg, CourseHero) to ChatGPT. In addition, we observe statistically significant learning losses proportional to the amount of presumed plagiarism, but there is no statistical difference on the proportionality between semesters.
Implications: Our findings suggest that unproctored exams become increasingly insecure and care needs to be taken to ensure the validity of summative assessments. More importantly, our results suggest that generative AI can be detrimental to students' learning. It seems necessary for educators to reduce the benefit of students using generative AI for counterproductive purposes.
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Reducing the Carbon Impact of Generative AI Inference (today and in 2035)
Generative AI, exemplified in ChatGPT, Dall-E 2, and Stable Diffusion, are exciting new applications consuming growing quantities of computing. We study the compute, energy, and carbon impacts of generative AI inference. Using ChatGPT as an exemplar, we create a workload model and compare request direction approaches (Local, Balance, CarbonMin), assessing their power use and carbon impacts.
Our workload model shows that for ChatGPT-like services, in- ference dominates emissions, in one year producing 25x the carbon-emissions of training GPT-3. The workload model characterizes user experience, and experiments show that carbon emissions-aware algorithms (CarbonMin) can both maintain user experience and reduce carbon emissions dramatically (35%). We also consider a future scenario (2035 workload and power grids), and show that CarbonMin can reduce emissions by 56%. In both cases, the key is intelligent direction of requests to locations with low-carbon power. Combined with hardware technology advances, CarbonMin can keep emissions increase to only 20% compared to 2022 levels for 55x greater workload. Finally we consider datacenter headroom to increase effectiveness of shifting. With headroom, CarbonMin reduces 2035 emissions by 71%.
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- NSF-PAR ID:
- 10428143
- Date Published:
- Journal Name:
- ACM Hot Carbon 2023
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract In the face of climate change, climate literacy is becoming increasingly important. With wide access to generative AI tools, such as OpenAI’s ChatGPT, we explore the potential of AI platforms for ordinary citizens asking climate literacy questions. Here, we focus on a global scale and collect responses from ChatGPT (GPT-3.5 and GPT-4) on climate change-related hazard prompts over multiple iterations by utilizing the OpenAI’s API and comparing the results with credible hazard risk indices. We find a general sense of agreement in comparisons and consistency in ChatGPT over the iterations. GPT-4 displayed fewer errors than GPT-3.5. Generative AI tools may be used in climate literacy, a timely topic of importance, but must be scrutinized for potential biases and inaccuracies moving forward and considered in a social context. Future work should identify and disseminate best practices for optimal use across various generative AI tools.
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
