As generative artificial intelligence (GenAI) tools such as ChatGPT become more capable and accessible, their use in educational settings is likely to grow. However, the academic community lacks a comprehensive understanding of the perceptions and attitudes of students and instructors toward these new tools. In the Fall 2023 semester, we surveyed 982 students and 76 faculty at a large public university in the United States, focusing on topics such as perceived ease of use, ethical concerns, the impact of GenAI on learning, and differences in responses by role, gender, and discipline. We found that students and faculty did not differ significantly in their attitudes toward GenAI in higher education, except regarding ease of use, hedonic motivation, habit, and interest in exploring new technologies. Students and instructors also used GenAI for coursework or teaching at similar rates, although regular use of these tools was still low across both groups. Among students, we found significant differences in attitudes between males in STEM majors and females in non-STEM majors. These findings underscore the importance of considering demographic and disciplinary diversity when developing policies and practices for integrating GenAI in educational contexts, as GenAI may influence learning outcomes differently across various groups of students. This study contributes to the broader understanding of how GenAI can be leveraged in higher education while highlighting potential areas of inequality that need to be addressed as these tools become more widely used.

Time-series and graphs have been extensively studied for their ubiquitous existence in numerous domains. Both topics have been separately explored in the field of deep learning. For time-series modeling, recurrent neural networks or convolutional neural networks model the relations between values across timesteps, while for graph modeling, graph neural networks model the inter-relations between nodes. Recent research in deep learning requires simultaneous modeling for time-series and graphs when both representations are present. For example, both types of modeling are necessary for time-series classification, regression, and anomaly detection in graphs. This article aims to provide a comprehensive summary of these models, which we call graph time-series models. To the best of our knowledge, this is the first survey article that provides a picture of related models from the perspective of deep graph time-series modeling to address a range of time-series tasks, including regression, classification, and anomaly detection. Graph time-series models are split into two categories: (a) graph recurrent/convolutional neural networks and (b) graph attention neural networks. Under each category, we further categorize models based on their properties. Additionally, we compare representative models and discuss how distinctive model characteristics are utilized with respect to various model components and data challenges. Pointers to commonly used datasets and code are included to facilitate access for further research. In the end, we discuss potential directions for future research.

There has been a trend among various stakeholders for AI governance, such as the government, industry, and academia, that advocates for a shift from traditional ethics approach to more recent policy-oriented approach. This paper starts by briefly introducing the motivation for such a change. To further help AI ethics researchers ground their discussion about how to operationalize abstract AI ethics into actionable policy, it introduces existing literature about AI policy development efforts with ethics focus. We also discuss the implications of the policy approach to normative AI governance for training the next generation of AI professionals. More specifically, this paper introduces two approaches that AI educators can employ to encourage their students to participate in policy discussions and practical exercises in AI-related courses. The insights derived from this paper hold particular significance for AI professionals, educators, and policymakers aiming to translate general AI ethics principles into operationalizable action items with broader policy implications in the design of AI systems.

With the widespread adoption of smart meters in buildings, an unprecedented amount of high-resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods to segment customers based on their load profiles, which help utilities and energy providers promote customized/personalized targeting for energy programs. Existing energy consumption segmentation techniques use assumptions that could reduce clusters’ quality in representing their members. Therefore, in this paper, we investigated a two-stage clustering method for capturing more representative load shape temporal patterns and peak demands through a cluster merging approach. In the first stage, load shapes are clustered (using classical clustering algorithms) by allowing a large number of clusters to accurately capture variations in energy use patterns, and cluster centroids are extracted by accounting for limited shape misalignment within the range of Demand Response (DR) timeframes. In the second stage, clusters with similar centroids and power magnitude ranges are merged using Complexity-Invariant Dynamic Time Warping. We used three datasets consisting of ~250 households (~15000 profiles) to demonstrate the efficacy of the framework, compared to baseline methods, and discuss the impact on energy management. The proposed investigated merging-based clustering also increased correlation between cluster centroids and the corresponding members by 3–9% for different datasets.

The explosive growth of artificial intelligence (AI) over the past few years has focused attention on how diverse stakeholders regulate these technologies to ensure their safe and ethical use. Increasingly, governmental bodies, corporations, and nonprofit organizations are developing strategies and policies for AI governance. While existing literature on ethical AI has focused on the various principles and guidelines that have emerged as a result of these efforts, just how these principles are operationalized and translated to broader policy is still the subject of current research. Specifically, there is a gap in our understanding of how policy practitioners actively engage with, contextualize, or reflect on existing AI ethics policies in their daily professional activities. The perspectives of these policy experts towards AI regulation generally are not fully understood. To this end, this paper explores the perceptions of scientists and engineers in policy-related roles in the US public and nonprofit sectors towards AI ethics policy, both in the US and abroad. We interviewed 15 policy experts and found that although these experts were generally familiar with AI governance efforts within their domains, overall knowledge of guiding frameworks and critical regulatory policies was still limited. There was also a general perception among the experts we interviewed that the US lagged behind other comparable countries in regulating AI, a finding that supports the conclusion of existing literature. Lastly, we conducted a preliminary comparison between the AI ethics policies identified by the policy experts in our study and those emphasized in existing literature, identifying both commonalities and areas of divergence.

As artificial intelligence (AI) grows in popularity and importance—both as a domain within broader computing research and in society at large—increasing focus will need to be paid to the ethical governance of this emerging technology. The attitudes and competencies with respect to AI ethics and policy among post-secondary students studying computer science (CS) are of particular interest, as many of these students will go on to play key roles in the development and deployment of future AI innovations. Despite this population of computer scientists being at the forefront of learning about and using AI tools, their attitudes towards AI remain understudied in the literature. In an effort to begin to close this gap, in fall 2024 we fielded a survey ( $$n=117$$ ) to undergraduate and graduate students enrolled in CS courses at a large public university in the United States to assess their attitudes towards the nascent fields of AI ethics and policy. Additionally, we conducted one-on-one follow-up interviews with 13 students to elicit more in-depth responses on topics such as the use of AI tools in the classroom, ethical impacts of AI, and government regulation of AI. In this paper, we describe the findings of our exploratory study, drawing parallels and contrasts to broader public opinion polling in the United States. We conclude by evaluating the implications of CS student attitudes on the future of AI education and governance.