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Abstract 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. 

Abstract BackgroundEngineering curricula are built around faculty and accreditors' perceptions of what knowledge, skills, and abilities graduates will need in engineering careers. However, the people making these decisions may not be fully aware of what industry employers require for engineering graduates. Purpose/HypothesisThe purpose of this study is to determine how industry employer‐sought professional and technical skills vary among engineering disciplines and levels of education. Design/MethodUsing a large sample (n = 26,103) of mined job advertisements, we use the O*NET skills database to determine the frequencies of different professional and technical skills for biomedical, civil, chemical, electrical, environmental, and mechanical engineers with bachelor's, master's, and PhD degrees. ResultsThe most frequently sought professional skill is problem‐solving; the most frequently sought technical skills across disciplines are Microsoft Office software and computer‐aided design software. Although not the most frequently requested skills, job advertisements including the Python and MATLAB programming languages paid significantly higher salaries than those without. ConclusionsThe findings of this study have important implications for engineering program leaders and curriculum designers choosing which skills to teach students so that they are best prepared to get and excel in engineering jobs. The results also show which skills students can prioritize investing their time in so that they receive the largest financial return on their investment.