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.

Background: Prior researchers developed an instrument to measure perceived design thinking ability of first‐year students interested in engineering, and they validated the instrument through exploratory factor analysis.Purpose/Hypothesis: Our study uses the previously developed instrument to evaluate perceived design thinking ability of senior engineering students. We make a cross‐sectional comparison of this measure on a national scale.Design/Method: We surveyed a national sample of senior engineering students in 2018 and conducted a cross‐sectional comparison with results from a 2012 national sample of first‐year students who were interested in declaring an engineering major. Two‐way analysis of variance tests compared average design thinking scores across sample groups. Confirmatory factor analysis was conducted to improve the design thinking instrument.Results: First‐year students who intended to declare an engineering major score significantly higher (2.80) on the design thinking scale than senior engineering students (2.59) with a medium effect size of 0.4. The senior engineering sample performs significantly worse on the feedback seeking and experimentalism instrument items, but significantly better on the integrative thinking and collaboration items. We found no significant differences in perceived design thinking ability among engineering disciplines among senior students.Conclusions: Feedback seeking and experimentalism are traits that engineering educators should develop in their students to improve perceived design thinking ability. Incorporation of user‐centered design and divergent thinking in the engineering classroom are recommended as avenues to foster feedback seeking and experimentalism. We also offer recommendations to improve the design thinking instrument for future research.

Self-report assessments are used frequently in higher education to assess a variety of constructs, including attitudes, opinions, knowledge, and competence. Systems thinking is an example of one competence often measured using self-report assessments where individuals answer several questions about their perceptions of their own skills, habits, or daily decisions. In this study, we define systems thinking as the ability to see the world as a complex interconnected system where different parts can influence each other, and the interrelationships determine system outcomes. An alternative, less-common, assessment approach is to measure skills directly by providing a scenario about an unstructured problem and evaluating respondents’ judgment or analysis of the scenario (scenario-based assessment). This study explored the relationships between engineering students’ performance on self-report assessments and scenario-based assessments of systems thinking, finding that there were no significant relationships between the two assessment techniques. These results suggest that there may be limitations to using self-report assessments as a method to assess systems thinking and other competencies in educational research and evaluation, which could be addressed by incorporating alternative formats for assessing competence. Future work should explore these findings further and support the development of alternative assessment approaches.

Background: Increasing the persistence of engineering transfer students can help meet the US national priority of increasing the number of engineering graduates. Many transfer students experience a decrease in their grade point average (GPA) at their receiving institution, known as transfer shock, which can lead to them leaving the institution. This GPA decrease is found to be more prevalent in engineering transfer students.Purpose/Hypothesis: The purpose of this study is to analyze a single institutional dataset to determine when transfer shock occurs, how it differs among engineering transfer student subgroups, and if transfer shock is a predictor of graduation within 4 years in engineering.Design/Method: A 10‐year dataset with 789 engineering transfer students was used in this study, and the engineering transfer students were split into four subgroups. Multiple statistical analyses were conducted, including Welch's F‐test, chi‐square, and logistic regression, to understand differences in transfer shock during the first three terms of enrollment as well as 4‐year graduation rates among each subgroup.Results: Transfer shock extends through the first three post‐transfer terms, resulting in transfer norming. The engineering transfer student subgroups experience different levels of transfer norming; however, the subgroups were not predictors of graduation. The predictors were the transfer GPA and the transfer norming in the first three post‐transfer terms of enrollment.Conclusions: Engineering transfer students are not a homogeneous population; there are key differences between lateral and vertical transfer students. More strategic, longitudinal programming and decision‐making should be considered by institutions.