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Generative Artificial Intelligence (GAI) has emerged in recent years as an innovative tool with promising potential for enhancing student learning across a broad spectrum of academic disciplines. GAI not only offers students personalized and adaptive learning experiences, but it is also playing an increasingly important role in various industries. As technologies evolve and society adapts to the growing AI revolution, it becomes necessary to train students of all disciplines to become proficient in using GAI. This work builds on studies that have established the effectiveness of intelligent tutoring systems, adaptive learning environments, and the use of virtual reality in education. This work-in-progress paper presents preliminary findings related to the relationship between university students’ area of study and the frequency at which they utilize GAI to aid their learning. Data for this study were collected using a survey distributed to students from eight different colleges at a large Western university as part of a larger ongoing project geared towards gaining insight into student perceptions and use of GAI in higher education. The goal of the overall project is to establish a foundational understanding of how disruptive technologies, like GAI, can promote learner agency. By exploring why and how students choose to engage with these technologies, the project seeks to find proactive approaches to integrate GAI technology into education, ultimately enhancing teaching and learning practices across various disciplines. 

This work-in-progress paper explores university students’ perspectives on Generative Artificial Intelligence (GAI) tools, such as ChatGPT, an increasingly prominent topic in the academic community. There is ongoing debate about whether faculty should teach students how to use GAI tools, restrict their usage to maintain academic integrity, or establish regulatory guidelines for sustained integration into higher education. Unfortunately, limited research exists beyond surface-level policies and educator opinions regarding GAI, and its full impact on student learning remains largely unknown. Therefore, understanding students' perceptions and how they use GAI is crucial to ensuring its effective and ethical integration into higher education. As GAI continues to disrupt traditional educational paradigms, this study seeks to explore how students perceive its influence on their learning and problem-solving. As part of a larger mixed-methods study, this work-in-progress paper presents preliminary findings from the qualitative portion using a phenomenological approach that answers the research question: How do university students perceive disruptive technologies like ChatGPT affecting their education and learning? By exploring the implications of Artificial Intelligence (AI) tools on student learning, academic integrity, individual beliefs, and community norms, this study contributes to the broader discourse on the role of emerging technologies in shaping the future of teaching and learning in education. 

Work-in-Progress: Uncovering AI Adoption Trends Among University Engineering Students for Learning and Career Preparedness-progress study explores self-reported data on AI use by university engineering students. The purpose of this study is to investigate how students are utilizing AI technologies and to understand their views on the role of AI in their future. The primary research question formulated was: How does the adoption of AI technologies for learning vary across demographic groups among university engineering students? Advances in technology and the emergence of AI tools have attracted attention from academia, research, and industry. The rapid growth of deep learning technologies has changed the landscape in the work environment, and universities may need to adapt to keep pace. Dynamic changes in the workplace have accelerated as these AI technologies are being leveraged to complete tasks at a high-speed rate. Research indicates that the workforce is increasingly demanding higher skill levels, including specialized AI skills. Formal education in AI basics could be crucial for future career readiness. Over 150 engineering students reported their demographics, including age, race, gender, year in school, and if they identify as having any form of disability. Currently, the survey remains open. The final study will incorporate more responses, and additional data will come from semi-structured interviews. This research explores the ways in which undergraduate and graduate students at a major R1 land-grant university in the western United States interact with AI tools. Students reported on using AI technologies, like ChatGPT, to aid in their learning. Preliminary findings suggest that freshman students are less likely to have used AI technologies than those later in their college careers. Encouragingly, students closest to entering the workforce are the ones with the most exposure to these technologies. Interestingly, students who identify as having any form of a disability or condition that impacts their learning (e.g., learning disability, neurodiversity, physical disability, etc.) initially reported lower usage of AI technologies compared to their classmates. The lower use by freshmen and increasing exposure to generative AI throughout students’ university experience is noteworthy. Students were also asked for their views on the formal integration of AI technologies into the College of Engineering courses. It could be valuable for universities to explore adding formal training to help equip students for the workforce. We anticipate that this study will highlight how exposure to AI technologies may prove essential for engineering students in preparing for a rapidly evolving workplace, as AI has the potential to enhance real-world problem-solving skills and help students become more equipped for workplace demands. 

Spatial ability is a well-known predictor of success in science, technology, engineering, and mathematics (STEM) fields. The purpose of this study was to investigate and understand the spatial strategies that were used by blind and low-vision (BLV) individuals as they solved problems on the tactile mental cutting test (TMCT), an instrument that was designed to measure the spatial ability of BLV audiences. The TMCT is an accessible adaptation of the older, 1938 version of the mental cutting test (MCT) that has been used extensively in spatial ability research. Additionally, this paper seeks to compare these strategies with existing strategies that have been investigated with sighted populations. The BLV community is underrepresented in engineering and in spatial ability research. By understanding how BLV students understand and solve spatial problems and concepts, educators can develop and enhance educational content that is relevant to this population. By incorporating perspectives from the BLV community and making STEM curricula accessible to this population, more BLV individuals may be encouraged to pursue STEM or engineering career pathways. 

This article presents tactile drafting techniques developed in collaboration with blind educators and students that have the potential to increase BLV students’ access to drafting and engineering graphic curriculum in K-12 and higher education. This work builds on previous work funded by the National Science Foundation (Goodridge et al., 2019; Ashby et al., 2018; Lopez et al., 2020; Goodridge et al., 2021a; Goodridge et al., 2021b) and it is the authors’ hope that some of the practices included herein will allow BLV youth to further develop technological and engineering literacy in related technology and engineering graphics courses. 

Spatial skills are fundamental to learning and developing expertise in engineering. This paper describes a new virtual and physical manipulatives (VPM) technology that this research team recently developed to enhance undergraduate engineering students’ spatial skills. This technology consists of ten manipulatives spanning a variety of levels of geometrical complexity. Each manipulative is authentic due to their real-world engineering applications that were chosen to stimulate student interest in engineering. A computer program was developed to connect virtual and physical manipulatives, allowing students to receive spatial training anytime, anywhere through the Internet. Quasi-experimental research, involving an intervention group (n = 37) and a control group (n = 34), was conducted. Each group completed a pre- and post-test using the same assessment instrument that measured students’ spatial skills. Normality tests were conducted. The results show that the data involved in the present study did not have a normal distribution. Thus, non-parametric statistical analysis was performed, including descriptive analysis, correlation analysis, and Mann-Whitney U tests. The results show that the mean value of normalized learning gains is 41.2% for the intervention group, which is 33% higher than that for the control group (8.2%). A statistically significant difference exists between the intervention and control groups in terms of normalized learning gains (P < 0.01). The new VPM technology developed from the present study has a medium effect size (0.34) on improving students’ spatial skills. 

The current study examined the neural correlates of spatial rotation in eight engineering undergraduates. Mastering engineering graphics requires students to mentally visualize in 3D and mentally rotate parts when developing 2D drawings. Students’ spatial rotation skills play a significant role in learning and mastering engineering graphics. Traditionally, the assessment of students’ spatial skills involves no measurements of neural activity during student performance of spatial rotation tasks. We used electroencephalography (EEG) to record neural activity while students performed the Revised Purdue Spatial Visualization Test: Visualization of Rotations (Revised PSVT:R). The two main objectives were to 1) determine whether high versus low performers on the Revised PSVT:R show differences in EEG oscillations and 2) identify EEG oscillatory frequency bands sensitive to item difficulty on the Revised PSVT:R.  Overall performance on the Revised PSVT:R determined whether participants were considered high or low performers: students scoring 90% or higher were considered high performers (5 students), whereas students scoring under 90% were considered low performers (3 students). Time-frequency analysis of the EEG data quantified power in several oscillatory frequency bands (alpha, beta, theta, gamma, delta) for comparison between low and high performers, as well as between difficulty levels of the spatial rotation problems.   Although we did not find any significant effects of performance type (high, low) on EEG power, we observed a trend in reduced absolute delta and gamma power for hard problems relative to easier problems. Decreases in delta power have been reported elsewhere for difficult relative to easy arithmetic calculations, and attributed to greater external attention (e.g., attention to the stimuli/numbers), and consequently, reduced internal attention (e.g., mentally performing the calculation). In the current task, a total of three spatial objects are presented. An example rotation stimulus is presented, showing a spatial object before and after rotation. A target stimulus, or spatial object before rotation is then displayed. Students must choose one of five stimuli (multiple choice options) that indicates the correct representation of the object after rotation. Reduced delta power in the current task implies that students showed greater attention to the example and target stimuli for the hard problem, relative to the moderate and easy problems. Therefore, preliminary findings suggest that students are less efficient at encoding the target stimuli (external attention) prior to mental rotation (internal attention) when task difficulty increases.  Our findings indicate that delta power may be used to identify spatial rotation items that are especially challenging for students. We may then determine the efficacy of spatial rotation interventions among engineering education students, using delta power as an index for increases in internal attention (e.g., increased delta power). Further, in future work, we will also use eye-tracking to assess whether our intervention decreases eye fixation (e.g., time spent viewing) toward the target stimulus on the Revised PSVT:R. By simultaneously using EEG and eye-tracking, we may identify changes in internal attention and encoding of the target stimuli that are predictive of improvements in spatial rotation skills among engineering education students.  

In teaching mechanics, we use multiple representations of vectors to develop concepts and analysis techniques. These representations include pictorials, diagrams, symbols, numbers and narrative language. Through years of study as students, researchers, and teachers, we develop a fluency rooted in a deep conceptual understanding of what each representation communicates. Many novice learners, however, struggle to gain such understanding and rely on superficial mimicry of the problem solving procedures we demonstrate in examples. The term representational competence refers to the ability to interpret, switch between, and use multiple representations of a concept as appropriate for learning, communication and analysis. In engineering statics, an understanding of what each vector representation communicates and how to use different representations in problem solving is important to the development of both conceptual and procedural knowledge. Science education literature identifies representational competence as a marker of true conceptual understanding. This paper presents development work for a new assessment instrument designed to measure representational competence with vectors in an engineering mechanics context. We developed the assessment over two successive terms in statics courses at a community college, a medium-sized regional university, and a large state university. We started with twelve multiple-choice questions that survey the vector representations commonly employed in statics. Each question requires the student to interpret and/or use two or more different representations of vectors and requires no calculation beyond single digit integer arithmetic. Distractor answer choices include common student mistakes and misconceptions drawn from the literature and from our teaching experience. We piloted these twelve questions as a timed section of the first exam in fall 2018 statics courses at both Whatcom Community College (WCC) and Western Washington University. Analysis of students’ unprompted use of vector representations on the open-ended problem-solving section of the same exam provides evidence of the assessment’s validity as a measurement instrument for representational competence. We found a positive correlation between students’ accurate and effective use of representations and their score on the multiple choice test. We gathered additional validity evidence by reviewing student responses on an exam wrapper reflection. We used item difficulty and item discrimination scores (point-biserial correlation) to eliminate two questions and revised the remaining questions to improve clarity and discriminatory power. We administered the revised version in two contexts: (1) again as part of the first exam in the winter 2019 Statics course at WCC, and (2) as an extra credit opportunity for statics students at Utah State University. This paper includes sample questions from the assessment to illustrate the approach. The full assessment is available to interested instructors and researchers through an online tool.