This research explores the integration of ChatGPT, an advanced AI language model, in engineering and computer science education. It investigates ChatGPT's effectiveness in enhancing learning outcomes, engagement, and skill development among first-year engineering students. Utilizing a mixed-method approach in an introductory programming course, the study compares the impact of ChatGPT, traditional teaching assistant support, and a combined method on student performance and perceptions. Anticipated findings aim to illuminate the benefits and challenges of AI tutoring, focusing on personalized learning experiences and ethical considerations in AI integration. This research contributes to the discourse on AI in education, highlighting its potential to transform educational practices and outcomes in engineering and computer science fields.

In this paper, we present a novel conceptual framework for integrating Artificial Intelligence (AI) into engineering education, with the goal of harmonizing technical skills with intrinsic human qualities, such as creativity and ethical reasoning, essential in the Industry 5.0 landscape. This work is based on a comprehensive literature review and empirical data from student and teacher surveys, which are analyzed in order to assess the potential of AI to foster these human-centered qualities. Our framework proposes dynamic, interdisciplinary learning environments and personalized educational trajectories. It emphasizes continuous adaptation, lifelong learning and addresses the ethical issues of AI application. This framework not only advances engineering education to meet the demands of Industry 5.0, but also fosters holistic, human-centered learning experiences, preparing technically competent engineers equipped with vital interpersonal skills.

This work in progress research paper presents findings from a survey conducted about ChatGPT that was circulated in both spring 2023 and 2024 at an R1 engineering university, with responses from faculty doubling from the first to the second year. As Generative Artificial Intelligence (GAI) and online tools continue to reshape engineering education, it can be difficult for educators to keep up. Emerging technologies like ChatGPT by OpenAI have spurred conversations across academia, and questions about its capabilities and potential uses in the classroom have led to countless ethical discussions about academic integrity and how educators need to adapt to rapid growth in technological advancement. Understanding faculty perceptions of emerging technologies like ChatGPT by OpenAI becomes an essential part of embracing these advancements. This study aims to answer the following research question: What are the current perceptions of faculty towards emerging technologies like ChatGPT by OpenAI in engineering education, as assessed by the Technology Acceptance Model (TAM), and what factors influence their attitudes towards its potential integration into engineering curricula? The TAM assesses survey data by examining responses related to perceived usefulness and ease of use of a technology. It analyzes the extent to which these factors influence attitudes towards, and intention to use the technology, thus predicting its acceptance. Applied to the context of this survey instrument developed by the authors we should glean a good understanding of how faculty may have begun to accept the technology of GAI from the initial survey to the most current survey. Preliminary results shed light on faculty familiarity with ChatGPT and their perspectives on its potential utility for student learning as well as their concerns. By examining faculty attitudes towards integrating ChatGPT into engineering curricula, this research contributes to ongoing discussions on the role of GAI in higher education and provides insight into the extent of which educators currently embrace advancements in a technology-enhanced world.

The Engineer of 2050 requires a multidimensional education that not only incorporates advanced technical knowledge but also emphasizes global competence, growth miudset, and a proactive approach to lifelong learning. This workshop will explore how the DANCE (Designing Adaptations for the Next Changes in Education) model facilitates such development through a structured yet adaptable framework. The model emphasizes the need for faculty to not simply respond to changes but to anticipate and prepare for them, enabling a dynamic and forward-thinking educational environment.

From overhead transparencies to interactive documents, teaching and learning engineering has changed dramatically over the years. A flipped or hybrid course is a pedagogical model that reverses typical lecture and homework elements of a course. Class time is used for discussions and group activities with lecture materials being introduced in an on-line format. As research has shown, this model allows students to typically demonstrate higher levels of learning. This paper will detail the associated learning by students through the author's transition from a typical lecture-based sophomore-level engineering class to a hybrid model. The author will address how learning can be increased through the use of active engagement tools, such as iClickers. To address this question and gauge the overall impact on student learning, a post-course survey was used to measure the perceived value for students. In addition, evidence for improved student engagement was gathered qualitatively to compare with previous experiences. Results show that active engagement tools helped students become active learners, increased interaction with peers and instructor, and helped them evaluate whether material was understood to motivate further learning. Moreover, students benefitted from the instructor being able to pinpoint misconceptions earlier due to instant feedback from students in the class.

Anecdotally, engineering faculty members complain students taking sophomore engineering science courses are not prepared with respect to mathematics and mechanics-based physics. However, evidence has rarely been systematically collected and analyzed to determine the veracity of these assertions. Therefore, the paper intends to address two questions: 1) With respect to first-year mathematics and first-year physics mechanics knowledge, what do engineering faculty members expect students to know and be able to do when they begin a sophomore statics and dynamics course? 2) To what extent do students satisfy these expectations? To address these questions, the following steps were taken. First, engineering faculty members who taught a sophomore statics and dynamics course at Texas A&M University were asked for problems involving first-year mathematics and physics mechanics they thought students should be able to solve entering the course. Learning outcomes were abstracted, and two instruments were developed and administered near the beginning of the statics and dynamics course. After administering the instruments and analyzing results, faculty members have a better idea of the background of their students. Furthermore, there is evidence to examine the extent to which students are prepared in first-year mathematics and physics mechanics to begin a core engineering science course.