, timemanagement, leadership, ethical principles, and interpersonal skills. In alignment withaccreditation requirements, we can visualize this mapping by adding components in core coursesthroughout the curriculum (e.g., assignments) and planning for or reinforcing dedicated courses(e.g., communication skills and machine learning within chemical process simulation). A designspine within our UG Chem Eng curriculum would require, therefore, a critical path of coursespreparing students for the capstone project, fed by technical and soft skills acquired in core andoptional courses, while adding assignments/projects in core and optional courses, for dedicatedunit operations and reaction systems. An example of implementing non-traditional technical skillsand
experience, perceived reliability of AI-generated content, and the extentto which AI aligns with their learning goals [9-12]. Moreover, concerns about the accuracy of AIoutputs and ethical considerations, such as potential biases in AI algorithms, have been raised byboth students and educators [13-16].Studies involving generative AI tools in STEM education suggest a mixed response: studentsappreciate the efficiency and accessibility of AI tools but remain cautious about over-reliance andthe lack of critical evaluation skills when using AI-generated solutions. This highlights the needfor educational interventions that not only incorporate AI tools but also teach students how tocritically evaluate and effectively integrate these technologies into
learners, canfoster a more personalized learning experience. A key aspect of this is targetedfeedback, which plays a vital role in student development. This study presents astrategy that enables instructors in chemical engineering courses to create bespokeproblem sets and solutions tailored for their students. Ethical AI use and intellectualproperty contributions are discussed extensively in the text. The issues consideredwere (1) bias in AI-generated problem statements; (2) academic integrity andplagiarism; (3) data privacy and student information; (4) openness and explanation;(5) intellectual property and copyright; and most importantly, (6) the general frameworkfor ethical use of AI in engineering education.This approach leverages Python
bothscalable and personalized to individual needs. The incorporation of anonymity protocols ensuresthat personal data is protected, fostering trust among participants. However, challenges such asFigure 2: Model performance comparison for Perceived Stress Scale (PSS) label prediction. Thechart compares ROC-AUC and accuracy scores across various machine learning models.ensuring widespread adoption and addressing ethical considerations remain.The framework provides a forward-thinking approach for academic institutions seeking toimplement modern mental health support systems. It offers actionable, data-driven insights thatcan inform institutional policies and identify students who may be vulnerable to mental healthchallenges. Moreover, the system’s
facilities, online resources, and services through the institution's library, makerspace,and laboratory. Ethical approval for the study was obtained from University of New South WalesHuman Research Ethics Committee prior to data collection (Project Reference Number:HC200047). Student participants were informed of the study’s purpose and their rights, andwritten informed consent was obtained from 69 students.AI Analysis of Student Teams Meeting TranscriptsDESN2000 was delivered in person but students are required to meet outside of class to plan andcomplete their project tasks throughout the term. Geographic and scheduling constraints meantin-person meetings can be challenging for some students as UNSW Sydney is a commutercampus. Most student teams
methods of engineering; introduce skills which are basic to engineering; and acquaintstudents with the interaction of skills, techniques, logic, ethical responsibility[2], and creativity inengineering problem formulation and solving. Although the curriculum is common, the actualschedule for each student is based on their incoming background and their anticipated major. Thescience and general education requirements are the same regardless of whether they enter the FirstYear Engineering Program or as a first-year student or as a transfer student. Upon the successfulcompletion of the first-year curriculum, students choose their major from any of the tendepartments or programs.First year students (and transfer students) also participate in an
purpose of the survey both in class and through course announcementson the learning management system. No incentive was provided to students filling in the survey.The survey was anonymous, with no way of tracking respondents. The study was reviewed andapproved by the institutional research ethics board, ID # H24-03237. The list of questionsprovided in the survey as well as closed-ended question answer choices are provided in anappendix at the end of this publication. The survey was adapted from a previous study focusingon Gen. AI usage in capstone design courses [8].36 responses were received of which 32 appeared to be fully completed. The 4 incompleteresponses were removed from the analysis as they did not provide sufficient data for
studyto more rigorously evaluate its effectiveness. For instance, incorporating A/B testing could providerobust evidence of v-UOL’s impact on students’ preparation for lab courses. After the preliminary pilotstudy, the system is considered user-friendly and safe and with the ethic approval by the universitycommittee, we are extending the study to large student body, involving over 150 third-year chemicalengineering students enrolled in the UOL course, who will voluntarily participate in different pre-labexercises. These exercises will include traditional paper-based SOP and P&ID training, as well as screen-and VR-based v-UOL applications. The study will then evaluate the student’s performance, learningeffectiveness, memory retention and
the lungs and on the skin. More recently, she began conducting research in engineering education with a focus on the development of engineering students as effective learners and ethical thinkers.Charles Stanier, The University of Iowa ©American Society for Engineering Education, 2025 WiP: Empowering TAs through Metacognitive and Communication Skills DevelopmentIntroductionThis paper presents a structured approach to teaching assistant (TA) training in a chemicalengineering department at an R1 university, tailored primarily for undergraduate TAs. Theinitiative was conceived in the 2021-2022 academic year, when a committee of faculty andstudents consolidated feedback
Thermodynamics – Ideal Gas Non-Reactive Mass Balances Law & Other Equations of Renewable Energy Sector State Reactive Mass Balances Engineering Ethics Defense Industry Process Flow Diagrams Basic ProgrammingPrevious work has shown that of the various identity constructs– interest in the subject area,recognition (i.e., the beliefs that they are seen as a good student in the subject area by their peers,parents, and faculty), and performance / competence beliefs (i.e., beliefs in the ability to performwell and understand concepts) [1] – the strongest direct path to the construction of anengineering identity is recognition [2]; however, performance / competence
(2019).12. Millman, K. J. & Aivazis, M. Python for scientists and engineers. Computing in Science and Engineering vol. 13 Preprint at https://doi.org/10.1109/MCSE.2011.36 (2011).13. Goktas, P., Karakaya, G., Kalyoncu, A. F. & Damadoglu, E. Artificial Intelligence Chatbots in Allergy and Immunology Practice: Where Have We Been and Where Are We Going? Journal of Allergy and Clinical Immunology: In Practice 11, (2023).14. Ray, P. P. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber- Physical Systems vol. 3 Preprint at https://doi.org/10.1016/j.iotcps.2023.04.003 (2023).
Institutional Review Board (IRB) and deemed exemptunder educational research guidelines. Ethical considerations, including informed consent andvoluntary participation, were followed to protect student confidentiality and ensure compliancewith institutional policies.ImplementationCHE CALCULATOR®’s application is best illustrated through specific examples of its use inchemical engineering courses. In the Thermodynamics course, students used the tool to calculatevapor-liquid equilibrium (VLE) properties for multicomponent systems. It is an innovative, Excel-based computational tool designed to streamline the process of determining thermodynamicproperties. It eliminates the need for students to conduct extensive searches across multiplewebsites or
. 2. W. Perry, Forms of intellectual and ethical development in the college years: A scheme. New York, NY: Holt, Rinehart, and Winston, 1970. 3. B. Christe, “The Importance of Faculty-Student Connections in STEM Disciplines: A Literature Review,” Journal of STEM Education, vol. 14, pp. 22-26, 2013. 4. E. A. Kuley, “Understanding Opportunities and Barriers to Engineering Student Success and Persistence,” M.S. dissertation, Dept. Civil, Geological, and Environmental Eng., University of Saskatchewan, Saskatoon, 2018. 5. M. Micari and P. Pazos, “Connecting to the professor: Impact of the student-faculty relationship in a highly challenging course,” College Teaching, vol. 60, pp. 41-47, 2012. 6. R. Suresh
(LBSC 2) 4 Design good solutions to several actual food-engineering problems. 5 Attain familiarity with some of the many current safety, cultural, business, regulatory, political, nancial, and ethical implications of food and food production. Re ect on the historical bases for these implications. 6 Practice persuasive communication, experimental design, and life-long-learning skills such as nding your own information, identifying and addressing potential market needs, and persevering in the face of failure (LBSC 3.)fi fi ffi fi ff ff
education. During her time at Iowa, she has built a research program focused on developing better drug delivery systems to treat infections in the lungs and on the skin. More recently, she began conducting research in engineering education with a focus on the development of engineering students as effective learners and ethical thinkers. ©American Society for Engineering Education, 2025 A 52-Week, Scaffolded Faculty Journey into Engineering Culture and ClimateIntroductionEngineering culture and climate play a crucial role in shaping the academic environment andexperiences of students, staff, and faculty in engineering educational institutions. Research hasshown that
identifytrends and the need for additional support for students in each category. Plans for improvedstudent engagement as a result of this study are presented.The Student PopulationIntroduction to Chemical Engineering (Intro) is offered as the first course in ChemicalEngineering at the U of A and covers topics such as chemical engineering as a profession, jobopportunities, ethics, communication skills, unit conversions, limiting reactant calculations andmaterial balances for reacting and non-reacting systems. Prior to 2013, the course was part of atwo-course freshman-level sequence that also included Introduction to Chemical Engineering II(Intro II), which emphasized ideal and real gases, steam table use, humidification and energybalances for reacting
towardsthe Society 5.0 global vision. Coupled with the use of conscious, ethical Artificial Intelligence tools (ChatGPT, JasperAI, Copilot, Gemini, etc.) and learning modalities (active/experiential/inquiry-driven, flipped-classroom, etc.) willempower students to individualize learning experiences/outcomes. However, e-learning must be supplemented byopen discussions [13], and project-based/textbook-based learning, especially for foundational subjects. Withinchemical engineering, core subjects and topics like calculus, transport phenomena, chemical thermodynamics,separation processes, and plant/process design (undergraduate capstone) must be taught through a mix of pedagogicalstrategies. Our results reveal an increase (especially since 2017
ChemE educators seekinnovative ways to engage and retain students, interventions like PORPs offer valuable insightsinto how contextualized learning can shape students’ perceptions of the field and their futurecareer prospects.Institutional Review Board ConsiderationsThis study, titled “Impact of People-Oriented Recitation Problems,” was reviewed anddetermined to be exempt under the 2018 Common Rule 45 CFR 46.104.d by the CarnegieMellon University Review Board (IRB). The exemption was granted on August 26, 2024, undercategories (1) educational settings and (2)(i)-(iii) tests, surveys, interviews, or observation.Limited IRB review was conducted where necessary, ensuring compliance with ethical researchstandards. The study's IRB determination is