, ultimately fostering a culture of professionalism and ethical responsibility in engineering. By providing empirical evidence of disciplinary incidents and their causes, this study contributes to evidence- based practices for engineering education and professional development, enhancing the engineering education community’s understanding of professionalism and ethics.1 IntroductionThe engineering profession is built on a foundation of trust, integrity, and ethical conduct.However, like any profession, engineering is not immune to instances of misconduct, negli-gence, and unethical behavior. The Ordre des ingénieurs du Québec (OIQ)’s disciplinaryregister provides a unique window into the types of complaints filed against engineers
undergraduate understanding andincorporation of ethical and psychological competencies that promote a balanced view ofconsumer persuasion, engagement, outcomes, and wellness. Our proposed curriculum andassessment model integrates practical guidelines for ethical product development with theultimate goal of giving capstone students a framework for understanding product design as afoundation for consumer choice architecture.This study introduces students to eight ethical and psychological constructs: privacy, informedconsent, unintended outcomes and safeguards, participatory design, choice architecture, usermotivation and engagement, measurement of user outcomes, and AI/ML. In the study, weextend previous work by the authors and aim to 1) develop and
be included incurricula of accredited institutions [1]. As a result, college textbook authors began to includeinformation about professional and ethical responsibility in their publications helping to furtherembed ethics in engineering curricula [2]. Over time, these trends have increased scholarlyinterest in the teaching of ethics in engineering educationThough there are two different goals for teaching engineering ethics—on the one hand cognitiveunderstanding, and on the other hand social and moral understanding and behavior—theinstructional methods used to accomplish both appear to be similar. To wit, they have often bothinvolved active learning (specifically use of ethical dilemmas), case studies, and problem-basedlearning [1]. A few
, GenAI could provide information on environmental and societal concerns thatwere lacking in the textbook. In reviewing the GenAI-generated case studies, students reportedincreased confidence in their ability to recognize GenAI text and judge it for bias based on pre-and post-assignment surveys. While continued integration of GenAI into coursework is essentialfor developing graduates who can critically evaluate GenAI and use it effectively, the textbookremains a valuable resource for the course. Future steps include new assessments to betteraddress the myriad of ethical issues introduced by GenAI. IntroductionGenerative artificial intelligence (GenAI) is changing all aspects of society, including education[1]. Students can now access GenAI to
four times per semester, approximately once per month, using the skills-based, learner-centered BOPS method. Finally, this paper describes the contents of the workshop, including thecompetencies the workshop aims to cultivate and exercises used to do so. This paper is not meantto be an exhaustive description of either the IREI project or workshop but, rather, a sketch of themotivations for and nature of workshop so far.Background and objectivesNational legislation in the US, such as the America COMPETES Act and, more recently, theCHIPS and Science Act, highlights the importance of research integrity in innovation andcompetitiveness of the US economy [1], [2]. Given federal funding mandates, researchinstitutions have developed interventions and
-making inprofessional engineering contexts.IntroductionEthical reasoning is a critical competency for engineers, as their decisions often carry profoundsocietal, environmental, and safety implications. Traditional assessments of ethical reasoning,such as the Defining Issues Test (DIT) [1] and the Engineering Ethical Reasoning Instrument(EERI) [2], are modeled on Kohlberg’s justice-based moral development framework [3]. Whilethese assessments provide quantitative measures of ethical judgment, they often fail to capturethe complexity and context-dependence of ethical decision-making in real-world engineeringpractice.A key limitation of these static, principle-based assessments is that they emphasize abstractreasoning over situated, in-the-moment
ever, it is imperative thatprofessionals in engineering and technology engage with the normative dimensions of their workand consider how to best uphold high ethical standards. Multiple ethical frameworks andguidelines have been promulgated to support such objectives in educating engineering studentsand guiding engineering professionals, including relevant professional codes (e.g., [1]),accreditation requirements (e.g., [2]), responsible conduct of research (RCR) guidelines [3], andcorporate policies related to ethics, compliance, and social responsibility [4-5]. However, theseand other elements constituting engineering ethics require frequent revision in consonance withthe dynamic nature of technology. Indeed, the need for expanded and
an Associate Professor of Mathematics and Data Analytics and Director of Institutional Effectiveness at Doane University. ©American Society for Engineering Education, 2025Work-in-Progress [WIP] Baseline Results for The Impact of the Liberal Arts on the Ethical Development of Engineers Joel R. TerMaat (1), Kristopher J. Williams (2), and Christopher D. Wentworth (1) (1) Department of Engineering and Physics, Doane University (2) Director of Institutional Effectiveness, Doane UniversityAbstractPrevious research suggests that liberal arts institutions provide improved moral reasoningdevelopment in students compared with other types of institutions, but the
“public good” is characterized in the ethical codes andwebsites of engineers’ professional associations. While engineers are expected to hold the public paramount overclient and employer needs, historic accounts of engineers’ professional formation suggest that scientific authorityand the economic bottom line have been powerful drivers of engineers’ work since the turn of the 20th century [1, 2].How do these three occupational authorities—science, business, and public service—shape the contemporarymessaging systems of engineers’ professional organizations and to what extent do these messages differ acrossindustrial and national contexts? My critical analysis of eight engineering organization websites suggests anamplification of scientific and
University’s Experience Teaching and Assessing Student Learning of Professional Skills Using the EPSA MethodIntroductionProficiency in engineering professional skills, such as ethics, communication skills, andteamwork, are critical for success in the multidisciplinary, intercultural team interactions thatcharacterize 21st century engineering careers. Boeing’s list of “Desired Attributes of anEngineer” specifically include “Good communications skills”, “High ethical standards”, “Aprofound understanding of the importance of teamwork”, “Understanding of the context in whichEngineering is practiced”, and “Curiosity and a desire to learn for life”[1]. Engineering programaccrediting bodies worldwide recognize this importance and have required
’. ©American Society for Engineering Education, 2025Exploring engineering students’ understanding of their social responsibilitythrough a living library of ethics case studiesIntroductionEthics education is increasingly recognized as a crucial component of the undergraduateengineering curricula. Nonetheless, many engineering students show reluctance or outrightdisengagement when exposed to ethical issues [1] [2]. Traditionally, the engineeringcurriculum privileges technico-scientific knowledge, seeing it divorced from ethics andsocietal considerations, and relegating ethics tends to standalone courses or ancillary topicswithin broader coursework [3], [4]. This hierarchization of disciplines reflects a deeper‘depoliticization’ of engineering programs
topreserve critical thinking and foundational writing skills. Both groups called for clearerinstitutional policies and structured guidelines for the ethical use of AI tools in educationalcontexts.The findings underscore the need for a balanced and proactive framework to leveragegenerative AI’s benefits while safeguarding educational integrity. Key recommendationsinclude: (1) establishing clear institutional policies on permissible AI use; (2) developing AIliteracy modules to foster critical engagement; (3) implementing process-oriented assessmentmodels, such as version history reviews and reflective writing logs, to emphasize students'intellectual contributions; (4) promoting active faculty involvement in guiding ethical AI use;and (5) adopting
making connected to technical knowledge. By incorporating modern case studiesand speculative design, this course provides biomedical engineers with the critical thinking andethical reasoning skills necessary to navigate the challenges of emergent technologies inprofessional practice and can be adapted to any engineering discipline.Introduction At the core of the National Society for Professional Engineers Code of Ethics is that“Engineering is an important and learned profession” [1]. Thus, it logically follows that the canons,values, and professional obligations of engineers are formally and informally taught to studentengineers during their education. However, recent meta-analyses by [2] and [3] of current practicesin engineering ethics
wrong. They influence what someone considers to be ethical andvirtuous. While many attempts have been made to define what is ethical, virtuous, and moral,there is no universal agreement, which is one reason why this is a challenging subject. Studentslearn how to calculate many things using formulas, but there are no formulas for virtuousbehavior.Some might argue that engineering is morally neutral, that it is strictly guided by well-acceptedmathematics, science, and engineering principles. However, that is a somewhat naïve viewbecause there may be considerable gray areas. Busby and Coeckelbergh (2003) [1] wrote, “. . .the picture of engineering as morally neutral is misleading. . . . Telling someone to develop adesign for a hazardous
foster discussion about those differences?Because classroom settings and participants vary, and other teachers may wish to adaptrather than adopt our exercise, we chose a qualitative approach to interpret theexperiences of students when placed in the circumstances of this activity. Our analyticoutput consisted of themes for competing moral standpoints and discussion. We presentthese in our discussion of the student experience as examples to illustrate the variety ofresponses gained from this exercise and the moral priorities that they indicate.2. Background and LiteratureTeachers of engineering ethics have many ways of approaching their task. Hess andFore’s [1] review of engineering ethics interventions provides a picture of the breadth
©American Society for Engineering Education, 2025 Work in Progress: STEMtelling as a Method towards Ethical Awareness in Machine LearningAbstractThe recent surge in artificial intelligence (AI) developments has been met with an increase inattention towards incorporating ethical engagement in machine learning discourse and development.This attention is noticeable within engineering education, where comprehensive ethics curricula aretypically absent in engineering programs that train future engineers to develop AI technologies [1].Artificial intelligence technologies operate as black boxes, presenting both developers and users witha certain level of obscurity concerning their decision-making processes and a
includes multiple international clinics, engineers, pedagogy experts, the COSPH, CoN, and SOM as part of an interdisciplinary project to improve medical device design education and methods. ©American Society for Engineering Education, 2025 Engaging Undergraduate Studentsin Ethical Thinking Through Fun and MoviesAbstractThis paper describes a classroom approach and activities that have been successful in increasingundergraduate student understanding and engagement with ethics in a first-year design course.Gamification, the incorporation of game-like elements into non-game contexts, has been shownto increase student motivation and engagement in learning activities [1], [2], [3]. By creating afun and engaging
ofethical decision-making skills among recent graduates 1,2 . Therefore, it is critical for educators todevelop more effective approaches for teaching students engineering ethics.Traditional approaches to engineering ethics education have been largely limited to the use ofcodes of ethics of engineering societies and regulatory boards and the so-called “disaster cases”as case studies 3 . Engineering ethics has been expressed primarily in rules, and these rules areprimarily negative or prohibitive in nature. However, the use of rules is limiting. 1) Rules cannotadequately account for the place of discretion, judgment, and background knowledge in meetingsome professional obligations. 2) This rule-based approach, along with a focus on technicalethics
engineeringdisciplines.IntroductionThis paper advocates for a paradigm shift in engineering ethics through the lens ofintersectionality, a concept rooted in social science that examines how overlapping socialidentities, such as race, gender, and disability, intersect with systems of power and oppression[1]. The Intersectionality-Informed Ethics Principles (IIEP) framework offers a structuredapproach to integrating these considerations into engineering decision-making, helpingprofessionals address both technical and societal dimensions of their work. For instance, IIEPenables engineers to design sustainable energy systems that prioritize equitable access formarginalized communities while maintaining technical rigor. Through theoretical insights andpractical applications, this
VMI's CE program, highlightingthe synergy between technical education, ethical leadership, and military values.1. IntroductionEngineers are expected to uphold ethical standards as an essential element of their profession[1,2]. Ethical codes are commonly established by engineering societies, such as the AmericanSociety of Civil Engineers (ASCE) [3], the Institute of Electrical and Electronics Engineers(IEEE) [4], the American Society of Mechanical Engineers (ASME) [5], and the NationalSociety of Professional Engineers (NSPE) [6]. These codes of ethics provide lists of genericrules of practice for engineering professionals in how they approach their professional duties,including interactions with others [3-6]. Because ethics is important to the
regulatoryrequirements for obtaining a P.Eng., however typically it requires four main components: • Approved 4-year undergraduate engineering degree. • Four years of engineering industry experience. • Passing an ethics exam near the end of the Member-in-Training period (typically an 80- 100 multiple choice exam) • Submission of competency assessment. In the province of Alberta, this includes demonstrating competence across 22 engineering competencies through a 1-page essay on each.At the University of Calgary, there is one course which covers ethics and professionalism, with astrong emphasis on the requirements and regulations towards becoming a P.Eng. Students acrossall disciplines (chemical, mechanical, electrical
.” (translated with deepl) [1: p.74].In the general discussion, this requirement is reflected, for example, in the concept of the t-shaped engineer, whose strength is seen in the great variety of interdisciplinary skills, which,in addition to mastering foreign languages, include cultural and communicative skills. In addi-tion, young engineers are expected to think systemically and holistically, as well as to be ableto critically reflect on their own actions [2], [3]. A critical examination of the concept of the t-shaped engineer and a literature review in the context of the ASEE can be found in [4].The aim of these approaches is to lay a foundation for a technology and product developmentprocess that takes into account the non-technical and non-economic
been employed to screen resumes and identify the best candidates.While this may streamline recruitment, it has also led to instances of bias, where certaindemographic groups are unfairly excluded or prioritized. These biases often stem from historicaldata used to train the models, which may reflect existing inequalities in the workforce. Suchoutcomes not only raise ethical concerns but also risk violating anti-discrimination laws.Addressing these issues requires developing algorithms that account for fairness and biasmitigation, alongside rigorous testing and transparency in how decisions are made. Without suchmeasures, machine learning risks reinforcing systemic inequalities rather than promotinginclusivity and diversity in the workplace. 1
Methods to Inform Criteria for Broadening Participation in Institutions and Organizationsintroduction2022’s Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act [1]mandates efforts to “ensure collaboration and coordination across federal agencies, the privatesector, and with state and local governments to facilitate timely and effective reviews of allfederally funded projects.” The 4b requirement includes “measures of the institution’s ability toattract and retain a diverse and nontraditional student population in the fields of science,technology, engineering, and mathematics, which may include the ability to attract women,minorities, and individuals with disabilities.” To retain the workforce enabled by this act
privateinvestment into creating a more GAI-powered world. However, there remain many unanswered questions about theethical and moral impact of this emergent technology, both in terms of the harms caused by the outputs of GAI toolstowards historically marginalized identities (e.g., [1]–[4]) as well as the ecological impacts of producing and runninglarge GAI systems on a global scale (e.g., [5]–[7]). In such a climate, there arises a strong necessity for trainingengineering students and future industry professionals in the ethical usage of GAI tools, such that they maychampion ethical and harm-informed GAI design and incorporation strategies to their employers. Towards this end, we developed and taught a 10-week college course on considerations and
, and incorporate these in education ofengineers of tomorrow. Naturally, the educational implications of the findings will also bediscussed.Keywords— AI Policies, AI Ethics Frameworks, Global AI Policies, AI Ethics Education2 IntroductionThe concept of Artificial Intelligence (AI) has existed for years, with first being published by Alan Turing,in his paper ”Computer Machinery and Intelligence” [1]. From the time of simple ”Turing Test”, thedomain of AI has seen a massive boost, specifically after the advent of Large Language Models(LLMs) [2], that powers the Generative AI tools, such as Chat GPT. With such a rapid rise, AI has becomea game changer in many industries [3], including healthcare [4], education [5], finance [6], and
aims to build a more comprehensive understanding ofdoctoral-level ethics education.Research Aims and QuestionsThe primary goal of this study is to illuminate how doctoral students interpret and applyethical principles in engineering and to identify critical gaps in ethics education. Theinvestigation is guided by the following research questions: 1. How do doctoral engineering students at the Thayer School of Engineering conceptualize ethics within the context of their training and future careers? 2. In what ways do these conceptualizations shape their approach to professional decision-making? 3. What challenges and gaps in ethics education are perceived by doctoral students, and where might curricular or