Paper ID #41786Examining ChatGPT in Educational Settings: Ethics, Challenges, and OpportunitiesDr. Mudasser Fraz Wyne, National University I hold a Ph.D. in Computer Science, an M.Sc. in Engineering, and a B.Sc. in Electrical Engineering. Currently, I am honored to serve as a Professor of Computer Science and as the Chair for the Department of Computer Science and Information Systems at the School of Technology and Engineering, National University, San Diego, USA. Additionally, I am entrusted with the role of Academic Program Director for the MS in Computer Science. Over the years, I have played key leadership roles
Paper ID #46252Educating a Responsible AI Workforce: Piloting a Curricular Module on AIPolicy in a Graduate Machine Learning CourseMr. James Weichert, Virginia Polytechnic Institute and State University James Weichert is a recent M.S. graduate from the Virginia Tech Computer Science Department, where he studied AI ethics, policy and education. Starting fall 2025, James will be joining the faculty at the University of Washington Paul G. Allen School of Computer Science & Engineering as an Assistant Teaching Professor.Hoda Eldardiry, Virginia Polytechnic Institute and State University Eldardiry is an Associate Professor of
adoption in theirworkplace, address its ethical implications, and enable better communication about AI initiativeswithin the organization. It also demystifies the technology, and ensures leaders can responsiblynavigate AI-driven changes.Most AI-related courses mainly focus on teaching programming languages and handling big data.A closer look at AI adult education reveals gaps and limitations in content suitable forprofessional adults – e.g in leadership, decision-making, ethics, governance and cultural aspectsof organizational change. Furthermore, AI education pedagogy for adult learners,is stillunderstudied. Literature suggests that adults are self-directed, experience-based learners.Therefore, their learning should involve self-planning
engineering education [2], [3]. Despite thewidespread use of GenAI tools, they are still relatively new in engineering education. Thisintroduces uncertainties, including issues regarding ethics, accessibility, and algorithmic bias [2],[4]. There are also concerns around the lag between the rapidly growing uses of GenAI tools andthe current policies regarding their uses in engineering education [5], [6].In addition to ChatGPT, there have been other GenAI and large language model (LLMs) basedtools, with widespread uses for students, educators, and researchers in engineering education [2],[3]. This has created opportunities for innovation within engineering education along withchallenges of using them in learning environments [3]. Due to the recent
early introduction to the softwaredesign process and a consideration of ethical issues that are inherent in technology. A widevariety of projects that inevitably result from this process, also give students in class exposure toa wide range of possibilities when it comes to programming and where programming can beapplied, even at their early programmer level. Although this process is intensive and requiressignificant instructor time and was primarily done in classes of up to 43 students, the approachdescribed can be scaled to larger classes through trained teaching assistants and how to approachthis is discussed. The value of increased engagement, continued engagement and learning afterthe end of the course, and, confidence boost overall makes
between the groups and anotable preference for a more structured and practical educational approach, especially amongstudents with a more robust foundational knowledge. This highlights the relevance of personalizedand applied teaching methods in real-world contexts.This approach examines how AI tools can be effectively integrated into an educationalenvironment, preparing students to face future technological challenges with an innovativeperspective on information systems management.Keywords: Artificial Intelligence (AI), Information Systems (IS), Alternative Evaluation,Automatic Code Generation, Operational Efficiency, Decision Making, Automation, AI Ethics,Information Management, AI Tools.IntroductionIn the digital era, Artificial Intelligence
, Computer Science Education, Machine Learning, PersonalizedLearning, Ethical AI, Research, Graduate Programs, Undergraduate Programs.INTRODUCTIONThe technological innovations of the 21st century have fundamentally transformed how the worldoperates [1], creating entirely new areas of expertise and workforce demands [2,3,4,5]. Theinterdisciplinary interest from scholars in linguistics, psychology, education, and neuroscience aswell as other disciplines, who examine AI through the lens of their respective fields, such as itsnomenclature, perceptions, and knowledge poses challenges in defining AI [6]. This hasnecessitated the development of AI categories within specific disciplinary contexts.There is a pressing need for widespread education across all
important feature of a COVML which is crucial toaccommodate the increasing number of students and evolving educational needs[7].It is essential to teach students about ethical cybersecurity practices. This will provide them with thenecessary skills to responsibly test and secure computer systems. By emphasizing the importance ofethical behavior in cybersecurity, we can help to create a culture of trust, integrity, and responsibility inthe field. This will benefit individual students and contribute to a more secure and stable digitallandscape for all users[7, 26].2. COVML Safe Environment for Learning and Testing:Cybersecurity refers to the practice of protecting computer systems, networks, and data fromunauthorized access, damage, or theft. VM
covers linear regression, neural networks, sparsity, and dictionary learning. The goal of this part is for students to see a vari- ety of ML methods that they can understand most of, but they do not code these applications from scratch. Instead, students use Python libraries. 3. Part 3 (3 weeks) What other cool ML things are out there? This part briefly introduces advanced ML algorithms and the ethics of ML.The following subsections describe the learning activities in each course part and Fig. 1 summa-rizes the schedule for the Fall 2024 semester. Key activities are highlighted in blue text in both thefollowing text and in Fig. 1.3.1 Part 1Given the desire to present ML algorithms from first principles and the lack of
usefulness of thetool, specifically noting that they did not have the opportunity to simply copy and paste what anAI tool suggests. Instead, they had a chance to rethink and revise their writing through the KVIStool. In addition, the visualized graph appears to help students capture the overall focus of theirwriting, rather than losing sight of their main idea by concentrating too narrowly on a specificaspect.As AI technologies grow more advanced, concerns about over-reliance, ethical use, and misusehave become increasingly significant. Addressing issues such as authenticity, feedback quality,bias, and digital literacy is critical to harnessing the potential of generative AI in engineeringeducation and ensuring equitable learning opportunities. The
projects, by providing alternate viewpoints and that will increaseteam’s performance.5- As a new freshman Student, by asking many primitive questions from the instructor. As theresult instructor will be more prepared for the harder questions from other students.6- As a Simulator in which students can practice their project presentations.7- As a Flashcard for practicing and preparing for exam.8- For collecting Feedback regarding lectures or course.9- As a Student Advisor, by providing teaching plan, submitting course incomplete applicationform, registration, course progress, pre-requisite requirement, etc.Creating these nine options requires several best practices to ensure that they are effective,ethical, and user friendly. You can also use
computer ethics have evolved in the CS discipline over the past 50 years and found thatinterest in the topic only spiked around 2017-2018, despite the topics being explored to someextent in the mid-1970s, 1980s, 1990s, and 2000s [7].Researchers have addressed the issue of academic misconduct in the CS discipline in a variety ofways. One approach is the creation of CS specific policies to address the unique nature of work inthe discipline, which is often not covered by institution level policies for student conduct. Forexample, a study proposed a model for developing and implementing an academic ethics policy(which encompasses academic integrity) that specifically addresses the challenges imposed byinformation technology, through evidence-based
, 5. Network security, 6. Operating systems security, 7. Cloud security, 8. Software security, 9. Vulnerability analysis, 10. Penetration testing/ethical hacking, 11. Risk management, 12. Digital forensics, 13. Cybersecurity law and policy.BiometricsBiometrics information is playing a significant role in the field of cybersecurity. Three majorareas of biometric information processing in cybersecurity are listed below. 1. Access control: Biometric information is used to verify and authenticate any individual requesting access to confidential information and/or a secure facility. 2. Forensics: Biometric information can be analyzed to identify the person responsible for a malicious activity. 3. Biometric
answers without fostering deeper understanding, which may hinder long-term learning. Whilemany students feel confident in their ability to use AI tools ethically, some express concerns about over-reliance on AI for completing assignments, which could compromise academic integrity. Opinions onhow well AI use is managed in assessments are mixed. Some students believe that redesignedassessments—such as scaffolded projects or oral defenses—effectively minimize the risk of AI misuseby requiring critical thinking and authentic engagement. Others, however, suggest that furtherimprovements are needed to ensure assessments remain resistant to AI-driven shortcuts. These insightshighlight the dual role of AI as both a helpful resource and a potential
. LaFerriere, “Enabling Meaningful Labor: Narratives of Participation in a Grading Contract,” J. Writ. Assess., vol. 13, no. 2, p. 1, 2020, doi: 10.35360/njes.316.[12] A. M. Shubert, “Contracts for a Time of Crisis : What I Learned from Grading in a Pandemic,” vol. 1, no. 17, 2021.[13] T. S. Harding, M. J. Mayhew, C. J. Finelli, and D. D. Carpenter, “The Theory of Planned Behavior as a Model of Academic Dishonesty in Engineering and Humanities Undergraduates,” Ethics Behav., vol. 17, no. 3, pp. 255–279, Sep. 2007, doi: 10.1080/10508420701519239.[14] T. VanDeGrift, H. Dillon, and L. Camp, “Changing the Engineering Student Culture with Respect to Academic Integrity and Ethics,” Sci. Eng. Ethics, pp. 1–24, Nov. 2016, doi:10.1007
[Machine Organization and Assembly Language] (n = 13) ● remove, CS[Introduction to Systems Programming] (n = 2) ● remove, same [Data Structures and Introduction to Algorithms] (n = 1) ● remove, CS[Computers, Ethics, and
. Shih, B. D. Chambers, and M. James, “On the challenges of transferring teaching practices in engineering ethics and an asset-based approach to developing ethics instruction,” in 2024 ASEE Annual Conference & Exposition, 2024.[10] I. S. Osunbunmi, S. Cutler, V. Dansu, Y. Brijmohan, B. R. Bamidele, A. N. Udosen, L. C. Arinze, A. V. Oje, D. Moyaki, M. J. Hicks et al., “Board 45: Generative artificial intelligence (gai)-assisted learning: Pushing the boundaries of engineering education.” in 2024 ASEE Annual Conference & Exposition, 2024.[11] K. Lee, “Augmented reality in education and training,” TechTrends, vol. 56, pp. 13–21, 2012.[12] K. Petal, Y.-Z. Lin, and P. Satam, “Edutalk sentiment dataset,” https://gitlab.com
, focusingparticularly on manifestations of algorithmic thinking. Our work was guided by the followingresearch question:1. How are students’ algorithmic thinking skills manifested in their approaches to solving problems using programming? MethodsResearch SettingIn this research, we focus on one section of an introductory computer science course for first-year engineering students at a private, highly selective research university in the northeasternUnited States. Because the course is for engineering students, there is a heavy emphasis onmodeling, data analysis, and statistics. The course is also a testbed for the inclusion of ethics andsociotechnical thinking within engineering classrooms. The section in this study
a security measure, 6) the implementation of security defenses, which includessecurity policy, vulnerability assessment, intrusion detection, virus protection, auditing,accounting, and logging, 7) methods to harden an operating system (either Windows or Linux),8) firewalling, and 9) practical experiments that make use of operating system tools for securitypurposes.Information Security: This course emphasizes the integration of information technology aspectspertinent to network and application layer security, while providing students the opportunity toobtain Security+ certification and/or Certified Ethical Hacker (CEH) certification. This revisedcourse encompasses topics included in the Security+ and CEH examinations. Included arenetwork
following the six-phase processoutlined by Braun and Clarke [6]. This involved familiarization with the data, generating initialcodes, searching for themes, reviewing themes, defining and naming themes, and producing thefinal report. The analysis focused on identifying recurring patterns in students’ experiences,particularly regarding their use of external online courses and their perceptions of institutionalsupport. To ensure reliability and validity, multiple researchers independently coded the databefore reaching a consensus on the identified themes. This triangulation of data helped minimizebias and ensured the findings accurately represented participants' experiences [7].Ethical ConsiderationsEthical approval for this study was obtained prior
three attack vectors (4.4% of all theknowledge in the NICE Framework) TKSA Num- TKSA Description Phishing/Social Malware Web- ber Engineering Based Attacks K0003 Knowledge of laws, regulations, poli- * * * cies, and ethics as they relate to cyber- security and privacy. K0006 Knowledge of specific operational im- * * * pacts of cybersecurity lapses. K0066 Knowledge of Privacy
, minimum and maximum scores, and time spenton quizzes.This study does not include direct measures of learning outcomes, such as final grades orassessments beyond the embedded video quizzes. It focuses on student interaction patterns, and giventhe course-specific context and small sample size, the findings should be interpreted as exploratory.Our university's Institutional Review Board (IRB) approved the research protocol for this study,ensuring that all data collection, analysis, and reporting processes met ethical and legal standards,with a strong emphasis on protecting student privacy. Identifiable data were anonymized by replacingpersonal identifiers with unique numerical identifiers.Course Context and Design RationaleBackgroundThis paper
can executean attack on a device and then protect the device from that attack would be something a lot ofstudents who want to advance in security will find enjoyable.” We considered these valuablesuggestions from the students and are planning to address some of the suggestions in the futureteaching of the IoT Security class. Regarding the recommendation to include offensive securitytechniques, we intend to consult with the university’s general counsel to ensure compliance withinstitutional guidelines and ethical standards.Educational Content and Learning Opportunities The course’s structured learning materialsand assignments were deemed extremely helpful. The students praised the clarity and relevanceof the shared slides and the variety of
thatask for a specialty in one area or the other (i.e., UI designer, UX researcher), studentsdemonstrating the ability to do both will make them more marketable and competitive in the job,especially if they are able to do so in an accessibility-first approach. Students were pleased withlearning how to conduct ethical and responsible research while also acquiring skills to translateresearch findings to design solutions. Similar to our findings through the data we collected,Letaw et al. (2022) assert that embedded inclusive design throughout a multi-year studyimproves student retention of inclusive practices and normalizes accessibility within the designprocess. The authors argue that this integration is especially effective when educators
needed. When students post and reply to messages, and read the messages of their peers and give them feedback, this improves the quality of the learning environment dynamics and the richness of the content delivery.• Community Policies: These specify rules and standards of ethical behavior that must be followed. These should be shared with teachers and students at the start of the program. This helps to avoid confusion and inappropriate behavior. Web-based learning requires more internal self-regulation and external supervision. Community policies can provide a schema to help keep specific learning groups engaged in their online courses from beginning to end.In an educational context, the Activity model is a reminder that
them keep up with the technological changes. Overallmore African American teachers participated over four years. The teachers who participated inthe surveys reported that the program had increased their confidence in research andincorporated STEM in their classrooms. In addition, the program has provided flexibility to theteachers as they start their research two weeks after the students (REUs), which required theteachers to work more at developing their teams.ConfidenceMost of the effects were seen in the teachers' confidence in producing research articles forpublication, understanding research literature, and understanding the ethical issues surroundingresearch. Teachers' confidence levels barely changed between 2019 and 2020 but increased
student-centered approach to teaching and learning. [33].ITL aligns to guidelines provided by the Accreditation Board for Engineering and Technology (ABET)[34] to establish that students should be learning engineering in ways that look like the work engineers do([4], [35]). To meet ABET learning objectives and teaching through Inquiry, students work on teams toidentify, design, and solve complex problems and to create ways to test their ideas that meet specificneeds and constraints of health, culture, environment and economics, while communicating effectively todifferent stakeholders and exercising ethical and professional judgments. ABET learning objectives arerepresentative of Inquiry and not Transmission teaching ([4], [14]). Even
of McCourt School of Public Policy at Georgetown University, Washington, DC. She is involved in projects in the intersection of education, data mining, machine learning, ethics, and fairness. Her research interests include data mining, recommender systems, predictive models within educational contexts, and the fairness concerns that arise from their use. Her goal is to help students succeed using data and machine learning models.Dr. Peter J Clarke, Florida International University Peter J. Clarke received his B.Sc. degree in Computer Science and Mathematics from the University of the West Indies (Cave Hill) in 1987, M.S. degree from SUNY Binghamton University in 1996 and Ph.D. in Computer Science from Clemson
in size in the last decade. Many faculty members at UBC havetransitioned their courses to use Automated Assessment Tools so it is becoming less common forstudents to have their course work manually assessed by faculty or teaching assistants.5 Research DesignThis study answers the research question: How do course assessment practices affect students’perspectives of learning technical writing?5.1 Research EthicsThe research study was approved by the UBC Behavioural Research Ethics Board before theresearch commenced.I am a computer science faculty member at UBC. In this study, I was conscious of the power thatI have in relation to the participants who are UBC computer science students. In any study, thereis a risk that participants