Incorporating ethics into computing education has become a priority for the SIGCSE community. Many computing departments and educators have contributed to this endeavor by creating standalone computing ethics courses or integrating ethics modules and discussions into preexisting curricula. In this study, we hope to support this effort by reporting on computing educators' attitudes toward including ethics in their computing classroom, with a special focus on the structures that hinder or help this endeavor. We surveyed 138 higher education computing instructors to understand their attitudes toward including ethics in their classes, what barriers might be preventing them from doing so, and which structures best support them. We found that even though instructors were generally positive about ethics as a component of computing education, there are specific barriers preventing ethics from being included in some computing courses. In this work, we explore how to alleviate these barriers and outline support structures that could encourage further integration of ethics and computing in higher education.

/pdf/incorporating-ethics-in-computing-courses-barriers-support-3qacuije.pdf

Incorporating Ethics in Computing Courses: Barriers, Support, and Perspectives from Educators

This study examines the roles of teachers in creating a positive and supportive learning environment, with a focus on the application of ethics in reading, writing, and speaking. The research method is literature studies, and the data sources are taken from a variety of articles in relevant scientific journals and books. The research phases begin with the formulation of goals, searching and gathering relevant references, identifying and classifying various references, and, finally, analyzing the results of the research. The study's findings demonstrate the critical role that teachers play in establishing a productive learning environment, particularly when it comes to incorporating the ethics of speaking, writing, and reading into routine instruction. Teachers can support students in developing good communication skills and strong moral foundations by raising awareness of ethical ideals and helping students apply these ideas in their daily lives. According to these studies, creating a learning atmosphere that values justice, honesty, and tolerance for other viewpoints can help students learn more effectively. 

pdf/fostering-effective-learning-environments-educator-43d9d8uqn6.pdf

Fostering Effective Learning Environments: Educator Approaches to Applying Reading, Writing, and Speaking Ethics

Most computing students enter the industry once they graduate. As future software engineers, they will be in powerful positions, making decisions that impact their personal lives, others, and society. Thus, preparing graduates for their careers is crucial by addressing ethical considerations, decision problems, and other concepts related to morals, values, and legal aspects (e.g., data protection, privacy, security, etc.) as part of computing curricula. In this paper, we propose the integration of data ethics into computing programs and provide a framework for an ethics module, including relevant competency-based learning objectives. The proposed module is based on a curricular analysis of all 71 German data science degree programs focusing on ethics courses. The course contents and competency goals were analyzed and classified based on their cognitive complexity. As the results proved the lack of competency-based learning outcomes, we designed observable competency goals, meaning knowledge, skills, and dispositions taken in the context of a task. In addition, we provide suggestions for contents, pedagogical instructions, and assessments in such a course. The proposed module serves as a first draft and resource to support other educators aiming to design such a course and who are willing to integrate it into computing curricula.

With Great Power Comes Great Responsibility - Integrating Data Ethics into Computing Education

In today's technological era, educators need to safeguard the security and privacy of their data by employing Computational Intelligence (CI). CI is an aspect of Artificial Intelligence (AI) that concentrates on developing algorithms and systems capable of solving tough societal problems. Several methodologies informed by biological systems and natural events used in CI include Artificial Neural Networks (ANN), fuzzy systems, evolutionary computation, and swarm intelligence. Therefore, CI systems learn and adapt over time, improving performance through experience and interaction with their environment. This versatility and durability render CI valuable for various applications, including data analysis, pattern identification, control systems, and decision-making processes. Confidentiality is critical for securing sensitive information. CI uses sophisticated computational techniques to improve learning experiences, instructional practices, and educational outcomes. 

Security and Privacy of Educational Computational Intelligence

Education plays an indispensable role in fostering societal well-being and is widely regarded as one of the most influential factors in shaping the future of generations to come. As artificial intelligence (AI) becomes more deeply integrated into our daily lives and the workforce, educational institutions at all levels are directing their focus on resources that cater to AI education. Yet, informal education, including online learning on social media platforms like YouTube, plays an increasingly significant role for both students and the general public. Offering greater accessibility compared to formal education, millions of individuals use YouTube for educational resources on AI today. Due to the substantial societal impact of AI, it is crucial for introductory AI courses to meaningfully address the ethical implications associated with AI. Our work investigates the current landscape of introductory AI courses on YouTube, and the potential for introducing ethics in this context. We qualitatively analyze the 20 most watched introductory AI courses on YouTube, coding a total of 92.2 hours of educational content viewed by close to 50 million people. We find that these introductory AI courses do not meaningfully engage with ethical or societal challenges of AI (RQ1). When defining and framing AI, introductory AI courses foreground excitement around AI’s transformative role in society, over-exaggerate AI’s current and future abilities, and anthropomorphize AI (RQ2). In teaching AI, we see a widespread reliance on corporate AI tools and frameworks as well as a prioritization on a hands-on approach to learning rather than on conceptual foundations (RQ3). In promoting key AI practices, introductory AI courses abstract away entirely the socio-technical nature of AI classification and prediction, for example by favoring data quantity over data quality (RQ4). Given the power of openly available introductory courses to shape enduring beliefs around AI and its field at the onset of a learning journey, we extend our analysis with recommendations that aim to integrate ethical reflections into introductory AI courses. We recommend that introductory AI courses should (1) highlight ethical challenges of AI to present a more balanced perspective, (2) raise ethical issues explicitly relevant to the technical concepts discussed and (3) nurture a sense of accountability in future AI developers.

Visions of a Discipline: Analyzing Introductory AI Courses on YouTube

The computing education research community now has at least 40 years of published research on teaching ethics in higher education. To examine the state of our field, we present a systematic literature review of papers in the Association for Computing Machinery (ACM) computing education venues that describe teaching ethics in higher-education computing courses. Our review spans all papers published to SIGCSE, ICER, ITiCSE, CompEd, Koli Calling, and TOCE venues through 2022, with 100 papers fulfilling our inclusion criteria. Overall, we found a wide variety in content, teaching strategies, challenges, and recommendations. The majority of the papers did not articulate a conception of “ethics,” and those that did used many different conceptions, from broadly-applicable ethical theories, to social impact, to specific computing application areas (e.g., data privacy, hacking). Instructors used many different pedagogical strategies (e.g., discussions, lectures, assignments) and formats (e.g., standalone courses, incorporated within a technical course). Many papers identified measuring student knowledge as a particular challenge, and 59% of papers included mention of assessments or grading. Of the 69% of papers that evaluated their ethics instruction, most used student self-report surveys, course evaluations, and instructor reflections. While many papers included calls for more ethics content in computing, specific recommendations were rarely broadly applicable, preventing a synthesis of guidelines. To continue building on the last 40 years of research and move toward a set of best practices for teaching ethics in computing, our community should delineate our varied conceptions of ethics, examine which teaching strategies are best suited for each, and explore how to measure student learning.

Teaching Ethics in Computing: A Systematic Literature Review of ACM Computer Science Education Publications

Public coverage of ethical scandals in tech companies often portrays tech workers as uncaring or short-sighted in their ethical consideration. But how do tech students regard the connection between ethics and their future jobs? To explore ethics in the transition between academia and industry, we interviewed graduating computing students at the University of Colorado Boulder about their perspectives on ethical concerns in tech, decisions during job searches, and their ethics education. These interviews revealed that while students may value and understand ethics in tech, their belief that companies do not value ethics makes ethical consideration in the workplace daunting. In response, we suggest improving support for tech workers through academia-industry collaboration and additions to computing ethics curricula to help students stand up for responsible tech.

Entering the Techlash: Student Perspectives on Ethics in Tech Job Searches

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ella Sarder

Author Tools

Chat about Author

Papers

Teaching Ethics in Computing: A Systematic Literature Review of ACM Computer Science Education Publications

Entering the Techlash: Student Perspectives on Ethics in Tech Job Searches