Computing ethics and socially responsible computing (SRC) education has taken a prominent role in computing and technology programs in post-secondary education. Much of the prior work in this space describes the various approaches and strategies that scholars and educators have developed to teach ethics and SRC in technology and computing courses. There is limited work that explore and discuss how students within these courses describe the relationships and connections across various elements within technology ecosystems, such as computing technologies, users of these systems, and their impacts to various communities. Understanding students' perspectives when discussing the impacts of technology systems can give us a better sense of their own considerations when designing computing systems, as well as insights on how we can create learning designs that better support them in thinking about responsible computing. To explore this, I qualitatively analyzed students' responses to prompts that asked them to describe the pros and cons of different technology systems. I developed case studies that foreground themes within students' discussions and demonstrate how students articulated critical relationships across system and technology designs, their impacts, and various considerations within human-technology ecosystems. I also describe the implications of the work to learning designs that support students in thinking critically about the designs and impacts of technology, particularly in the use of narratives and speculative exploration in teaching socially responsible computing.
This study examines how B.S. ABET-accredited IT programs can align with industry needs while meeting the requirements of ABET standards. By analyzing current curricula against emerging technology trends (such as AI, cloud computing, and cybersecurity), we identify skill gaps and propose a flexible framework for enhancement. The study evaluates accredited programs and IT trends, revealing disparities in specialized domains. We recommend modular updates through electives and concentrations, offering institutions adaptable strategies to enhance graduate employability while maintaining accreditation.
This paper investigates the successes and challenges faced by under-graduate students in selecting topics for senior capstone projects in Computer Science. Conducted at California State University, Northridge over two semesters, the study compares instructor-driven (strict) and student-driven (dynamic) topic selection models. Survey responses and project evaluations from 64 students across 12 teams were analyzed to assess outcomes related to satisfaction, collaboration, and soft skills development. The findings indicate that student-driven topic selection promotes higher levels of motivation, ownership, and communication skills, though it introduces challenges around scoping, feasibility, and consistent evaluation. Instructor-driven models, while fostering academic rigor and deeper exposure to research-based work, may reduce student engagement and creative investment due to limited topic autonomy. These trade-offs highlight the value of a hybrid capstone structure, which blends student choice with structured faculty guidance to balance autonomy, mentorship, and educational outcomes.
Large language models (LLMs) are rapidly transforming programming education, yet little is known about how students' AI literacy and skills shape human-AI collaboration. We conducted an exploratory study with 23 computer science and robotics students who completed discipline-specific programming tasks in which the LLM produced the code and students guided and debugged it. AI literacy, measured with the SNAIL instrument, was generally high, with Practical Application rated highest and Critical Appraisal lowest, suggesting a risk of overconfidence. No robust links emerged between AI literacy, programming skill, task duration, or perceived chatbot output quality. Qualitative feedback revealed both enthusiasm for experimentation and frustration with AI errors. Findings indicate that brief, well-structured tasks can support productive AI-student collaboration, but stronger emphasis on critical evaluation, domain-specific skills, and appropriate scaffolding is needed.
Service-learning, an experiential learning pedagogy, has been shown to be effective in attracting and retaining students in computing, thereby contributing to the mission of broadening participation in computing (BPC). This paper presents a sustainable, project-based outreach model designed to engage students in educating communities about computing concepts. The model is grounded in a successful, multi-component service-learning program established at our undergraduate college to address the need to increase the number of students who persist in information technology (IT) and related majors.. Our unique program has proven to be sustainable through an established elective course and strong departmental support. This paper reviews the foundational components that have supported the outreach model over its 13 years of existence. More significantly, we highlight recent enhancements, including a new framework that unifies the format of our outreach activities and an online platform to document existing and new initiatives, facilitating adoption by others. The newly introduced outreach components include a series of career development events featuring IT alumni, as well as training workshops for K-12 teachers. We hope to inspire other institutions to adopt our model-or adapt its components-to enhance their own outreach initiatives and BPC efforts.
