Software development is undergoing a revolutionary transformation, fueled by remarkable advancements in Large Language Models (LLMs). This wave of innovation is reshaping the entire landscape and holds the promise of streamlining the development process, leading to increased productivity and efficiency. By providing text prompts, developers can now receive entirely generated code outputs, representing a fundamental shift in how software is built. This paradigm change can accelerate development cycles and unlock new levels of creativity and ingenuity, resulting in the realization of novel applications and business outcomes. However, this paradigm shift also brings new challenges and necessitates acquiring additional skills for software developers to fully harness the capabilities of LLM-powered tools. These skills include prompt engineering for software development, structural complexity management, debugging of AI errors, and compliance with ethical guidelines and principles. The special session will introduce our NSF-sponsored 3-year project, which aims to integrate LLMs into the standard CS curriculum. To the best of our knowledge, this project is among the first department-level initiatives to renovate CS curriculum, rather than individual courses, with the new developments of LLMs. Our project focuses on (a) enhancing students' problem-solving and programming skills by leveraging LLMs as a learning tool in core programming courses, (b) improving students' software development skills by integrating LLM-powered tools into the software engineering course sequence, and (c) educating students on ethical and responsible AI practices. The special session will discuss the objectives and methods of our project, as well as the current results and lessons learned.This NSF-supported project aims to integrate LLMs into the standard CS curriculum. The revolutionized computer science education will cultivate a new generation of AI-powered responsible developers. The objectives are to enhance student programming, software development, and problem-solving skills; educate students on ethical and responsible AI practices; and develop faculty development materials and workshops. Our presentation will discuss the objectives and methods of our project, currently in year 1 of a 3-year timeline.

This tutorial will provide a foundation for faculty members either teaching a course in artificial intelligence for the first time or renewing a course that has been dormant. The growing ubiquity of AI and machine learning dictates that departments offer a course in this area, regardless of whether any such expertise exists locally. Furthermore, there are many possible approaches, from a general overview to a tight focus on a particular application area, either as a single elective or a specialization area within an undergraduate degree. An increasingly wide range of resources is available, but intelligent selection from the plethora of information can be a challenge, particularly in smaller programs where no local expertise is available. The tutorial is in two parts. Part 1 focuses on background knowledge, discussing major divisions within the broad field of AI, research trends and application areas, and commonly used tools. Part 2 addresses classroom implementation, assessment, textbook options, and online resources including code libraries, free-to-use data sets, development environments, and visualization tools. Participants will have the opportunity to brainstorm and discuss options for course focus. Sample syllabi using differing course approaches, sample exams and assignments, and a non-exhaustive list of useful resources will be provided.

Many third party services exist to help faculty both in and out of the classroom. This tutorial would cover 3 such tools; gradescope.com[1], piazza.com[2], and socrative.com[3]. Learning management systems like blackboard[4] and Canvas[5] provide useful features, but don't always deliver everything teaching faculty needs. The three tools chosen will address grading, out of class communication Q&A, and in-class quizzes and activities. Using tools can help faculty alleviate some of their workload while adding value to the experience the students receive. We will walk through real world examples, so that faculty can see how they would be used in practice.

Peer-instruction, hybrid, or 'flipped classroom' models have been gaining interest as a way of increasing student learning and engagement as well as improving retention. In this panel, educators with experiences reorienting classroom pedagogy in introductory CS courses and upper-level undergraduate MIS courses will discuss their experiences, and the rewards and challenges of using these models. Technical tools and needed infrastructure will also be discussed.

This paper reports on a set of classroom interventions at a public urban university to reduce the failure and withdrawal rate in CS1. Though unsuccessful in the original goal of reducing the withdrawal or failure rate, the interventions provided valuable information suggesting contributors to poor course outcomes and have spurred course revisions going forward.

This poster reports on a set of classroom interventions at a public urban university to reduce the failure and withdrawal rate in CS1. Though unsuccessful in their original goal of reducing the number of students withdrawing from the course or receiving poor grades, the interventions provided valuable information suggesting contributors to poor course outcomes. These included concurrent full-time enrollment and full-time employment and lack of foundational skills. We also found that at-risk students could be identified with a one-page quiz the third week of the semester. We have made several course revisions going forward, including restructuring programming assignments and adding online practice problems, and revised our academic advising procedures.

ACM hosts an annual student programming contest [1]. Contests are also offered regularly by several conferences [2], student organizations, and professional societies [3]. Although specific organizational features vary, there are common requirements for organizers and host sites to be aware of. This panel discussion will address computing and infrastructure requirements, developing contest problems, scoring and judging issues, organizational and technical challenges, and how much pizza and soda it takes to keep several teams of student programmers functioning. Confirmed panel members have each been site director for an ACM or conference-based programming contest.

ACM and IEEE support the inclusion of social and professional issues in the computer science curriculum in the 2001 Computing Curricula Computer Science volume and the recent C2008 Computing Curricula Computer Science volume. The IEEE-CS/ACM Joint Task Force on Computing Curricula note: Undergraduates also need to understand the basic cultural, social, legal, and ethical issues inherent in the discipline of computing. They should understand where the discipline has been, where it is, and where it is heading. They should also understand their individual roles in this process, as well as appreciate the philosophical questions, technical problems, and aesthetic values that play an important part in the development of the discipline. ([4], p. 152)

This article discusses the design and implementation of a writing-intensive course on ethics and professionalism for third-year undergraduate computer science and information technology students at the University of Missouri-Kansas City. The course includes a large "current events" component; this has been an effective way to improve student engagement and motivation.