The latest in the series of computer science curricular guidelines produced by the ACM in collaboration with IEEE-Computer Society and the AAAI is CS2023 (csed.acm.org). Given that generative AI had only been available for a short while at the time of publication of CS2023, the task force that drafted CS2023 could at best speculate on its impact on the curricular guidelines. Two years later, this special session explores how the design of some of the knowledge areas of CS2023 most affected by generative AI could/should change in terms of content, pedagogy, assessment and policies. Given the ubiquity of generative AI in computer science education and its rapid evolution, re-examination of its impacts on computer science curriculum on an ongoing basis is both warranted and needed.

CS educators teach programming. Generative AI tools can program. So we're clearly wasting our time teaching CS to students in the AI era, right? There's no need for junior programmers, enrollments are dropping, and we should all plan to be plumbers or apple farmers. Or perhaps there is a chance to use the AI crisis as an opportunity to do make some much-needed changes to our programs. For in computing education, as in so many other topics, AI is not an answer, it is only an accelerant. For topics with sound foundations, it's a boon, reducing friction and toil. For topics with long-standing shortcomings, it's adding fuel to the fire. In this talk I'll give some industry perspective, identify what seems outdated in CS and SE pedagogy, and try to sketch out some more fruitful directions for revision to our assignments, classes, and overall curricula.

The current ACM/IEEE/AAAI Computer Science Curricula is approaching ten years of age. The CS2023 Steering Committee began efforts to update this important document in spring 2021 (csed.acm.org). The aim of this session is to seek feedback from the community on an advanced draft of the Society, Ethics and Professionalism (SEP) knowledge area. Much has transpired in the last decade within these domains including the role of social media in our daily lives, professional roles, and even our elections. Today, fake news, data leaks, hacks, and scandals involving personal information are rife. The impact of computing on society has never been more high-stakes, our ethics have never been tested in these ways, and the need for up-to-date professionalism has never been greater. In this light we seek to gather as many diverse views as possible to help us shape a Society, Ethics & Professionalism knowledge area that can serve the community and our students for the next decade.

To deal with a shortage of computer scientists, industry is challenging the traditional view that students need a deep theoretical knowledge of computer systems. Some believe that time spent on assignments that do not reflect technical/development practices would be better used building practical skills. This is at odds with the pedagogical strategy of building mental models through students' implementation of tools and libraries that would otherwise be used as off-the-shelf components. Although project-based learning is a well-studied area, there is little to no literature that studies the link between these programming activities and the target cognitive processes. Researchers must understand the advantages and disadvantages of these types of projects in building the correct mental models and any transfer learning it enables. In this Birds of a Feather, we seek to create a working group of academics and professionals that are interested in an evidence-based understanding rather than reliance upon anecdotes. This group will look at the strategic implementation projects and how well they achieve their linked learning outcomes. This working group will discuss how to empirically define the value of these activities and gain a better understanding of how they affect a student's cognitive processes.

Industry practitioners and CS educators seem to operate in different worlds these days. My fellow industry leaders often have surprising ideas about what all can be covered in a 4 year degree program. We are seemingly unaware of the huge challenge in making novices into algorithmic thinkers and programmers, to say nothing of imparting mastery in the ever-expanding array of computing sub-disciplines. At the same time, the day-to-day operations in industry have a very different set of core skills and tools than what is traditionally presented in a CS curriculum. Communication skills, experimentation, reasoning, code comprehension, caching, threading, and concurrency models are a huge fraction of the toolkit for a software practitioner. Hashing is essential. Constants matter. Implementing data structures really doesn't. In practical terms, almost nobody should be using a linked list anymore. This talk will surface what I see as the disconnects on both sides, and suggestions for what we can do about it. (I will also probably be wrong, since I can only speak from my perspective and experience - but that's where important dialogues start.)

As Computer Science Curricula 2013 (CS2013) approaches its 10th anniversary plans are underway to update this important document. The CS202X task force started these efforts in Spring 2021. The aim of this session is to seek feedback from the community on a draft of the Software Engineering (SE) knowledge area. As Dave Parnas once said, "Software Engineering is the multi-person construction of multi-version programs," it is distinct from programming by virtue of being acutely impacted by issues of time (compatibility, versioning, version skew, schema evolution) and coordination or teamwork (communication, coordination, planning, etc). These are challenging issues to convey effectively given classroom practicalities, but it is essential for our graduates that go on to industry roles to have some awareness of these matters. We hope that by hosting a BoF at SIGCSE we can gather input from current instructors, especially to hear what is and is not working well from the CS2013 guidelines. We also hope such a BoF session can serve as a networking session for educators and education-aligned industry representatives so that we have a better feedback loop as Software Engineering practices evolve.

No abstract available.

No abstract available.

New accreditation requirements focus on education as a "continuous improvement process." The most important part of such a process is that information gets fed back into the system to improve the quality of the output. This requirement is often interpreted to imply a course-level feedback loop that iterates on offerings of courses or entire academic years. This paper provides a smaller and more immediate feedback loop. This technique gives instructors feedback onthe quality of each question on a test or quiz, as well as a numeric score for the difficult of the question. A simple tool implementing this procedure can be used to help train instructors on which questions are difficult, as well as what types of questions are correlated with ability, and how to design a meaningful instrument of assessment. Performing this analysis at the end of a course offering could help demonstrate continuous improvement to accreditation committees. Performing this analysis immediately after the administration of a test or quiz can point out topics that the class as a whole have failed to understand, thus giving instructors more insight into student knowledge.

Well-run organizations collect, archive and analyze data relating to the effectiveness of their important processes. Educational institutions discard a wealth of student scores that could be analyzed. Each score contains important information about the student as well as the item (i.e., problem or question). This paper describes our project to develop an outcomes-based assessment system that mines per-item scores to track each student's skills and knowledge. Statistical inference techniques from both educational statistics and data mining will quantitatively determine each student's acquired competency, with minimal input from faculty. The culmination of item-level assessment gives individual faculty feedback on their courses, and gives curriculum committees feedback on which objectives are sufficiently met by their respective curricula.