Computing educators strive to align curricula with workforce needs yet rarely articulate how professional competencies, behaviors, and responsibilities develop across different educational pathways. As the community moves beyond the 2027 curriculum revision cycle, bridge pathways spanning vocational, undergraduate, graduate, and non-computing entry routes are rapidly expanding. At the same time, widespread AI use requires curricula to more explicitly address professional judgment, accountability, and ethical reasoning, in addition to technical skills. This working group brings participants together to map, compare, and synthesize real curriculum and pathway artifacts using shared academic and workforce frameworks. Participants will build a competency-oriented pathway framework, identify recurring bridge pathway designs and gaps, and produce practical, post-2027 guidance that supports coherent, scalable, and internationally transferable computing curricula.

Allocating students to projects is a commonplace task in computing education. These decisions underpin student-supervisor allocation, the formation of tutee and capstone groups, and pair programming. These allocations play a critical role for individual learner outcomes and the success of collaborative interventions. For example, imbalance in either gender, ethnicity, or nationality can negatively impact learner outcomes. Despite the critical importance of these allocation choices, we see little consensus on how these are implemented. The allocation task can be challenging and time-consuming for instructors of even moderately-sized classes, and the fairness implications can be difficult to assess. Inadvertently, an instructor may allocate in a way that amplifies existing biases or disproportionately harms those from disadvantaged or protected groups. From students' perspectives, a lack of transparency on the allocation process may also lead to issues of trust. The Working Group will undertake a study of allocation practices by bringing together educational and ML literature to develop and evaluate the fairness of allocation methods, and develop educator guidelines to promote pedagogically grounded allocation practices.

The field of data systems has seen quick advances due to the popularization of data science, machine learning, and real-time analytics. In industry contexts, system features such as recommendation systems, chatbots and reverse image search require efficient infrastructure and data management solutions. Due to recent advances, it remains unclear (i) which topics are recommended to be included in data systems studies in higher education, (ii) which topics are a part of data systems courses and how they are taught, and (iii) which data-related skills are valued for roles such as software developers, data engineers, and data scientists. This working group aims to answer these points to explain the state of data systems education today and to uncover knowledge gaps and possible discrepancies between recommendations, course implementations, and industry needs. We expect the results to be applicable in tailoring various data systems courses to better cater to the needs of industry, and for teachers to share best practices.

At ITiCSE 2021, Working Group 3 examined the evidence for teaching practices that broaden participation for women in computing, based on the National Center for Women & Information Technology (NCWIT) Engagement Practices framework. One of the report's recommendations was "Make connections from computing to your students' lives and interests (Make it Matter) but don't assume you know what those interests are; find out! " The goal of this 2023 working group is to find out what interests women students by bringing together data from our institutions on undergraduate module enrollment, seeing how they differ for women and men, and what drives those choices. We will code published module content based on ACM curriculum guidelines and combine these data to build a hierarchical statistical model of factors affecting student choice. This model should be able to tell us how interesting or valuable different topics are to women, and to what extent topic affects choice of module - as opposed to other factors such as the instructor, the timetable, or the mode of assessment. Equipped with this knowledge we can advise departments how to focus curriculum development on areas that are of value to women, and hence work towards making the discipline more inclusive.

Teaching students problem-solving skills is the biggest challenge for many computing programs. Students often do not consider what questions they need to ask while designing a solution. This paper introduces how technical writing techniques and problem-solving strategies can be combined to help students find those questions and develop programming code through writing instructions and documenting their design process. Preliminary results indicate that this approach helps developing problem-solving skills and improves the student success rate in the first programming course.

At the direction of the ACM Education Board, the IT2017 Task Group was formed with the charge of updating the joint ACM and IEEE Computer Society Curriculum Guidelines for Undergraduate Degree Programs in Information Technology, known as IT2008. The revised document, called IT2017, should be appropriately forward looking given the significant advances in information technology that have occurred since 2008. Participants attending the BOF will contribute their insights and assist with the revision process to update IT2008. Discussions will center on delineating knowledge areas and learning outcomes specific to IT; exploring the current and future roles of IT in computing disciplines; recommending changes to improve the usefulness of the report; and planning further communications to fully engage the academic and professional community in the revision process. The objective is to ensure that the updated document is a forward-looking curriculum framework of the disciplinary content and practices in the field of information technology and remains relevant into 2020 and beyond for incoming students, computing departments with IT programs, accreditation bodies, and employers in the U.S. and anywhere else in the world.