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2026 Working Group Proposals

Below are descriptions of the 12 Working Groups (WG) open for membership applications. Please read the descriptions below, and if you find a WG you are interested in, use the link (below) to apply to participate. During each round of applications, you will be submitting one application only for your preferred working group. Multiple applications for the same application round will result in disqualification.

Schedule

1) Membership application round 1 form opens Thursday, 22 January
2) Membership application round 1 form closes Monday, 16 February EoA
3) Notifications for selections of first choice Sunday, 22 February
Note: You will also be notified in case you were not selected for your first choice of WG
4) Membership application round 2 opens Monday, 23 February
Note: Only WGs (if any) that are not full will be available for selection during the second round of applications
5) Membership application round 2 form closes Tuesday, 10 March EoA
6) Notifications for selections of second choice Tuesday, 17 March

Groups that attract a sufficient number of members to create a viable group will proceed and work on their project from March to September to produce their extended abstract and final research report. All members are expected to contribute to the shape and direction of the working group and contribute to the abstract published at the time of the conference (Vols 1-2), as per ACM authorship policy. Revisions in response to the external review of the final report and the camera ready manuscript are due in November. Pending satisfactory reviews of the report, it will be published in the Conference Proceedings (Vol 3: ITiCSE WGR 2026).

Participation

As a reminder, all members are expected to (a) participate in WG activities from February/March to November/December, as needed by your WG, (b) register for the conference (including the WG fee), and (c) be present at the 2026 ITiCSE Conference in Madrid, Spain. 

Note that there is a separate WG Conference fee that provides the work space (not lodging) for WGs in Madrid as well as lunch and morning and afternoon breaks during the WG working period.

Application

Application Round 1 Form link
Application Deadline Round 1: 16 February AoE
Application Round 2 Form link
Application Deadline Round 2: 10 March AoE

All membership Decisions expected by: 17 March

Please email (potential) WG Leaders to ask questions about a specific WG (as listed below).
Please email WG Co-Chairs with any/all questions about the process: iticse2026wg@easychair.org 

Ellie Lovellette and Sara Hooshangi
ITiCSE 2026 WG chairs 

Working Group Proposal List

WG1 – Leveraging GenAI Tools for Accessibility

Leaders:

Description: 

Are you interested in how generative AI tools can support students with disabilities in computing education? Do you want to explore whether tools like ChatGPT, GitHub Copilot, and other LLMs are truly accessible and inclusive? Join our working group to investigate these critical questions.

As GenAI tools become increasingly prevalent in computing education, there is urgent need to understand how well they serve diverse learners, particularly students with disabilities and neurodivergent students.  Despite extensive research on GenAI integration, accessibility and inclusivity considerations remain largely unexplored.

Our working group will conduct a systematic literature review following PRISMA 2020 guidelines to examine how current GenAI tools align with accessibility standards and Universal Design principles.

What we’ll do: 

We will systematically review the literature to catalog GenAI tools used in computing education, analyze their features and accessibility accommodations, assess alignment with UDL principles, identify barriers students with disabilities face, and expose critical gaps in research and tool design. Our deliverables will include a comprehensive tool inventory, accessibility assessment, gap analysis with recommendations.

Who should join: 

We seek members with expertise or strong interest in one or more of the following areas: computing education research, accessibility and inclusive design, educational and assistive technologies, Universal Design for Learning, or GenAI/LLMs in education. We particularly welcome applications from early career academics and doctoral students, and aim to represent various institutional contexts and geographic locations.

Expectations of members: 

Before the conference the working group will meet twice a month from March to July to discuss logistics, research, and make progress on writing. The meetings will typically last 1-2 hours. During the meetings, the working group will discuss the paper’s structure, content, sources and divide the work according to expertise. In addition, the working group will discuss the timeline and set deadlines for completion of individual pieces of the work. We aim to have the introduction and systematic literature review methodology section completed by the time we meet in Madrid.

During the conference, all group members are expected to attend face-to-face and are expected to remain on site from 8:30am to 5:00pm each day of the intensive three-day period at the conference. Group members are expected to come prepared and ready to work the entire time to generate a draft of the paper by the deadline.

After the intensive three-day working time at the ITiCSE conference, we will resume twice-monthly meetings until the paper is submitted. We anticipate some work being done in semi-autonomous subgroups based on objectives, interest, and expertise. 

WG2 – Exploring the Impact of Gen-AI on Team-Based Computing Capstone Projects

Leaders:

Motivation:

Team-based capstone projects are a cornerstone of computing education, preparing students for professional software development through collaboration, communication, and authentic, open-ended project work. The rapid emergence of GenAI tools is reshaping how students plan, implement, test, and document capstone projects, raising new challenges and opportunities for teaching, assessment, and graduate preparation. Although GenAI is increasingly embedded in industry practice, there remains limited shared understanding of how it is used and governed in capstone courses, how students and instructors perceive its impact, and how well capstone experiences align with evolving workplace expectations. This Working Group (WG) seeks to address these gaps by examining GenAI use in computing capstones from different stakeholder perspectives and developing evidence-based guidance for educators.

Goals:

This Working Group has the following goals:

  • Review and synthesize research on GenAI in computing education, with a focus on capstone contexts.
  • Investigate how GenAI is used, perceived, and governed in capstone courses from the perspectives of students, instructors, and employers.
  • Examine how instructors are adapting capstone design, supervision, and assessment practices in response to GenAI.
  • Identify alignments and gaps between capstone practices and industry expectations for GenAI-related competencies.
  • Develop evidence-based recommendations to support rigorous and industry-aligned capstone experiences in the GenAI era.

Methodology:

The WG will adopt a mixed-methods approach, including:

  • A structured literature review and analysis examining GenAI use, benefits, risks, policy considerations, and emerging workforce expectations, drawing on academic literature, job advertisements, hiring guidelines, and GenAI-related competency statements.
  • Quantitative data collection through surveys distributed to capstone students, instructors, and employers.
  • Qualitative data collection via semi-structured interviews with participants from each stakeholder group.
  • Analysis and synthesis of findings to identify common themes, challenges, and promising practices, informing actionable recommendations.

Expectations of members:

Working Group members are expected to actively contribute to the research and collaborative process, including:

  • Supporting or obtaining local ethical approval (IRB) for human-subjects research, where required.
  • Contributing to the literature review.
  • Assisting with survey and interview design, distribution, and data collection.
  • Participating in data analysis and synthesis activities.
  • Attending regular virtual meetings (approximately twice monthly prior to the conference).
  • Collaborating on drafting the final Working Group report and related dissemination outputs.
  • Attending ITiCSE 2026 in person in Madrid, Spain, to participate in intensive Working Group sessions and present outcomes.

Member selection criteria:

We welcome participation from individuals with diverse backgrounds, perspectives, and career stages, including early-career researchers, doctoral students, junior and senior academics. We seek members who are:

  • Involved in teaching, coordinating, or supporting computing capstone courses.
  • Interested in computing education research, pedagogy, curriculum design, or assessment.
  • Experienced in or curious about the use of GenAI in software development or education.
  • Able to facilitate access to relevant participants (e.g., students, instructors) for surveys or interviews.
  • Willing to actively engage in collaborative research and attend ITiCSE 2026 in person.

Meeting frequency and timeline:

  • Online meetings: Approximately twice monthly for planning, data collection, data analysis, and writing. 
  • Research milestones: Prior to the conference, the Working Group aims to complete the following:
    • Data collection: Ethics approvals; surveys and interviews with students, instructors, and employers.
    • Cross-checked thematic analysis of interview data.
    • Preliminary analysis of survey data to identify key trends.
  • Conference participation: Members must attend ITiCSE 2026 in Madrid, Spain, in person and be present for the full conference, including designated Working Group sessions.

WG3 – Agency for Whom and To What Ends: An Exploratory Analysis of Agentic AI in Computing Education

Leaders:

Motivation:

Agentic AI, where AI systems have their own forms of agency, will present some of the most critical challenges that computing education will face in the very near future including regulatory gaps and amplified cascading effects. This working group proposes to conduct a landscape analysis on ethical questions arising from Agentic AI tool use in computing education contexts. 

Goals and expected deliverables:

This working group aims to contribute a landscape study, exploring: 1) What are emerging challenges and opportunities related to Agentic AI?, 2) In what ways are institutions leveraging Agentic AI and what are the potential ethical and societal impacts?, and 3) What are the implications (e.g. challenges, opportunities, limitations) of integrating Agentic AI into computing education? 

The two expected deliverables will include: 1) development of a protocol and a literature scoping review of Agentic AI in education, including its challenges, impacts, and opportunities, and 2) results of a qualitative study on Agentic AI efforts in computing education. Combined, this proposed project aims to identify promising principles, use cases, challenges, and ways to navigate the implementation of Agentic AI in ethical and principled ways. 

Methodology:

The goal is to produce an exploratory analysis of Agentic AI in computing education through a literature review and qualitative study. We acknowledge from the outset the critical role that grey literature (GL) will play in our work. We will, therefore, approach the literature review by taking a Multi-Vocal literature review approach.

The qualitative research component will interview individuals or conduct focus groups with teams who lead Agentic AI in computing education across a range of institutions (e.g., public, private, urban, rural) representing diverse countries. Whether we will conduct interviews or focus groups will depend on contextual, infrastructure, and organizational factors. Our IRB protocol will reflect this flexible approach to engaging with the participants.

Expectations of members:

We estimate contributions from mid-March to the end of June (3-4 hours/week). We intend to complete the initial literature scoping review and interviews in education prior to ITiCSE. During ITiCSE, we will finalize the draft of our report. We expect all members to attend the pre-conference working days on-site (July 10-12, 2026). Similarly to pre-conference work, we expect all members to work August to November drafting and revising the WG report (3-4 hours/week).

Member selection criteria:

We welcome group members with various backgrounds, and researchers, educators, junior and senior academics alike. We hope every member will bring their expertise, enthusiasm, and commitment to co-writing this WG landscape report to achieve meaningful results. We seek WG members who are:

  • Academics and educators interested in this topic and who have taught courses in AI, computing ethics, computing education, or are currently teaching or have demonstrated interest in teaching these topics in the future.
  • Academics who are currently teaching/researching or have taught/researched GenAI in higher education
  • Researchers with interests in Agentic AI and its applications and implications in education
  • Professionals and industry willing to contribute their expertise, insight, and foresight on Agentic AI and its implications in education.
  • Represent diverse geographical regions.

Meeting frequency & timeline:

  1. Synchronous and asynchronous biweekly pre-ITiCSE 2026 meetings (estimate 2-3 hours/week). 
  2. In-person during the 2026 ITiCSE in Madrid, Spain including the designated working group pre-conference days. 
  3. Synchronous and asynchronous biweekly post-ITiCSE 2026 meetings to finalise the working group report/paper (estimate 2-3 hours/week).

Please reach out to the group leaders with any questions – we welcome your contributions!

WG4 – An Expanded View of Replication Studies in Computing Education Research

Leaders:

Motivation: 

Researchers can adopt a replication study design to confirm, strengthen, and advance Computing Education Research (CER). Still, prior research indicates that replication is infrequent in CER, even though the community encourages its use. Previous research argues that the CER community uses different terms to describe a replication study. We are interested in confirming CER’s use of different terms to describe replication studies and determining whether there is a disparity between its application and the community’s encouragement of using this study design.

Approach and expected outcomes: 

We will conduct a Systematic Literature Review (SLR) across influential international CER venues to understand how the community presents and conducts replication studies. We will examine researchers’ motivations for using this study design and possibly identify venues receptive to it. In addition, we will interview Computing Education researchers, conference leaders, and journal editors to understand their experiences and perceptions with the study design in their respective venues. Their insights could promote greater adoption of replication in future studies. Our work will highlight and confirm the terms the CER community uses to present replication studies, enabling researchers and educators to better identify replication studies in CER.

Contributions: We expect to identify additional replication studies using other terminology and to share researchers’ insights on how we can encourage the community to adopt replication study designs. We expect to collect suggestions and recommendations from the CER community on how we can encourage (or other areas of interest) that require time but that educators currently do not have. By investigating how the CER applies the term “replication study”, our work may refine its definition to better align with its usage in CER.

Expectations from members: 

We seek individuals interested in researching the replication study design in Computing Education Research (CER). No replication study experience is necessary for this WG. We welcome all researchers. In this project, members will perform a systematic literature review and may participate in interviews with members of the CER community. Members will use mixed methods research to analyse the collected data. We have already received ethics (IRB) approval to interview CER researchers.

The WG will use video conferencing software for asynchronous communication to address the members’ different time zones. We will meet for 1-2 hours weekly, offering two to three virtual meetings to accommodate the globally distributed group.

In your application, please explain your interest in this WG. Please include your experience and/or background in your application, and explain how it relates to this Working Group’s goals.

WG5 – Interpretivist Research Paradigms in Computer Science Education: a Scoping Review

Leaders:

Motivation and goals:

The purpose of this working group is to conduct a systematic scoping review of how interpretivist research paradigms are articulated, operationalised, and reported within computer science education research. The group is motivated by a recognised misalignment between the predominantly constructivist assumptions that underpin pedagogical practice in CSEd and the largely positivist traditions that continue to dominate published research, alongside persistent ambiguity in how qualitative and interpretivist methodologies are described and evaluated.

The working group will focus on identifying and analysing existing literature that employs interpretivist approaches, examining how paradigmatic positions, analytical decisions, and reflexive practices are made explicit, and where gaps or inconsistencies remain.

Through collaborative analysis, the group aims to develop shared understanding of methodological quality, clarify links between research questions, paradigms, and methods, and co-produce practical guidance to support rigorous conduct, reporting, and review of interpretivist research in the field.

The group will meet regularly for approximately 1.5 hour to 2 hours per week over the course of the project, combining synchronous discussion with asynchronous collaboration to accommodate timezone differences, with members contributing through defined tasks related to literature identification, coding, synthesis, and guidance development.

Inclusion criteria for members:

Selection of members will take into account the holistic needs of the group. The working group welcomes applications from both early career and experienced researchers, recognising the value of combining methodological expertise with developing scholarly perspectives. We will expect members to fit all essential requirements and be at least in one member category. The essential requirements are as follows:

  • An interest in broadening participation and impact of qualitative research in Computer Science Education.
  • Willingness to take part in collecting and synthesizing academic literature.
  • Membership in at least one of the member categories.

Inclusion criteria for:

We welcome members who do not have direct experience of qualitative methods but are interested in learning about interpretivist approaches.

  • Computing Domain Researchers
    • Background in core Computer Science domains (e.g., programming, software engineering, human–computer interaction, algorithms).
    • Experience working across a range of computing education contexts, such as K-12 education, university-level teaching, or informal learning environments.
  • Learning Scientists
    • Background in the learning sciences, education, or educational psychology, with a focus on how people learn in formal or informal settings.
    • Experience with interpretivist, constructivist, or sociocultural research paradigms (e.g., design-based research, ethnography, discourse analysis, narrative inquiry) in computer science contexts.
    • Familiarity with theories of learning, cognition, identity, or participation that inform qualitative data interpretation.

Requirements of the Working Group members:

  • Attending the weekly meetings. Recordings will be provided for any one-off meetings that need to be missed.
  • Collecting, sharing and discussing academic literature.
  • Active participation in the production of the extended abstract and final report.
  • Registering and attending ITiCSE ’26. 
  • Adhere to the following deadlines for:
    • Extended abstract submission 15th April
    • Draft report submission to WG chairs 12th July
    • Report submission to external reviewers 13th September
    • Camera ready report submission 15th December

WG6 – Globalizing Computing Terminology

Leaders:

Motivation:

Computing education is global in scope. Yet, educational terms are often local in nature, local either to a geographic region (e.g., North America, Latin America) or a specific educational heritage (e.g., Anglophone, Francophone, Lusophone). Given the diversity of practices and heritages, a common understanding of educational concepts and terms would better facilitate exchange of ideas and adoption of practices. In other words, there is a need to globalize computing education terminology while respecting local needs. 

Goals:

The primary goal of the Working Group is to involve international computing education experts from diverse geographic regions and educational heritages in the aggregation, empirical validation and curation of a globalized terminology resource for computing education.

Methodology:

The process of creating a computing terminology resource involves three steps:

  • Aggregation: Shortlist academic terms from diverse regions, along with their definitions;
  • Empirical Validation: Verify the correctness and equivalences of terms through a mixed-methods approach involving both quantitative and qualitative techniques.
  • Curation: Present the terminology of different regions in a format that facilitates easy comparison and use by computing educators and researchers. 

The Working Group will perform tasks contributing to each of these three steps before, during and after the conference. 

Meeting frequency:

Working group meetings will be held every 2 weeks via a suitable collaboration platform such as Zoom, Microsoft Teams, or Google Meet. Meetings will be scheduled to accommodate members’ commitments and time zone differences.

Member activities:   

Working group members will be expected to contribute approximately 2-3 hours per week on average for the duration of the working group (starting in mid-March 2026). 

  • Collect data by identifying appropriate sources and conducting surveys in their educational region before the conference.
  • Conduct quantitative corpus-based analysis using LLM-assisted tools to identify term usage patterns and frequencies across the global literature.
  • Perform qualitative contextual analysis of assigned terms/term clusters, examining how they are interpreted across different regional publications.
  • Contribute to writing the report through the end of 2026.

Member selection criteria:

Preference will be given to applicants who bring diverse geographic perspectives or experience with academic frameworks across multiple countries. We welcome computing educators, practitioners, and researchers who have demonstrated expertise, or a strong interest, in one or more of the following areas: cross-regional terminology research, LLM-assisted corpus analysis, or qualitative content analysis.

WG7 – Bringing Inquiry-driven Programming into Practice: Towards a Research Agenda for Epistemic Programming

Leaders:

Motivation:

Programming has always been one of the most popular topics in CSEd. However, while programming used to be difficult to access, it is now much more accessible through tools, environments and communities and is used in various disciplines such as biology, mathematics, and physics, as well as in interdisciplinary contexts, particularly as a means of gaining knowledge. This flavour of programming for gaining insights is characterized through a conversional approach, in which a human and a computer interact in an intertwined process of programming and reflecting, which we denote as “epistemic programming”. 

This programming perspective is not only relevant for future computing professionals. In particular, laypersons and young learners can use this approach to gain insights into socially or personally meaningful questions, thus positioning programming as a form of general education. 

Although this perspective aligns with foundational ideas such as Constructionism, Literate Programming, and Personal Dynamic Media, and despite existing implementations and some empirical research, there is currently no shared framework or community focusing on examining this perspective and on developing and evaluating instructional materials for teaching. 

Goals:

As a working group, our goal is to establish a research community around epistemic programming and to systematically collect and analyse existing approaches and implementations. Based on this work, we aim to develop an initial process framework that can serve as a foundation for future research and practice. To achieve this, we seek to bring together diverse perspectives and disciplines, including perspectives from fields beyond computer science that already use programming as a means of knowledge generation.

Member selection criteria:

We invite participants who have experience with this knowledge-oriented perspective on programming, whether as educators, practitioners, or researchers, and who are interested in collaboratively developing a common (interaction-based) framework. Such a framework can support future interdisciplinary research and, on a long term, help enable learners to cultivate programming as a universally applicable skill for self-determined exploration and understanding of the world.

Expectations of members:

In February, the WG will hold an initial two-hour online meeting to introduce members and their backgrounds and to identify additional associates with experience in EP. Until the ITiCSE conference, the WG will meet online biweekly or monthly to plan and conduct the literature review and interviews, address ethical considerations, and collect examples of EP practice. Between meetings, members will carry out parts of the review and interviews and share their insights in the meetings. Beginning in June, these contributions will be discussed to derive key characteristics of EP and to develop an initial draft of an interaction-based framework.

At ITiCSE, the WG will consolidate findings from the literature review, interviews, and examples, refine the framework for inclusion in the draft report, and discuss directions for future research after the WG.

After the conference, the biweekly meetings will continue until December. The deliverables (including the final report and research agenda) will be completed by September and presented in an open online meeting in December, addressing participants from interdisciplinary contexts, to afterwards continue the international research community of EP.

WG8 – Mastery Learning Models in Computing Education: Toward a Shared Framework for Selection, Design, and Evaluation

Leaders:

Motivation:

Mastery learning approaches are increasingly adopted in computing education to support heterogeneous cohorts, enable frequent formative feedback, and improve attainment of critical competencies. However, it is difficult for computing educators to choose the most suitable model of mastery learning, considering pacing, assessment structures, feedback mechanisms, and institutional and operational constraints. Building on a recent synthesis of mastery learning models for computing education [1], this working group will produce a comparative framework that supports the selection and adoption of mastery learning models in diverse course contexts using key dimensions relevant to the pedagogical and operational aspects of a specific implementation. 

Methodology:

This Working Group will develop a comparative framework for mastery learning models in computing education. 

Building from the model set identified in prior synthesis work [1], we will (1) operationalise and compare models across key dimensions (pacing, assessment, feedback/remediation, integrity, scalability), (2) develop a comparison framework for mastery learning implementations, including guidelines for selection and adaptation of models and (3) identify research gaps and propose a research agenda.

This working group is focused on comparing and synthesising different models of mastery learning. We will develop a shared comparative framework that makes design choices, trade-offs, and contextual constraints explicit. Through collaborative analysis of literature and real course implementations, we aim to produce practical guidance for selecting and adapting mastery learning models, alongside recommendations for evaluation and future research.

We particularly welcome participants with experience in mastery-based course design, assessment and feedback, learning analytics, student support and equity, or large-scale teaching contexts, as well as early-career researchers seeking to contribute to a high-impact, community-oriented synthesis.

We seek members with experience in: mastery learning implementation

(CS1–CS3 and beyond), assessment design, academic integrity, learning analytics, empirical CS education research, student support and equity, and/or scalable feedback tooling. 

Commitment: 

Fortnightly meetings pre-conference (alternating time zones), intensive collaboration during the Working Group days at ITiCSE 2026, and structured writing/review work pre and post-conference.

[1] Claudia Szabo, Miranda C Parker, Michelle Friend, Johan Jeuring, Tobias Kohn, Lauri Malmi, and Judithe Sheard. Models of mastery learning for computing education. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1, pages 1092–1098, 2025.

WG9 – Computing Science Education on the AI Innovation Landscape

Leaders:

Motivation and goals:

Recent advances in artificial intelligence, including the widespread adoption of generative AI and foundational models, are reshaping computing science education. CS programs face a dual challenge: preparing graduates to participate in an AI-driven workforce, while simultaneously adapting pedagogy, assessment, and academic integrity practices in response to students’ increasing use of AI tools. To date, many institutional and instructional responses have been reactive and ad hoc, driven by short-term pressures rather than long-term pedagogical strategy.

This Working Group aims to move the CS education community beyond provisional experimentation toward a coherent, sustainable, and pedagogically grounded integration of AI. By synthesizing perspectives from students, educators, and industry, the group will characterize the current landscape of AI adoption in CS education and develop evidence-based recommendations to inform curriculum design, teaching practices, and policy decisions.

Research questions and methodology:

The Working Group will address four guiding questions:

  1. What is the current state of AI adoption in CS education?
  2. How do students perceive the role of AI in their CS education?
  3. What are the educators’ perspectives on how AI is shaping the future of CS education?
  4. What are current industry practices regarding the use of AI, and what skills are emphasized in early-career roles?

We will employ a mixed-methods approach, beginning with an exploratory literature review of recent work in venues such as ACM ITiCSE, SIGCSE TS, ICER, and prior ITiCSE Working Groups. This review will be complemented by surveys targeting undergraduate and graduate students, CS educators, and industry professionals. Survey data will be analyzed using qualitative and quantitative methods, and findings will be synthesized with the literature to identify recurring themes, tensions, and gaps. The primary outcome will be an ITiCSE Working Group report articulating insights, implications, and recommendations for the future of CS education in an AI-rich landscape.

Expectations of members:

Working Group members are expected to commit approximately 3-4 hours per week, including one hour of synchronous meeting time, from mid-April through July 2026, with continued involvement through September and lighter follow-up commitments through the end of the year. Activities will include participating in weekly online meetings, contributing to the literature review, designing and administering surveys, assisting with ethics approval processes, collecting and analyzing data, and collaboratively writing and revising the Working Group report. Members are expected to attend the in-person intensive Working Group days at ITiCSE 2026.

Member selection criteria:

We seek participants from diverse geographic regions and career stages (PhD students through established scholars). We welcome scholars with an interest and experience in undergraduate CS curriculum design, Artificial Intelligence courses or research, CS education research, data-driven research, and a motivation for performing high-quality research to join us. Selection will prioritize diversity of perspectives, demonstrated interest in collaborative research, and the ability to commit the required time and effort. We strongly encourage applications from individuals who are eager to engage in reflective, evidence-based discussions shaping the future of computing science education.

WG10 – Teamwork in Computing Education: Skills, Values, and Virtues

Leaders:

Motivation and goals:

Teamwork is critical to computing education and for graduates’ thriving in the workplace. Yet successful outcomes can require scaffolding, guidance, support, and mechanisms for evaluation and feedback. This working group (WG) will explore teamwork in tertiary computing education and develop practical materials for instructors, with regard to the skills, values, and virtues that students may require from team project conceptualization through completion and evaluation. Specifically, we seek to achieve:

  1. Identify how scholars describe the integration of skills, values, and virtues for successful formal and informal teams in tertiary computing and engineering coursework through established literature.
  2. Explore educators’ perspectives on the skills, values, and virtues to understand how they may be applied during formal teamwork in computing education.
  3. Appraise community successes and prioritize their needs, identifying open concerns around integrating teamwork into computing education and best practices for cultivating students’ skills, values, and virtues.
  4. Develop a participatory action research plan to co-design teamwork-focused resources that could be integrated into computing courses with students and faculty. 

Methodology:

We will conduct a multi-part study and co-design effort. The investigation will first begin with a systematic literature review (SLR) around teamwork in computing and engineering education (Phase 1a). In parallel, we will conduct a qualitative investigation of educator perspectives around fostering skills, values, and virtues relevant for teamwork (Phase 1b). Based on the results of these inquiries, we will identify needs in computing education as well as best practices (Phase 2). The WG will then develop a participatory research plan to co-design teamwork-focused resources for adoption in computing courses (Phase 3). These materials will then be shared to support adoption across institutions.

Expectations of WG members:

All WG members will be expected to attend regular virtual meetings and to contribute to the research process. The WG will meet biweekly, and subgroups will meet weekly. These meetings will be scheduled virtually to accommodate members’ commitments and differing time zones. We expect all members will contribute approximately 3-4 hours a week, 4-months before and after the conference, and attend ITiCSE 2026 in person. The research activities will be distributed among WG members based on their expertise, availability, and institutional contexts.

Member selection criteria:

We aim to attract international educators and scholars from a variety of backgrounds and cultures, with a range of teaching and research experience related to teaching teamwork. We encourage applications from early-career researchers and instructors and/or first-time working group participants with interest and/or experience in developing teamwork skills, values, and virtues. 

We are especially interested in attracting members who have experience leading long-term team projects in computing. Similarly, WG members must be willing to reach out to faculty within their professional network to participate in the information gathering and co-design phases. WG leaders will provide assistance with the language and will provide mentoring and support throughout the research process. 

In your application, please explain your interest in this WG. Please also include your experience in the application and how it relates to the four objectives of this WG. If you are unsure about applying, please reach out to the co-leads by email. We are happy to answer questions, and we hope you will join us!

WG11 – Towards Improving CS Students’ Generative AI Literacy

Leaders:

Motivation:

The widespread adoption of Generative AI (GenAI) tools by students across different educational levels highlights the need for them to develop robust GenAI literacy, including an understanding of how such systems operate, their limitations, and their implications for responsible use. However, misconceptions about GenAI — such as perceiving such systems as mere search engines or database lookup systems — are commonly observed both among students and the broader public, while the availability of teaching resources remains fragmented, and learning objectives lack alignment.

Goals:

This Working Group has 2 main goals:

  • Identify a set of GenAI literacy learning goals/objectives which give Computer Science students the base information for responsibly using these tools (e.g., fundamental behavior, limitations);
  • Prepare pedagogical materials that can be shared with the computing education community. This might include a multiple of media such as documents, slides, videos, and/or pedagogical games (e.g., card games).

Methodology:

The proposed work focuses on gathering and synthesizing perspectives and insights from the WG members, the broader CSE community, and recent GenAI-literacy research, with the goal of identifying shared GenAI literacy learning objectives and producing pedagogical materials to support their teaching.

  • Evidence gathering and synthesis: Systematically collect and synthesize perspectives from WG members, the broader research community, and recent GenAI literacy studies.
  • Learning objective development: Identify, validate, and prioritize GenAI literacy learning objectives through surveys and structured community feedback.
  • Material design and refinement: Develop pedagogical materials aligned with validated learning objectives, including instructional content and learning activities. Evaluate materials through interactive testing and feedback to refine both learning objectives and instructional design.

Expectations of members:

We expect members to contribute to various phases of the work, such as: (a) participating in online meetings held approximately every two weeks; (b) reading and summarizing relevant literature; (c) designing and administering surveys; (d) analysing survey results; (e) designing, implementing, and testing educational materials; and (f) writing, reviewing, and editing the group’s reports.

Member selection criteria:

We welcome any computing educator. We will, however, prioritize educators with some level of experience with the integration of GenAI in CS courses.

WG12 – Workforce-Informed Competency Pathways in Computing Education: Toward a Post-2027 Curriculum Framework

Leaders:

Motivation:

Computing programs worldwide face increasing pressure to demonstrate workforce relevance while supporting diverse learner pathways and maintaining academic rigor. As the community looks beyond the 2027 curriculum revision cycle, there is a growing need for shared, international approaches to articulating competencies, professional judgment, and progression across vocational, undergraduate, graduate, and non-traditional pathways.

This ITiCSEWorking Group brings together educators, researchers, and curriculum leaders to examine how workforce-aligned competency frameworks can be systematically integrated into computing curricula without prescribing specific program structures. Using curriculum artifacts and established frameworks such as EQF and SFIA, participants will collaboratively analyze pathway designs, identify misalignments, and develop a transferable competency-oriented pathway framework.

Goals:

  • RQ1. How are technical skills, cross-disciplinary skills, and professional behaviors described across computing pathway transitions?
  • RQ2. Where do curricula, workforce expectations, and student progression not align, including expectations for AI-related professional judgment and responsibility?
  • RQ3. What bridge pathway designs appear across institutions and support fair, scalable, workforce-aligned computing pathways after 2027?

Methodology:

  • Artifact collection and mapping: Participants will contribute curriculum documents, pathway descriptions, and articulation materials from their institutions. These artifacts will be mapped to workforce-aligned competency frameworks to identify how skills, professional behaviors, and responsibility levels are described across pathways.
  • Comparative analysis across pathways: Working in small groups, participants will compare artifacts across institutions and educational levels to identify common patterns, gaps, and misalignments between curricula, workforce expectations, and student progression, including expectations for AI-related professional judgment.
  • Synthesis and framework development: The group will synthesize findings to identify recurring bridge pathway designs and practices. These results will be used to develop shared design principles and post-2027 guidance for equitable, scalable, workforce-aligned computing pathways.

Before the conference, participants will meet online to share artifacts, agree on mapping approaches, and complete initial analysis in small groups. During the ITiCSE conference, the working group will meet in person to compare findings, identify common patterns and gaps, and draft the core structure of the working group report.

After the conference, participants will continue collaborative writing and revision through regular online meetings to finalize the report for publication.

Member selection:

We invite applications from individuals with relevant experience and interest in computing curriculum development and alignment with industry-relevant competencies. 

Specifically, we seek:

  1. Academics and educators are involved in the design, revision, or delivery of computing curricula across vocational, undergraduate, or graduate programs.
  2. Researchers in computing education with interests in competency-based education, workforce alignment, curriculum frameworks, or educational pathways.
  3. Academics or administrators with experience in articulation agreements, bridge pathways, or cross-institutional curriculum alignment.
  4. Representatives from diverse geographical regions.
  5. Individuals who are familiar with international and national classification systems such as CIP (Classification of Instructional Programs), SOC (Standard Occupational Classification), CIP Canada, ISCED-F (International Standard Classification of Education – Fields of Education and Training), ISCO (International Standard Classification of Occupations), ASCED (Australian Standard Classification of Education), ANZSCO (Australian and New Zealand Standard Classification of Occupations), JACS (Joint Academic Coding System)/HECoS (Higher Education Classification of Subjects), SOC (Standard Occupational Classification – UK), KldB (Klassifikation der Berufe), NSF (Nomenclature des Spécialités de Formation), ROME (Répertoire opérationnel des métiers et des emplois), National codes mapped to ISCED-F, and ISCO-08

Applicants should be willing to actively contribute to collaborative analysis and synthesis and to participate fully in the working group activities.