Working Groups Shortlisted Proposals

Below are details of the four Working Groups (WGs) open for membership applications. Please read the descriptions below, and if you find a WG that interests you, use the link provided below to apply for participation. Please note that WG members are expected to actively contribute in accordance with the expectations and responsibilities outlined by the respective WG leaders.

In-person attendance at the conference is mandatory for all Working Group (WG) participants. Although the conference officially begins on December 3, Day 0 (December 2) is reserved exclusively for WG participants. On this day, the group will meet for a full-day dedicated to advancing the project. The WG may continue its collaborative work throughout the conference as needed.

In addition, the conference program will include a session on Day 2 (December 4) or Day 3 (December 5) during which each WG will deliver a brief presentation outlining its project and progress to conference attendees.

Please scroll further down for more information on the complete process. If you have any questions about a WG, please contact the respective WG leaders via email.

Application Link (Round 2): https://forms.gle/RZ4ArngHUVFDpRU2A

Application Deadline: 29th June 11:55PM IST

Understanding Refute Problem Difficulty for Learners in Indian Computing Education

Leader:

• Viraj Kumar – UNSW, Bengaluru (viraj.kumar.cs@gmail.com)
• S. Sangeetha – PSG Institute of Technology and Applied Research (sangeetha@psgitech.ac.in)
• Nimisha Agarwal – Institute of Infrastructure, Technology, Research and Management (nimishaagarwal@iitram.ac.in)

Members:

• Arun Raman – BITS Pilani Goa
• Ravindra Keskar – Visvesvaraya National Institute of Technology (VNIT) Nagpur
• Megha Sharma – Cambridge Institute of Technology
• Aalok Thakkar – Ashoka University
• Shabana K M – IIT Madras
• NANDIGAMA VENKATESH – JNTUGV College of Engineering, Vizianagaram
• Clif Kussmaul – Green Mango Associates, LLC
• Sonika Pal – IIT Bombay, Research Scholar
• Andreas Lingnau – German University of Applied Sciences (DHAW)

Abstract: Refute Problems present students with buggy code for a given task and ask students to demonstrate why the code is buggy. Little is known about what makes a Refute Problem difficult for students. This Working Group (WG) investigates possible causes of difficulty such as

1. Comprehending the given implementation (code-level difficulty)
2. Comprehending the given problem specification (task-level difficulty)

We hypothesize that task-level difficulty is particularly relevant in the context of Indian computing education, where several students may have limited fluency in the language in which the task is expressed (e.g., English). This WG will limit the scope to Refute Problems in the context of Introductory Programming (CS1), where they were first proposed and have been most extensively studied. Drawing on cognitive load theory, code comprehension, and assessment design, as well as an analysis of existing Refute Problems, instructor reflections, and exploratory studies with undergraduate learners from participating Indian institutions, this WG aims to develop a framework for characterizing Refute Problem difficulty together with design guidelines, annotated examples, and tools to help instructors tailor the difficulty of Refute Problems for their students. This will support the creation of more equitable, scalable, and cognitively calibrated Refute Problems for assessments.

Detailed Description: Detailed description can be downloaded at WG1_detailed.pdf

Assessment Integrity in Indian CS Education: GenAI-Era Capabilities, Traditional Examinations, and OBE Accreditation

Leader:

• Samridhi Singhal – IILM University, Gurugram (samridhi.singhal@iilm.edu)
• Megha Rana – IILM University, Gurugram (megha.rana@iilm.edu)
• Shailza Kanwar – Bennett University, Greater Noida (shailza.kanwar@bennett.edu.in)

Members:

• Adri Jovin John Joseph – Sri Ramakrishna Institute of Technology
• Jyoti Yadav – K.R. Mangalam University
• Anju Mishra – Ajay Kumar Garg Engineering College
• Puneet Raghav – Shri Vishwakarma Skill University Dudola Palwal
• NAGARAJ P – SRM INSTITUTE OF SCIENCE AND TECHNOLOGY
• Riya Dobhal

Abstract: The rapid adoption of Generative AI tools among the students has created a situation that Indian undergraduate Computer Science Education is structurally unprepared to handle, Students can now use tools such as ChatGPT and GitHub Copilot to generate the examination responses for almost all conventional assessment types like lab programs, theory answers, assignment submissions and even Viva preparation. Faculty at most Indian Institutions do not have the authority to modify the examination and laboratory formats prescribed by their affiliating universities. At the same time, institutions are required to demonstrate NAAC and NBA accreditation compliance by computing and reporting Course Outcome attainment percentages mapped to Program Outcomes. When the assessments underlying these figures may be susceptible to Gen-AI use, the reliability of the attainment data built on them becomes an open and important question – one that warrants rigorous empirical investigation rather than assumption. This three-way intersection that is – Gen-AI enabled student behaviour, rigid University mandated assessment formats and OBE based accreditation requirements is a distinctly Indian Education structural problem. Global computing education research on GenAI and academic integrity has not been designed for a context where faculty have limited reformative authority and where accreditation frameworks create strong institutional incentives around attainment reporting. Existing literature has not, to our knowledge, exclaimed this three-way structural challenge in the Indian Computing education context. Faculty are navigating it alone, students receive contradictory signals about what constitutes honest work, and accreditation bodies may be receiving documentation whose reliability warrants scrutiny. This Working Group sets out to empirically investigate the nature and extent of this problem.

Detailed Description: Detailed description can be downloaded at WG2_detailed.pdf

Foundations at Risk: Investigating Core Programming Competency Among Indian CS Undergraduates in the Age of AI/ML Hype and Generative AI Tools 

Leader: 

• Preeti Mehta – IILM University, Gurugram (preeti.mehta@iilm.edu)
• Neha Bansal – IILM University, Gurugram (neha.bansal@iilm.edu)
• Suchi Kumari – Shiv Nadar University, Greater Noida (suchi.kumari@snu.edu.in)

Members:

• Varsha Venkatasubramanian – Atria University, Bengaluru
• Manu Madhavan – Indian Institute of Information Technology, Kottayam
• Ranjitha M – Kristu Jayanti University, Bengaluru
• Jackulin Mahariba A – SRM Institute of Science and Technology (SRMIST)
• Suyel Namasudra – National Institute of Technology Agartala
• Sudha Rajesh – SRM Institute of Science and Technology (SRMIST)

Abstract: There is a worrying trend across Indian undergraduate CS programmes. Students are confidently using ChatGPT to write code, but are unable to debug a simple loop, reason about a recursive function, or trace pointer errors in C. Three intersecting pressures seem to be contributing to this shift: (a) a placement-driven culture that values credentials over deep mastery; (b) AI/ML hype that encourages students to skip foundational courses in favour of advanced electives; and (c) increasing reliance on Generative AI tools that allow code generation without real understanding. While faculty concern about this issue is widespread across India, the CS education literature has paid virtually no systematic empirical attention to this phenomenon. This working group seeks to help fill that gap by undertaking an open, evidence-based exploration of whether and to what extent foundational competencies are changing, why they are changing, and how institutions might respond.

Detailed Description: Detailed description can be downloaded at WG3_detailed.pdf

Local-Language Reasoning Small Language Models for Computer Science Education in Low Resource Languages

Leader: 

• Vikram Vincent – FreeThoughtLabs Pvt Ltd (vikram.vincent@freethoughtlabs.com)

Members:

• Annette Elizabeth Shoney – Student
• Sarbani Banerjee Belur – Assistant Professor, IIIT Dharwad
• BINDU K R – Amrita vishwa vidyapeetham
• Indra R – IITB, Research Scholar
• Swaroop Joshi – BITS Pilani (Goa) – Assistant Professor
• Suja Jayachandran – Associate Professor , Vidyalankar Institute of Technology
• Bavishya Sankaranarayanan – Indian Institute of Information Technology Kottayam, Research Intern
• Anya Rajan – IIT Kanpur, Student
• Thrupthi R – Christ University, PhD scholar
• Parthasarathy PD – BITS Pilani, Adjunct Professor (WILP Division), CSIS

Abstract: Low resource languages such as Kannada, Tamil, Telugu, Malayalam, and Hindi remain under-resourced for NLP despite being spoken by millions and having rich, morphologically complex structures. Recent work highlights persistent data sparsity, script and grammar complexity, and the need for dedicated resources and models tailored to these languages. Studies on low-resource sentiment analysis in Tamil and Tulu, for instance, show that transformer-based multilingual models significantly outperform traditional ML baselines but still suffer from limited annotated data and language-specific nuances. Concurrently, national initiatives (e.g., the National Language Translation Mission and NEP) have motivated the translation of educational content. However, these translation-centric initiatives typically focus on static materials like lectures or documents. There remains a critical gap at the intersection of: (a) Edge-deployable Small Language Models (SLMs) (1–7B parameters), (b) Dravidian and other Indian languages, (c) Code reasoning tasks (e.g., programming contest problems, debugging, step-by-step logic), To address this gap, this working group will build upon ongoing experiments in foundational ML tools and local-language code reasoning. We will focus on the rigorous dataset creation and model training phases, ensuring robust pedagogical and technical evaluation before widespread curriculum integration.

Detailed Description: Detailed description can be downloaded at WG4_detailed.pdf

Please submit your applications to join a WG via this link before 29th June 2026 11:55PM IST.