2026 ASEE Annual Conference & Exposition

When GenAI Arrived, Forum Participation Fell: Reshaping Help-Seeking in Introductory Computing Courses

Presented at Computers in Education (CoED): Learning, Engagement & Inclusion (3 of 9) -- M508A

The rapid growth of openly-available generative artificial intelligence (GenAI) tools has impacted higher education. These tools are readily available sources of academic assistance, raising important questions about how students' engagement with existing help-seeking resources may change. In computing education, where help-seeking has traditionally relied on peer interaction and instructional support through online discussion forums, the presence of GenAI may alter both the frequency and purpose of forum participation. Understanding these shifts is critical for interpreting student engagement and designing effective support structures.
Introductory computer science (CS) courses present a particularly important context for examining the impact of GenAI on academic help-seeking. These courses are typically characterized by large enrollments, diverse student backgrounds, and a heavy reliance on asynchronous help-seeking mechanisms such as online discussion forums. Additionally, many GenAI tools are advertised as supporting coding tasks similar to those in introductory programming courses. As a result, participation metrics drawn from these forums have historically been used as indicators of engagement and learning. However, the emergence of GenAI as an alternative help-seeking resource may reshape how students interact with forums, especially in introductory programming courses where students are still developing their problem-solving and help-seeking strategies.
This paper investigates how student participation in online discussion forums has changed before and after the widespread availability of GenAI tools starting in Fall 2023, and how the relationship between forum participation and academic performance differs across these periods. Using longitudinal forum log and course performance data from multiple semesters of introductory programming courses (CS1 and CS2), we conduct a quantitative analysis of participation frequency, usage of anonymous posting features, and contribution types across pre-GenAI and post-GenAI eras. For the purposes of this study, we consider semesters prior to Fall 2023 as pre-GenAI, and those from Fall 2023 onward as post-GenAI. We further examine correlations between forum participation metrics and final course grades to assess whether previously observed relationships persist in the current GenAI context.
Our results reveal notable shifts in forum participation with the introduction of GenAI tools, including changes in participation levels, anonymity use, and the interpretability of participation metrics as indicators of academic performance. While discussion forums remain an active component of the help-seeking landscape, their role and signaling value appear to have evolved alongside new AI-driven alternatives. These findings provide an updated empirical baseline for interpreting discussion forum engagement in introductory computing courses and highlight the need to reconsider how help-seeking behaviors are measured and understood in the presence of GenAI.

Authors

Matthew Stephen Zahn North Carolina State University at Raleigh [biography]

Matthew is a Ph.D. candidate in Computer Science at North Carolina State University. His research focuses on computer science education, with a strong emphasis on student help-seeking behaviors in introductory computer science courses. His work explores how students engage with help-seeking resources, their perceptions of those resources, and how teaching assistants can provide more effective support through structured guidance. His research is driven by a strong passion for teaching and a commitment to improving student learning experiences in computing education. Matthew was the recipient of the Outstanding TA Award (2024) in recognition of his instructional contributions. He is actively looking for teaching faculty opportunities.
Anurata Prabha Hridi North Carolina State University at Raleigh [biography]

Anurata is a freshly minted PhD in Computer Science from NC State University. Her research focuses on applying distributed machine learning methods in education for better privacy and personalization for students, instructors, and institutions. Her interest in helping instructors become more supportive of student success through privacy-preserved interaction prediction and learning forecasting has motivated her to pursue an academic career. She is actively looking for faculty opportunities and would like to collaborate on research ideas and projects.
Dr. Lina Battestilli http://orcid.org/https://0000-0002-1450-9700 North Carolina State University at Raleigh [biography]

Dr. Lina Battestilli is a Full Teaching Professor of Computer Science at North Carolina State University, where she has been on the faculty since 2012. Before joining NC State, she spent seven years in industry, including at IBM Research, contributing to next-generation networking projects. She holds a Ph.D. and M.S. in Computer Science from NC State and a B.S. in Electrical Engineering from Kettering University.

A 2023 recipient of NC State’s Outstanding Teaching Award, Dr. Battestilli teaches introductory computing and networking courses and coordinates the undergraduate Computer Science Capstone Course. Her research focuses on computer science education and broadening participation in computing. She served as Senior Program Chair for SIGCSE TS 2024 and is a member of the Steering Committee for the SIGCSE Virtual Conference. She also advises the Women in Computer Science/ACM‑W Chapter and is a former DELTA and 3C Faculty Fellow.
Sarah Heckman North Carolina State University at Raleigh [biography]

Sarah Heckman is an Alumni Distinguished Undergraduate Professor in the Department of Computer Science at NC State University. She serves as Director of Undergraduate Programs and was the first teaching-track faculty member hired in the department. Heckman is a three-time graduate of NC State, earning her bachelor’s, master’s, and Ph.D. degrees in computer science.

Her teaching focuses on core courses in software engineering and programming languages. Her research interests include computer science education and software engineering. As a graduate student, she was a three-time recipient of the IBM Ph.D. Fellowship.

Heckman has received the NC State Outstanding Teacher Award and the Alumni Association Outstanding Teacher Award as a representative of the College of Engineering. She is a member of the NC State Academy of Outstanding Teachers.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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