AI-Assisted Collaboration in Computing Education: Rethinking Teamwork Pedagogy
Document Type
Conference Proceeding
Source of Publication
2nd International Conference on Human AI Interaction and Experience Design Haxd 2026
Publication Date
6-1-2026
Abstract
This position paper explores emerging research on AI-Assisted collaboration in computing education, building upon existing literature on teamwork and group work in higher education. The analysis focuses on recent trends that redefine how students engage in team-based learning through large language models (LLMs) and specialized AI copilots. It identifies five critical research directions: (1) human-AI pair programming as a new collaboration model, (2) shifting team culture and coordination patterns with AI tools, (3) AI as a co-facilitator of ideation and reflection, (4) the affective and motivational impacts of AI-supported teamwork, and (5) the rise of LLMs as hybrid teaching assistants and evaluators. Through conceptual analysis and structured comparison, the paper argues that the integration of AI requires a theory-driven, evidence-based pedagogical approach rooted in the principles of distributed cognition. The paper concludes with a proposed framework for responsible, effective AI-enhanced teamwork pedagogy.
DOI Link
ISBN
[9798331550547]
Publisher
IEEE
First Page
4
Last Page
11
Disciplines
Computer Sciences | Education
Keywords
Human-AI Collaboration, Human-AI Complementarity, Human-AI Teams, Team Performance
Scopus ID
Recommended Citation
Calonge, David Santandreu; Smail, Linda; Kamalov, Firuz; and Ruth, Tyrone, "AI-Assisted Collaboration in Computing Education: Rethinking Teamwork Pedagogy" (2026). All Works. 8182.
https://zuscholars.zu.ac.ae/works/8182
Indexed in Scopus
yes
Open Access
no