Rethinking Citation of AI Sources in Student-AI Collaboration within HCI Design Education

2025-06-10

AI Summary

In HCI design education, the dynamic and ephemeral nature of generative AI–produced content challenges traditional citation norms—grounded in authorship attribution, verifiability, and source stability—raising critical concerns regarding authorial agency, assessment fairness, and pedagogical transparency. This study employs qualitative analysis and course-based action research across 35 team projects and 175 student reflective texts. It introduces, for the first time, a reconceptualization of AI citation as a reflective pedagogical practice, proposing a dual-dimensional framework: “AI Contribution Statements” and “Process-Aware Citation,” both foregrounding metacognitive engagement and design process transparency. Findings reveal substantial heterogeneity and inconsistency in students’ current citation practices. Building on this, the study develops a pedagogically grounded citation framework specifically tailored to generative AI’s characteristics, offering actionable assessment criteria and instructional guidelines for AI-augmented design education.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Philosophy and Ethics of AI: Artificial General Intelligence
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

The growing integration of AI tools in student design projects presents an unresolved challenge in HCI education: how should AI-generated content be cited and documented? Traditional citation frameworks -- grounded in credibility, retrievability, and authorship -- struggle to accommodate the dynamic and ephemeral nature of AI outputs. In this paper, we examine how undergraduate students in a UX design course approached AI usage and citation when given the freedom to integrate generative tools into their design process. Through qualitative analysis of 35 team projects and reflections from 175 students, we identify varied citation practices ranging from formal attribution to indirect or absent acknowledgment. These inconsistencies reveal gaps in existing frameworks and raise questions about authorship, assessment, and pedagogical transparency. We argue for rethinking AI citation as a reflective and pedagogical practice; one that supports metacognitive engagement by prompting students to critically evaluate how and why they used AI throughout the design process. We propose alternative strategies -- such as AI contribution statements and process-aware citation models that better align with the iterative and reflective nature of design education. This work invites educators to reconsider how citation practices can support meaningful student--AI collaboration.

Problem

• How to cite AI-generated content in HCI education
• Traditional citation frameworks fail with dynamic AI outputs
• Inconsistent AI citation practices reveal pedagogical gaps

Innovation

• AI contribution statements for transparency
• Process-aware citation models for iteration
• Reflective practice for metacognitive engagement