Eliciting User Requirements for AI-Enhanced Learning Environments using a Participatory Approach

2025-06-24

AI Summary

This study addresses the tension between user needs and design expectations in educational AI systems. Drawing on a two-phase participatory workshop involving teachers, students, and edtech developers, it investigates real-world requirements for AI-enhanced learning environments. Guided by activity theory, the study employs a hybrid thematic analysis—combining deductive and inductive approaches—to systematically identify and analyze structural contradictions across goals, tools, and rules among multiple stakeholders. It introduces a novel “contradiction-driven” approach to user need generation, moving beyond conventional requirement elicitation paradigms. The research distills six core design expectations—including explainability, pedagogical adaptability, and ethical controllability—and translates them into actionable design principles and practical guidelines. These contributions advance human-centered design theory and practice for educational AI systems. (149 words)

Technology Category

• Humans and AI: User Experience and Usability
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

This paper explores the needs &expectations of educational stakeholders for AI (Artificial Intelligence)-enhanced learning environments. Data was collected following two-phased participatory workshops. The first workshop outlined stakeholders'profiles in terms of technical and pedagogical characteristics. The qualitative data collected was analysed using deductive thematic analysis with Activity Theory, explicating the user needs. The second workshop articulated expectations related to the integration of AI in education. Inductive thematic analysis of the second workshop led to the elicitation of users'expectations. We cross-examined the needs and expectations, identifying contradictions, to generate user requirements for emerging technologies. The paper provides suggestions for future design initiatives that incorporate AI in learning environments.

Problem

• Identify user needs for AI-enhanced learning environments
• Explore expectations of AI integration in education
• Generate user requirements for emerging educational technologies

Innovation

• Participatory workshops for user requirements
• Activity Theory for thematic analysis
• Cross-examination of needs and expectations