Exploring Contradictions in Human-AI Collaboration in HEIs in a Developing Country Using Activity Theory

This study explores the tensions in the adoption and use of AI collaborative tools in Higher Education Institutions (HEIs) within a developing country context. Current Information Systems (IS) research on human-AI collaboration in HEIs predominantly focuses on AI's role in adaptive teaching and learning, often overlooking the conflicts and tensions that arise. Additionally, AI is frequently viewed as a tool rather than a collaborative agent, creating a knowledge gap regarding its collaborative potential and the associated challenges. Using an interpretive case study approach and activity theory as the theoretical framework, this research analyzes interview data through hermeneutics to uncover tensions between HEIs and AI as collaborative agents, as well as within lecturers, students, administrative staff, and the broader HEI context. These tensions are identified as transformative mechanisms that can enable developing countries to fully leverage human-AI collaboration. The study provides practical and policy implications for optimizing AI integration in HEIs.