Beyond Replacement: How AI Reconfigures Academic Roles

Abstract

The literature examining the impact of artificial intelligence (AI) on higher education often boils down to the question of whether AI will replace teachers. This study examines the issue more broadly — through the lens of the diverse nature of academic tasks. A multidimensional analysis is conducted to show how academic work is being redistributed among tasks that AI can replace, augment, or cannot meaningfully displace. The study uses qualitative content analysis on a purposive corpus of 46 documents, including scientific papers, reports, policy and practitioner texts, and higher education commentary published between 2020 and 2026. A 3×3 analytical matrix was developed, reflecting the degree of distribution of functions between humans and AI ("Replace," "Augment," or "Human-dominant") across three basic dimensions of academic activity (teaching, research, and socio-technical work). The findings show that AI is strongest in codifiable and routine tasks such as basic educational content generation, objective grading, literature processing, drafting, and administrative support. By contrast, tasks involving judgment, ethics, interpretation, mentoring, relationship-building, assessment design, governance, and public trust remain human-dominant. The research concludes that AI does not eliminate academic work; it reorders it, shifting value from content transmission toward learning design, interpretive expertise, and institutional stewardship.