The Double-Edged Algorithm: A Review of AI-Tool Dependency in Education and Education Research

Abstract

automated tutors for literature search and coding assistantshave moved
from novelty to necessity across classrooms, faculty offices, and research
laboratories in only a few years. This review examines the phenomenon
of AI-tool dependency in education and education research: the degree
to which learners, educators, and researchers have come to rely on AI
systems to complete cognitive and academic work that they previously
performed unaided. We organise the literature and observed practice
into a taxonomy distinguishing learner, educator, and researcher depen-
dency, and propose a conceptual feedback-cycle model explaining how
convenience-driven adoption can deepen into structural reliance. Re-
ported benefits include efficiency gains, personalised support, and ex-
panded research throughput, while reported risks include erosion of crit-
ical thinking and independent problem-solving, academic-integrity
concerns, propagation of model bias into scholarly output, and homogenisa-
tion of student and research writing. We argue that dependency is best
understood not as a binary state but as a spectrum shaped by task de-
sign, disciplinary norms, and institutional guidance, and we propose a
set of design and policy principles- transparency, scaffolded autonomy,
verification literacy, and assessment redesignintended to preserve the
benefits of AI assistance while containing its costs.