🤖 AI Summary
This study addresses the paradox of why users continue to engage with large language models (LLMs) despite harboring distrust, challenging conventional satisfaction-driven models by proposing an “active negotiation” framework that reconceptualizes continuance as a dynamic cycle intertwining risk, mitigation labor, and rationalization. Employing a qualitative research design, the authors conducted in-depth interviews with 36 graduate students. The primary contribution lies in developing a theoretical model that delineates the invisible labor embedded in sustained LLM use, while introducing EFL linguistic equity as an additional rationalization mechanism. By elucidating the socio-psychological logic underpinning technological dependence, this work advances the theoretical understanding of compliance behavior in human-computer interaction.
📝 Abstract
Large language models (LLMs) have become fixtures of academic work even as their users describe them as degrading their writing, thinking, and skills. Dominant adoption frameworks read continued use as evidence of satisfaction, and cannot explain continued use of a distrusted tool. We interviewed 36 graduate student workers, balanced between English-as-a-foreign-language (EFL) and non-EFL speakers, and introduce the Active Negotiation framework: a model of sustained LLM use as a recurring cycle of risk, mitigation, and justification. A failure surfaces a risk, mitigation labor addresses it, and a justification renders the residual risk tolerable until the next failure reopens the cycle. The cycle runs across three dimensions: practical, auditing output; internal, auditing one's own cognition and identity; and social, managing how peers and institutions perceive use. EFL participants invoke linguistic parity as a further justification. We reframe continued adoption as compliance sustained by invisible labor.