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Large language models (LLMs) are increasingly used to support writing, reasoning, translation, and decision-making, often on the assumption that easier access to information improves judgment. This integrative conceptual review argues that this assumption is incomplete because LLMs interact not with neutral information processors, but with users who bring prior beliefs, directional motivations, cognitive-effort constraints, and varying willingness to verify. The article develops the concept of artificial confidence: a relational and systemically reinforced form of unwarranted certainty that emerges when prompt-shaped, fluent, and seemingly authoritative AI outputs are experienced as independent validation. Drawing on research in judgment and decision-making, motivated reasoning, automation bias, processing fluency, epistemic vigilance, LLM sycophancy, and systems thinking, the review distinguishes artificial confidence from related constructs and proposes a socio-technical feedback model linking user motivations, prompt framing, model accommodation, perceived validation, reduced verification, and institutional normalization. The framework also identifies boundary conditions under which LLMs can improve judgment by preserving epistemic friction, source checking, uncertainty awareness, and accountability. The article concludes by offering operational definitions, behavioral indicators, testable hypotheses, and design and governance implications for AI-augmented systems in which human judgment remains revisable, accountable, and evidence-sensitive.
Guy Hochman (Mon,) studied this question.