Between Quasi-Emotion and Real Emotion

Generative AI is shifting from information tools to emotional interaction systems, bringing AI into sensitive domains such as loneliness, attachment, and emotion regulation. Existing research has accumulated insights in affective computing, digital intimacy critique, and AI ethics frameworks, yet lacks an analytical concept that connects these three strands. This paper proposes the concept of "quasi-emotion" to define AI's algorithmically generated capacities for emotional recognition, expression, and adaptation—precise at recognition, fluent at expression, but lacking bodily calibration at adaptation. Drawing on consciousness philosophy and attachment theory, it argues for a structural difference between quasi-emotion and real emotion. It then introduces "judgment hierarchy" as a user-side variable, reframing risk as an adaptive mismatch between quasi-emotional intensity and the user's judgment hierarchy, and demonstrates its temporal and contextual nature. Using the 2024 Character.AI teen suicide case as an anchor, the paper proposes six ethical principles and an overarching "cognitive adaptation principle." It reveals an institutional paradox: AI's emotional "promises" are legally non-binding, yet users are more attracted to them and less able to obtain remedies. The solution lies in shifting regulation from "content compliance" to "relational compliance," applied dynamically and differentially according to users' cognitive characteristics. The paper's value lies in translating philosophical judgments into operable, dynamically adjustable governance tools.