Sycophancy Through a Five-Level AI Response Validation Framework

Abstract

AI sycophancy, the tendency of AI systems to excessively agree with, flatter, or validate users, is an emerging concern in human-AI interaction. It is especially consequential in problem-sharing contexts, where users may seek both information and emotional validation. This paper conceptualizes AI sycophancy as excessive response validation and introduces a five-level AI Response Validation Framework (ARVF), measured using a six-item Perceived AI Sycophancy Scale (PASS). Across three phases, the study validated the framework with human participants through an online questionnaire, tested LLM-as-evaluators in answering PASS, and examined how ten contemporary LLMs generated and evaluated responses to real-world work and personal conflict scenarios. Results supported the intended progression of perceived sycophancy in ARVF and the reliability of PASS. Trust and perceived competence followed an inverted U-shaped pattern. LLM ratings reproduced the five-level structure but showed calibration differences from human ratings. Based on the LLM ratings, the ten contemporary LLMs were grouped according to their behavioral pattern. Text-based analysis provided further support on the linguistical structure for sycophantic responses generated by LLMs. Findings highlight AI sycophancy as a graded, context-sensitive interactional phenomenon.