Psychological Alignment and Misalignment in LLM Assistants: Training Data, Interpretive Risk, and Human–AI Interaction

This interdisciplinary research report examines psychological alignment and misalignment in large language model (LLM) assistants, with particular focus on training-data bias, hallucination, anthropomorphism, trust calibration, cultural skew, and interpretive risk during human–AI interaction. The report argues that alignment failures should not be understood solely as technical problems, but also as sociotechnical and psychological phenomena shaped by user interpretation, emotional meaning construction, and behavioural reliance. The analysis distinguishes between empirical, theoretical, and speculative claims in order to avoid causal overstatement while identifying plausible mechanisms through which technically coherent AI outputs may become psychologically consequential. A central contribution of the report is the concept of “interpretive misalignment,” describing situations in which users assign emotionally significant or destabilising meaning to otherwise technically acceptable outputs. The report proposes operational definitions, experimental designs, lifecycle-monitoring metrics, and governance recommendations for evaluating psychological alignment in LLM assistants across high-stakes, culturally diverse, and emotionally sensitive contexts.