Language Cues for Expressing Artificial Personality: A Systematic Literature Review for Conversational Agents

Users attribute artificial personality (AP) to conversational agents (CAs) based on perceived language respectively verbal cues. This review synthesizes studies on this topic, encompassing research not only on chat- and voicebots but also on social robots, drawing from interdisciplinary databases. This approach led to an identification of 200 verbal signals, nearly four times more as in previous reviews. The signals were classified according to the personality dimensions of the BFM as well as its facets. Besides, the relevance of theories of personality other than the BFM are discussed. Furthermore, six methodological challenges in the empirical study of verbal cues expressing AP are identified. Practical implications include providing practitioners an overview of verbal signals, while offering opportunities for research improvement based on identified challenges. Enhanced understanding of verbal signals related to AP aids in evaluating implementation quality, not only in rule-based CAs but also in LLM-based systems.