Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This study investigated whether training individuals on the strategic use of evidence (SUE) interview technique using large language model (LLM)-based AI suspects improves their ability to detect deception in subsequent interviews with human mock suspects. Participants were trained with either instruction alone or instruction combined with AI suspect simulations, and the results showed that both training groups used evidence-statement inconsistencies more effectively in their judgments compared to a control group. Furthermore, the group trained with AI suspects demonstrated better accuracy in judging the veracity of human mock suspects, suggesting a potential benefit of AI-enhanced training for SUE.
Practicing strategic interviewing techniques against LLM-powered "AI suspects" can improve a human interviewer's ability to spot inconsistencies and detect lies in real human interviews.
OBJECTIVE The strategic use of evidence (SUE) is a technique that aims to improve the ability to differentiate between liars and truth-tellers. However, while theoretical training provides guidance on interview techniques, it lacks opportunities for practical application. HYPOTHESES We created two large language model driven artificial intelligence (AI) suspects with whom participants could simulate interviews and hypothesized that these simulations would enhance the transfer of training to later interactions with human mock suspects. METHOD The study included 156 Chinese laypersons (78 interviewers and 78 human mock suspects). The two AI suspects followed response rules representing simplified and prototypical examples of liars' and truth-tellers' behaviors under the SUE model. Interviewers were randomly allocated to one of three types of training: (a) instruction and AI exercise, (b) instruction, and (c) control. After the training, the participants interacted with either a lying or truthful human mock suspect. RESULTS Receiving interventions made interviewers use evidence-framing matrix (an important tactic within the SUE framework) more frequently, thereby eliciting more inconsistencies between the lying human mock suspects' statements and the evidence (i.e., evidence-statement inconsistencies) as well as more inconsistencies within their own statements (i.e., within-statement inconsistencies). Both instruction and instruction and AI exercise groups used evidence-statement (in)consistencies more to make their judgments about whether human mock suspects were lying or truthful compared to those in the control group. In addition, the instruction and AI exercise group was better at accurately judging whether the human mock suspects were lying or truthful compared to the control group. CONCLUSIONS Overall, this study provided preliminary evidence that simulated SUE with AI suspects transferred to interactions with human mock suspects in a controllable experimental setting, but that the advantage over instruction-only was not particularly robust. (PsycInfo Database Record (c) 2026 APA, all rights reserved).