Scarce longitudinal evidence examines LLMs' persuasiveness and humanness along time-evolving psychological frameworks. We introduce Talk2AI, a longitudinal framework quantifying psycho-social, reasoning and affective dimensions of LLMs' persuasiveness about polarizing societal topics. In a four-way longitudinal setup, Talk2AI's 770 participants engaged in structured conversations with one of four leading LLMs on topics like climate change, social media misinformation, and math anxiety. This produced 3,080 conversations over 60,000 turns. After each wave, participants reported conviction in their initial topic stance, perceived opinion change, LLM's perceived humanness, a self-donation to the topic and a textual explanation. Feedback time series showed longitudinal inertia in convictions, indicating some human anchoring to initial opinions even after repeated exposure to AI-generated arguments. Interestingly, NLP analyses revealed that both humans and LLMs relied on fallacious reasoning in 1 conversational quip every 6, countering the ``LLMs as superior systems" stereotype behind LLMs' cognitive surrender. LLMs' perceived humanness was most learnable from sociodemographic, psychological and engagement features ($R^2=0.44$), followed by opinion change ($R^2=0.34$), conviction ($R^2=0.26$) and personal endowment ($R^2=0.24$). Crucially, explainable AI (XAI) indicated: (i) the presence of individuals more susceptible to LLM-based opinion changes; (ii) psychological susceptibility to LLM-convincing consisted of having more trust in LLMs, being more agreeable and extraverted and with a higher need for cognition. A multiverse approach with mixed-effects models confirmed XAI results, alongside strong individual differences. Talk2AI provides a grounded framework and evidence for detecting how GenAI can influence human opinions via multiple psycho-social pathways in AI-human digital platforms.

翻译：少有纵向研究从动态演化的心理框架考察大语言模型的说服力与类人性。我们提出Talk2AI纵向框架，量化LLMs在极化社会议题上的心理社会、推理与情感维度说服力。在四波纵向实验中，770名参与者与四款主流LLM就气候变化、社交媒体虚假信息及数学焦虑等议题进行结构化对话，共生成3080轮对话（超6万次交互）。每轮实验后，参与者报告初始立场信念强度、感知观点变化、LLM的感知类人性、议题相关自我捐赠行为及文本解释。时间序列反馈显示信念具有纵向惯性——即使反复接触AI生成论点，人类仍对初始观点存在锚定效应。有趣的是，NLP分析揭示人类与LLM均在每6次对话交锋中依赖一次谬误推理，这反驳了"LLM作为优越系统"导致人类认知投降的刻板印象。LLM的感知类人性最易从社会人口学、心理学及参与特征中习得（R²=0.44），其次为观点变化（R²=0.34）、信念强度（R²=0.26）与个人捐赠（R²=0.24）。关键而言，可解释人工智能表明：（i）存在对LLM观点变化更敏感的群体；（ii）对LLM说服的心理易感性表现为更信任LLM、更具宜人性与外向性，且认知需求更高。基于混合效应模型的多宇宙分析验证了XAI结果，并揭示了显著个体差异。Talk2AI为检测生成式AI如何通过多重心理社会路径影响人类观点提供了实证框架与证据基础。