Talk2AI: A Longitudinal Dataset of Human--AI Persuasive Conversations

Abstract

Talk2AI is a large-scale longitudinal dataset of 3,080 conversations (totaling 30,800 turns) between human participants and Large Language Models (LLMs), designed to support research on persuasion, opinion change, and human-AI interaction. The corpus was collected from 770 profiled Italian adults across four weekly sessions in Spring 2025, using a within-subject design in which each participant conversed with a single model (GPT-4o, Claude Sonnet 3.7, DeepSeek-chat V3, or Mistral Large) on three socially relevant topics: climate change, math anxiety, and health misinformation. Each conversation is linked to rich contextual data, including sociodemographic characteristics and psychometric profiles. After each session, participants reported on opinion change, conviction stability, perceived humanness of the AI, and behavioral intentions, enabling fine-grained longitudinal analysis of how AI-mediated dialogue shapes beliefs and attitudes over time.

Figures (2)

• Figure 1: Talk2AI experiment workflow and data generation pipeline. The data collection workflow begins with participant registration (Step 1), recording sociodemographic metadata, topic assignment, and LLM architecture assignment. The repeated session loop encompasses Steps 2 through 4. Step 2 records psychometric profiles, capturing Need for Cognition, Big Five personality traits, and trust in LLMs (TILLMI). Step 3 logs the conversational interaction, comprising 10 turns between the user and the model. Step 4 collects persuasion feedback. Users repeat this loop for four sessions, with a one-week delay between each session. The data processing pipeline begins at Step 5; the system evaluates responses for completeness and variance, discarding data that fails these criteria. Step 6 applies exploratory and confirmatory factor analysis and longitudinal measurement invariance testing to generate the data_table.csv file. Step 7 translates the conversational logs into English, Spanish, Dutch, German, Portuguese, and French. The bottom panel displays data usage examples: longitudinal sequence modeling, NLP and argumentation modeling, predictive persuasion analytics, and unsupervised user typology clustering.
• Figure 2: PRISMA-style flow diagram of the data cleaning pipeline. Sequential filters excluded incomplete conversational sessions (Filter 1), zero-variance psychometric responses (Filter 2), and participants failing cumulative completion thresholds (Filter 3), yielding a final validated cohort of 770 fully profiled participants from an initial pool of 2,644 registrants.