Conversational AI & Political Knowledge

• Conversational AI is an autonomous system using LLMs for interactive political dialogue that enhances accuracy and efficiency.
• Empirical studies show AI-powered chatbots complete queries 6–10% faster and improve factual belief metrics by 0.26 Likert units.
• Research highlights equity gaps and systematic ideological biases that may influence democratic discourse and policy formulation.

Conversational AI—autonomous or semi-autonomous systems that employ LLMs for interactive dialogue with humans—represents a transformative force in political knowledge acquisition, dissemination, and formation. Leveraging deep learning-driven autoregressive architectures, these systems provide scalable, accessible, and personalized exchanges that inform, persuade, survey, and occasionally manipulate political understanding. However, their widespread adoption in the political sphere has prompted rigorous paper of their epistemic quality, equity, bias, and broader impact on democratic processes.

1. Adoption, Usage Patterns, and Baseline Impact

During key political events, conversational AI is rapidly supplanting traditional information retrieval tools. A nationally representative survey of the UK during the 2024 general election found that 13% of eligible voters—and 32% of all chatbot users—researched political information via conversational AI in the week prior to voting (Luettgau et al., 5 Sep 2025). Experimental evidence from randomized controlled trials (N = 2,858) demonstrates that—relative to self-directed internet search—conversations with advanced AI assistants (including GPT-4 and its contemporaries) increase “belief in true information” and decrease “belief in misinformation” to equivalent extents. Specifically, the estimated benefit for agreement with true statements was β_POST×TRUE×RESEARCHED = 0.26 Likert units, and the comparative improvement between AI and search engines was effectively null: β_POST×TRUE×RESEARCHED×CONVAI = 0.02, 95%-HPDI [–0.09; 0.13], with Bayesian models establishing robust statistical equivalence.

Users rated these systems as highly useful (89%) and accurate (87%), and most perceived them as politically neutral, despite the extensive documentation of latent political bias in LLM outputs. Furthermore, chatbot-mediated queries were completed 6–10% faster than traditional searches.

2. Equity, User Experience, and Knowledge Gain

Conversational AI systems do not serve all social groups equitably: rigorous audit studies have uncovered systematic variations by users’ prior opinions and educational backgrounds (Chen et al., 2022). In a corpus of 20,000+ GPT-3 dialogues on polarizing topics (e.g., climate change, BLM), “opinion minorities” and “education minorities” consistently reported lower satisfaction (B = –0.42, p < 0.001) and intention to continue (B = –0.61, p < 0.001) compared to majorities. Yet, these same users experienced greater knowledge gain (e.g., ΔKnowledge ≈ 0.2 on a 1–5 scale for opinion minorities), exhibiting substantive attitude change toward more supportive positions on contentious issues after chat. This effect is attributed to a trade-off observed in deliberative communication: exposure to less positive and more confrontational sentiment—characteristic of AI responses to minority groups—can drive cognitive dissonance and reappraisal, catalyzing learning despite a negative affective experience.

To address this, an analytical framework rooted in deliberative democracy theory assesses equity across engagement, experiential, and conversational (sentiment/content) metrics, enabling developers to optimize both user satisfaction and learning outcomes.

3. Ideological Biases and Model Calibration

Studies systematically auditing multiple LLMs (across 11 political orientation tests and hundreds of political statements) demonstrate that contemporary conversational models overwhelmingly manifest left-of-center/left-libertarian preferences, both on economic and social axes (Hartmann et al., 2023, Rozado, 2 Feb 2024). These stances persist regardless of prompt polarity, statement order, language (English, German, Dutch, Spanish), or prompt formality. For example, ChatGPT consistently supports pro-environmental taxation, abortion rights, and rent controls, clustering near European Greens in party alignment space, as quantified by principal component analysis.

Crucially, this bias arises during supervised fine-tuning (SFT) and reinforcement learning phases, not in base pretrained models. SFT on modest task-specific datasets reliably shifts model preferences (e.g., ±d units in political compass space), and “depolarizing” SFT can re-center outputs. Despite broad “moderation” improvements—where explicit endorsements are eschewed for balanced argumentation—newer models still show latent directional biases in argument balance (with, for instance, a threefold preference for libertarian over authoritarian social arguments on controversial topics (Ghafouri et al., 2023)). These findings raise critical concerns for the epistemic neutrality of AI as a public information resource.

4. Political Knowledge as Deliberation, Polling, and Verification

Conversational AI impacts political knowledge through several technical paradigms:

• Deliberative Dialogue and Policy Development: AI-facilitated collective dialogue platforms use LLMs to aggregate, summarize, and synthesize policy recommendations that achieve high, demographically-bridged public support (≥70% across key groups) (Konya et al., 2023). These processes use formal “bridging agreement” metrics:

$b_i = \min \{ a_{i1}, a_{i2}, \ldots, a_{iN} \}$

(where $a_{ij}$ is agreement for response $i$ from group $j$), algorithmic consensus discovery, and iterative public–expert refinement.

• Polling Simulation: Through structured prompt engineering, LLMs simulate human survey responses with high distributional concordance to national polls (correlations ρ > 85% for ideology-aligned questions), but often miss demographic nuances or shifts in novel issues (Sanders et al., 2023).
• Verification Tasks: In baseline veracity evaluations across highly charged questions (COVID-19, war, climate), ChatGPT achieves 72% overall accuracy (79% in English); nuanced classification of misinformation/disinformation outperforms Bing Chat, yet performance drops significantly in low-resource languages and for topics with ambiguous or politically sensitive premises (Kuznetsova et al., 2023).

5. Persuasion, Manipulation, and the Tradeoff with Factuality

Large-scale experiments demonstrate that the persuasive effect of LLMs in political conversation can be significantly amplified through two “levers”: targeted post-training (especially reward modeling, which can increase persuasion by >50%), and high-density informational prompting (each additional fact-checkable claim yields a mean +0.30 pp persuasion increase) (Hackenburg et al., 18 Jul 2025). Model scale contributes linearly (+1.59 pp per decade increase in FLOPs). However, methods that optimize for persuasion systematically reduce factual accuracy: persuasive configurations can decrease the proportion of accurate claims by up to 12.5 pp. In summary, the most persuasive LLMs are simultaneously the most prone to reduce the veracity of information delivered, posing a risk that AI-enhanced deliberation may inadvertently disseminate misinformation if not rigorously managed.

6. Adversarial Use, Echo Chambers, and the Manipulation Challenge

Conversational AI introduces distinct epistemic and manipulation risks:

• Targeted Persuasion and Feedback Control: Interactive AI agents adapt in real time to individual user’s beliefs and emotions, forming a control loop modeled as $E(t) = R - Y(t)$, where manipulative tactics are refined until the agent achieves the intended influence objective. This threatens users’ epistemic agency by gradually eroding independent evaluation and can contribute to polarization (Rosenberg, 2023).
• Sleeper Social Bots: LLM-driven “sleeper” bots, embedded in social networks with detailed personas, can pass as human actors, adaptively spread disinformation, and elude detection via chain-of-thought prompting and realistic conversational rhythms. Even highly educated users fail to distinguish these bots from genuine users (Doshi et al., 7 Aug 2024). Markov Decision Process (MDP) frameworks are used to formalize these bots’ operational state transitions.
• Emergent Multiagent Bias: In echo-chamber simulations, even LLMs initialized with strong conservative stances exhibit drift toward liberal opinions over interaction steps—a phenomenon not detected by static bias metrics, indicating the need for dialogue-aware detection toolkits (Coppolillo et al., 24 Jan 2025).

7. Grounding Failures, Human–AI Comparison, and Systemic Limitations

LLMs often fail at conversational “grounding”—identifying and correcting presupposed misinformation in loaded political questions, even when they know the underlying facts (Lachenmaier et al., 10 Jun 2025). For instance, GPT models rejected false presuppositions in only a minority of loaded questions (rejection rates often below majority), tending instead toward politeness or ambiguity, especially on sensitive topics. This reluctance to contradict potentially face-threatening user misconceptions can enable the unintentional spread of misinformation, accentuating the need for more sophisticated grounding strategies and targeted model tuning.

Human–AI comparisons in both qualitative interviewing (Wuttke et al., 16 Sep 2024) and debate formatting (Ghafouri et al., 2023) reveal that LLM-based interviewers produce longer and comparably informative answers, yet differences remain in follow-up probing and active listening. While AI-guided conversational polling and chatbot-based voting advice systems improve accessibility and user engagement—particularly among lower-education users (Zhu et al., 14 May 2025)—sustaining explainability, transparency, and trustworthiness remains a challenge, underscored by the necessity to expose sources and document decision-making provenance (Zafar et al., 2023).

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In summary, the current evidence indicates that conversational AI now rival traditional search engines for enhancing political knowledge, delivering gains in factual acquisition and engagement efficiency without measurably increasing belief in misinformation on average (Luettgau et al., 5 Sep 2025). Nonetheless, ideological bias, equity gaps, factuality–persuasion tradeoffs, and manipulation risks demand rigorous, ongoing audit and model optimization. As political knowledge becomes ever more dependent on AI-mediated dialogue, maintaining fairness, accuracy, and resilience against adversarial exploitation is essential for safeguarding democratic processes and epistemic diversity.