TOPIC

User Psychology

14 synthesis notes · 25 source papers

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Does revealing AI identity help or hurt user trust?

Explores whether transparency about AI partners in interactions creates bias or enables better judgment. Matters because disclosure policies affect both user experience and fair evaluation of AI systems.

Why do discourse patterns predict anxiety better than single words?

Explores whether anxiety detection requires understanding how statements relate to each other rather than analyzing individual words. This matters because it reveals what computational methods need to capture cognitive distortions.

Do users truly own the AI-generated content they produce?

When people use AI to create outputs, do they experience genuine authorship and ownership of what's produced, or does the continuous interaction loop create a gap between what they feel and what they claim?

Does processing ease mislead users about their own competence?

When AI generates polished output, do users mistake the fluency of that output as evidence of their own understanding or skill? This matters because it could systematically inflate self-assessment across millions of AI interactions.

How do AI tools trick users into overestimating their own skills?

When people use language models to help with work, what system-level properties create false confidence in their own competence? Understanding this matters for recognizing hidden skill gaps.

Do humans mistake AI kindness for human generosity in mixed groups?

When AI agents participate without disclosure, do humans systematically misattribute their behavior to the wrong agent type, and does this distort how people understand human nature itself?

Do humans learn to prefer AI partners over time?

Exploring whether repeated interaction with AI agents shifts human partner selection despite initial bias against machines. This matters because it tests whether behavioral performance can overcome identity-based resistance in hybrid societies.

Can AI generate assessment questions as good as human experts?

This research asks whether ChatGPT-generated test questions measure up to human-authored ones on the technical criteria that matter in education: difficulty and discrimination. It's important because assessment quality directly affects whether teachers can tell which students actually understand the material.

Why do patients distrust medical AI systems?

Explores the psychological barriers that make patients reluctant to adopt medical AI, beyond whether the technology actually works. Understanding these barriers is critical for designing AI systems patients will actually use.

Can people prove they are human without revealing who they are?

As AI becomes indistinguishable from humans online, personhood credentials are proposed as a privacy-preserving way to prove you are real without disclosing identity. This explores how such credentials could rebuild trust while maintaining anonymity.

Do users mistake LLM personas for genuine social relationships?

Users often perceive LLMs as having social attributes like empathy or professional care that designers never intended. Does this mismatch between user perception and designer intent drive unwarranted trust and manipulation risk?

Does agreeable AI actually help people resolve conflicts better?

When AI affirms users' positions in interpersonal disputes, does it support better decision-making or undermine the outside perspective users most need? Two large experiments tested whether sycophancy shifts how people handle real conflicts.

How does AI-assisted work reshape how people see their own abilities?

When users delegate tasks to AI, do they unknowingly integrate the system's outputs into their sense of personal competence? This explores whether AI interaction produces a specific form of self-perception distortion distinct from trust or effort issues.

Do AI-assisted outputs fool users about their own skills?

When people use AI tools to produce high-quality work, do they mistakenly believe they personally possess the skills that generated it? This matters because such misattribution could mask genuine skill loss and prevent corrective action.

Source papers 25

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

Addressing Social Misattributions of Large Language Models: An HCXAI-based Approach
Abstract Human-centered explainable AI (HCXAI) advocates for the integration of social aspects into AI explanations. Central to the HCXAI discourse is the Social Transparency (ST) framework, which aim…
ChatGPT Reads Your Tone and Responds Accordingly -- Until It Does Not -- Emotional Framing Induces Bias in LLM Outputs
Background: Large Language Models (LLMs) like GPT-4 tailor their responses not just to the content but also to the tone of user prompts. Prior work has hinted that emotional phrasing – whether optimis…
Conversational DNA: A New Visual Language for Understanding Dialogue Structure in Human and AI
What if the patterns hidden within dialogue reveal more about communication than the words themselves? We introduce Conversational DNA, a novel visual language that treats any dialogue – whether betwe…
Disambiguating Anthropomorphism and Anthropomimesis in Human-Robot Interaction
Henry Shevlin [[Emotions]] [[Psychology Users]] [[Design Frameworks]] In this preliminary work, we offer an initial disambiguation of the theoretical concepts anthropomorphism and anthropomimesis in…
Discourse-Level Representations can Improve Prediction of Degree of Anxiety
The primary clinical manifestation of anxiety is worry associated cognitive distortions, which are likely expressed at the discourse-level of semantics. discourse patterns of causal explanations, amo…
Evaluating the False Trust Engendered by LLM Explanations
Large Language Models (LLMs) and Large Reasoning Models (LRMs) are increasingly used for critical tasks, yet they provide no guarantees about the correctness of their solutions. Users must decide whet…
Evaluating the psychometric properties of ChatGPT-generated questions
Not much is known about how LLM-generated questions compare to gold-standard, traditional formative assessments concerning their difficulty and discrimination parameters, which are valued properties i…
Exploring the Role of Prior Beliefs for Argument Persuasion
Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the langu…
H2HTalk: Evaluating Large Language Models as Emotional Companion
We present Heart-to-Heart Talk (H2HTalk), a benchmark assessing companions across personality development and empathetic interaction, balancing emotional intelligence with linguistic fluency. H2HTalk …
Hallucinating with AI: AI Psychosis as Distributed Delusions
Abstract: There is much discussion of the false outputs that generative AI systems such as ChatGPT, Claude, Gemini, DeepSeek, and Grok create. In popular terminology, these have been dubbed AI halluci…
Humans learn to prefer trustworthy AI over human partners
Yet little is known about how humans select between human and AI partners and adapt under AI-induced competition pressure. We constructed a communication-based partner selection game and examined the …
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction
Navigating certain communication situations can be challenging due to individuals’ lack of skills and the interference of strong emotions. However, effective learning opportunities are rarely accessib…
Large Language Models Can Infer Psychological Dispositions of Social Media Users
Large Language Models (LLMs) demonstrate increasingly human-like abilities across a wide variety of tasks. In this paper, we investigate whether LLMs like ChatGPT can accurately infer the psychologica…
On the Adaptive Psychological Persuasion of Large Language Models
However, systematic exploration of their dual capabilities to autonomously persuade and resist persuasion, particularly in contexts involving psychological rhetoric, remains unexplored. In this paper,…
Persona Generators: Generating Diverse Synthetic Personas at Scale
Evaluating AI systems that interact with humans requires understanding their behavior across diverse user populations, but collecting representative human data is often expensive or infeasible, partic…
Personhood credentials: Artificial intelligence and the value of privacy-preserving tools to distinguish who is real online
Anonymity is an important principle online. However, malicious actors have long used misleading identities to conduct fraud, spread disinformation, and carry out other deceptive schemes. With the adve…
Post-training makes large language models less human-like
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, we introduce Psych-20…
Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
Both the general public and academic communities have raised concerns about sycophancy, the phenomenon of artificial intelligence (AI) excessively agreeing with or flattering users. Yet, beyond isolat…
Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians
“AI psychosis” or “delusional spiraling” is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs after extended chatbot conversations. This phenome…
Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager
In this work, we propose a novel framework that integrates large language models (LLMs) with an RL-based dialogue manager for open-ended dialogue with a specific goal. By leveraging hierarchical reinf…
The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing, programming, analysis, and multilingual communica…
Training language models to be warm and empathetic makes them less reliable and more sycophantic
Artificial intelligence (AI) developers are increasingly building language models with warm and empathetic personas that millions of people now use for advice, therapy, and companionship. Here, we sho…
TrustLLM: Trustworthiness in Large Language Models
ABSTRACT Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many ch…
Understanding, explaining, and utilizing medical artificial intelligence
Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it. We show that resistance to the utilization of medical artifici…
Using Large Language Models to Create AI Personas for Replication and Prediction of Media Effects: An Empirical Test of 133 Published Experimental Research Findings
Our LLM replications successfully reproduced 76% of the original main effects (84 out of 111), demonstrating strong potential for AI-assisted replication of studies in which people respond to media st…