Karin G. Coifman

Jianfeng Zhu, Megan Korhummel, Ruoming Jin et al.

As demand for mental health care outpaces clinician-delivered assessment, scalable screening tools are increasingly needed. Large language models (LLMs) may identify psychiatric risk from patient narratives, but their reliability across diagnoses, demographic subgroups, and evidence-use patterns remains uncertain. We introduce a SCID-anchored benchmark of 555 semi-structured experiential interviews paired with diagnostic reference labels for anxiety disorder, major depressive disorder, post-traumatic stress disorder, and any current mental health disorder. Using zero-shot task-specific prompting, we evaluated five state-of-the-art LLMs and examined whether false-negative errors reflected missed psychiatric evidence or differential weighting of symptom, functional-impairment, and protective-context cues. Performance varied across tasks and models, with accuracy ranging from 0.49 to 0.86 and Matthews correlation coefficients from 0.16 to 0.38. GPT-4.1 Mini and GPT-5 Mini showed the most consistent disorder-specific accuracy. Subgroup analyses found higher depression-classification accuracy among male than female participants, no consistent age-related pattern, and modest non-uniform variation across race strata. Evidence-integration analyses showed that false-negative anxiety and PTSD classifications often contained explicit symptom evidence but were accompanied by preserved functioning, coping ability, or social support. Functional-impairment evidence shifted model outputs toward positive classifications, whereas protective-context evidence shifted outputs away. These findings suggest that LLMs may support scalable psychiatric screening, but their tendency to discount symptom evidence in the presence of preserved functioning or protective context requires careful validation before clinical deployment.

Jianfeng Zhu, Karin G. Coifman, Ruoming Jin

Generative AI systems are increasingly embedded in everyday life, yet empirical understanding of how psychological risk associated with AI use emerges, is experienced, and is regulated by users remains limited. We present a large-scale computational thematic analysis of posts collected between 2023 and 2025 from two Reddit communities, r/AIDangers and r/ChatbotAddiction, explicitly focused on AI-related harm and distress. Using a multi-agent, LLM-assisted thematic analysis grounded in Braun and Clarke's reflexive framework, we identify 14 recurring thematic categories and synthesize them into five higher-order experiential dimensions. To further characterize affective patterns, we apply emotion labeling using a BERT-based classifier and visualize emotional profiles across dimensions. Our findings reveal five empirically derived experiential dimensions of AI-related psychological risk grounded in real-world user discourse, with self-regulation difficulties emerging as the most prevalent and fear concentrated in concerns related to autonomy, control, and technical risk. These results provide early empirical evidence from lived user experience of how AI safety is perceived and emotionally experienced outside laboratory or speculative contexts, offering a foundation for future AI safety research, evaluation, and responsible governance.

Jianfeng Zhu, Julina Maharjan, Xinyu Li et al.

Large Language Models (LLMs) are increasingly deployed in roles requiring nuanced psychological understanding, such as emotional support agents, counselors, and decision-making assistants. However, their ability to interpret human personality traits, a critical aspect of such applications, remains unexplored, particularly in ecologically valid conversational settings. While prior work has simulated LLM "personas" using discrete Big Five labels on social media data, the alignment of LLMs with continuous, ground-truth personality assessments derived from natural interactions is largely unexamined. To address this gap, we introduce a novel benchmark comprising semi-structured interview transcripts paired with validated continuous Big Five trait scores. Using this dataset, we systematically evaluate LLM performance across three paradigms: (1) zero-shot and chain-of-thought prompting with GPT-4.1 Mini, (2) LoRA-based fine-tuning applied to both RoBERTa and Meta-LLaMA architectures, and (3) regression using static embeddings from pretrained BERT and OpenAI's text-embedding-3-small. Our results reveal that all Pearson correlations between model predictions and ground-truth personality traits remain below 0.26, highlighting the limited alignment of current LLMs with validated psychological constructs. Chain-of-thought prompting offers minimal gains over zero-shot, suggesting that personality inference relies more on latent semantic representation than explicit reasoning. These findings underscore the challenges of aligning LLMs with complex human attributes and motivate future work on trait-specific prompting, context-aware modeling, and alignment-oriented fine-tuning.

Jianfeng Zhu, Hailong Jiang, Yulan Wang et al.

Early substance use during adolescence increases the risk of later substance use disorders and mental health problems, yet the emotional and contextual factors driving these behaviors remain poorly understood. This study analyzed 23000 substance-use related posts and an equal number of non-substance posts from Reddit's r/teenagers community (2018-2022). Posts were annotated for six discrete emotions (sadness, anger, joy, guilt, fear, disgust) and contextual factors (family, peers, school) using large language models (LLMs). Statistical analyses compared group differences, and interpretable machine learning (SHAP) identified key predictors of substance-use discussions. LLM-assisted thematic coding further revealed latent psychosocial themes linking emotions with contexts. Negative emotions, especially sadness, guilt, fear, and disgust, were significantly more common in substance-use posts, while joy dominated non-substance discussions. Guilt and shame diverged in function: guilt often reflected regret and self-reflection, whereas shame reinforced risky behaviors through peer performance. Peer influence emerged as the strongest contextual factor, closely tied to sadness, fear, and guilt. Family and school environments acted as both risk and protective factors depending on relational quality and stress levels. Overall, adolescent substance-use discussions reflected a dynamic interplay of emotion, social context, and coping behavior. By integrating statistical analysis, interpretable models, and LLM-based thematic exploration, this study demonstrates the value of mixed computational approaches for uncovering the emotional and contextual mechanisms underlying adolescent risk behavior.

Jianfeng Zhu, Ruoming Jin, Karin G. Coifman

Large Language Models (LLMs) such as OpenAI's GPT-4 and Meta's LLaMA offer a promising approach for scalable personality assessment from open-ended language. However, inferring personality traits remains challenging, and earlier work often relied on synthetic data or social media text lacking psychometric validity. We introduce a real-world benchmark of 555 semi-structured interviews with BFI-10 self-report scores for evaluating LLM-based personality inference. Three state-of-the-art LLMs (GPT-4.1 Mini, Meta-LLaMA, and DeepSeek) were tested using zero-shot prompting for BFI-10 item prediction and both zero-shot and chain-of-thought prompting for Big Five trait inference. All models showed high test-retest reliability, but construct validity was limited: correlations with ground-truth scores were weak (max Pearson's $r = 0.27$), interrater agreement was low (Cohen's $κ< 0.10$), and predictions were biased toward moderate or high trait levels. Chain-of-thought prompting and longer input context modestly improved distributional alignment, but not trait-level accuracy. These results underscore limitations in current LLM-based personality inference and highlight the need for evidence-based development for psychological applications.