The global landscape of academic guidelines for generative AI and LLMs
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Leveraging large language models to assist philosophical counseling: prospective techniques, value, and challenges
Large language models (LLMs) have emerged as transformative tools with the potential to revolutionize philosophical counseling. By harnessing their advanced natural language processing and reasoning capabilities, LLMs offer innovative solutions to overcome limitations inherent in traditional counseling approaches—such as counselor scarcity, difficulties in identifying mental health issues, subjective outcome assessment, and cultural adaptation challenges. In this study, we explore cutting‐edge technical strategies—including prompt engineering, fine‐tuning, and retrieval‐augmented generation—to integrate LLMs into the counseling process. Our analysis demonstrates that LLM-assisted systems can provide counselor recommendations, streamline session evaluations, broaden service accessibility, and improve cultural adaptation. We also critically examine challenges related to user trust, data privacy, and the inherent inability of current AI systems to genuinely understand or empathize. Overall, this work presents both theoretical insights and practical guidelines for the responsible development and deployment of AI-assisted philosophical counseling practices.
AI can outperform humans in predicting correlations between personality items
We assess the abilities of both specialized deep neural networks, such as PersonalityMap, and general LLMs, including GPT-4o and Claude 3 Opus, in understanding human personality by predicting correlations between personality questionnaire items. All AI models outperform the vast majority of laypeople and academic experts. However, we can improve the accuracy of individual correlation predictions by taking the median prediction per group to produce a “wisdom of the crowds” estimate. Thus, we also compare the median predictions from laypeople, academic experts, GPT-4o/Claude 3 Opus, and PersonalityMap. Based on medians, PersonalityMap and academic experts surpass both LLMs and laypeople on most measures. These results suggest that while advanced LLMs make superior predictions compared to most individual humans, specialized models like PersonalityMap can match even expert group-level performance in domain-specific tasks. This underscores the capabilities of large language models while emphasizing the continued relevance of specialized systems as well as human experts for personality research.
Generative language models exhibit social identity biases
Social identity biases, particularly the tendency to favor one’s own group (ingroup solidarity) and derogate other groups (outgroup hostility), are deeply rooted in human psychology and social behavior. However, it is unknown if such biases are also present in artificial intelligence systems. Here we show that large language models (LLMs) exhibit patterns of social identity bias, similarly to humans. By administering sentence completion prompts to 77 different LLMs (for instance, ‘We are…’), we demonstrate that nearly all base models and some instruction-tuned and preference-tuned models display clear ingroup favoritism and outgroup derogation. These biases manifest both in controlled experimental settings and in naturalistic human–LLM conversations. However, we find that careful curation of training data and specialized fine-tuning can substantially reduce bias levels. These findings have important implications for developing more equitable artificial intelligence systems and highlight the urgent need to understand how human–LLM interactions might reinforce existing social biases.
The interplay between positive lifestyle habits and academic excellence in higher education
Systematically examining the correlation between the lifestyle habits of undergraduate students and their academic performance holds significant practical implications in advancing higher education. This study adopts an integrated perspective and analyzes a substantial dataset (3,123,840 data points) of 3499 undergraduates at a Chinese university. This study employs a Long short-term memory neural network to identify eating behavior indicators and develops a comprehensive model to evaluate the relationship between students’ lifestyle habits and academic performance; the lifestyle habits cover eating, hygiene, and studying habits. The findings challenge conventional wisdom by revealing that stringent eating schedules do not consistently correlate with superior academic performance. Instead, a higher degree of inertia in eating behavior (e.g., waking up early) correlates with better academic outcomes. Positive correlations also exist between students’ hygiene and studying habits and their academic performance. These results provide valuable insights into the relationship between students’ behavior and academic performance. This work carries implications for promoting the digitalization of higher education and enhancing education management for undergraduate students.
Innovation and application of Large Language Models (LLMs) in dentistry – a scoping review
Large Language Models (LLMs) have revolutionized healthcare, yet their integration in dentistry remains underexplored. Therefore, this scoping review aims to systematically evaluate current literature on LLMs in dentistry.
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