The Incomplete Bridge: How AI Research (Mis)Engages with Psychology

2025-07-30

AI Summary

This study investigates the current state of interdisciplinary engagement between artificial intelligence—particularly large language models (LLMs)—and psychology, focusing on mechanisms of conceptual misappropriation and structural barriers to integration. Method: Drawing on 1,006 LLM papers published at top-tier AI conferences (2023–2025) and their 2,544 cited psychological sources, we conduct bibliometric analysis, systematic content coding, and cross-disciplinary text mining to construct the first comprehensive AI–psychology interdisciplinary research map. Contribution/Results: We identify disproportionate citation concentration in cognitive and social psychology, uncover recurrent misuse patterns—including conceptual conflation, inadequate operationalization, and theoretical decontextualization—and reveal significant neglect of developmental and cultural psychology. The study yields an actionable interdisciplinary integration framework and practical guidelines, thereby advancing theoretical rigor and empirical validity in AI-based modeling of human behavior.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Understanding People, Theories, Concepts and Methods

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Social sciences have accumulated a rich body of theories and methodologies for investigating the human mind and behaviors, while offering valuable insights into the design and understanding of Artificial Intelligence (AI) systems. Focusing on psychology as a prominent case, this study explores the interdisciplinary synergy between AI and the field by analyzing 1,006 LLM-related papers published in premier AI venues between 2023 and 2025, along with the 2,544 psychology publications they cite. Through our analysis, we identify key patterns of interdisciplinary integration, locate the psychology domains most frequently referenced, and highlight areas that remain underexplored. We further examine how psychology theories/frameworks are operationalized and interpreted, identify common types of misapplication, and offer guidance for more effective incorporation. Our work provides a comprehensive map of interdisciplinary engagement between AI and psychology, thereby facilitating deeper collaboration and advancing AI systems.

Problem

• Analyzes AI-psychology interdisciplinary synergy via 1,006 LLM papers
• Identifies psychology domains referenced and underexplored areas in AI research
• Examines psychology theory misapplication and guides effective integration

Innovation

• Analyzes 1,006 LLM papers and 2,544 psychology citations
• Identifies psychology domains and integration patterns
• Offers guidance for effective interdisciplinary collaboration