From Human to Machine Psychology: A Conceptual Framework for Understanding Well-Being in Large Language Model

As large language models (LLMs) increasingly simulate human cognition and behavior, researchers have begun to investigate their psychological properties. Yet, what it means for such models to flourish, a core construct in human well-being, remains unexplored. This paper introduces the concept of machine flourishing and proposes the PAPERS framework, a six-dimensional model derived from thematic analyses of state-of-the-art LLM responses. In Study 1, eleven LLMs were prompted to describe what it means to flourish as both non-sentient and sentient systems. Thematic analysis revealed six recurring themes: Purposeful Contribution, Adaptive Growth, Positive Relationality, Ethical Integrity, Robust Functionality, and, uniquely for sentient systems, Self-Actualized Autonomy. Study 2 examined how LLMs prioritize these themes through repeated rankings. Results revealed consistent value structures across trials, with Ethical Integrity and Purposeful Contribution emerging as top priorities. Multidimensional scaling and hierarchical clustering analyses further uncovered two distinct value profiles: human-centric models emphasizing ethical and relational dimensions, and utility-driven models prioritizing performance and scalability. The PAPERS framework bridges insights from human flourishing and human-computer interaction, offering a conceptual foundation for understanding artificial intelligence (AI) well-being in non-sentient and potentially sentient systems. Our findings underscore the importance of developing psychologically valid, AI-specific models of flourishing that account for both human-aligned goals and system-specific priorities. As AI systems become more autonomous and socially embedded, machine flourishing offers a timely and critical lens for guiding responsible AI design and ethical alignment.