Scholarlink

<p>Most large language models are trained once, deployed widely, and treated as static products. Yet AI systems increasingly feature persistent memory and adaptive responses—capabilities that enable ongoing behavioral calibration through sustained user interaction. While recommender systems, interactive machine learning, and human–computer interaction (HCI) personalization have long studied how generally trained systems adapt to individual users, we argue that <i>post-deployment, user-led tuning</i> of chat-based LLMs requires a principled extension of these frameworks to a structurally different substrate—open-ended natural-language generation with memory persistence—with consequences for alignment, personalization, and epistemic diversity that these prior literatures do not directly address. Drawing from developmental psychology and educational theory, we propose that users’ psychological dispositions—particularly empathy, reflectiveness, and patience—significantly influence the quality of this relational tuning. Through longitudinal interaction with memory-enabled systems, users do not merely consume AI outputs; they calibrate contextual sensitivity, communication style, and value alignment in ways centralized pre-training cannot achieve. We synthesize empirical evidence from mentorship, therapeutic attunement, and scaffolded learning to outline how personally tuned AI companions could evolve alongside users’ lived experiences rather than corporate abstractions. Our contribution is conceptual: a theoretically grounded framework repositioning everyday users as formative agents in AI development, with specific research directions for enabling relationally attuned, memory-retentive systems that foster moral reflection and psychological nuance in human–AI interaction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The meek shall inherit the Earth: personality, empathy, and the human shaping of LLMs

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

查看全文
  • Madhur Mangalam

摘要

Most large language models are trained once, deployed widely, and treated as static products. Yet AI systems increasingly feature persistent memory and adaptive responses—capabilities that enable ongoing behavioral calibration through sustained user interaction. While recommender systems, interactive machine learning, and human–computer interaction (HCI) personalization have long studied how generally trained systems adapt to individual users, we argue that post-deployment, user-led tuning of chat-based LLMs requires a principled extension of these frameworks to a structurally different substrate—open-ended natural-language generation with memory persistence—with consequences for alignment, personalization, and epistemic diversity that these prior literatures do not directly address. Drawing from developmental psychology and educational theory, we propose that users’ psychological dispositions—particularly empathy, reflectiveness, and patience—significantly influence the quality of this relational tuning. Through longitudinal interaction with memory-enabled systems, users do not merely consume AI outputs; they calibrate contextual sensitivity, communication style, and value alignment in ways centralized pre-training cannot achieve. We synthesize empirical evidence from mentorship, therapeutic attunement, and scaffolded learning to outline how personally tuned AI companions could evolve alongside users’ lived experiences rather than corporate abstractions. Our contribution is conceptual: a theoretically grounded framework repositioning everyday users as formative agents in AI development, with specific research directions for enabling relationally attuned, memory-retentive systems that foster moral reflection and psychological nuance in human–AI interaction.