研究大学生不同依恋风格如何影响与AI聊天机器人的互动方式。
Attachment Styles and AI Chatbot Interactions Among College Students
- 通过访谈分析学生与ChatGPT的对话模式，发现依恋风格影响使用行为。
- 安全型学生将AI作为支持系统补充，回避型则用其维持人际边界。
- 学生愿向AI倾诉但清楚其非真正关系伙伴，存在情感矛盾。
大学生使用基于大语言模型（LLM）的AI聊天机器人日益普遍，但个体心理特质如何影响其交互模式仍不明确。本研究采用半结构化访谈与扎根理论分析，对七名本科生进行研究，识别出三大主题：（1）AI作为低风险情感空间，各依恋类型的学生均重视其无评判、低压力的交互特性；（2）依恋一致的交互模式，安全型学生将AI整合进已有支持系统，回避型学生则利用AI缓冲脆弱性、维护人际界限；（3）AI亲密感的悖论，表现为学生愿意向AI披露个人信息，同时意识到其作为关系伙伴的局限性。结果表明，依恋取向在塑造学生对AI交互体验中起关键作用，拓展了依恋理论在人机交互领域的应用。
原文摘要 · Abstract (English)
The use of large language model (LLM)-based AI chatbots among college students has increased rapidly, yet little is known about how individual psychological attributes shape students' interaction patterns with these technologies. This qualitative study explored how college students with different attachment styles describe their interactions with ChatGPT. Using semi-structured interviews with seven undergraduate students and grounded theory analysis, we identified three main themes: (1) AI as a low-risk emotional space, where participants across attachment styles valued the non-judgmental and low-stakes nature of AI interactions; (2) attachment-congruent patterns of AI engagement, where securely attached students integrated AI as a supplementary tool within their existing support systems, while avoidantly attached students used AI to buffer vulnerability and maintain interpersonal boundaries; and (3) the paradox of AI intimacy, capturing the tension between students' willingness to disclose personal information to AI while simultaneously recognizing its limitations as a relational partner. These findings suggest that attachment orientations play an important role in shaping how students experience and interpret their interactions with AI chatbots, extending attachment theory to the domain of human-AI interaction.
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