面向可扩展的心理急救培训:对大型语言模型在基于模拟的学习中的潜力的自动民族学探索
Rodrigo A Figueroa1,2, Margarita Bernales3, Chris M Hoeboer2
1Department of Psychiatry, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
European journal of psychotraumatology
|February 2, 2026
概括
大型语言模型 (LLM) 显示了心理急救 (PFA) 模拟培训的前景,为建立情感性提供了可访问的实践. 为了充分发挥教育潜力,需要进一步的细化和验证.
科学领域:
- 数字健康数字健康
- 教育中的人工智能
- 心理健康培训 心理健康培训
背景情况:
- 心理急救 (PFA) 培训是必不可少的,但成本高昂,通常需要结构化,基于模拟的方法.
- 大型语言模型 (LLM) 为可访问,灵活和可重复的PFA模拟培训提供了潜在的解决方案.
- 基于LLM的培训可以克服地理障碍,到达最需要PFA的孤立地区.
研究的目的:
- 探索LLMs在心理急救模拟培训中的潜力.
- 评估LLM聊天机器人在模拟PFA培训场景中的有效性.
- 确定在心理健康急救教育中使用LLM的挑战和机会.
主要方法:
- 在这项研究中,采用了数字自民族学方法.
- 使用ChatGPT-3.5-turbo.进行了三次角色扮演模拟.
- 研究人员同时担任教练和学习者,在现实的PFA场景中评估聊天机器人的表现.
主要成果:
- 聊天GPT有效地模拟了现实的角色,并与教练合作.
- 在PFA模拟期间,LLM为学习者提供了有价值的反.
- 挑战包括刻板印象的表征,身份混和遵守教练指令.
结论:
- 法律学士在提升心理急救模拟培训方面显示出显著的前景.
- 仔细的细化,经验验证和道德监督对于实现LLM在这个领域的全部教育潜力至关重要.
- 基于LLM的PFA培训需要进一步发展,以解决已识别的局限性,并确保有效的,道德的实施.
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