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dc.contributor.authorBokai Chen
dc.contributor.authorWeiwei Zheng
dc.contributor.authorLiang Zhao
dc.contributor.authorXiaojun Ding
dc.date.accessioned2025-12-13T14:09:57Z
dc.date.accessioned2026-05-18T06:00:07Z
dc.date.available2026-05-18T06:00:07Z
dc.date.issued2025-12-13T14:09:57Z
dc.identifier.urihttps://doi.org/10.1057/s41599-025-04657-7
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/22438
dc.description.abstractAbstract Large language models (LLMs) have emerged as transformative tools with the potential to revolutionize philosophical counseling. By harnessing their advanced natural language processing and reasoning capabilities, LLMs offer innovative solutions to overcome limitations inherent in traditional counseling approaches—such as counselor scarcity, difficulties in identifying mental health issues, subjective outcome assessment, and cultural adaptation challenges. In this study, we explore cutting‐edge technical strategies—including prompt engineering, fine‐tuning, and retrieval‐augmented generation—to integrate LLMs into the counseling process. Our analysis demonstrates that LLM-assisted systems can provide counselor recommendations, streamline session evaluations, broaden service accessibility, and improve cultural adaptation. We also critically examine challenges related to user trust, data privacy, and the inherent inability of current AI systems to genuinely understand or empathize. Overall, this work presents both theoretical insights and practical guidelines for the responsible development and deployment of AI-assisted philosophical counseling practices.
dc.publisherSpringer Nature
dc.subject.lccHistory of scholarship and learning. The humanities; Social Sciences
dc.titleLeveraging large language models to assist philosophical counseling: prospective techniques, value, and challenges
dc.typeArticle
dc.description.doi10.1057/s41599-025-04657-7
dc.title.journalHumanities & Social Sciences Communications
dc.identifier.oaioai:doaj.org/journal:558425ba863e4a1da6a35dca18801997


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