AliceBot: Designing AI-Driven Applications for Mental Health Management

Min Zhao; Yunpeng Bai; Wanming Zhong; Wenzhe Cun; Yuanjun Li; Mengya Zhu; Yuan Feng; Dengkai Chen

doi:10.1007/978-3-031-93712-5_16

AliceBot: Designing AI-Driven Applications for Mental Health Management

Min Zhao
, Yunpeng Bai
, Wanming Zhong
, Wenzhe Cun
, Yuanjun Li
, Mengya Zhu
, Yuan Feng
, Dengkai Chen

School of Mechanical Engineering

Northwestern Polytechnical University Xian
Ministry of Industry and Information Technology
Ningbo University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Digital health leverages emerging technologies to enhance mental healthcare. In response to the increasing prevalence of mental health issues among young people, this study developed a AI-Driven Applications for Mental Health Management-Alicebot. The objective was to integrate large language models (LLMs) such as ChatGPT into mental health management applications to enhance cohesiveness, sustainability, and effectiveness, thereby supporting mental health management. Following the ADDIE instructional design model, the chatbot underwent three iterative design phases. The core functions included Conversational Interaction-Alicebot, which provide chat support as well as sentiment data collection; Multimedia Resource Support; and an Emotion Tracker for monitoring mood changes. Results indicated that AI-driven mental health management applications are more advantageous in terms of user experience and engagement. However, due to the limited sample size, further research is required to establish the generalizability of these findings. This study provides empirical support and a reference for AI-driven mental health tools.

Original language	English
Title of host publication	Virtual, Augmented and Mixed Reality - 17th International Conference, VAMR 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings
Editors	Jessie Y. C. Chen, Gino Fragomeni
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	260-279
Number of pages	20
ISBN (Print)	9783031937118
DOIs	https://doi.org/10.1007/978-3-031-93712-5_16
State	Published - 2025
Event	17th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2025, held as part of the 27th HCI International Conference, HCII 2025 - Gothenburg, Sweden Duration: 22 Jun 2025 → 27 Jun 2025

Publication series

Name	Lecture Notes in Computer Science
Volume	15789 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2025, held as part of the 27th HCI International Conference, HCII 2025
Country/Territory	Sweden
City	Gothenburg
Period	22/06/25 → 27/06/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Anxiety Management
Artificial Intelligence (AI)
Chatbots
Digital Health
Large Language Models (LLMs)
Mental Health Applications

Access to Document

10.1007/978-3-031-93712-5_16

Cite this

Zhao, M., Bai, Y., Zhong, W., Cun, W., Li, Y., Zhu, M., Feng, Y., & Chen, D. (2025). AliceBot: Designing AI-Driven Applications for Mental Health Management. In J. Y. C. Chen, & G. Fragomeni (Eds.), Virtual, Augmented and Mixed Reality - 17th International Conference, VAMR 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings (pp. 260-279). (Lecture Notes in Computer Science; Vol. 15789 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-93712-5_16

Zhao, Min ; Bai, Yunpeng ; Zhong, Wanming et al. / AliceBot : Designing AI-Driven Applications for Mental Health Management. Virtual, Augmented and Mixed Reality - 17th International Conference, VAMR 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings. editor / Jessie Y. C. Chen ; Gino Fragomeni. Springer Science and Business Media Deutschland GmbH, 2025. pp. 260-279 (Lecture Notes in Computer Science).

@inproceedings{eb052a7c93b848e495e8fdb51808d261,

title = "AliceBot: Designing AI-Driven Applications for Mental Health Management",

abstract = "Digital health leverages emerging technologies to enhance mental healthcare. In response to the increasing prevalence of mental health issues among young people, this study developed a AI-Driven Applications for Mental Health Management-Alicebot. The objective was to integrate large language models (LLMs) such as ChatGPT into mental health management applications to enhance cohesiveness, sustainability, and effectiveness, thereby supporting mental health management. Following the ADDIE instructional design model, the chatbot underwent three iterative design phases. The core functions included Conversational Interaction-Alicebot, which provide chat support as well as sentiment data collection; Multimedia Resource Support; and an Emotion Tracker for monitoring mood changes. Results indicated that AI-driven mental health management applications are more advantageous in terms of user experience and engagement. However, due to the limited sample size, further research is required to establish the generalizability of these findings. This study provides empirical support and a reference for AI-driven mental health tools.",

keywords = "Anxiety Management, Artificial Intelligence (AI), Chatbots, Digital Health, Large Language Models (LLMs), Mental Health Applications",

author = "Min Zhao and Yunpeng Bai and Wanming Zhong and Wenzhe Cun and Yuanjun Li and Mengya Zhu and Yuan Feng and Dengkai Chen",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 17th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2025, held as part of the 27th HCI International Conference, HCII 2025 ; Conference date: 22-06-2025 Through 27-06-2025",

year = "2025",

doi = "10.1007/978-3-031-93712-5\_16",

language = "英语",

isbn = "9783031937118",

series = "Lecture Notes in Computer Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "260--279",

editor = "Chen, \{Jessie Y. C.\} and Gino Fragomeni",

booktitle = "Virtual, Augmented and Mixed Reality - 17th International Conference, VAMR 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings",

}

Zhao, M, Bai, Y, Zhong, W, Cun, W, Li, Y, Zhu, M, Feng, Y & Chen, D 2025, AliceBot: Designing AI-Driven Applications for Mental Health Management. in JYC Chen & G Fragomeni (eds), Virtual, Augmented and Mixed Reality - 17th International Conference, VAMR 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings. Lecture Notes in Computer Science, vol. 15789 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 260-279, 17th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2025, held as part of the 27th HCI International Conference, HCII 2025, Gothenburg, Sweden, 22/06/25. https://doi.org/10.1007/978-3-031-93712-5_16

AliceBot: Designing AI-Driven Applications for Mental Health Management. / Zhao, Min; Bai, Yunpeng; Zhong, Wanming et al.
Virtual, Augmented and Mixed Reality - 17th International Conference, VAMR 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings. ed. / Jessie Y. C. Chen; Gino Fragomeni. Springer Science and Business Media Deutschland GmbH, 2025. p. 260-279 (Lecture Notes in Computer Science; Vol. 15789 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - AliceBot

T2 - 17th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2025, held as part of the 27th HCI International Conference, HCII 2025

AU - Zhao, Min

AU - Bai, Yunpeng

AU - Zhong, Wanming

AU - Cun, Wenzhe

AU - Li, Yuanjun

AU - Zhu, Mengya

AU - Feng, Yuan

AU - Chen, Dengkai

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Digital health leverages emerging technologies to enhance mental healthcare. In response to the increasing prevalence of mental health issues among young people, this study developed a AI-Driven Applications for Mental Health Management-Alicebot. The objective was to integrate large language models (LLMs) such as ChatGPT into mental health management applications to enhance cohesiveness, sustainability, and effectiveness, thereby supporting mental health management. Following the ADDIE instructional design model, the chatbot underwent three iterative design phases. The core functions included Conversational Interaction-Alicebot, which provide chat support as well as sentiment data collection; Multimedia Resource Support; and an Emotion Tracker for monitoring mood changes. Results indicated that AI-driven mental health management applications are more advantageous in terms of user experience and engagement. However, due to the limited sample size, further research is required to establish the generalizability of these findings. This study provides empirical support and a reference for AI-driven mental health tools.

AB - Digital health leverages emerging technologies to enhance mental healthcare. In response to the increasing prevalence of mental health issues among young people, this study developed a AI-Driven Applications for Mental Health Management-Alicebot. The objective was to integrate large language models (LLMs) such as ChatGPT into mental health management applications to enhance cohesiveness, sustainability, and effectiveness, thereby supporting mental health management. Following the ADDIE instructional design model, the chatbot underwent three iterative design phases. The core functions included Conversational Interaction-Alicebot, which provide chat support as well as sentiment data collection; Multimedia Resource Support; and an Emotion Tracker for monitoring mood changes. Results indicated that AI-driven mental health management applications are more advantageous in terms of user experience and engagement. However, due to the limited sample size, further research is required to establish the generalizability of these findings. This study provides empirical support and a reference for AI-driven mental health tools.

KW - Anxiety Management

KW - Artificial Intelligence (AI)

KW - Chatbots

KW - Digital Health

KW - Large Language Models (LLMs)

KW - Mental Health Applications

UR - https://www.scopus.com/pages/publications/105007993371

U2 - 10.1007/978-3-031-93712-5_16

DO - 10.1007/978-3-031-93712-5_16

M3 - 会议稿件

AN - SCOPUS:105007993371

SN - 9783031937118

T3 - Lecture Notes in Computer Science

SP - 260

EP - 279

BT - Virtual, Augmented and Mixed Reality - 17th International Conference, VAMR 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings

A2 - Chen, Jessie Y. C.

A2 - Fragomeni, Gino

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 22 June 2025 through 27 June 2025

ER -

Zhao M, Bai Y, Zhong W, Cun W, Li Y, Zhu M et al. AliceBot: Designing AI-Driven Applications for Mental Health Management. In Chen JYC, Fragomeni G, editors, Virtual, Augmented and Mixed Reality - 17th International Conference, VAMR 2025, Held as Part of the 27th HCI International Conference, HCII 2025, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 260-279. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-93712-5_16

AliceBot: Designing AI-Driven Applications for Mental Health Management

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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