single-jc.php

JACIII Vol.29 No.1 pp. 41-52
doi: 10.20965/jaciii.2025.p0041
(2025)

Research Paper:

Enhancing Individual Self-Efficacy Through a Self-Growing Memory Artificial Intelligence Agent Integrated with a Diary Application

Yuchen Guo*, Chyan Zheng Siow*,† ORCID Icon, Wei Hong Chin*, Bakir Hadžić**, Akihiro Yorita***, Takenori Obo* ORCID Icon, Matthias Rätsch**, and Naoyuki Kubota* ORCID Icon

*Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

Corresponding author

**Reutlingen University
150 Alteburgstraße, Reutlingen, Baden-Württemberg 72762, Germany

***Daiichi Institute of Technology
1-10-2 Kokubu-chuo, Kirishima, Kagoshima 899-4395, Japan

Received:
June 18, 2024
Accepted:
October 3, 2024
Published:
January 20, 2025
Keywords:
self-growing memory system, AI interactive, experience exact, self-efficacy, loneliness
Abstract

This paper introduces an artificial intelligence (AI) interactive system featuring a self-growing memory network designed to enhance self-efficacy, reduce loneliness, and maintain social interaction among the elderly. The system dynamically analyzes and processes user-written diaries, generating empathic and personalized responses tailored to each individual. The system architecture includes an experience extraction model, a self-growing memory network that provides a contextual understanding of the user’s daily life, a chat agent, and a feedback loop that adaptively learns the user’s behavioral patterns and emotional states. By drawing on both successful and challenging experiences, the system crafts responses that reinforce the self-efficacy of the user, fostering a sense of accomplishment and engagement. This approach improves the psychological well-being of elderly users and promotes their mental health and overall quality of life through consistent interaction. To validate our proposed method, we developed a diary application to facilitate user interaction and collect diary entries. Over time, the system’s capacity to learn and adapt further refines the user experience, suggesting that AI-driven solutions hold significant potential for mitigating the effects of declining self-efficacy on mental health and social interactions. With the proposed system, we achieve an average system usability scale score of 77.3 (SD = 5.4) and a general self-efficacy scale score of 34.2 (SD = 3.5).

User interface of the diary application

User interface of the diary application

Cite this article as:
Y. Guo, C. Siow, W. Chin, B. Hadžić, A. Yorita, T. Obo, M. Rätsch, and N. Kubota, “Enhancing Individual Self-Efficacy Through a Self-Growing Memory Artificial Intelligence Agent Integrated with a Diary Application,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.1, pp. 41-52, 2025.
Data files:
References
  1. [1] C. M. Perissinotto, I. S. Cenzer, and K. E. Covinsky, “Loneliness in older persons: A predictor of functional decline and death,” Archives of Internal Medicine, Vol.172, No.14, pp. 1078-1084, 2012. https://doi.org/10.1001/archinternmed.2012.1993
  2. [2] Japan Foundation for Aging and Health, “Social issues of elderly people with dementia,” 2016 (in Japanese). https://www.tyojyu.or.jp/net/byouki/ninchishou/shakai-mondai.html [Accessed November 8, 2019]
  3. [3] A. Bolotnikova, P. Gergondet, A. Tanguy, S. Courtois, and A. Kheddar, “Task-space control interface for SoftBank humanoid robots and its human-robot interaction applications,” 2021 IEEE/SICE Int. Symp. on System Integration, pp. 560-565, 2021. https://doi.org/10.1109/IEEECONF49454.2021.9382685
  4. [4] A. Bandura, “Self-efficacy: Toward a unifying theory of behavioral change,” Psychological Review, Vol.84, No.2, pp. 191-215, 1977. https://doi.org/10.1037/0033-295X.84.2.191
  5. [5] C.-Y. Chang, G.-J. Hwang, and M.-L. Gau, “Promoting students’ learning achievement and self-efficacy: A mobile chatbot approach for nursing training,” British J. of Educational Technology, Vol.53, No.1, pp. 171-188, 2022. https://doi.org/10.1111/bjet.13158
  6. [6] P. S. Fry and D. L. Debats, “Self-efficacy beliefs as predictors of loneliness and psychological distress in older adults,” The Int. J. of Aging and Human Development, Vol.55, No.3, pp. 233-269, 2002. https://doi.org/10.2190/KBVP-L2TE-2ERY-BH26
  7. [7] W. X. Zhao et al., “A survey of large language models,” arXiv:2303.18223, 2023. https://doi.org/10.48550/arXiv.2303.18223
  8. [8] H. Yildiz Durak, “Conversational agent-based guidance: Examining the effect of chatbot usage frequency and satisfaction on visual design self-efficacy, engagement, satisfaction, and learner autonomy,” Education and Information Technologies, Vol.28, No.1, pp. 471-488, 2023. https://doi.org/10.1007/s10639-022-11149-7
  9. [9] R. Williams et al., “21-day stress detox: Open trial of a universal well-being chatbot for young adults,” Social Sciences, Vol.10, No.11, Article No.416, 2021. https://doi.org/10.3390/socsci10110416
  10. [10] N. Ameen, J.-H. Cheah, and S. Kumar, “It’s all part of the customer journey: The impact of augmented reality, chatbots, and social media on the body image and self-esteem of Generation Z female consumers,” Psychology & Marketing, Vol.39, No.11, pp. 2110-2129, 2022. https://doi.org/10.1002/mar.21715
  11. [11] N. Sakane et al., “The effect of a mHealth App (KENPO-app) for specific health guidance on weight changes in adults with obesity and hypertension: Pilot randomized controlled trial,” JMIR mHealth and uHealth, Vol.11, Article No.e43236, 2023. https://doi.org/10.2196/43236
  12. [12] “Natural language processing @ Cardiff University.” https://cardiffnlp.github.io/ [Accessed March 6, 2024]
  13. [13] S. Elbagir and J. Yang, “Twitter sentiment analysis using natural language toolkit and VADER sentiment,” Proc. of the Int. MultiConf. of Engineers and Computer Scientists 2019, pp. 12-16, 2019.
  14. [14] C. Kaur and A. Sharma, “Twitter sentiment analysis on coronavirus using Textblob,” EasyChair Preprint No.2974, 2020.
  15. [15] N. Kaur and A. Solanki, “Sentiment knowledge discovery in Twitter using CoreNLP library,” 2018 8th Int. Conf. on Cloud Computing, Data Science & Engineering (Confluence), pp. 574-580, 2018. https://doi.org/10.1109/CONFLUENCE.2018.8442439
  16. [16] J. Yao, “Automated sentiment analysis of text data with NLTK,” J. of Physics: Conf. Series, Vol.1187, No.5, Article No.052020, 2019. https://doi.org/10.1088/1742-6596/1187/5/052020
  17. [17] T. G. Dietterich, “Approximate statistical tests for comparing supervised classification learning algorithms,” Neural Computation, Vol.10, No.7, pp. 1895-1923, 1998. https://doi.org/10.1162/089976698300017197
  18. [18] Sentence Transformer, “all-MiniLM-L6-v2.” https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 [Accessed March 15, 2024]
  19. [19] W. H. Chin, N. Kubota, and C. K. Loo, “An episodic-procedural semantic memory model for continuous topological sensorimotor map building,” M. K. Habib (Ed.), “Cognitive Robotics and Adaptive Behaviors,” IntechOpen, 2022. https://doi.org/10.5772/intechopen.104818
  20. [20] W. H. Chin, W. Dou, N. Kubota, and C. K. Loo, “A robust growing memory network for lifelong learning of intelligent agents,” 2022 Int. Joint Conf. on Neural Networks, 2022. https://doi.org/10.1109/IJCNN55064.2022.9892827
  21. [21] R. Dale, “GPT-3: What’s it good for?,” Natural Language Engineering, Vol.27, No.1, pp. 113-118, 2021. https://doi.org/10.1017/S1351324920000601
  22. [22] S. Shimoyama, “Narrative approach,” The Japanese J. of Dialysis & Caring, Vol.16, No.11, pp. 1236-1240, 2010 (in Japanese).
  23. [23] NLP-JAPAN Learning Center, “A simple explanation of the basics of coaching! Introduces the meaning and effects,” (in Japanese). https://life-and-mind.com/coaching-7958 [Accessed March 29, 2024]
  24. [24] A. Wenzel, “Basic strategies of cognitive behavioral therapy,” Psychiatric Clinics of North America, Vol.40, No.4, pp. 597-609, 2017. https://doi.org/10.1016/j.psc.2017.07.001
  25. [25] R. Schwarzer and M. Jerusalem, “Generalized self-efficacy scale,” J. Weinman, S. Wright, and M. Johnston (Eds.), “Measures in health psychology: A user’s portfolio. Causal and control beliefs,” NFER-Nelson, pp. 35-37, 1995.
  26. [26] A. Bangor, P. T. Kortum, and J. T. Miller, “An empirical evaluation of the system usability scale,” Int. J. of Human–Computer Interaction, Vol.24, No.6, pp. 574-594, 2008. https://doi.org/10.1080/10447310802205776
  27. [27] J. Sauro and J. R. Lewis, “Quantifying the User Experience: Practical Statistics for User Research,” Morgan Kaufmann, 2012.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Feb. 07, 2025