Trust Predicts Actual Use of AI Chatbot as a Virtual Nutrition Assistant Among Dietetic Students in Taiwan: A Path Analysis

Yen Nhi Hoang¹, Seu-Hwa Chen², Chun-Chao Chang^{3

4

5}, Annie W Lin⁶, Trong Hung Nguyen^{7

8

9

10}, Le Xuan Hung¹¹, Tuong Vi Hoang¹², Dang Khanh Ngan Ho¹³, Wen-Ling Lin¹⁴, Chih-Yuan Yao¹⁵, Dian Handayani¹⁶, Jung-Su Chang^{1

5

17

18

19}

Affiliations

¹ School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan.
² Department of Anatomy and Cell Biology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
³ Division of Gastroenterology and Hepatology, Department of Internal Medicine 110, Taipei Medical University Hospital, Taipei, Taiwan.
⁴ Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
⁵ TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei, Taiwan.
⁶ The Hormel Institute, University of Minnesota, Austin, MN, USA.
⁷ Department of Adult Nutrition Counselling, National Institute of Nutrition, Hanoi, Vietnam.
⁸ Department of Clinical Nutrition and Dietetics, National Hospital of Endocrinology, Hanoi, Vietnam.
⁹ Department of Nutrition and Food Safety, Thai Binh University of Medicine and Pharmacy, Thai Binh, Vietnam.
¹⁰ Department of Nursing-Nutrition, and Food Safety, Viet Nam University of Traditional Medicine, Hanoi, Vietnam.
¹¹ Department of Research Methodology and Biostatistics, School of Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.
¹² Application and Software Development E-comm AI PTE.LTD, Singapore, Singapore.
¹³ Faculty of Nursing and Midwifery Hong Bang International University, Ho Chi Minh City, Vietnam.
¹⁴ National Center for Geriatrics and Welfare Research, National Health Research Institutes, Taiwan.
¹⁵ Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
¹⁶ Health Science Faculty, Universitas Brawijaya, East Java, Indonesia.
¹⁷ Graduate Institute of Metabolism and Obesity Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan.
¹⁸ Nutrition Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
¹⁹ Chinese Taipei Society for the Study of Obesity (CTSSO), Taipei, Taiwan.

PMID: 41220106
DOI: 10.1111/jhn.70156

Trust Predicts Actual Use of AI Chatbot as a Virtual Nutrition Assistant Among Dietetic Students in Taiwan: A Path Analysis

Yen Nhi Hoang et al. J Hum Nutr Diet. 2025 Dec.

. 2025 Dec;38(6):e70156.

doi: 10.1111/jhn.70156.

Authors

Affiliations

¹ School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan.
² Department of Anatomy and Cell Biology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
³ Division of Gastroenterology and Hepatology, Department of Internal Medicine 110, Taipei Medical University Hospital, Taipei, Taiwan.
⁴ Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
⁵ TMU Research Center for Digestive Medicine, Taipei Medical University, Taipei, Taiwan.
⁶ The Hormel Institute, University of Minnesota, Austin, MN, USA.
⁷ Department of Adult Nutrition Counselling, National Institute of Nutrition, Hanoi, Vietnam.
⁸ Department of Clinical Nutrition and Dietetics, National Hospital of Endocrinology, Hanoi, Vietnam.
⁹ Department of Nutrition and Food Safety, Thai Binh University of Medicine and Pharmacy, Thai Binh, Vietnam.
¹⁰ Department of Nursing-Nutrition, and Food Safety, Viet Nam University of Traditional Medicine, Hanoi, Vietnam.
¹¹ Department of Research Methodology and Biostatistics, School of Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.
¹² Application and Software Development E-comm AI PTE.LTD, Singapore, Singapore.
¹³ Faculty of Nursing and Midwifery Hong Bang International University, Ho Chi Minh City, Vietnam.
¹⁴ National Center for Geriatrics and Welfare Research, National Health Research Institutes, Taiwan.
¹⁵ Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
¹⁶ Health Science Faculty, Universitas Brawijaya, East Java, Indonesia.
¹⁷ Graduate Institute of Metabolism and Obesity Sciences, College of Nutrition, Taipei Medical University, Taipei, Taiwan.
¹⁸ Nutrition Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
¹⁹ Chinese Taipei Society for the Study of Obesity (CTSSO), Taipei, Taiwan.

PMID: 41220106
DOI: 10.1111/jhn.70156

Abstract

Background: Although studies have demonstrated ChatGPT's potential as an AI nutritionist, no research has yet investigated the factors influencing its adoption and actual use of ChatGPT as a virtual nutrition assistant among dietetic students.

Objective: In this study, we examined factors influencing ChatGPT's actual usage among dietetic students, focusing on the role of trust in its adoption for academic and clinical nutrition tasks.

Methods: A web-based survey was conducted among dietetic students in Taiwan with prior experience using ChatGPT. The survey was designed based on an extended Technology Acceptance Model. Partial least squares structural equation modeling was applied to assess the structural model and test hypotheses.

Results: In total, 150 dietetic students (82% female, mean age = 20.66 ± 3.96 years) completed the survey. Regarding specific nutrition-related tasks, ChatGPT's actual use scores were highest for "understanding nutrition knowledge" and "analyzing the calorie and nutrient content of a dietary record", while lower scores were observed for "preparing for the registered dietitian exam" and "analyzing patients' nutritional status and providing dietary recommendations". The PLS-SEM model respectively explained 28.2% and 44.6% of the variance in actual use (AU) for academic and clinical nutrition tasks. Trust (TR) and behavioral intention to use (BIU) independently predicted AU for academic tasks, including understanding nutrition knowledge (BIU: β = 0.336 and TR: β = 0.341) and preparing for the registered dietitian exam (BIU: β = 0.310 and TR: β = 0.311). For clinical tasks, such as analyzing the calorie and nutrient contents of a dietary record (β = 0.523) and evaluating patients' nutritional status (β = 0.381), TR was the sole significant predictor.

Conclusions: Trust is a key factor driving the adoption and actual use of ChatGPT as an AI nutrition assistant, particularly in clinical nutrition tasks.

Keywords: AI nutritionis; AI‐enabled dietitian; ChatGPT; Technology Acceptance Model; artificial intelligence; educational technology; trust.

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References

1. World Health O. Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization; 2021.
1. Organization WH. Ethics and governance of artificial intelligence for health: large multi‐modal models. WHO guidance. World Health Organization; 2024.
1. G. Eysenbach, “The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers,” JMIR Med Educ 9 (March 2023): e46885, https://doi.org/10.2196/46885.
1. F. Dzuali, K. Seiger, R. Novoa, et al., “ChatGPT May Improve Access to Language‐Concordant Care for Patients With Non–English language Preferences,” JMIR Med Educ 10 (2024): e51435, https://doi.org/10.2196/51435.
1. H. Naveed, A. U. Khan, S. Qiu, et al., “A Comprehensive Overview of Large Language Models,” ACM Trans Intell Syst Technol 16, no. 5 (2025): 1–72, https://doi.org/10.1145/3744746.

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Grants and funding

Jung-Su Chang was supported by grants from the National Science and Technology Council, Taiwan (MOST 111-2410-H-038-019-MY2 and NSTC 113-2410-H-038-017-MY2).

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Trust Predicts Actual Use of AI Chatbot as a Virtual Nutrition Assistant Among Dietetic Students in Taiwan: A Path Analysis

Affiliations

Trust Predicts Actual Use of AI Chatbot as a Virtual Nutrition Assistant Among Dietetic Students in Taiwan: A Path Analysis

Authors

Affiliations

Abstract

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources