AI-DASA: AI-Based Depression, Anxiety, and Stress Assessment
Received: 7 July 2025 | Revised: 24 July 2025, 2 August 2025, 15 August 2025, and 21 August 2025 | Accepted: 22 August 2025 | Online: 29 September 2025
Corresponding author: José Santisteban
Abstract
This article presents the design, implementation, and validation of AI-DASA, an innovative web-based predictive system designed to detect emotional disorders, specifically anxiety, depression, and stress, in university students at an early stage. AI-DASA is based on advanced Natural Language Processing (NLP) techniques and utilizes large-scale language models to analyze free-text documents written by students. The system was evaluated in a three-week experiment involving expert psychologists and 200 university students who interacted with the platform. Validation was conducted at three levels: operational efficiency compared to the traditional process, usability from the perspective of mental health experts, and overall satisfaction perception among students. The results show that AI-DASA achieved an average accuracy of 94% in detecting disorders, significantly reducing the evaluation time from 48 hours to just 20 minutes per case and eliminating the need for direct human intervention in the initial diagnostic phase. Both experts and students reported high levels of satisfaction and usability. This system represents a promising tool for emotional screening in educational settings, particularly in environments with limited access to mental health professionals, thus contributing to the earlier and more efficient detection of emotional problems in the student population.
Keywords:
depression, anxiety, stress, emotional disorders, Natural Language Processing (NLP)Downloads
References
"Mental disorders," World Health Organization. https://www.who.int/news-room/fact-sheets/detail/mental-disorders.
C. El Morr et al., "Predictive Machine Learning Models for Assessing Lebanese University Students’ Depression, Anxiety, and Stress During COVID-19," Journal of Primary Care & Community Health, vol. 15, Jan. 2024, Art. no. 21501319241235588. DOI: https://doi.org/10.1177/21501319241235588
C. Chau and P. Vilela, "Determinantes de la salud mental en estudiantes universitarios de Lima y Huánuco," Revista de Psicología, vol. 35, no. 2, pp. 387–422, 2017. DOI: https://doi.org/10.18800/psico.201702.001
"Salud mental: casos atendidos por Minsa se incrementaron en casi 20 % durante el 2022." https://elperuano.pe/noticia/210845-salud-mental-casos-atendidos-por-minsa-se-incrementaron-en-casi-20-durante-el-2022.
Z. Guo, A. Lai, J. H. Thygesen, J. Farrington, T. Keen, and K. Li, "Large Language Models for Mental Health Applications: Systematic Review," JMIR Mental Health, vol. 11, Oct. 2024, Art. no. e57400. DOI: https://doi.org/10.2196/57400
"La salud mental de los adolescentes," World Health Organization. https://www.who.int/es/news-room/fact-sheets/detail/adolescent-mental-health.
A. Ahmed et al., "Machine learning models to detect anxiety and depression through social media: A scoping review," Computer Methods and Programs in Biomedicine Update, vol. 2, 2022, Art. no. 100066. DOI: https://doi.org/10.1016/j.cmpbup.2022.100066
S. Goyal, R. Dutta, S. Dev, K. N. Raju, and M. W. Bhatt, "MindLift: AI-powered mental health assessment for students," Neuroscience Informatics, vol. 5, no. 2, Jun. 2025, Art. no. 100208. DOI: https://doi.org/10.1016/j.neuri.2025.100208
A. Daza Vergaray, J. C. H. Miranda, J. B. Cornelio, A. R. López Carranza, and C. F. Ponce Sánchez, "Predicting the depression in university students using stacking ensemble techniques over oversampling method," Informatics in Medicine Unlocked, vol. 41, 2023, Art. no. 101295. DOI: https://doi.org/10.1016/j.imu.2023.101295
Md. M. Islam, S. Hassan, S. Akter, F. A. Jibon, and Md. Sahidullah, "A comprehensive review of predictive analytics models for mental illness using machine learning algorithms," Healthcare Analytics, vol. 6, Dec. 2024, Art. no. 100350. DOI: https://doi.org/10.1016/j.health.2024.100350
U. Madububambachu, A. Ukpebor, and U. Ihezue, "Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review," Clinical Practice & Epidemiology in Mental Health, vol. 20, no. 1, Jul. 2024, Art. no. e17450179315688. DOI: https://doi.org/10.2174/0117450179315688240607052117
G. H. A. Masud, R. I. Shanto, I. Sakin, and M. R. Kabir, "Effective depression detection and interpretation: Integrating machine learning, deep learning, language models, and explainable AI," Array, vol. 25, Mar. 2025, Art. no. 100375. DOI: https://doi.org/10.1016/j.array.2025.100375
S. Juanita, A. H. Daeli, M. Syafrullah, W. Anggraeni, and M. H. Purnomo, "A machine learning approach for text pattern diagnosis in mental health consultations," Decision Analytics Journal, vol. 15, Jun. 2025, Art. no. 100572. DOI: https://doi.org/10.1016/j.dajour.2025.100572
K. Krakowski, D. Oliver, M. Arribas, D. Stahl, and P. Fusar-Poli, "Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study," Biological Psychiatry, vol. 96, no. 7, Oct. 2024, Art. no. 604–614. DOI: https://doi.org/10.1016/j.biopsych.2024.05.022
A. Alshahrani, M. M. Almatrafi, J. I. Mustafa, L. S. Albaqami, and R. A. Aljabri, "A Children’s Psychological and Mental Health Detection Model by Drawing Analysis based on Computer Vision and Deep Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 15533–15540, Aug. 2024. DOI: https://doi.org/10.48084/etasr.7812
Y. Kuang et al., "Federated learning-based prediction of depression among adolescents across multiple districts in China," Journal of Affective Disorders, vol. 369, pp. 625–632, Jan. 2025. DOI: https://doi.org/10.1016/j.jad.2024.10.027
H. Park and J. Lee, "Designing a Conversational Agent for Sexual Assault Survivors: Defining Burden of Self-Disclosure and Envisioning Survivor-Centered Solutions," in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Feb. 2021, pp. 1–17. DOI: https://doi.org/10.1145/3411764.3445133
B. Kang and M. Hong, "Development and Evaluation of a Mental Health Chatbot Using ChatGPT 4.0: Mixed Methods User Experience Study With Korean Users," JMIR Medical Informatics, vol. 13, Jan. 2025, Art. no. e63538. DOI: https://doi.org/10.2196/63538
D. Shin, H. Kim, S. Lee, Y. Cho, and W. Jung, "Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study," Journal of Medical Internet Research, vol. 26, Sep. 2024, Art. no. e54617. DOI: https://doi.org/10.2196/54617
D. B. Olawade, O. Z. Wada, A. Odetayo, A. C. David-Olawade, F. Asaolu, and J. Eberhardt, "Enhancing mental health with Artificial Intelligence: Current trends and future prospects," Journal of Medicine, Surgery, and Public Health, vol. 3, Aug. 2024, Art. no. 100099. DOI: https://doi.org/10.1016/j.glmedi.2024.100099
D. F. Wong, Z. Wei, and M. Yang, Natural Language Processing and Chinese Computing: 13th National CCF Conference, NLPCC 2024, Hangzhou, China, November 1–3, 2024, Proceedings, Part III. Springer Nature, 2024. DOI: https://doi.org/10.1007/978-981-97-9437-9
M. K. Kabir, M. Islam, A. N. B. Kabir, A. Haque, and M. K. Rhaman, "Detection of Depression Severity Using Bengali Social Media Posts on Mental Health: Study Using Natural Language Processing Techniques," JMIR Formative Research, vol. 6, no. 9, Sep. 2022, Art. no. e36118. DOI: https://doi.org/10.2196/36118
D. Owen, A. J. Lynham, S. E. Smart, A. F. Pardiñas, and J. Camacho Collados, "AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges," Journal of Medical Internet Research, vol. 26, Nov. 2024, Art. no. e59225. DOI: https://doi.org/10.2196/59225
F. Aramburu, W. Minaya, and J. Santisteban, "Natural Language Processing Algorithms to detect mental disorders in College Students in Lima, Perú," in 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China, May 2025, pp. 455–460. DOI: https://doi.org/10.1109/ICAIBD64986.2025.11081967
R. Panghiyangani, I. Aflanie, F. Sulistiawati, Y. Rafikaningrum, and W. Syafitri, "Estimation of body height based on the bone length of the limbs in adult males from Dayak Ngaju," in Proceedings of the 19th International Federation of Associations of Anatomists Congress, Aug. 2019.
J. Patel, C. Hung, and T. R. Katapally, "Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review," Psychiatry Research, vol. 343, Jan. 2025, Art. no. 116277. DOI: https://doi.org/10.1016/j.psychres.2024.116277
T. Alqahtani et al., "The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research," Research in Social and Administrative Pharmacy, vol. 19, no. 8, pp. 1236–1242, Aug. 2023. DOI: https://doi.org/10.1016/j.sapharm.2023.05.016
Q. Zheng, F. Liu, S. Xu, J. Hu, H. Lu, and T. Liu, "Artificial intelligence empowering research on loneliness, depression and anxiety — Using Covid-19 as an opportunity," Journal of Safety Science and Resilience, vol. 4, no. 4, pp. 396–409, Dec. 2023. DOI: https://doi.org/10.1016/j.jnlssr.2023.10.002
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Copyright (c) 2025 Wendy Minaya, Francis Aramburu, José Santisteban
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