Application of Artificial Intelligence in Health, Psychology and Education

Editors

1. Department of Physiology, School of Medicine, Pusan National University, Yangsan, Republic of Korea
2. Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
Interests: artificial intelligence and neuroscience; neuroscience; machine learning; health informatics; medical education
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Faculty of Educational Sciences, An-Najah National University, P.O. Box 7, Nablus, Palestine
2. Faculty of Graduate Studies, Al-Qasemi Academic College of Education, P.O. Box 124, Baqa, Israel
Interests: education; technology in education; psychology in education; qualitative methods in education; quantitative methods in education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Artificial intelligence (AI) has been making waves across various fields, with health, psychology, and education. This Special Issue aims to explore the innovative applications of AI technologies and their impact on these sectors. 

AI in Health 

In health, AI is redefining the continuum of care from early detection to long‑term management. Deep‑learning systems already rival—and in some cases surpass—expert radiologists in oncology, cardiology, and neurology, while image‑analysis platforms comb pathology slides in seconds to spot cellular abnormalities that conventional methods can miss. Beyond imaging, predictive analytics mine electronic health records to flag high‑risk patients before symptoms appear, enabling timely, cost‑saving prevention. AI also drives personalized treatment: machine‑learning algorithms fuse genomic, proteomic, and longitudinal data to craft patient‑specific regimens, pharmacogenomic tools fine‑tune drug choices to minimize side effects, and continuous remote monitoring updates therapy in real time for chronic‑disease management. 

At the system level, AI streamlines operations and elevates patient engagement. Predictive models forecast admissions, optimize bed use, and guide staffing; natural‑language processing automates documentation to free clinicians for direct care; and chatbots handle scheduling, billing questions, and reminders, boosting satisfaction while easing administrative load. Virtual assistants extend support beyond the clinic by answering health queries, triaging symptoms, and delivering post‑discharge instructions—measures that strengthen adherence and curb readmissions. 

AI in Psychology 

In psychology, AI impacts psychological variables, as motivation, self-efficacy, well-being, openness, agreeableness, and emotions regulation. Being responsive to human needs and their questions, AI tools can enhance the previous variables and others. They can also lessen one’s stress, anxiety, and negative emotions. Researchers in the psychology field are invited to contribute to the Special Issue, writing about the different psychological variables in AI environments. Descriptive, correlational, and experimental research are welcome, in addition to review research. 

AI in Education 

In education, as artificial intelligence (AI) continues to reshape various sectors, its impact on education has become increasingly significant. This Special Issue, with a sub-focus on education, welcomes manuscripts on AI in teaching, learning, well-being, education policy, and curriculum development. Aiming to explore the diverse ways AI technologies are being integrated into educational environments, this Special Issue invites scholars, educators, practitioners, and researchers to contribute articles that address the intersection of AI with the above areas in educational contexts. 

  • Key Topics:

    AI Applications in Educational Settings

    • Innovative AI tools and platforms for teaching and learning;
    • Case studies on successful AI integrations in classrooms. 

    Personalized Learning Experiences

    • AI-driven adaptive learning technologies;
    • Strategies for fostering individualized learning pathways;
    • Using AI to support academic output among educators and learners. 

    Learning Analytics and Performance

    • Employing AI for data-driven insights into student performance;
    • The role of predictive analytics in enhancing educational outcomes. 

    Ethical Considerations and Challenges

    • Navigating the ethical landscape of AI in education;
    • Addressing biases and equity issues in AI algorithms;
    • Ethical implications of AI on student and teacher. 

    Future Directions and Innovations

    • The role of AI in shaping the future of education;
    • Emerging trends in AI technology and their potential impacts. 

We invite you to submit original articles (both qualitative and quantitative analysis, both cross-sectional and longitudinal studies) and systematic review works. 

We look forward to receiving your contributions to this Special Issue and to the ongoing discourse on the transformative potential of AI in health, psychology, and education.

Dr. Hyunsu Lee
Prof. Dr. Wajeeh Daher
Dr. Dat Bao
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. European Journal of Investigation in Health, Psychology and Education is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • medical imaging
  • precision medicine
  • pharmacogenomics
  • predictive analytics
  • digital health
  • healthcare management
  • clinical decision support
  • motivational variables
  • well-being
  • emotions’ regulation with AI tools
  • AI Applications in educational settings
  • personalized learning experiences
  • learning analytics and performance
  • ethical considerations and challenges
  • future directions and innovations

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Published Papers (5 papers)

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Research

18 pages, 2052 KB  
Article
Robust Unsupervised Analysis of Longitudinal Functional Trajectories in Older Inpatients Reveals Stable Recovery Profiles: The AIRCOT Study
by Sergio Martinez-Zujeros, Pedro J. Zufiria, Aránzazu Vázquez Sasot and María Luisa Delgado-Losada
Eur. J. Investig. Health Psychol. Educ. 2026, 16(8), 111; https://doi.org/10.3390/ejihpe16080111 - 30 Jul 2026
Viewed by 477
Abstract
Functional decline in older adults represents a major challenge in geriatric rehabilitation, and machine learning (ML) clustering techniques may help identify functional recovery profiles and support personalized rehabilitation strategies. This study characterizes functional recovery profiles in older adults admitted for rehabilitation using unsupervised [...] Read more.
Functional decline in older adults represents a major challenge in geriatric rehabilitation, and machine learning (ML) clustering techniques may help identify functional recovery profiles and support personalized rehabilitation strategies. This study characterizes functional recovery profiles in older adults admitted for rehabilitation using unsupervised clustering techniques. A retrospective longitudinal study was conducted including 957 older adults admitted to a geriatric rehabilitation unit between 2019 and 2025. Clinical and functional variables, including the Modified Barthel Index (MBI), Daniels and Worthingham’s Muscle Testing, and Functional Ambulation Category, were collected from medical records. Considering the baseline functional status and the temporal changes in MBI scores, a clustering of the functional trajectories has been performed using a k-means algorithm based on the silhouette score. The robustness of the resulting segmentation has been evaluated by comparing alternative partitions obtained from bootstrap-sampling, hierarchical clustering, and the centroids of Gaussian Mixture Models. Four distinct functional recovery profiles were identified, showing different trajectories of independence, ambulation, and muscle strength during rehabilitation. Two clusters demonstrated favorable recovery and higher functional resilience, whereas the remaining profiles were characterized by chronic impairment or severe damage with limited recovery. These findings support the usefulness of unsupervised ML clustering techniques for identifying clinically meaningful recovery profiles and may facilitate patient stratification and individualized intervention planning in geriatric rehabilitation. Full article
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33 pages, 815 KB  
Article
Positive Affect and Academic Skill Development Through ChatGPT in Higher Education
by Bonginkosi A. Thango, Lerato Matshaka, Alaa M. S. Azazz and Ibrahim A. Elshaer
Eur. J. Investig. Health Psychol. Educ. 2026, 16(7), 100; https://doi.org/10.3390/ejihpe16070100 - 13 Jul 2026
Viewed by 685
Abstract
This study investigates how positive affect during ChatGPT-4 use is associated with perceived academic skill development in higher education, emphasising the mediating role of attitudes toward ChatGPT and the moderating role of learning orientation, grounded in Broaden-and-Build Theory and the Technology Acceptance Model. [...] Read more.
This study investigates how positive affect during ChatGPT-4 use is associated with perceived academic skill development in higher education, emphasising the mediating role of attitudes toward ChatGPT and the moderating role of learning orientation, grounded in Broaden-and-Build Theory and the Technology Acceptance Model. The enrichment of academic skills through ChatGPT is proposed to be maximised when students who experience positive affect develop positive attitudes toward the tool and channel their interactions through deep learning orientations. This study uses quantitative analysis with n = 12,035 active ChatGPT-using students from 135 countries. PLS-SEM using bootstrapping with 200 resamples is employed for data analysis. The results confirm that positive affect is positively associated with perceived academic skill development (H1: β = 0.199, p < 0.001) and attitude (H2: β = 0.267, p < 0.001), and that attitude is significantly associated with skill development (H3: β = 0.354, p < 0.001). Attitude partially mediates the positive affect to skill development relationship (H4: β = 0.095, t = 24.923, p < 0.001). Learning orientation negatively moderates both the attitude skill development (H5: β = −0.021) and positive affect skill development (H6: β = −0.045) pathways, indicating a desirable difficulty effect: when learning orientation is high, less evaluative scaffolding is needed for skill acquisition, consistent with cognitive challenge theory. What this implies for practice is that access alone is not enough. Institutions need to cultivate AI interactions that feel emotionally positive and, alongside them, a learning-oriented form of engagement; only when the two are developed together does ChatGPT’s potential for academic skill development carry across the diverse global contexts of higher education. Full article
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28 pages, 3352 KB  
Article
Development and Validation of the ATRAI Questionnaire to Assess Attitudes Toward Large Language Models in Clinical Setting (ATRAI-LLM)
by Roman V. Reshetnikov, Yuriy A. Vasilev, Yuliya F. Shumskaya, Dina A. Akhmedzyanova, Yulya A. Alymova, Anton V. Vladzymyrskyy, Ilya A. Tyrov, Olga V. Omelyanskaya and Ivan A. Blokhin
Eur. J. Investig. Health Psychol. Educ. 2026, 16(7), 94; https://doi.org/10.3390/ejihpe16070094 - 30 Jun 2026
Viewed by 479
Abstract
Background: Large language models (LLMs) are increasingly integrated into real-world medical practice as chatbots for answering clinical queries. However, the perceptions of this technology among its end-users remain understudied. Existing research on physicians’ attitudes toward LLMs relies on non-validated questionnaires, raising concerns about [...] Read more.
Background: Large language models (LLMs) are increasingly integrated into real-world medical practice as chatbots for answering clinical queries. However, the perceptions of this technology among its end-users remain understudied. Existing research on physicians’ attitudes toward LLMs relies on non-validated questionnaires, raising concerns about the accuracy and reliability of the findings. The aim of this study is to develop and validate a questionnaire to assess physicians’ attitudes toward LLM-based chatbots used as a reference tool for answering queries. Methods: The instrument was based on the previously developed and validated ATRAI-14 questionnaire assessing radiologists’ attitudes toward artificial intelligence. Items for the new questionnaire were formulated and refined through focus group testing. Validation involved 562 physicians of various specialties working in medical institutions within the Moscow healthcare system. Some respondents had prior experience working with medical LLMs. We assessed face, content, construct, and criterion validity. Criterion validity was evaluated through correlation between respondents’ self-assessed attitudes toward LLMs measured by visual analogue scale (VAS), and construct validity through confirmatory factor analysis. Results: The resulting ATRAI-LLM questionnaire comprised 19 items (8 in the background part and 11 in the main part). The questionnaire demonstrated acceptable internal consistency (Cronbach’s α = 0.770, McDonald’s ωt = 0.830). It encompasses three domains: “Willingness to Use”, “Implementation Perspective”, and “Hopes and Fears.” Confirmatory factor analysis supported the three-factor structure, with satisfactory fit indices achieved (RMSEA = 0.05, CFI = 0.97, TLI = 0.96, SRMR = 0.03). Criterion validity was confirmed as acceptable with moderate correlation between the final score and VAS scores (Spearman’s rho 0.68, p < 0.001). Conclusions: ATRAI-LLM is a validated instrument for assessing physicians’ attitudes toward LLMs as a knowledge base. Full article
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13 pages, 737 KB  
Article
Development and Validation of AI Help-Seeking Behavior Scale Among Undergraduate University Students
by Othman A. Alfuqaha, Rasha M. Abdelrahman and Kyle Msall
Eur. J. Investig. Health Psychol. Educ. 2026, 16(7), 90; https://doi.org/10.3390/ejihpe16070090 - 29 Jun 2026
Viewed by 1378
Abstract
(1) Background: Artificial intelligence tools have become integrated into undergraduate students from academic assignments to seek help with psychological concerns, particularly during the crises period. Scales measuring Artificial Intelligence-help-seeking behavior (AI-HSB) are still limited. This study aims to develop a new bilingual scale [...] Read more.
(1) Background: Artificial intelligence tools have become integrated into undergraduate students from academic assignments to seek help with psychological concerns, particularly during the crises period. Scales measuring Artificial Intelligence-help-seeking behavior (AI-HSB) are still limited. This study aims to develop a new bilingual scale (Arabic and English) to assess AI-HSB by providing a reliable and useful tool for researchers worldwide. (2) Methods: We conducted a methodological cross-sectional design among 416 undergraduate students in United Arab Emirates (AUE) between the period of 1 October 2025 and 10 December 2025, using an online Google Form. The development, translation, validation, and reliability processes were conducted for the AI-HSB scale. (3) Results: It has been found that 13 items (two factors) are strong indications of factorial validity, reliability, and construct validity of AI-HSB scale. The two factors explained about 58% of the total variance. The confirmatory factor analysis confirmed the two-factor structure with all items loading above recommended thresholds and the goodness-of-fit indices of AI-HSB all exceeded 0.90. (4) Conclusions: The AI-HSB is a valid and reliable tool for assessing AI-based psychological help-seeking behavior among university students in the UAE. This scale will allow universities, counselors, and policymakers to use a well-validated scale to measure the extent to which students are using AI for psychological coping. Full article
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24 pages, 2572 KB  
Article
DIALOGUE: A Generative AI-Based Pre–Post Simulation Study to Enhance Diagnostic Communication in Medical Students Through Virtual Type 2 Diabetes Scenarios
by Ricardo Xopan Suárez-García, Quetzal Chavez-Castañeda, Rodrigo Orrico-Pérez, Sebastián Valencia-Marin, Ari Evelyn Castañeda-Ramírez, Efrén Quiñones-Lara, Claudio Adrián Ramos-Cortés, Areli Marlene Gaytán-Gómez, Jonathan Cortés-Rodríguez, Jazel Jarquín-Ramírez, Nallely Guadalupe Aguilar-Marchand, Graciela Valdés-Hernández, Tomás Eduardo Campos-Martínez, Alonso Vilches-Flores, Sonia Leon-Cabrera, Adolfo René Méndez-Cruz, Brenda Ofelia Jay-Jímenez and Héctor Iván Saldívar-Cerón
Eur. J. Investig. Health Psychol. Educ. 2025, 15(8), 152; https://doi.org/10.3390/ejihpe15080152 - 7 Aug 2025
Cited by 12 | Viewed by 7438
Abstract
DIALOGUE (DIagnostic AI Learning through Objective Guided User Experience) is a generative artificial intelligence (GenAI)-based training program designed to enhance diagnostic communication skills in medical students. In this single-arm pre–post study, we evaluated whether DIALOGUE could improve students’ ability to disclose a type [...] Read more.
DIALOGUE (DIagnostic AI Learning through Objective Guided User Experience) is a generative artificial intelligence (GenAI)-based training program designed to enhance diagnostic communication skills in medical students. In this single-arm pre–post study, we evaluated whether DIALOGUE could improve students’ ability to disclose a type 2 diabetes mellitus (T2DM) diagnosis with clarity, structure, and empathy. Thirty clinical-phase students completed two pre-test virtual encounters with an AI-simulated patient (ChatGPT, GPT-4o), scored by blinded raters using an eight-domain rubric. Participants then engaged in ten asynchronous GenAI scenarios with automated natural-language feedback. Seven days later, they completed two post-test consultations with human standardized patients, again evaluated with the same rubric. Mean total performance increased by 36.7 points (95% CI: 31.4–42.1; p < 0.001), and the proportion of high-performing students rose from 0% to 70%. Gains were significant across all domains, most notably in opening the encounter, closure, and diabetes specific explanation. Multiple regression showed that lower baseline empathy (β = −0.41, p = 0.005) and higher digital self-efficacy (β = 0.35, p = 0.016) independently predicted greater improvement; gender had only a marginal effect. Cluster analysis revealed three learner profiles, with the highest-gain group characterized by low empathy and high digital self-efficacy. Inter-rater reliability was excellent (ICC ≈ 0.90). These findings provide empirical evidence that GenAI-mediated training can meaningfully enhance diagnostic communication and may serve as a scalable, individualized adjunct to conventional medical education. Full article
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