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Digital transformation in higher education has presented medical students with new challenges, which has increased the difficulty of organising their own studies. The main objective of this study is to evaluate the effectiveness of a chatbot in assessing the stress levels of medical students in everyday conversations and to identify the m...
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Citations
... In addition, chatbots offer scalable, accessible digital therapy for addressing the mental health needs of students in HE, potentially improving academic outcomes and retention rates (Biro et al., 2023;Lin et al., 2021). Medical chatbots also assist in administrative tasks such as scheduling appointments, managing patient records, providing information and support, enhancing engagement with medical students, and managing stress levels (Moldt et al., 2022;Moldt et al., 2023). ...
... They employed the chatbot to inquire about web programming learning materials, including code explanations, coding tasks, and problem-solving, resulting in successful outcomes (Hamzah et al., 2021). In the study which showed no significant difference, the researchers reported that the chatbot had limitations and needed revision to enable effective use in the future (Moldt et al., 2022). ...
... The survey uses Google Forms to assess the use of social media and self-reported mental well-being using validated scales. This survey includes questionnaire items and scales fromTing et al. (2023),Tandoc et al. (2015),Islam et al. (2021),Pate et al. (2023),Moldt et al. (2022),Caballo et al. (2012),Jones et al. (2013), and Hirschfeld (2002). ...
... Education, various years of study, beginning of semester [49] Medicine, junior college, China [46] Medicine, first year, Germany [51] Medicine, post-& undergraduate, China [46] Education, various years of study, Germany [52] Education, undergraduate, Spain [58] Across all subjects & years of study, Germany, beginning of semester [48] Dental, two weeks before final exams, Spain [56,57] Across all subjects & years of study, Germany, mid of semester [48] Medicine, 2nd year, Germany [53] Education, various years of study, mid of semester [49] Across all subjects & years of study, Germany, end of semester [48] Across all subjects & years of study, UK [55] Education, various years of study, end of semester [49] Across all subjects & years of study, Germany [50] Average Gö of LW4-14 of groups A1 & B1 (N=228) ...
[Version accepted for publication in Phys. Rev. Phys. Educ. Res. is available as a full file at: https://arxiv.org/abs/2404.05682v3]
The current dropout rate in physics studies in Germany is about 60\%, with the majority of dropouts occurring in the first year. Consequently, the physics study entry phase poses a significant challenge for many students. Students' stress perceptions can provide more profound insights into the processes and challenges during that period. In a panel study featuring 67 measuring points involving up to 128 participants at each point, we investigated students' stress perceptions with the Perceived Stress Questionnaire (PSQ), identified underlying sources of stress, and assessed self-estimated workloads across two different cohorts. This examination occurred almost every week during the first semester, and for one cohort also in the second semester, yielding a total of 3,241 PSQ data points and 5,823 stressors. The PSQ data indicate a consistent stress trajectory across all three groups studied that is characterized by significant dynamics between measuring points, spanning from M=20.1, SD=15.9 to M=63.6, SD=13.4 on a scale from 0 to 100. Stress levels rise in the first weeks of the lecture, followed by stable, elevated stress levels until the exams and a relaxation phase afterward during the lecture-free time and Christmas vacation. In the first half of the lecture period, students primarily indicated the weekly exercise sheets, the physics lab course, and math courses as stressors; later on, preparation for exams and the exams themselves emerged as the most important stressors. Together with the students' self-estimated workloads that correlate with the PSQ scores, we can create a coherent picture of stress perceptions among first-year physics students, which builds the basis for supportive measures and interventions.
... (Lin & Yu, 2023) melalui analisis bibliometrik mengidentifikasi bahwa chatbot terutama diterapkan dalam pendidikan bahasa, layanan pendidikan, dan pendidikan kesehatan, menunjukkan aplikasi yang beragam serta tantangan yang dihadapi, seperti kemajuan teknologi dan persepsi siswa. (Moldt et al., 2022) menemukan bahwa chatbot dapat secara efektif menilai tingkat stres pada mahasiswa kedokteran, menunjukkan bahwa meningkatkan kemampuan teknis dan sosial chatbot dapat meningkatkan penerimaan pengguna. Lebih lanjut, (Lai et al., 2023) membahas potensi dampak negatif AI dalam pendidikan terhadap kemampuan beradaptasi sosial remaja, menekankan perlunya integrasi yang seimbang untuk menghindari efek buruk. ...
This research aims to evaluate student perceptions of the use of Generative Pre-trained Transformer (GPT) AI in learning educational technology in the Educational Technology Study Program, Mandalika Education University. The research method used is a quantitative survey with a descriptive approach. The research participants consisted of 118 students who were selected as respondents. The research instrument is a closed questionnaire with a 5-point Likert scale designed to measure various aspects of GPT AI use, including understanding of the material, learning motivation, learning feedback, accessibility, interaction with learning material, relevance to learning objectives, and quality of education received. The data collected was analyzed using descriptive statistics to provide a general overview, normality test to test data distribution, and Pearson correlation analysis to explore the relationship between variables. The research results show that students generally have a positive perception of the use of GPT AI in learning. The average rating for various aspects of using GPT AI is above the middle value (3.0), indicating that students feel helped in understanding the course material, are more motivated to learn, and receive useful feedback. However, the normality test showed that the data were not normally distributed, indicating significant variation in student perceptions. Pearson correlation analysis identified a significant relationship between learning motivation and interaction with learning materials (r = 0.104), as well as between recommendations for using GPT AI and increased interaction with learning materials (r = 0.166). This research concludes that GPT AI has great potential to improve students' understanding of material and learning motivation, but its implementation requires adequate technical and pedagogical support to maximize its benefits. These findings provide important insights for the development of more effective AI implementation strategies in educational technology and underscore the need for approaches tailored to individual student needs. ABSTRAKPenelitian ini bertujuan untuk mengevaluasi persepsi mahasiswa terhadap penggunaan Generative Pre-trained Transformer (GPT) AI dalam pembelajaran teknologi pendidikan di Program Studi Teknologi Pendidikan, Universitas Pendidikan Mandalika. Metode penelitian yang digunakan adalah survei kuantitatif dengan pendekatan deskriptif. Partisipan penelitian terdiri dari 118 mahasiswa yang dipilih sebagai responden. Instrumen penelitian berupa kuesioner tertutup dengan skala Likert 5 poin yang dirancang untuk mengukur berbagai aspek penggunaan GPT AI, termasuk pemahaman materi, motivasi belajar, umpan balik pembelajaran, aksesibilitas, interaksi dengan materi belajar, relevansi dengan tujuan pembelajaran, dan kualitas pendidikan yang diterima. Data yang dikumpulkan dianalisis menggunakan statistik deskriptif untuk memberikan gambaran umum, uji normalitas untuk menguji distribusi data, serta analisis korelasi Pearson untuk mengeksplorasi hubungan antar variabel. Hasil penelitian menunjukkan bahwa mahasiswa secara umum memiliki persepsi positif terhadap penggunaan GPT AI dalam pembelajaran. Rata-rata penilaian terhadap berbagai aspek penggunaan GPT AI berada di atas nilai tengah (3,0), menunjukkan bahwa mahasiswa merasa terbantu dalam memahami materi kursus, lebih termotivasi untuk belajar, dan mendapatkan umpan balik yang bermanfaat. Meskipun demikian, uji normalitas menunjukkan bahwa data tidak terdistribusi normal, mengindikasikan adanya variasi signifikan dalam persepsi mahasiswa. Analisis korelasi Pearson mengidentifikasi hubungan signifikan antara motivasi belajar dan interaksi dengan materi belajar (r = 0,104), serta antara rekomendasi penggunaan GPT AI dan peningkatan interaksi dengan materi belajar (r = 0,166). Penelitian ini menyimpulkan bahwa GPT AI memiliki potensi besar untuk meningkatkan pemahaman materi dan motivasi belajar mahasiswa, namun implementasinya memerlukan dukungan teknis dan pedagogis yang memadai untuk memaksimalkan manfaatnya. Temuan ini memberikan wawasan penting bagi pengembangan strategi implementasi AI yang lebih efektif dalam teknologi pendidikan dan menggarisbawahi perlunya pendekatan yang disesuaikan dengan kebutuhan individu mahasiswa.
... This approach allows reducing the level of stress in communication due to the involvement of digital assistants. Consequently, this impacts the favorable reception of information among fellow students (Moldt et al., 2022). Our article also presents aspects of the digital transformation of medical education. ...
Future doctors are expected to possess a high level of professional skills, as reflected by their acquisition of proficient communication competence. The purpose of the article is to determine the role of digital technologies in the formation of communicative competence of prospective doctors. The objective was attained through the use of observation, analysis and weight coefficient, efficiency coefficient, Spearman's correlation coefficient. It was established that the development of communicative competence skills primarily allows to ensure the relevant orientation to the communication conditions (1.6) as well as informativity (1.53). To cultivate the communication proficiencies of prospective medical professionals, the authors developed corresponding approaches that included the use of digital technologies. The study of theoretical material involved the use of the SlideDog application; conducting practical classes via Medvoice Platform. The formation of professional competence involved the role-playing of relevant situations, based on the materials of the Pediatric Dentistry Academy, CARE-NExT-PG. After determining the level of students’ communication skills development in Group 1 (40.2) and Group 2 (40.1), it was established that they attained a high level. The development of communication skills contributed to the formation of students’ communication and social skills, as well as skills of abstract thinking and statistical information processing. The practical significance of the study lies in the elaboration of effective approaches to the development of prospective doctors’ communication skills drawing on the use of digital technologies. Research perspectives may be linked to the comparison of the level of communicative competence among medical students across various academic levels.
... The study found that the chatbot test group had significantly lower depression (PHQ-9) and anxiety (GAD-7) levels than the bibliotherapy group [70]. On top of this, another experiment employed the Perceived Stress Questionnaire (PSQ20) and qualitative chatbot analysis and found that chatbots (Melinda) can accurately detect medical students' stress levels in daily conversations [71]. ...
Artificial intelligence (AI) has transformed our interactions with the world, spawning complex apps and gadgets known as intelligent agents. ChatGPT, a chatbot hybrid of AI and human-computer interaction, converse with humans and have a wide range of possible uses. Chatbots have showed potential in the field of medical education and health sciences by aiding learning, offering feedback, and increasing metacognitive thinking among undergraduate and postgraduate students. OpenAI's ChatGPT, an advanced language model, has substantially enhanced chatbot capabilities. Chatbots are being used in the medical related field for teaching & learning, mental state categorisation, medication recommendation, health education and awareness. While chatbots have been well accepted by users, further study is needed to fully grasp their use in medical and healthcare settings. This study looked at 32 research on ChatGPT and chatbots in medical-related fields and medical education. Medical education, anatomy, vaccines, internal medicine, psychiatry, dentistry, nursing, and psychology were among the topics discussed in the articles. The study designs ranged from pilot studies to controlled experimental trials. The findings show the exponential growth and potential of ChatGPT and chatbots in healthcare and medical education, as well as the necessity for more research and development in this sector.
... One study investigated the effectiveness of a chatbot named Melinda in evaluating the stress levels of medical students during everyday conversations. They also looked into the essential condition for accepting a chatbot as a conversational partner, using validated stress instruments such as the Perceived Stress Questionnaire (PSQ20) [18]. Another study conducted in Italy involved the development of a chatbot to measure the engagement and effectiveness of Atena, a psychoeducational chatbot designed to assist with healthy coping mechanisms for stress and anxiety among university students [19]. ...
The COVID-19 pandemic has worsened the psychological and social stress levels of university students due to physical illness, enhanced dependence on mobile devices and internet, a lack of social activities, and home confinement. Therefore, early stress detection is crucial for their successful academic performance and mental well-being. The advent of machine learning (ML)-based prediction models can have a crucial impact in predicting stress at its early stages and taking necessary steps for the well-being of individuals. This study aims to develop a reliable machine learning-based prediction model for perceived stress prediction and validate the model using real-world data collected through an online survey among 444 university students from different ethnicity. The machine learning models were built using supervised machine learning algorithms. Principal Component Analysis (PCA) and the chi-squared test were employed as feature reduction techniques. Moreover, Grid Search Cross-Validation (GSCV) and Genetic Algorithm (GA) were employed for hyperparameter optimization (HPO). According to the findings, around 11.26% of individuals were identified with high levels of social stress. In comparison, approximately 24.10% of people were found to be suffering from extremely high psychological stress, which is quite alarming for students' mental health. Furthermore, the prediction results of the ML models demonstrated the most remarkable accuracy (80.5%), precision (1.000), F1 score (0.890), and recall value (0.826). The Multilayer Perceptron model was shown to have the maximum accuracy when combined with PCA as a feature reduction approach and GSCV for HPO. The convenience sampling technique used in this study only considers self-reported data, which may have biased results and lack generalizability. Future research should consider a large sample of data and focus on tracking long-term impacts with coping strategies and interventions. The results of this study can be used to develop strategies to mitigate adverse effects of the overuse of mobile devices and promote student well-being during pandemics and other stressful situations.
... In addition to the significance and social relevance of the data described, reference should be made to certain limitations contained in this study design. While the PSQ is a sufficiently validated and tested survey instrument for perceived life stress [47,49,72,73], here it could be shown that some factor loadings drop below 0.7; the factors tension and demands especially load quite inhomogeneously. These obtained findings encourage a discussion of the extent to which exceptional demanding situations such as the COVID-19 pandemic influence the factor structure of the PSQ. ...
Numerous research results have already pointed towards the negative influence of increased mental stress on educational processes and motivational criteria. It has also been shown that the global public health crisis induced by COVID-19 was related to anxiety symptoms and elevated levels of distress. To holistically elucidate the dynamics of the pandemic-related mental stress of first-year medical students, the associated parameters of three different cohorts were measured at the beginning of the pandemic-related restrictions on university life in Germany (20/21), at the peak of the COVID-19-related restrictions (21/22) and during the easing of the restrictions in the winter term 22/23. In a repeated cross-sectional study design, the constructs of worries, tension, demands and joy were collected from first-year medical students (n = 578) using the Perceived Stress Questionnaire. The results demonstrate significantly increased values of the constructs worries (p < 0.001), tension (p < 0.001) and demands (p < 0.001) at the peak of the pandemic related restrictions compared to the previous and following year as well as significantly decreasing values of general joy of life during the observed period of 3 years (all p-values < 0.001). A confirmatory factor analysis was performed to verify the questionnaire's factor structure regarding the addressed target group during the pandemic (CFI: 0.908, RMSEA: 0.071, SRMR: 0.052). These data, collected over a period of three years, provide information regarding dynamically manifesting mental stress during the COVID-19 pandemic, and refer to new areas of responsibility for the faculties to adequately counteract future crisis situations.
... Future physicians will not only need to be flexible in responding to different healthcare contexts but will also require the competence to adequately deal with procedures and applications involving AI and the accompanying big data [7]. The growing complexity of medicine and increasing specialization of knowledge require the integration of AI as well as the interaction with digital assistance systems already in the curriculum of medical studies [8][9][10]. According to current literature, although AI competencies are essential for medical practice, they are not comprehensively taught in medical education [7,11,12]. ...
Artificial intelligence (AI) in medicine and digital assistance systems such as chatbots will play an increasingly important role in future doctor – patient communication. To benefit from the potential of this technical innovation and ensure optimal patient care, future physicians should be equipped with the appropriate skills. Accordingly, a suitable place for the management and adaptation of digital assistance systems must be found in the medical education curriculum. To determine the existing levels of knowledge of medical students about AI chatbots in particular in the healthcare setting, this study surveyed medical students of the University of Luebeck and the University Hospital of Tuebingen. Using standardized quantitative questionnaires and qualitative analysis of group discussions, the attitudes of medical students toward AI and chatbots in medicine were investigated. From this, relevant requirements for the future integration of AI into the medical curriculum could be identified. The aim was to establish a basic understanding of the opportunities, limitations, and risks, as well as potential areas of application of the technology. The participants (N = 12) were able to develop an understanding of how AI and chatbots will affect their future daily work. Although basic attitudes toward the use of AI were positive, the students also expressed concerns. There were high levels of agreement regarding the use of AI in administrative settings (83.3%) and research with health-related data (91.7%). However, participants expressed concerns that data protection may be insufficiently guaranteed (33.3%) and that they might be increasingly monitored at work in the future (58.3%). The evaluations indicated that future physicians want to engage more intensively with AI in medicine. In view of future developments, AI and data competencies should be taught in a structured way during the medical curriculum and integrated into curricular teaching.
Virtual avatar deployment has opened a new window for many industries in service personalization and customization, offering customers “anytime–anywhere” services. It becomes increasingly essential for marketing practitioners and scholars to clearly understand how avatar attributes and traits are reshaping customer value perceptions in the digital world. Despite the exponential growth of research interest within this area in the last decade, the literature on avatar attributes and consumer‐perceived values remains fragmented and isolated. Given this circumstance, this review draws on the theory–context–characteristics–methods framework and explores the extant literature to provide a collective and comprehensive insight into dominant theories, contexts, characteristics, and methodologies in the context of avatar‐based marketing (ABM). A systematic literature review was applied to rigorously review 57 published articles on avatars and ABM from 2005 to 2023. Using in‐depth content analysis, the current review sheds light on the opportunity to extend the theoretical foundations for the ABM literature. It also identifies gaps in understanding avatar attributes and the associated consumer values, with potential implications for consumer behavior. Finally, it sums up a comprehensive future research agenda.
