TEACHING INFECTIOUS DISEASES AND EPIDEMIOLOGY AT A UNIVERSITY: TEACHER OR ARTIFICIAL INTELLIGENCE. ONE-TIME RESEARCH



Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

Introduction: The digital transformation of higher education is accompanied by the active implementation of artificial intelligence (AI) technologies, which are seen as a tool for increasing the accessibility and personalization of learning. Of particular interest is the potential of generative models (GPT-4, etc.) in creating educational content, including speech synthesis and animated avatars, which raises the question of whether traditional lecturers can be partially or completely replaced. However, there is a lack of empirical research evaluating the effectiveness of comprehensive AI lectures compared to expert lecturers in real-world medical education settings.

Aim: To conduct a comparative assessment of the effectiveness of educational material comprehension by medical students in the context of a traditional lecture delivered by a human lecturer versus a lecture fully generated using artificial intelligence technologies (including text generation, voice synthesis, and an animated avatar).

Methods: Within a single lecture on infectious diseases for 5th-year medical students, the topic "Leptospirosis" was delivered by an expert lecturer. The topic "Hemorrhagic Fever with Renal Syndrome" was presented via an AI-generated video: text was synthesized using GPT-4, GPT-3.5 Turbo, GPT-4o, and Perplexity models based on clinical guidelines and verified for accuracy by the expert; voiced using AI-synthesized speech (ElevenLabs) modeled on the lecturer's voice; visualized by an animated avatar (D-ID) based on the lecturer's photo. Following the lecture, 99 students completed a test on both topics (10-point scale) and filled out an online survey (Yandex Forms) comparing the delivery methods and assessing AI's potential. Data was processed using Microsoft Excel.

Key results: The average test score for the lecturer-delivered topic was 8.9/10, compared to 5.9/10 for the AI-delivered topic. According to the survey, 94.9% of students found the lecturer's information delivery more accessible. The lecturer's delivery was rated 4.76/5, while the AI's delivery was rated 3.01/5. However, 89% of respondents acknowledged the potential for AI technology development in education.

Conclusions: The AI-generated lecture, despite achieving a satisfactory result (5.9/10) and recognized potential by students, demonstrated statistically and subjectively significantly lower effectiveness in knowledge transfer compared to the traditional expert lecturer. The results confirm the current superiority of the human factor (experience, adaptability) in the lecture-based learning format but highlight the progress of AI and its prospects as a supportive educational tool.

Full Text

Rationale

Relevance. The digital transformation of education is actively introducing artificial intelligence (AI) tools, promising increased accessibility and personalization of learning, as well as improved quality [1, 2]. Particular interest is drawn to the potential of AI to replace traditional lectures — a format criticized for passivity but still widespread in higher education, especially in medicine. However, the rapid development of generative AIs capable of creating complex educational content (text, speech, video) raises the acute question of their actual effectiveness compared to an expert human lecturer [3]. The lack of empirical studies directly comparing student comprehension of material from an AI lecturer versus a human in controlled conditions hinders the assessment of the pedagogical value of new technologies and the development of sound strategies for their integration [4].

An analysis of publications shows a growing volume of theoretical works and analytical reviews discussing the opportunities and risks of AI in education [5, 6, 7, 8]. A number of studies demonstrate the successful use of AI as a supportive tool (tutor, assignment generator, automation tool) [9, 10, 11]. However, there is a clear shortage of studies experimentally evaluating the effectiveness of AI in the role of the primary "lecturer," delivering comprehensive educational material instead of a human, particularly in clinical disciplines. There is a significant "blind spot" in understanding the extent to which AI, even when generating technically correct and expert-verified content, can compete with a live teacher in terms of depth of knowledge acquisition and student engagement [12].

Objective

To conduct a comparative analysis of the effectiveness of knowledge transfer to medical students between a traditional lecture delivered by an expert lecturer and a lecture fully created and presented using artificial intelligence technologies.
Specific tasks: to develop and verify (with expert input) a methodology for creating an AI lecture (text, voice-over, avatar) imitating a specific lecturer; to conduct a comparative session where one topic is presented by the lecturer and another (of comparable complexity) by an AI lecture; to assess the objective effectiveness of learning the material for each topic using standardized testing; to identify students' subjective perception of the comparative accessibility of information delivery and lecture quality; to analyze students' opinions on the potential of AI in the educational process; to perform statistical analysis of the obtained data to identify significant differences.
Scientific novelty lies in the fact that this study contributes by providing one of the first empirical evaluations of the effectiveness of a comprehensive AI lecture (text + synthesized voice modeled on a specific lecturer + animated avatar) in direct comparison with the traditional format within a real educational setting at a medical faculty. The novelty lies in the methodology of directly substituting the lecturer with an AI analog while controlling for persona and topic, as well as in the comprehensive assessment (objective testing + subjective survey).
It is hypothesized that the traditional lecture delivered by an expert lecturer will provide a statistically significantly higher level of knowledge acquisition by students and receive a higher subjective assessment of the quality of material presentation compared to the lecture fully created and presented by artificial intelligence, despite expert verification of the content.

Methods

  • Study Design: Interventional, single-center, cross-sectional, continuous, controlled, non-randomized, non-blinded study. The study was completed at the time of publication.

  • Setting: Tver State Medical University.

  • Eligibility Criteria:

    • Inclusion criteria: 132 students who attended the lecture on May 18, 2024.

    • Exclusion criteria: Students who were absent from the lecture or did not participate in testing/survey.

  • Study Duration/Timeline: The study was conducted on May 18, 2024, from 1:20 PM to 2:50 PM.

  • Intervention Description: The researchers replaced the traditional lecture delivery with a lecture fully created and presented using artificial intelligence technologies, while maintaining thematic unity and organizational conditions of the session. After the lecture, testing on both topics and a survey comparing the lecturers and evaluating the effectiveness of AI were conducted.
    The study was conducted as a within-group comparative experiment within a single standard lecture (90 minutes) on the topic: "Leptospirosis. Hemorrhagic Fever with Renal Syndrome (HFRS)." The lecture was divided into two equal parts in terms of time (40 minutes each + 5-minute break):

    • Control Condition (Traditional Lecture): The topic "Leptospirosis" was delivered in person by N.A. Grishkina, PhD, Associate Professor of the Department of Infectious Diseases and Epidemiology, Tver State Medical University. Standard lecture teaching methods (oral presentation, slides, answering questions) were used.

    • Experimental Condition (AI Lecture): The topic "Hemorrhagic Fever with Renal Syndrome" was presented in the form of a video completely created using artificial intelligence (AI) technologies, imitating the lecturer (N.A. Grishkina).

Methodology for Creating the AI Lecture:

  1. Generation of Text Content:

    • Tools: Generative AI language models were used: OpenAI ChatGPT-4 (May 2024 version), ChatGPT-3.5 Turbo (May 2024 version), ChatGPT-4o (May 2024 version), and Perplexity.ai (Copilot mode, May 2024).

    • Prompts: The following requirements were specified: "Generate a lecture text for 5th-year medical students on the topic 'Hemorrhagic Fever with Renal Syndrome.' Style: scientific, using current medical terminology. Basis: current clinical guidelines of the Russian Ministry of Health 'HFRS' (2023) and international ECDC/WHO guidelines. Structure: 1. Definition, etiology. 2. Epidemiology. 3. Pathogenesis. 4. Clinical picture (stages). 5. Diagnostics. 6. Treatment. 7. Prevention. Volume: ~3500 characters (equivalent to 40 minutes of oral presentation). The text should be coherent and logical."

    • Process: The text was generated iteratively using different models. The highest quality fragments were selected and compiled into a single text.

    • Verification: The resulting final text was not edited by a human and was submitted to N.A. Grishkina for verification of:

      • Reliability: Compliance with current scientific data and clinical guidelines.

      • Completeness: Coverage of key aspects of the topic according to the curriculum.

      • Correctness of terminology.

    • The text was approved by the expert without substantive edits.

  2. Voice-over Synthesis:

    • Tool: Online platform ElevenLabs.io (paid subscription, May 2024).

    • Process:

      • Recording a voice sample of N.A. Grishkina (reading a 3-minute excerpt of neutral fictional text) in a quiet room using a Fifine k669b microphone.

      • Uploading the recording to the platform and creating a voice model "Voice_Grishkina N.A." with automatic settings.

      • Synthesizing an audio file based on the verified lecture text using the created model. Synthesis settings were automatic.

      • Quality control: The resulting audio track was listened to by the study authors for speech artifacts and naturalness of intonation. No significant distortions were found.

  3. Creation of Visual Representation (Avatar):

    • Tool: Online platform D-ID.com (paid subscription, May 2024).

    • Process:

      • Uploading a photograph of N.A. Grishkina (front view, neutral expression, high resolution).

      • Uploading the synthesized audio track (section 3.2).

      • Generating an animated video avatar using the platform's AI. Standard settings were used.

    • Final Product: A 40-minute video with a "talking" avatar of N.A. Grishkina, synchronized with the synthesized audio lecture.

  • Study Outcomes: Quality and accessibility of material, reliability of terminology use, potential for AI technology development.

  • Outcome Registration Methods: Online survey developed by the study authors using Yandex Forms, results of the conducted testing.

    • Knowledge Testing:

      • Tool: Standardized written test developed by the Department of Infectious Diseases and Epidemiology, Tver State Medical University, for final control on the topics "Leptospirosis" and "HFRS."

      • Test Structure: 10 open-ended questions for each topic. The questions were designed according to learning objectives and assessed knowledge of etiology, epidemiology, pathogenesis, clinical presentation, diagnosis, and treatment. The difficulty of questions across topics was deemed comparable by the department's faculty.

      • Administration: The test was administered immediately after the lecture. Time allowed: 30 minutes. Anonymous answer sheets were collected.

      • Assessment: Answers were graded on a 10-point scale by a lecturer not involved in the study, using uniform criteria. The maximum score for each topic was 10.

    • Survey:

      • Tool: Online survey developed by the study authors using Yandex Forms.

      • Survey Structure:

        1. Rate the accessibility of the material presentation on the topic "Leptospirosis" (lecturer) on a scale from 1 (not accessible at all) to 10 (very accessible).

        2. Rate the accessibility of the material presentation on the topic "HFRS" (AI lecture) on a scale from 1 to 10.

        3. Compare: Who, in your opinion, delivered the information more accessibly? (Options: Lecturer; AI; Equally).

        4. Rate the quality of the AI's elaboration of the "HFRS" topic: (Excellent; Good; Satisfactory; Poor).

        5. Rate the quality of the AI's use of medical terminology: (Excellent; Good; Satisfactory; Poor).

        6. Do you see potential in using similar AI technologies in the educational process? (Yes; No; Unsure).

      • Administration: A link to the survey was sent to students via the university's LMS 24 hours after the lecture. Anonymous responses were collected over 3 days.

  • Group Analysis: 75% of students present at the lecture were selected.

  • Statistical Analysis: Data processing was performed using Microsoft Excel 365 (Microsoft Corp., USA) with built-in statistical functions and the Analysis ToolPak. Quantitative data are presented as M ± SD (mean ± standard deviation). Normality of distribution was checked using the Shapiro-Wilk test. The paired Student's t-test was used to compare mean test scores and accessibility ratings between the two lecture parts. Pearson's chi-squared test (χ²) was used for qualitative data analysis (survey responses). The significance level was set at p < 0.001. Cohen's d was calculated to estimate effect size.

Results

  • Study Objects: 99 fifth-year medical students who attended the lecture and completed the survey.

  • Main Results of the Study:

    1. According to data from 99 students, the mean score for the "Leptospirosis" topic (lecturer) was 8.27 ± 1.61, which is significantly higher than the result for the "HFRS" topic (AI lecture) – 5.58 ± 2.04 (paired t-test: t = 14.16, p < 0.001; Cohen's d = 1.42). The dispersion of scores was significantly higher for the AI-delivered material (SD = 2.04 vs. 1.61) (Table 1).

    2. Accessibility of Presentation Rating: The accessibility of the material presented by the lecturer was rated by students at an average of 8.92 ± 1.13 points out of 10, significantly higher than the AI lecture rating (5.65 ± 2.33; paired t-test: t = 15.43, p < 0.001; d = 1.55). 96.97% of respondents indicated that the lecturer delivered the information more accessibly (Table 2).

    3. Quality of AI Lecture: The quality of the AI's topic elaboration was rated as "High quality" by 38.38% of students, "Insufficiently good" by 43.43%, and "Poor" by 18.18%. The correctness of using medical terminology was noted by 39.39% of respondents, while 53.54% indicated partial errors, and 7.07% indicated incorrect usage (Figures 1, 2).

    4. Potential of AI in Education: 89.9% of students see prospects for the use of AI technologies in the educational process, while 10.1% deny such a possibility (Figure 3).

Discussion

  • Summary of the Main Result: The obtained data demonstrate the fundamental advantage of a traditional lecturer over AI technologies in key aspects of the educational process. The significant difference in material comprehension (8.27 vs. 5.58 points; d=1.42) and accessibility rating (8.92 vs. 5.65; d=1.55) indicates that even with advanced generative models (GPT-4, D-ID) and expert content verification, AI is not yet capable of fully replicating the didactic competencies of a teacher. Notably, 96.97% of students subjectively confirmed this objective superiority. However, the high percentage acknowledging AI's potential (89.9%) suggests the promise of its integration as a supportive tool [13].

  • Comparison with Literature: Our findings are consistent with the meta-analysis by Chen et al. (2023), which showed that AI lags behind humans in forming systematic knowledge in medicine but is effective for training practical skills [14]. The discrepancy with studies demonstrating the success of AI lecturers in technical disciplines [15] may be explained by the specificity of medical education: the need for clinical thinking and interpretation of nuances.

  • Scientific and Practical Significance:

    • The results challenge the notion of replacing teachers with AI, substantiating the feasibility of hybrid models [16].

    • The irreplaceability of the human factor for complex cognitive tasks is confirmed [17].

    • A "performance threshold" for AI lectures (5.5-6.0/10) was determined.

    • Critical quality parameters for AI (terminology as a weak point) were identified.

    • A methodology for objective evaluation of AI tools in education is proposed.

  • Confirmation of Hypothesis: The primary hypothesis regarding the statistically significant superiority of the traditional lecturer (p < 0.001) was fully confirmed [18]. The secondary hypothesis regarding a "satisfactory" level of AI (mean score 5.58/10) was also proven, but with clarification: the current level of technology provides a minimally acceptable, but insufficient result for higher medical education.

  • Alignment with Study Objective: The objective — comparing the effectiveness of knowledge transfer — was achieved, yielding reproducible metrics. The results provide a clear answer to the "Lecturer vs. AI" debate: at the current state of technology, completely replacing a teacher with AI reduces educational quality, but its integration as a supplement is promising, provided the identified limitations are addressed [19].

  • Study Limitations:

    1. Contextual limitations: The experiment was conducted on a single discipline (infectious diseases) at a single university.

    2. Novelty effect: Using an AI avatar of a specific lecturer might have distorted perception.

    3. Short-term assessment: Data on long-term material retention are absent.

    4. Technological limitations: The quality of speech/animation synthesis (ElevenLabs, D-ID) could have influenced the results.

    5. Sample: Individual cognitive styles of students were not considered.

Conclusion

Upon completion of the experiment, statistical data were obtained allowing us to state that currently, AI is not ready to replace teachers in the educational process. For example, the majority of students indicated that the lecturer presented the material more accessibly and in detail. However, it is worth noting that the vast majority of students who participated in the survey consider the development of AI to be promising. Despite all the shortcomings currently present in artificial intelligence technology, it can be stated that in the future, it will gain wide recognition in society and will be actively used in many spheres, including teaching.

Authors' Contribution

  • Grishkina N.A. — delivery of the lecture on "Leptospirosis," evaluation of student testing.

  • Volkova O.V. — idea, organization.

  • Magomedov S.G. — refinement of the idea and study implementation, manuscript editing.

  • Bakalov K.A. — statistical data analysis, survey evaluation.
    All authors approved the manuscript (the version to be published) and agreed to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of it are appropriately investigated and resolved.

Acknowledgements

The authors express their gratitude to:

  • The administration of Tver State Medical University for organizational support of the study.

  • The 5th-year medical students who participated in the experiment.

  • The development teams of the used AI tools:

    • OpenAI (GPT-4, GPT-4o)

    • ElevenLabs (speech synthesis)

    • D-ID (avatar generation)

    • Perplexity.ai (search optimization)

Ethical Review

Funding Sources
None.

Disclosure of Interests
No relationships or activities with other persons or entities exist that could be perceived as influencing the work submitted in the manuscript over the past 36 months.

Statement of Originality
The data collected for this study were gathered for the first time.

Data Availability
The authors provide unrestricted access to the data posted on an external resource. The authors state that all data are presented in the article and its appendices.

Generative Artificial Intelligence
During the preparation of this work, the authors did not use generative AI for the purpose of creating the manuscript.

Submission and Review
The manuscript was submitted to the journal's editorial office on an initiative basis.

Disclaimer
The authors of the article are not responsible for any possible consequences resulting from the publication of the article.

Tables

Параметр

Лектор (Лептоспироз)

ИИ

(ГЛПС)

p-value

M ± SD

8.27 ± 1.61

5.58 ± 2.04

< 0.001

Me [Min; Max]

9.0 [3.5; 10.0]

5.5 [2.5; 9.0]

-

Table 1. Comparison of the efficiency of assimilation of the material (n = 99)
 

Параметр

Лектор

ИИ

M ± SD                 

8.92 ± 1.13

5.65 ± 2.33

%выбравших "доступнее"

96.97       

3.03       

Table 2. Subjective assessment of accessibility of presentation (n = 99)

 

Illustration 1. AI quality assessment
Illustration 2. AI Quality Assessment
Illustration 3. Students' opinions on the potential of AI.
×

About the authors

Said Guseynovich Magomedov

Federal State Budgetary Educational Institution of Higher Education "Tver State Medical University" of the Ministry of Health of the Russian Federation

Email: magomedov.dias@gmail.com
ORCID iD: 0009-0004-4719-8787

6th year medical student

Russian Federation, Tver, Sovetskaya Street 4

Olga Victorovna Volkova

Federal State Budgetary Educational Institution of Higher Education "Tver State Medical University" of the Ministry of Health of the Russian Federation

Email: o-volkova64@yandex.ru
ORCID iD: 0009-0008-8435-184X
SPIN-code: 8476-6410

Candidate of Medical Sciences, Associate Professor of the Department of Pathological Physiology

Russian Federation, Tver, Sovetskaya Street 4

Natalya Anatolyevna Grishkina

Federal State Budgetary Educational Institution of Higher Education "Tver State Medical University" of the Ministry of Health of the Russian Federation

Email: kalanta1@yandex.ru
ORCID iD: 0009-0001-9225-2285
SPIN-code: 4266-6801

Candidate of Medical Sciences, Associate Professor of the Department of Infectious Diseases and Epidemiology

Russian Federation, Tver, Sovetskaya Street 4

Kirill Andreevich Bakalov

Federal State Budgetary Educational Institution of Higher Education "Tver State Medical University" of the Ministry of Health of the Russian Federation

Author for correspondence.
Email: bakalov2k17@yandex.ru
ORCID iD: 0009-0006-9509-8988

6th year medical student

Tver, Sovetskaya Street 4

References

  1. Hasanova R. R., Romanova E. A. Artificial intelligence in Higher education: problems, opportunities, risks // Bulletin of the RUDN University. Series: Informatization of education. 2024. No. 4. URL: https://cyberleninka.ru/article/n/iskusstvennyy-intellekt-v-vysshey-shkole-problemy-vozmozhnosti-riski (date of request: 04.09.2025).
  2. Tozsin A., Ucmak H., Soyturk S. [et al.] The Role of Artificial Intelligence in Medical Education: A Systematic Review [Electronic resource] / A. Tozsin, H. Ucmak, S. Soyturk, A. Aydin, A.S. Gozen, M.A. Fahim, S. Güven, K. Ahmed // Surgical Innovation. - 2024. - Vol. 31, No. 4. - P. 415-423. - doi: 10.1177/15533506241248239. - PMID: 38632898. - URL: https://doi.org/10.1177/15533506241248239 (date of request: 08/15/2025).
  3. Cooper, G. Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence / G. Cooper // Journal of Science Education and Technology. — 2023. — Vol. 32. — P. 444–452. — doi: 10.1007/s10956-023-10039-y.
  4. Zawacki-Richter, O. Systematic Review of Research on Artificial Intelligence Applications in Higher Education – Where are the Educators? / O. Zawacki-Richter, V. I. Marín, M. Bond, F. Gouverneur // International Journal of Educational Technology in Higher Education. — 2019. — Vol. 16, Article 39. — doi: 10.1186/s41239-019-0171-0.
  5. Chen L., Chen P., Lin Z. Artificial Intelligence in Education: A Review // IEEE Access. – 2020. – Vol. 8. – P. 75264-75278. – doi: 10.1109/ACCESS.2020.2988510. - URL: https://ieeexplore.ieee.org/document/9069875 (date of request: 09/05/2024).
  6. Hwang G. J., Xie H., Wah B. W., Gašević D. Vision, challenges, roles and research issues of Artificial Intelligence in Education // Computers and Education: Artificial Intelligence. – 2020. – Vol. 1. – Art. 100001. – doi: 10.1016/j.caeai.2020.100001. - URL: https://www.sciencedirect.com/science/article/pii/S2666920X20300017 (date of request: 09/05/2024)
  7. Ouyang F., Jiao P. Artificial intelligence in education: The three paradigms // Computers and Education: Artificial Intelligence. – 2021. – Vol. 2. – Art. 100020. – doi: 10.1016/j.caeai.2021.100020. - URL: https://www.sciencedirect.com/science/article/pii/S2666920X21000060 (date of request: 09/05/2024)
  8. Hale J., Alexander S., Wright S. [et al.] Generative AI in Undergraduate Medical Education: A Rapid Review [Electronic resource] / J. Hale, S. Alexander, S. Wright, K. Gilliland // Journal of Medical Education and Curricular Development. - 2024. - Vol. 11. - Art. 10.1177/23821205241266697. - doi: 10.1177/23821205241266697. - URL: https://doi.org/10.1177/23821205241266697 (date of request: 08/15/2025).
  9. Luckin R., Cukurova M. Designing educational technologies in the age of AI: A learning sciences-driven approach // British Journal of Educational Technology. – 2019. – Vol. 50, No. 6. – P. 2824-2838. – doi: 10.1111/bjet.12861. - URL: https://bera-journals.onlinelibrary.wiley.com/doi/abs/10.1111/bjet.12861 (date of request: 09/05/2024).
  10. Holstein K., McLaren B. M., Aleven V. Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity // Journal of Learning Analytics. – 2019. – Vol. 6, No. 2. – P. 27-52. – doi: 10.18608/jla.2019.62.3. - URL: https://learning-analytics.info/index.php/JLA/article/view/62.3 (date of request: 09/05/2024).
  11. Fazlollahi A. M., Bakhaidar M., Alsayegh A. [et al.] Effect of Artificial Intelligence tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial [Electronic resource] / A.M. Fazlollahi, M. Bahaidar, A. Alsayeg, R. Yilmaz, A. Winkler-Schwartz, N.Mirci, I. Langleben, N. Ledvos, A. J. Sabbagh, K. Bajunaid, J. M. Harley, R. F. Del Maestro // JAMA Network Open. - 2022. - Vol. 5, No. 2. - P. e2149008. - doi: 10.1001/jamanetworkopen.2021.49008. - PMID: 35191972. - PMCID: PMC8864513. - URL: https://doi.org/10.1001/jamanetworkopen.2021.49008 (accessed: 08/15/2025).
  12. Kaplan-Rakowski R., Grotewold N., Hartwick P., Papin K. Generative AI and Teachers’ Perspectives on Its Implementation in Education // Journal of Interactive Learning Research. – 2023. – Vol. 34, No. 2. – P. 313-338. - URL: https://www.learntechlib.org/primary/p/222363 / (date of access: 09/05/2024)
  13. Salih S.M. Perceptions of Faculty and Students About Use of Artificial Intelligence in Medical Education: A Qualitative Study [Electronic resource] / S.M. Salih // Cureus. – 2024. – Vol. 16, No. 4. – Art. e57605. – doi: 10.7759/cureus.57605. – PMID: 38707183. – PMCID: PMC11069392. - URL: https://doi.org/10.7759/cureus.57605 (accessed: 08/15/2025).
  14. Chen L., Chen P., Lin Z. Artificial Intelligence in Education: A Review // IEEE Access. – 2020. – Vol. 8. – P. 75264-75278. – doi: 10.1109/ACCESS.2020.2988510. - URL: https://ieeexplore.ieee.org/document/9069875 (date of request: 09/05/2024).
  15. Jihyun Kim , Kelly Merrill Jr., Kun Xu , Stephanie Kelly Perceived credibility of an AI instructor in online education: The role of social presence and voice features/ Computers in Human Behavior. – 2022. – Vol. 136, No.6 – 107383, doi: 10.1016/j.chb.2022.107383, URL: https://doi.org/10.1016/j.chb.2022.107383
  16. Voronina O. V. digital educational platforms for the implementation of hybrid learning / Bulletin of the Siberian Institute of Business and Information Technology vol. 12. Number – 2., doi: 10.24412/2225-8264-2023-2-5-10
  17. Mikheev M. Yu., Hilal S., pepel L. N., Gudkova E. A. GPT –3 technology in the tasks of creating content in the cognitive visual sphere/ modern information technologies-2023. No. 37. pp. 7-10 Elibrary ID: 53846343 EDN: JWOTBG
  18. Gurov D. I., Leonov D. E., Kirillov G. M. the influence of artificial intelligence on modern students: analysis, ethical and philosophical aspects// Bulletin of Science No. 12 (81) Volume 3. pp. 1320-1331. 2024. ISSN 2712-8849 // Electronic resource: https://www.вестник-науки .Russian Federation/article/19888 (accessed: 09/06/2025)
  19. Nagi F., Salih R., Alzubaidi M. [et al.] Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review [Electronic resource] / F. Nagi, R. Salih, M. Alzubaidi, H. Shah, T. Alam, Z. Shah, M. Househ // Stud Health Technol Inform. – 2023. – Vol. 305. – P. 648-651. – doi: 10.3233/SHTI230581. – PMID: 37387115. - URL: https://doi.org/10.3233/SHTI230581 (date of access: 08/15/2025).].

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) Eco-vector

License URL: https://eco-vector.com/for_authors.php#07
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ЭЛ № ФС 77 - 80652 от 15.03.2021 . Учредитель ООО "Эко-Вектор Ай-Пи" (ОГРН 1157847215338).