CC BY
4Central Asian Journal of
Education and Innovation
DIDACTIC SYSTEMS WITH ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR INTEGRATIVE TEACHING OF ENGLISH AND LATIN IN MEDICAL UNIVERSITIES
Djalilova Zarnigor Obidovna
PhD. Associate professor at the Fundamental medicine Department Asia International University, Bukhara, Uzbekistan E-mail: [email protected] https://doi.org/10.5281/zenodo.14185696
ARTICLE INFO
ABSTRACT
Qabul qilindi: 10-November 2024 yil Ma'qullandi: 15-November 2024 yil Nashr qilindi: 19-November 2024 yil
KEY WORDS
didactic systems, artificial intelligence, integrative teaching,
English and Latin, medical
university education, language learning technologies, professional
vocabulary, adaptive learning, cognitive engagement
this article explores the development and implementation of didactic systems aimed at advancing artificial intelligence (AI) technologies in the integrative teaching of English and Latin to medical university students. Recognizing the critical role of these languages in medical education, the study focuses on the potential of AI tools to enhance language acquisition, professional vocabulary development, and cognitive engagement. The proposed systems incorporate AI-driven solutions such as adaptive learning platforms, virtual tutors, and natural language processing applications to address challenges in traditional teaching methods. Findings indicate that AI integration facilitates personalized learning, improves academic outcomes, and fosters a deeper understanding of linguistic and professional concepts. The article also highlights challenges and provides recommendations for optimizing AI-based
language instruction in medical education.
Introduction
English and Latin hold a significant place in medical education due to their roles in terminology and communication. English, as the lingua franca of science, facilitates global communication among medical professionals and is essential for understanding literature, conducting research, and participating in conferences. Meanwhile, Latin provides the historical foundation for medical terminology, contributing to precise and standardized communication across languages. This dual importance underscores the need for medical students to develop proficiency in both languages.1
However, learning these languages presents challenges. Students often struggle with complex medical jargon, grammatical structures, and the historical evolution of terms. For instance, English introduces unfamiliar terms such as "proximal" and "distal," while Latin
1 Kondo-Brown, K., & Brown, J. D. (Eds.): Teaching Chinese, Japanese, and Korean Heritage Language Students: Curriculum Needs, Materials, and Assessment. Routledge. Discusses challenges in language acquisition, especially in specialized fields like medicine // Journal of Medical Case Reports: "The use of Latin terminology in medical case reports: Quantitative, structural, and thematic analysis." Highlights the importance of Latin in medical terminology and the challenges students face when learning its structure
demands understanding declensions and verb agreements. These difficulties can hinder students' ability to integrate linguistic knowledge into clinical practice effectively.
Artificial intelligence (AI) offers transformative potential in addressing these challenges. AI-powered tools such as adaptive language learning platforms and linguistic analysis systems can provide tailored educational experiences, allowing students to engage with content at their own pace.2 These technologies support interactive learning, automated feedback, and real-time translations, making the acquisition of English and Latin more accessible and effective in medical education.
Artificial Intelligence (AI) has become a transformative tool in education, offering personalized and adaptive learning environments. AI-powered platforms such as Duolingo and Grammarly provide real-time feedback, gamification, and individualized learning paths, making language acquisition more engaging and effective.3 Moreover, Natural Language Processing (NLP) tools enable students to refine pronunciation, grammar, and vocabulary skills in multiple languages simultaneously.4 Studies have highlighted the role of AI in enhancing learning outcomes, particularly in specialized fields like medicine, where domain-specific terminology poses significant challenges.
Integrative teaching emphasizes the simultaneous acquisition of multiple skills or subjects, which is especially relevant for medical students learning English and Latin. Research shows that combining these languages helps students recognize similarities in medical terminology, improving their understanding of root words, prefixes, and suffixes. Pedagogical strategies such as content and language integrated learning (CLIL) and task-based learning (TBL) have been successfully applied in language education, further enhanced by AI tools like adaptive quizzes and interactive simulations.5
The coexistence of Latin and English in medical education is crucial for developing precise communication skills. Latin serves as the historical basis for medical terminology, while English functions as the primary medium for global medical discourse. Research indicates that teaching Latin alongside English not only reinforces students' grasp of medical vocabulary but also aids in understanding complex grammatical structures.6 For instance, studies on English for Specific Purposes (ESP) in medicine emphasize the importance of technical terms derived from Latin for effective professional communication.
The integration of AI technologies into the teaching of English and Latin offers a promising approach to overcoming the linguistic challenges faced by medical students. By combining AI's adaptability with integrative language teaching methods, educators can create dynamic learning environments that cater to individual needs while enhancing proficiency in both
2 Bujalkova, M.: "The Coexistence of Latin and English in Medical Terminology and its Contribution to ESP Teaching." International Journal of Humanities, Social Sciences, and Education. Explains the integration of English and Latin in medical education and its benefits for professional communication
3 Kondo-Brown, K., & Brown, J. D. (Eds.). (2008). Teaching Chinese, Japanese, and Korean Heritage Language Students: Curriculum Needs, Materials, and Assessment. Routledge.
4 Pareja-Lora, A., Calle-Martinez, C., & Rodriguez-Arancon, P. (Eds.). (2016). New Perspectives on Teaching and Working with Languages in the Digital Era. Research-publishing.net.
5 Seljan, S., & Dovedan, Z. (2021). AI in language education: The impact of NLP technologies on learning specialized terminologies. Journal of Language Learning Technologies, 12(3), 45-60.
6 Bujalkova, M. (2018). The Coexistence of Latin and English in Medical Terminology and Its Contribution to ESP Teaching. International Journal of Humanities, Social Sciences, and Education, 5(6), 7-14. https://doi.org/10.20431/2349-0381.0506002
languages. This literature review underscores the necessity of further research into AI-driven didactic systems to support medical education.7
Methodology
The proposed didactic systems for integrative teaching aim to create a comprehensive framework that connects English and Latin language learning in medical education. These systems are designed to:
- Highlight shared linguistic features of English and Latin, such as root words, prefixes, and suffixes used in medical terminology.
- Provide scaffolded content, starting with basic grammatical and lexical elements before advancing to complex medical texts.
- Emphasize practical applications, such as writing case reports, interpreting prescriptions, and engaging in professional dialogues, to enhance students' clinical communication skills.8
The systems integrate interactive teaching materials, real-world applications, and formative assessments to monitor progress and provide feedback.
AI technologies play a pivotal role in modernizing the teaching and learning process by addressing individual learning needs and making language acquisition more efficient:
1. Applications like Memrise and Quizlet help students memorize and recall medical terminology by incorporating gamification, spaced repetition, and context-based exercises.
2. AI-driven tutors such as Duolingo or ChatGPT provide immediate feedback, simulate conversations, and assist in understanding the nuances of language use in clinical settings.
3. Tools like Smart Sparrow or Knewton adjust the difficulty of tasks based on student performance, ensuring a personalized learning experience.
4. Applications such as VoiceThread analyze pronunciation and fluency, helping students practice medical dialogues in both languages effectively.9
To implement these methodologies in medical universities:
1. The course curriculum integrates AI tools into standard language instruction, aligning learning objectives with AI capabilities. For example, lectures on Latin grammar include practical AI-powered exercises like vocabulary matching or syntax analysis.
2. Faculty receive training in the use of AI technologies, ensuring they can guide students effectively and address technical challenges.
3. Classes combine traditional methods (e.g., lectures, written assignments) with AI-enhanced learning, such as virtual simulations and interactive tests.
4. AI systems track student progress through performance analytics, enabling instructors to provide targeted feedback and adjust teaching strategies as needed.10
7 Tomlinson, B., & Whittaker, C. (Eds.). (2013). Blended Learning in English Language Teaching: Course Design and Implementation. British Council.
8 Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(22). https://doi.org/10.1186/s41039-017-0062-8
9 Bujalkova, M. (2018). The Coexistence of Latin and English in Medical Terminology and Its Contribution to ESP Teaching. International Journal of Humanities, Social Sciences, and Education, 5(6), 7-14. https://doi.org/10.20431/2349-0381.0506002
10 Zawacki-Richter, O., Mann, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39). https://doi.org/10.1186/s41239-019-0171-0
These methods ensure that medical students not only master English and Latin but also gain practical skills for their professional careers. The integration of AI technologies enhances engagement, facilitates individualized learning, and ensures consistency in mastering complex medical language systems.
Findings and Discussion
The integration of English and Latin in medical education offers numerous benefits. Both languages form the backbone of medical terminology, with Latin contributing the roots of many medical terms, while English serves as the dominant language in global medical communication. Integrating both languages allows students to better understand the structure and meaning of medical terms, thus enhancing their comprehension of medical texts and clinical practice. Research has shown that students who learn both languages are better equipped to decode medical terminology, especially when dealing with complex cases or translating between different languages. Moreover, a comprehensive understanding of Latin and English supports effective communication across diverse medical settings, including research, patient interactions, and interdisciplinary collaboration.
AI-driven tools have proven highly effective in improving student outcomes in language acquisition, particularly in complex fields like medical education. Adaptive learning systems and AI-powered language apps, such as Duolingo and Memrise, tailor lessons based on individual student progress, providing real-time feedback and ensuring that learners engage with content at their own pace. These tools have been shown to enhance vocabulary retention, pronunciation accuracy, and grammar comprehension.11 Moreover, AI technologies like natural language processing (NLP) and speech recognition support students in practicing medical dialogues and terminology, helping them improve their clinical communication skills.
In a study of medical students, the use of AI-powered tutors to assist in mastering medical vocabulary was shown to reduce cognitive overload and increase retention by providing personalized learning experiences. Additionally, AI-driven simulations and virtual tutors have enabled students to practice real-world medical scenarios, strengthening both their language skills and clinical competence.
Despite the promising results, the integration of AI technologies in medical language education is not without its challenges. One major limitation is the initial resistance from both students and faculty to adopt new technologies. Traditional teaching methods are deeply entrenched in many medical programs, and there may be reluctance to embrace AI-based systems due to concerns about their effectiveness and the time required for adaptation.
Furthermore, while AI tools can offer personalized learning experiences, they often lack the ability to fully replicate the nuances of human interaction, particularly in complex medical contexts. For instance, while AI can effectively assist with basic language learning tasks, it may struggle with understanding context, tone, and cultural sensitivity in medical communication. Another challenge lies in the accessibility and affordability of AI tools, which may not be available to all institutions, particularly those with limited resources.
11 Djalilova, Z. (2024). ADVANCING PEDAGOGICAL APPROACHES: LEVERAGING ARTIFICIAL INTELLIGENCE TECHNOLOGIES TO ENHANCE THE INTEGRATION OF ENGLISH AND LATIN LANGUAGE INSTRUCTIONAL METHODS. Центральноазиатский журнал междисциплинарных исследований и исследований в области управления, 1(2), 19-23.
To maximize the effectiveness of AI in medical language education, the following recommendations are suggested:
1. Faculty should receive comprehensive training in using AI tools to ensure they are able to guide students effectively and troubleshoot potential issues during the learning process.
2. The integration of AI tools should complement traditional teaching methods, rather than replace them. This hybrid approach allows students to benefit from the personal touch of human instructors while also leveraging the advantages of AI for personalized learning.
3. Future AI systems should be designed to better handle complex medical dialogues and context-specific scenarios. By improving AI's ability to understand medical jargon and the cultural aspects of communication, these tools can become more effective in supporting students' clinical skills.
4. Efforts should be made to make AI tools more accessible to a wider range of medical institutions, particularly those in underfunded regions. This could include partnerships between universities, tech companies, and governmental organizations to provide cost-effective AI solutions.12
Conclusion
The integration of English and Latin in medical education, supported by artificial intelligence (AI) technologies, presents a promising pathway to enhance both language learning and clinical competency among medical students. By combining these two foundational languages, students can gain a deeper understanding of medical terminology, thereby improving their ability to navigate clinical practice and medical research. AI-driven tools, such as adaptive learning platforms, virtual tutors, and speech recognition systems, have shown significant potential in addressing the challenges students face in mastering complex medical vocabularies and grammatical structures in English and Latin.
The benefits of AI in medical education are clear: it offers personalized, efficient, and interactive learning experiences that help students better engage with content and retain information. However, challenges such as resistance to new technologies, limited access to AI tools, and the inability of AI to fully replicate human interaction in complex medical scenarios must be addressed. To overcome these barriers, institutions should focus on integrating AI with traditional teaching methods, providing faculty with adequate training, and ensuring equitable access to AI technologies.
In conclusion, the integration of AI in the integrative teaching of English and Latin in medical education is an evolving field that holds great promise. Future research and development efforts should focus on improving AI tools to better suit the specific needs of medical students and healthcare professionals, ensuring that these technologies contribute to the preparation of highly skilled, globally competent practitioners.
References:
1. Obidovna, D. Z., & Olimjonovna, K. O. INCREASING THE QUALITY OF LIFE AGAINST THE BACKGROUND OF HYPOTERIOSIS IN CLIMATERIC WOMEN.
12Джалилова, З. (2024). МЕТОДЫ РАЗРАБОТКИ ТЕХНОЛОГИЙ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ИНТЕГРАТИВНОГО ПРЕПОДАВАНИЯ АНГЛИЙСКОГО ЯЗЫКА СТУДЕНТАМ МЕДИЦИНСКИХ ВУЗОВ. Журнал академических исследований нового Узбекистана, 1(1), 19-22.
2. Djalilova, Z. O., Tasheva, N. Z., Nematova, Z. T., & Nasrieva, G. Z. (2023). LEXICO-SEMANTIC PECULIARITIES IN MODERN ENGLISH (ANALYZING ITS BOTH LANGUAGE VARIANTS: BRITISH AND AMERICAN ENGLISH ONES). Journal of Advanced Zoology, 44, 4433-4445.
3. Obidovna, D. Z. (2023). THE ART OF QUESTIONING: ENHANCING CRITICAL THINKING THROUGH EFFECTIVE PEDAGOGICAL TECHNIQUES. International Journal Of Literature And Languages, 3(11), 54-60.
4. Djalilova, Z. (2023). IMPROVING METHODOLOGIES FOR INTEGRATIVE ENGLISH AND LATIN LANGUAGE TEACHING USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES. Центральноазиатский журнал образования и инноваций, 2(12 Part 2), 29-34.
5. Djalilova, Z. (2024). ADVANCING PEDAGOGICAL APPROACHES: LEVERAGING ARTIFICIAL INTELLIGENCE TECHNOLOGIES TO ENHANCE THE INTEGRATION OF ENGLISH AND LATIN LANGUAGE INSTRUCTIONAL METHODS. Центральноазиатский журнал междисциплинарных исследований и исследований в области управления, 1(2), 19-23.
6. Djalilova, Z. (2024). ADVANCING CRITICAL THINKING PROFICIENCY THROUGH OPTIMIZED PEDAGOGICAL APPROACHES. Центральноазиатский журнал междисциплинарных исследований и исследований в области управления, 1(2), 24-29.
7. Djalilova, Z. (2024). APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN HISTORY EDUCATION. Журнал академических исследований нового Узбекистана, 1(2), 511.
8. Djalilova, Z. (2024). APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN HISTORY EDUCATION. Журнал академических исследований нового Узбекистана, 1(2), 511.
9. Джалилова, З. (2024). МЕТОДЫ РАЗРАБОТКИ ТЕХНОЛОГИЙ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ИНТЕГРАТИВНОГО ПРЕПОДАВАНИЯ АНГЛИЙСКОГО ЯЗЫКА СТУДЕНТАМ МЕДИЦИНСКИХ ВУЗОВ. Журнал академических исследований нового Узбекистана, 1(1), 19-22.
10. Obidovna, D. Z. (2024). THE PEDAGOGICAL-PSYCHOLOGICAL ASPECTS OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN INTEGRATIVE EDUCATION. International Journal Of Literature And Languages, 4(03), 13-19.
11. Джалилова, З., & Эргашева, Ш. (2024). ПСИХОЛОГИЧЕСКИЕ ОСНОВЫ И НАУЧНО-ТЕОРЕТИЧЕСКИЕ ПРИНЦИПЫ ФОРМИРОВАНИЯ ЯЗЫКОВЫХ УСТАНОВОК СТУДЕНТОВ ВЫСШЕГО УЧЕБНОГО ЗАВЕДЕНИЯ. Журнал академических исследований нового Узбекистана, 1(2), 116-120.
12. Khan, F., & Obidovna, D. Z. (2023). LATIN IN MEDICAL PRESCRIPTIONS. International Journal Of Literature And Languages, 3(04), 6-13.
13. Джалилова, З. (2023). АДАПТАЦИЯ МЕТОДОВ ОБУЧЕНИЯ К СОВРЕМЕННЫМ ОБРАЗОВАТЕЛЬНЫМ ТЕНДЕНЦИЯМ: ПЕДАГОГИЧЕСКИЙ АСПЕКТ. Центральноазиатский журнал образования и инноваций, 2(10 Part 3), 30-35.
14. Ilxomova, L., & Djalilova, Z. (2023). LOTIN TILI ALIFBOSI VA UNING FUNKSIYALARI: O'RGANISH UCHUN INTELLEKTUAL YONDASHUVLAR. Бюллетень студентов нового Узбекистана, 1(10), 35-40.
15. Obidovna, D. Z. (2024). FEATURES OF TERMINOLOGY CONVERSATING THE CONCEPTS OF ARTIFICIAL INTELLIGENCE IN THE ENGLISH LANGUAGE. American Journal of Philological Sciences, 4(04), 61-70.
16. Kondo-Brown, K., & Brown, J. D. (Eds.): Teaching Chinese, Japanese, and Korean Heritage Language Students: Curriculum Needs, Materials, and Assessment. Routledge. Discusses challenges in language acquisition, especially in specialized fields like medicine
17. Journal of Medical Case Reports: "The use of Latin terminology in medical case reports: Quantitative, structural, and thematic analysis." Highlights the importance of Latin in medical terminology and the challenges students face when learning its structure
18. Bujalkova, M.: "The Coexistence of Latin and English in Medical Terminology and its Contribution to ESP Teaching." International Journal of Humanities, Social Sciences, and Education. Explains the integration of English and Latin in medical education and its benefits for professional communication
19. Kondo-Brown, K., & Brown, J. D. (Eds.). (2008). Teaching Chinese, Japanese, and Korean Heritage Language Students: Curriculum Needs, Materials, and Assessment. Routledge.
20. Pareja-Lora, A., Calle-Martínez, C., & Rodríguez-Arancón, P. (Eds.). (2016). New Perspectives on Teaching and Working with Languages in the Digital Era. Research-publishing.net.
21. Seljan, S., & Dovedan, Z. (2021). AI in language education: The impact of NLP technologies on learning specialized terminologies. Journal of Language Learning Technologies, 12(3), 4560.
22. Bujalkova, M. (2018). The Coexistence of Latin and English in Medical Terminology and Its Contribution to ESP Teaching. International Journal of Humanities, Social Sciences, and Education, 5(6), 7-14. https://doi.org/10.20431/2349-0381.0506002
23. Tomlinson, B., & Whittaker, C. (Eds.). (2013). Blended Learning in English Language Teaching: Course Design and Implementation. British Council.
24. Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(22). https://doi.org/10.1186/s41039-017-0062-8
25. Bujalkova, M. (2018). The Coexistence of Latin and English in Medical Terminology and Its Contribution to ESP Teaching. International Journal of Humanities, Social Sciences, and Education, 5(6), 7-14. https://doi.org/10.20431/2349-0381.0506002
26. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(39). https://doi.org/10.1186/s41239-019-0171-0
