
تعداد نشریات | 20 |
تعداد شمارهها | 410 |
تعداد مقالات | 3,366 |
تعداد مشاهده مقاله | 4,894,930 |
تعداد دریافت فایل اصل مقاله | 3,299,608 |
چندزبانگی و حواس کلامی در یادگیری مهارتهای درک فارسی با اهداف پزشکی بهکمک رباتهای اجتماعی هوشمند | ||
پژوهش نامه آموزش زبان فارسی به غیر فارسی زبانان | ||
مقاله 9، دوره 13، شماره 2 - شماره پیاپی 28، مهر 1403، صفحه 203-226 اصل مقاله (1.61 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.30479/jtpsol.2025.21886.1703 | ||
نویسندگان | ||
سعید خزایی1؛ علی درخشان* 2؛ مجتبی کرباسی3 | ||
1دانشیار، مرکز تحقیقات فناوری اطلاعات در امور سلامت، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران | ||
2نویسنده مسئول، استاد، گروه زبان انگلیسی، دانشکده علوم انسانی و اجتماعی، دانشگاه گلستان، گرگان، ایران | ||
3استادیار، مرکز تحقیقات فناوری اطلاعات در امور سلامت، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران | ||
تاریخ دریافت: 20 فروردین 1404، تاریخ بازنگری: 18 مرداد 1404، تاریخ پذیرش: 20 مرداد 1404 | ||
چکیده | ||
هدف از انجام این پژوهش بررسی ظرفیت رباتهای اجتماعی هوشمند در برانگیختن حواس کلامی دانشجویان در درک فارسی با اهداف پزشکی بود. بهاین منظور، با استفاده از طرح ترکیبی نهفته عملکرد و رویکرد ۴۰۰ دانشجو غیرایرانی تک زبانه و دو زبانه مشغول به تحصیل در دانشگاه علوم پزشکی اصفهان در سال تحصیلی ۱۴۰۳ در تعامل با رباتهای اجتماعی و دستیارهای هوشمند بررسی شد. در حالیکه دادههای مربوط به عملکرد شرکتکنندگان با استفاده از معادلات برآوردیابی تعمیم یافته تحلیل کمی شد، دادههای حاصل از پاسخ شرکتکنندگان به پرسشهای مصاحبه به شیوه مضمون-محور تحلیل کیفی شد. یافتهها نشان داد که پودمانهای زبانآموزی مبتنی بر ربات اجتماعی در تلفیق با هوش مصنوعی از ظرفیت بالایی برای برانگیختن حواس کلامی دانشجویان دوزبانه و ارتقاء مهارت درک فارسی با اهداف پزشکی برخوردار است. الگوریتمهای پردازش زبان طبیعی با فراخوانی محتوا از کتابخانههای هوش مصنوعی امکان پیادهسازی بافتهای تمرین تعاملی شبیه به واقعیت را برای تمرین مهارتهای شنیدار و خواندار فارسی با اهداف پزشکی رقم زد. در این بین، دانشجویان دو زبانه با عملکرد بهتر، یادگیری فارسی با اهداف پزشکی بهکمک رباتهای هوشمند را لذت بخش میدانستند. طراحی و اجرا دورههای هوشمند زبانآموزی با اهداف دانشگاهی بر اساس یادگیری کاربردی زبان بحث میشود. | ||
کلیدواژهها | ||
ربات اجتماعی؛ چند زبانگی؛ حواس کلامی؛ فارسی با اهداف پزشکی؛ هوش مصنوعی | ||
عنوان مقاله [English] | ||
Multilingualism and verbal senses in learning Persian for Medical Purposes comprehension through Artificial Intelligence social robots | ||
نویسندگان [English] | ||
Saeed Khazaie1؛ Ali Derakhshan2؛ Mojtaba Karbasi3 | ||
1Associate Professor, Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. | ||
2Corresponding Author, Professor of Applied Linguistics, Department of English Language and Literature, Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran. | ||
3Assistant Professor, Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran | ||
چکیده [English] | ||
This study investigates the potential of Artificial Intelligence social robots to stimulate students’ verbal senses in understanding Persian for Medical Purposes. To this end, using an embedded design, the performance and approach of 400 non-Iranian monolingual and bilingual students studying at Isfahan University of Medical Sciences in the 2024 academic year were examined in their interactions with Artificial Intelligence robots and assistants. While the data related to the participants’ performance were analyzed quantitatively using Generalized Estimating Equations, Interview content analysis was employed to extract themes and subthemes from the data obtained from the participants’ responses to interview questions. The findings indicated that Artificial Intelligence robots have great potential to stimulate bilingual students’ verbal senses and promote Persian for Medical Purposes comprehension. Natural Language Processing algorithms, by calling content from Artificial Intelligence Libraries, made it possible to implement realistic interactive contexts for practicing Persian for Medical Purposes listening and reading. Meanwhile, bilingual students with better performance found learning Persian for Medical Purposes through Artificial Intelligence robots enjoyable. The design and implementation of an Artificial Intelligence robot-assisted language learning for academic purposes is discussed based on applied language learning. Extended Abstract: Introduction Artificial Intelligence (AI) social robots help establish interpersonal interaction and focus on developing language skills through enhancing students’ senses. Multilingualism stimulates the audio-visual senses of students as they specifically learn a new language to deal with their needs in authentic environments. Developers of robot-assisted language learning exploit different senses in their current works in university language learning. Chatbots are AI assistants in language learning in which students pose their questions to join a conversation. While a broad array of studies has investigated the potential of social robots and AI assistants in language learning at universities, only a few studies have been conducted in Persian for Medical Purposes learning concerning stimulating students’ senses and multilingualism. Research has indicated that AI social robots serve as real-like conversation partners, which subsequently help students learn receptive and productive skills through enhanced torso. Nevertheless, mediation of students’ proficiency in first and/or second language has not been taken into account. In addition, several studies have tackled the effect of robot’s senses on language learning. Their findings revealed that enhancing the senses of robots facilitates language leaning; however, they did not address the probable effect of multilingualism on heightening the audio-visual senses in learning language for specific purposes. To fill the research gaps, the researchers mediate the monolingual and bilingual in learning Persian for Medical Purposes listening and reading through audio-visual senses of AI robots and chatbots. Artificial Intelligence assistants allow users to predict reading and listening to address their language needs. Therefore, using tools of touch monitors in robots’ torsos or students’ mobile devices, the researchers in this study mediates students’ Persian for Medical Purposes to help them understand language needs in both universities and fields. Artificial Intelligence-based language education is affiliated with social psychology, which, in turn, is concerned with the study of how a learning process is influenced by the presence of others. Methodology In the 2024 academic year, 480 non-Iranian male and female students were selected through the design of experiments. First, to identify monolingual and bilingual students, a TOEFL-like test was administered. Three hundred and twelve students were bilingual and 88 students were monolingual. Eighty students, whose English proficiency scores were one standard deviation higher than the mean, were excluded from the study. Then, the Persian proficiency of 400 students was determined by a general Persian test. According to the Common European Framework of Reference for Languages, the participants were placed in three levels and were randomly assigned to control and experimental groups. In an introductory session, the participants were taught how to read and listen with chatbots or AI robots. Besides, the structure of interviews was explained. In weeks 2-16, the participants were first co-taught with the teachers; then, they joined conversation with chatbots or AI robots to practice Persian for Medical Purposes listening and reading. Finally, the participants’ listening and reading were assessed in university- and field-like situations. Parallel with the treatment, focus-group interviews were conducted. Results and Discussion The study explored the effectiveness of AI robots in learning Persian for Medical Purposes, focusing on listening and reading skills across two control and experimental groups. The control group practiced with chatbots, while the experimental group engaged with AI robots. The results indicated a significant difference in performance between the two groups. Students in the chatbot group exhibited lower learning outcomes compared to their fellows in the AI robot group. This finding suggests that while both technologies aim to enhance language learning, AI robots may be more effective in facilitating comprehension of medical concepts in universities and fields. Moreover, proficiency in Persian emerged as an important factor influencing student success. Those with higher proficiency levels performed better, emphasizing the importance of foundational language skills in achieving effective learning outcomes. In terms of interaction effects, the study revealed that the impact of time on learning varied significantly between the two groups. As students progressed through the sessions, the effectiveness of the chatbot group fluctuated, suggesting that the learning experience may not have been as consistent as that of the AI robot group. Additionally, the interaction between language background and time indicated that bilingual students experienced different learning trajectories, potentially influenced by their prior exposure to the language. While the benefits of bilingualism were not statistically significant in this setting, the complexity of language learning in medical settings cannot be overlooked. Overall, these findings highlight the nuanced relationship between technology, language proficiency, and learning outcomes in specialized fields. The results advocate for a more tailored approach in language instruction, suggesting that leveraging the strengths of AI robots could enhance educational experiences in medical language education. Future research should further explore these dynamics to refine pedagogical strategies and optimize learning environments for students in medical fields. Conclusion In conclusion, this study highlights the significant advantages of bilingualism in the context of learning Persian for Medical Purposes. The findings indicate that bilingual students easily engaged in conversation with AI robots compared to their monolingual counterparts, suggesting that their verbal senses were more effectively stimulated. Additionally, the superior outcomes observed in the AI-robot group, as opposed to the chatbot group, underscore the potential of interactive and immersive learning environments in enhancing Persian for Medical Purposes listening and reading. Although bilingual students did not excel in developing their listening and reading skills in Persian, they reported a higher level of enjoyment in the learning process. These results emphasize the importance of considering linguistic backgrounds in language education and suggest that incorporating AI technologies, such as social robots, can significantly enhance the learning experience for medical students. Future research could further explore the implications of these findings and the potential for broader applications in medical language education. | ||
کلیدواژهها [English] | ||
Social robot, multilingualism, verbal senses, Persian for medical purposes, Artificial Intelligence | ||
مراجع | ||
پیشقدم، رضا (1399). مفهوم آموزشی. نشر بوی کاغذ.
خزائی، سعید، درخشان، علی و کیانپور، مریم (2021). بررسی امکانپذیرى فراخوانش قید زمان و ترتیب به کمک ایماهای استعاری رباتهای اجتماعی در آموزش مهارت درک خوانداری محتوای پزشکی به زبان فارسی. پژوهشنامة آموزش زبان فارسی به غیر فارسی زبانان، 10(2)، 157-181.
تاجالدین، سیّدضیاءالدین و نعمتی سرخی، محبوبه (1391). بررسی تأثیر آموزش از طریق رایانه درمقایسه با روش سنتی بر میزان یادگیری زبان آموزان غیر فارسی زبان. پژوهشنامة آموزش زبان فارسی به غیر فارسی زبانان، 1(1)، 101-122.
References:
ACTFL-American Council on the Teaching of Foreign Languages. (2010). Maximum class size. Retrieved from https://www.actfl.org/search/node/class%20size
Ahmed, S. K. (2024). The pillars of trustworthiness in qualitative research. Journal of Medicine, Surgery, and Public Health, 2, 100051. https://doi.org/10.1016/j.glmedi.2024.100051
Al Wachami, N., Chahboune, M., Youlyouz, I., Mesradi, M. R., Lemriss, H., & Hilali, A. (2024). Improving the quality of care and patient safety in oncology, the contribution of simulation-based training: A scoping review. International Journal of Nursing Sciences. https://doi.org/10.1016/j.ijnss.2024.03.005
Bahari, A. (2023). Affordances and challenges of technology-assisted language learning for motivation: A systematic review. Interactive Learning Environments, 31(9), 5853-5873. https://doi.org/10.1080/10494820.2021.2021246
Banaruee, H., Khatin-Zadeh, O., & Farsani, D. (2023). The challenge of psychological processes in language acquisition: A systematic review. Cogent Arts & Humanities, 10(1), 2157961. https://doi.org/10.1080/23311983.2022.2157961
Chen, X., Cheng, G., Zou, D., Zhong, B., & Xie, H. (2023). Artificial intelligent robots for precision education. Educational Technology & Society, 26(1), 171-186.
Chen, Y. L., Hsu, C. C., Lin, C. Y., & Hsu, H. H. (2022). Robot-assisted language learning: Integrating artificial intelligence and virtual reality into English tour guide practice. Education Sciences, 12(7), 437. https://doi.org/10.3390/educsci12070437
Deedari, R., & Hossaini, Z. (2021). English for the students of medicine (I). SAMT.
Donesch-Jeżo, E. (2007). English for students of pharmacy and pharmacists. Wydawnictwo Przegląd Lekarski.
Engwall, O., & Lopes, J. (2022). Interaction and collaboration in robot-assisted language learning for adults. Computer Assisted Language Learning, 35(5-6), 1273-1309. https://doi.org/10.1080/09588221.2020.1799821
Feak, C. B. (2012). ESP and speaking. The handbook of English for specific purposes, 35-53. https://doi.org/10.1002/9781118339855.ch2
Fung, K. Y., Lee, L. H., Sin, K. F., Song, S., & Qu, H. (2024). Humanoid robot-empowered language learning based on self-determination theory. Education and Information Technologies, 1-30. https://doi.org/10.1007/s10639-024-12570-w
Gollin-Kies, S., Hall, D. R., & Moore, S. H. (2016). Language for specific purposes. Springer.
Guggemos, J., Seufert, S., & Sonderegger, S. (2020). Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT. British Journal of Educational Technology, 51(5), 1864-1883. https://doi.org/10.1111/bjet.13006
Gümüş, M. M., & Kukul, V. (2023). Developing a digital competence scale for teachers: validity and reliability study. Education and Information Technologies, 28(3), 2747-2765. https://doi.org/10.1007/s10639-022-11213-2
Guo, S., Zheng, Y., & Zhai, X. (2024). Artificial intelligence in education research during 2013–2023: A review based on bibliometric analysis. Education and Information Technologies, 1-23.
Hu, Y. H., Fu, J. S., & Yeh, H. C. (2023). Developing an early-warning system through robotic process automation: Are intelligent tutoring robots as effective as human teachers? Interactive Learning Environments, 1-14. https://doi.org/10.1080/10494820.2022.2160467
Kartal, G., & Yeşilyurt, Y. E. (2024). A bibliometric analysis of artificial intelligence in L2 teaching and applied linguistics between 1995 and 2022. ReCALL, 1-17. https://doi.org/10.1017/S0958344024000077
Khaki, N., Maboudi, A., & Jannati, P. (2023). English for dentistry students. RoyanPazhouh. Khazaie, S., & Derakhshan, A. (2024). Extending embodied cognition through robot's augmented reality in English for medical purposes classrooms. English for Specific Purposes, 75, 15-36. https://doi.org/10.1016/j.esp.2024.03.001
Khazaie, S., Derakhshan, A., & Kianpour, M. (2021). Exploring the viability of representing time and order adverbs through the co-verbal gestures of social robots in teaching reading Persian for Medical Purposes. Journal of Teaching Persian to Speakers of Other Languages, 10(2), 157-181. http://doi.org/10.30479/jtpsol.2022.16012.1548 [In Persian]
Kim, S., & Park, J. Y. (2024). Critical awareness toward Content-Language Integrated Education for Multilingual Learners (CA-CIEML): a survey study about teachers’ ideological beliefs and attitudes. Language Awareness, 1-23. https://doi.org/10.1080/09658416.2024.2321895
Lear, D. (2021). Domain analysis: Research‐based reverse design for languages for specific purposes. Foreign Language Annals, 54(1), 139-157. https://doi.org/10.1111/flan.12509
Liang, J. C., & Hwang, G. J. (2023). A robot-based digital storytelling approach to enhancing EFL learners’ multimodal storytelling ability and narrative engagement. Computers & Education, 201, 104827. https://doi.org/10.1016/j.compedu.2023.104827
Liao, J., Lu, X., Masters, K. A., & Zhou, Z. (2024). Meaning-focused foreign language instruction via telepresence robots: A geosemiotic analysis. ReCALL, 1-19. https://doi.org/10.1017/S095834402400003X
Liu, Y., & Li, T. (2024). Comparing the syntactic complexity of plain language summaries and abstracts: A case study of marine science academic writing. Journal of English for Academic Purposes, 101350. https://doi.org/10.1016/j.jeap.2024.101350
Obari, H., & Lambacher, S. (2019). Improving the English skills of native Japanese using artificial intelligence in a blended learning program. CALL and complexity–short papers from EUROCALL, 327-333.
Pappachan, P., Piyakanjana, S., Syofyan, H., & Nugroho, G. (2025). Innovative horizons: The role of AI and robotics in special education. In B. B. Gupta & F. Colace (Eds.), AI developments for industrial robotics and intelligent drones (pp. 231-256). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-2707-4.ch010
Pikhart, M. (2021). Human-computer interaction in foreign language learning applications: Applied linguistics viewpoint of mobile learning. Procedia Computer Science, 184, 92-98. https://doi.org/10.1016/j.procs.2021.03.123
Pishghadam, R. (2020). Educational concept. Booka Publication (Booye Kaqaz) [In Persian]
Riedmann, A., Schaper, P., & Lugrin, B. (2024). Integration of a social robot and gamification in adult learning and effects on motivation, engagement and performance. AI & SOCIETY, 39(1), 369-388. https://doi.org/10.1007/s00146-022-01514-y
Staller, K. M. (2022). Confusing questions in qualitative inquiry: Research, interview, and analysis. Qualitative Social Work, 21(2), 227-234. https://doi.org/10.1177/14733250221080533
Tajeddin, S. Z., & Nemati Sorkhi, M. (2012). A survey on the effect of computer-assisted language teaching, in contrast to the traditional method, on non-native Persian learners’ performance. Journal of Teaching Persian to Speakers of Other Languages, 1(1), 101-122. [In Persian]
Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. The SAGE handbook of qualitative research in psychology, 2(17-37), 25.
Ververi, C., Koufou, T., Moutzouris, A., & Andreou, L. V. (2020, April). Introducing robotics to an English for academic purposes curriculum in higher education: The student experience. In 2020 IEEE Global Engineering Education Conference (pp. 20-21). IEEE. https://doi.org/10.1109/EDUCON45650.2020.9125290
Wu, F., Chen, Y., & Han, D. (2022). Development countermeasures of college English education based on deep learning and artificial intelligence. Mobile Information Systems, 2022(1), 8389800. https://doi.org/10.1155/2022/8389800
Yang, Z., Keung, J. W., Sun, Z., Zhao, Y., Li, G., Jin, Z., Liu, Sh. & Li, Y. (2024). Improving domain-specific neural code generation with few-shot meta-learning. Information and Software Technology, 166, 107365. https://doi.org/10.1016/j.infsof.2023.107365
Yu, X., Soto-Varela, R., & Gutiérrez-García, M. Á. (2024). How to learn and teach a foreign language through computational thinking: Suggestions based on a systematic review. Thinking Skills and Creativity, 101517. https://doi.org/10.1016/j.tsc.2024.101517
Yuan, L., & Liu, X. (2025). The effect of artificial intelligence tools on EFL learners’ engagement, enjoyment, and motivation. Computers in Human Behavior, 162, 108474. https://doi.org/10.1016/j.chb.2024.108474
Zhai, C., & Wibowo, S. (2023). A systematic review on artificial intelligence dialogue systems for enhancing English as foreign language students’ interactional competence in the university. Computers and Education: Artificial Intelligence, 4, 100134. https://doi.org/10.1016/j.caeai.2023.100134
Zhang, R., Zou, D., & Cheng, G. (2025). ChatGPT affordance for logic learning strategies and its usefulness for developing knowledge and quality of logic in English argumentative writing. System, 128, 103561. https://doi.org/10.1016/j.system.2024.103561
Zinina, A., Kotov, A., Arinkin, N., & Zaidelman, L. (2023). Learning a foreign language vocabulary with a companion robot. Cognitive Systems Research, 77, 110-114. https://doi.org/10.1016/j.cogsys.2022.10.007
| ||
آمار تعداد مشاهده مقاله: 41 تعداد دریافت فایل اصل مقاله: 10 |