Artificial General Intelligence: Advantages in English Language Learning

Yulian Purnama (1), Sherly Gaspersz (2)
(1) Universitas Saizu Purwokerto, Indonesia,
(2) Universitas Victory Sorong, Indonesia

Abstract

Learning English has become a major focus in this modern era, where cross-cultural communication is increasingly important. However, the language learning process is often challenging for many individuals, especially those who do not have adequate access or resources. The advent of Artificial General Intelligence promises significant advances in language learning approaches, with the potential to increase the accessibility, speed and effectiveness of learning. This research aims to explore the potential benefits of Artificial General Intelligence in English language learning, especially in the context of accessibility, speed and effectiveness of learning. The research method uses a qualitative approach involving a comprehensive literature study on the latest developments in the use of Artificial General Intelligence in language learning. The research results show that Artificial General Intelligence has great potential to improve English language learning. Apart from that, there are also many benefits to be gained from using Artificial General Intelligence in English language learning. The conclusions in this research confirm that the use of Artificial General Intelligence in English language learning offers significant potential to increase the accessibility, speed and effectiveness of learning. However, challenges related to ethics, privacy and data security also need to be seriously considered in the development and implementation of this technology. With a careful and integrated approach, Artificial General Intelligence can be a valuable tool in supporting inclusive and effective English language learning for all individuals. The limitation of this research is that this research only conducted research at the educational unit level, specifically in English language learning.

Full text article

Generated from XML file

References

Ahmad, S. F., Alam, M. M., Rahmat, Mohd. K., Mubarik, M. S., & Hyder, S. I. (2022). Academic and Administrative Role of Artificial Intelligence in Education. Sustainability, 14(3), 1101. https://doi.org/10.3390/su14031101

Albahra, S., Gorbett, T., Robertson, S., D’Aleo, G., Kumar, S. V. S., Ockunzzi, S., Lallo, D., Hu, B., & Rashidi, H. H. (2023). Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data preprocessing and basic supervised concepts. Seminars in Diagnostic Pathology, 40(2), 71–87. https://doi.org/10.1053/j.semdp.2023.02.002

Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91. https://doi.org/10.1016/j.cities.2019.01.032

Amalia, A., Sipahutar, P. Y. C., Elviwani, E., & Purnamasari, F. (2020). Chatbot Implementation with Semantic Technology for Drugs Information Searching System. Journal of Physics: Conference Series, 1566(1), 012077. https://doi.org/10.1088/1742-6596/1566/1/012077

Aparicio, F., Morales-Botello, M. L., Rubio, M., Hernando, A., Muñoz, R., López-Fernández, H., Glez-Peña, D., Fdez-Riverola, F., De La Villa, M., Maña, M., Gachet, D., & Buenaga, M. D. (2018). Perceptions of the use of intelligent information access systems in university level active learning activities among teachers of biomedical subjects. International Journal of Medical Informatics, 112, 21–33. https://doi.org/10.1016/j.ijmedinf.2017.12.016

Bales, A. (2023). Will AI avoid exploitation? Artificial general intelligence and expected utility theory. Philosophical Studies. https://doi.org/10.1007/s11098-023-02023-4

Baraldi, A., Sapia, L. D., Tiede, D., Sudmanns, M., Augustin, H. L., & Lang, S. (2023). Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as necessary-but-not-sufficient precondition of the downstream in a new notion of Space Economy 4.0 - Part 1: Problem background in Artificial General Intelligence (AGI). Big Earth Data, 7(3), 455–693. https://doi.org/10.1080/20964471.2021.2017549

Bell, S. (2010). Project-Based Learning for the 21st Century: Skills for the Future. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 83(2), 39–43. https://doi.org/10.1080/00098650903505415

Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019). Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners’ Views. Journal of Medical Internet Research, 21(3), e12802. https://doi.org/10.2196/12802

Blum, L., & Blum, M. (2023). A Theoretical Computer Science Perspective on Consciousness and Artificial General Intelligence. Engineering, 25, 12–16. https://doi.org/10.1016/j.eng.2023.03.010

Buck, C., Doctor, E., Hennrich, J., Jöhnk, J., & Eymann, T. (2022). General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study. Journal of Medical Internet Research, 24(1), e28916. https://doi.org/10.2196/28916

Carlson, K. W. (2019). Safe Artificial General Intelligence via Distributed Ledger Technology. Big Data and Cognitive Computing, 3(3), 40. https://doi.org/10.3390/bdcc3030040

Cloudia Ho, Y.-Y. (2020). Communicative language teaching and English as a foreign language undergraduates’ communicative competence in Tourism English. Journal of Hospitality, Leisure, Sport & Tourism Education, 27, 100271. https://doi.org/10.1016/j.jhlste.2020.100271

Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 134–138. https://doi.org/10.1145/2330601.2330636

Denkenberger, D., Sandberg, A., Tieman, R. J., & Pearce, J. M. (2021). Long-term cost-effectiveness of interventions for loss of electricity/industry compared to artificial general intelligence safety. European Journal of Futures Research, 9(1), 11. https://doi.org/10.1186/s40309-021-00178-z

Denkenberger, D., Sandberg, A., Tieman, R. J., & Pearce, J. M. (2022). Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety. International Journal of Disaster Risk Reduction, 73, 102798. https://doi.org/10.1016/j.ijdrr.2022.102798

Dharamsi, A., Navale, A. M., & Jambhekar, S. S. (2023). General considerations on artificial intelligence. Dalam A Handbook of Artificial Intelligence in Drug Delivery (hlm. 9–34). Elsevier. https://doi.org/10.1016/B978-0-323-89925-3.00002-2

Edwards, D. J., McEnteggart, C., & Barnes-Holmes, Y. (2022). A Functional Contextual Account of Background Knowledge in Categorization: Implications for Artificial General Intelligence and Cognitive Accounts of General Knowledge. Frontiers in Psychology, 13, 745306. https://doi.org/10.3389/fpsyg.2022.745306

Efimov, A. (2020). Post-turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence. Dalam B. Goertzel, A. I. Panov, A. Potapov, & R. Yampolskiy (Ed.), Artificial General Intelligence (Vol. 12177, hlm. 83–94). Springer International Publishing. https://doi.org/10.1007/978-3-030-52152-3_9

El Hajjar, A., & Rey, J.-F. (2020). Artificial intelligence in gastrointestinal endoscopy: General overview. Chinese Medical Journal, 133(3), 326–334. https://doi.org/10.1097/CM9.0000000000000623

El Sherbini, A., Glicksberg, B. S., & Krittanawong, C. (2024). Artificial intelligence in general internal medicine. Dalam Artificial Intelligence in Clinical Practice (hlm. 15–24). Elsevier. https://doi.org/10.1016/B978-0-443-15688-5.00025-5

Johanes B. Bunyamin. (2018). AGI (Artificial General Intelligence): Peluang Indonesia Melompat Jauh ke Depan. Jurnal Sistem Cerdas, 1(2), 1–11. https://doi.org/10.37396/jsc.v1i2.8

Kashou, A. H., Medina-Inojosa, J. R., Noseworthy, P. A., Rodeheffer, R. J., Lopez-Jimenez, F., Attia, I. Z., Kapa, S., Scott, C. G., Lee, A. T., Friedman, P. A., & McKie, P. M. (2021). Artificial Intelligence–Augmented Electrocardiogram Detection of Left Ventricular Systolic Dysfunction in the General Population. Mayo Clinic Proceedings, 96(10), 2576–2586. https://doi.org/10.1016/j.mayocp.2021.02.029

Keller, S. D., Fleckenstein, J., Krüger, M., Köller, O., & Rupp, A. A. (2020). English writing skills of students in upper secondary education: Results from an empirical study in Switzerland and Germany. Journal of Second Language Writing, 48, 100700. https://doi.org/10.1016/j.jslw.2019.100700

Kelley, D., & Atreides, K. (2020). AGI Protocol for the Ethical Treatment of Artificial General Intelligence Systems. Procedia Computer Science, 169, 501–506. https://doi.org/10.1016/j.procs.2020.02.219

Kelley, D. J., & Atreides, K. (2020). Human Brain Computer/Machine Interface System Feasibility Study for Independent Core Observer Model Based Artificial General Intelligence Collective Intelligence Systems. Dalam A. V. Samsonovich (Ed.), Biologically Inspired Cognitive Architectures 2019 (Vol. 948, hlm. 193–201). Springer International Publishing. https://doi.org/10.1007/978-3-030-25719-4_25

Kelley, D., & Twyman, M. (2020). Biasing in an Independent Core Observer Model Artificial General Intelligence Cognitive Architecture. Procedia Computer Science, 169, 535–541. https://doi.org/10.1016/j.procs.2020.02.213

Kocaballi, A. B., Ijaz, K., Laranjo, L., Quiroz, J. C., Rezazadegan, D., Tong, H. L., Willcock, S., Berkovsky, S., & Coiera, E. (2020). Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners. Journal of the American Medical Informatics Association, 27(11), 1695–1704. https://doi.org/10.1093/jamia/ocaa131

Mahlknecht, A., Engl, A., Piccoliori, G., & Wiedermann, C. J. (2023). Supporting primary care through symptom checking artificial intelligence: A study of patient and physician attitudes in Italian general practice. BMC Primary Care, 24(1), 174. https://doi.org/10.1186/s12875-023-02143-0

Martínez, E., & Winter, C. (2021). Protecting Sentient Artificial Intelligence: A Survey of Lay Intuitions on Standing, Personhood, and General Legal Protection. Frontiers in Robotics and AI, 8, 788355. https://doi.org/10.3389/frobt.2021.788355

Mikki, S. (2023). Artificial General Intelligence and Noncomputability: A Dynamical Framework. Journal of Artificial Intelligence and Consciousness, 10(01), 71–101. https://doi.org/10.1142/S2705078522500163

Molhoek, B. (2022). The scope of human creative action: Created co-creators, imago Dei and artificial general intelligence. HTS Teologiese Studies / Theological Studies, 78(3). https://doi.org/10.4102/hts.v78i2.7697

Raikov, A. (2022). The architecture of non-local semantics for artificial general intelligence. International Journal of Applied Systemic Studies, 9(4), 425. https://doi.org/10.1504/IJASS.2022.126763

Retracted: Relationship between Artificial Intelligence-Based General Anesthetics and Postoperative Cognitive Dysfunction. (2022). Journal of Healthcare Engineering, 2022, 1–1. https://doi.org/10.1155/2022/9829807

Roli, A., Jaeger, J., & Kauffman, S. A. (2022). How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence. Frontiers in Ecology and Evolution, 9, 806283. https://doi.org/10.3389/fevo.2021.806283

Sarraf, D., Vasiliu, V., Imberman, B., & Lindeman, B. (2021). Use of artificial intelligence for gender bias analysis in letters of recommendation for general surgery residency candidates. The American Journal of Surgery, 222(6), 1051–1059. https://doi.org/10.1016/j.amjsurg.2021.09.034

Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014

Shelepa, A. A., Petraikin, A. V., Artyukova, Z. R., Abuladze, L. R., Kudryavtsev, N. D., Ahmad, E. S., Semenov, D. S., Zakharov, A. A., & Belyaev, M. G. (2022). Artificial intelligence for bone mineral density assessment: General population data. Digital Diagnostics, 3(1S), 23–24. https://doi.org/10.17816/DD105714

Slavin, B. B. (2023). An architectural approach to modeling artificial general intelligence. Heliyon, 9(3), e14443. https://doi.org/10.1016/j.heliyon.2023.e14443

Sofyan Siregar, A. (2023). Pemanfaatan Media Belajar Berbasis Artificial Intelegency dalam Pembelajaran Di MAN 2 Padangsidimpuan. Al-Murabbi: Jurnal Pendidikan Islam, 1(2), 250–262. https://doi.org/10.62086/al-murabbi.v1i2.483

Szegedy, C. (2020). A Promising Path Towards Autoformalization and General Artificial Intelligence. Dalam C. Benzmüller & B. Miller (Ed.), Intelligent Computer Mathematics (Vol. 12236, hlm. 3–20). Springer International Publishing. https://doi.org/10.1007/978-3-030-53518-6_1

Taylor, C. R. (2019). Editorial: Artificial intelligence, customized communications, privacy, and the General Data Protection Regulation (GDPR). International Journal of Advertising, 38(5), 649–650. https://doi.org/10.1080/02650487.2019.1618032

Williams, A. E. (2020). A Model for Artificial General Intelligence. Dalam B. Goertzel, A. I. Panov, A. Potapov, & R. Yampolskiy (Ed.), Artificial General Intelligence (Vol. 12177, hlm. 357–369). Springer International Publishing. https://doi.org/10.1007/978-3-030-52152-3_38

Yakar, D., Ongena, Y. P., Kwee, T. C., & Haan, M. (2022). Do People Favor Artificial Intelligence Over Physicians? A Survey Among the General Population and Their View on Artificial Intelligence in Medicine. Value in Health, 25(3), 374–381. https://doi.org/10.1016/j.jval.2021.09.004

Young, A. T., Amara, D., Bhattacharya, A., & Wei, M. L. (2021). Patient and general public attitudes towards clinical artificial intelligence: A mixed methods systematic review. The Lancet Digital Health, 3(9), e599–e611. https://doi.org/10.1016/S2589-7500(21)00132-1

Zhu, L., Zhang, W., Kou, J., & Liu, Y. (2019). Machine learning methods for turbulence modeling in subsonic flows around airfoils. Physics of Fluids, 31(1), 015105. https://doi.org/10.1063/1.5061693

Authors

Yulian Purnama
yulianpurnama@uinsaizu.ac.id (Primary Contact)
Sherly Gaspersz
Purnama, Y., & Gaspersz, S. (2024). Artificial General Intelligence: Advantages in English Language Learning. Scientechno: Journal of Science and Technology, 3(1), 159–171. https://doi.org/10.55849/scientechno.v3i1.1049

Article Details

Revitalize Use Language English on Unit Education

Sam Hermansyah, Nasmilah Nasmilah; Abidin Pammu, Noer jjihad Saleh; Matteson Niva
Abstract View : 120
Download :124

Use of Whatsapp as A Learning Media to Increase Students' Learning Interest

Sue Holly, Burkewitz Maulik, Irvine Samuel
Abstract View : 1025
Download :899

Moral Aqidah Learning Using Video-Based Technology

Tsai Nicholas, Grub James, Kebede Robert
Abstract View : 556
Download :422