IDEAS home Printed from https://ideas.repec.org/a/ddj/fseeai/y2023i3p142-147.html
   My bibliography  Save this article

The Potential of Natural Language Technology in Transforming Educational Processes

Author

Listed:
  • Adrian LUPASC

    (Dunarea de Jos University of Galati, Romania)

Abstract

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the development and implementation of systems capable of understanding, analyzing, and producing human language coherently. This technology enables computers to interpret and respond to natural language, including both speech and writing. Through machine learning algorithms and language processing, NLP can facilitate a wide range of applications, from automatic translation and text processing to aiding in learning and human-machine interaction. Its ability to comprehend the context, meaning, and nuances of natural language makes it essential in transforming communication and learning processes, with a significant impact in crucial areas such as education.

Suggested Citation

  • Adrian LUPASC, 2023. "The Potential of Natural Language Technology in Transforming Educational Processes," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 142-147.
  • Handle: RePEc:ddj:fseeai:y:2023:i:3:p:142-147
    DOI: 10.35219/eai15840409373
    as

    Download full text from publisher

    File URL: http://eia.feaa.ugal.ro/images/eia/2023_3/Lupasc.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.35219/eai15840409373?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yue Kang & Zhao Cai & Chee-Wee Tan & Qian Huang & Hefu Liu, 2020. "Natural language processing (NLP) in management research: A literature review," Journal of Management Analytics, Taylor & Francis Journals, vol. 7(2), pages 139-172, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    2. Alin-Gabriel Vaduva & Simona-Vasilica Oprea & Dragos-Catalin Barbu, 2023. "Understanding Customers' Opinion using Web Scraping and Natural Language Processing," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 537-544, August.
    3. Lu, Qinli & Chesbrough, Henry, 2022. "Measuring open innovation practices through topic modelling: Revisiting their impact on firm financial performance," Technovation, Elsevier, vol. 114(C).
    4. Zimei Liu & Kefan Xie & Ling Li & Yong Chen, 2020. "A paradigm of safety management in Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 632-645, July.
    5. Jing Li & Daniel Shapiro & Anastasia Ufimtseva, 2024. "Regulating inbound foreign direct investment in a world of hegemonic rivalry: the evolution and diffusion of US policy," Journal of International Business Policy, Palgrave Macmillan, vol. 7(2), pages 147-165, June.
    6. Segun Akinola & Arnesh Telukdarie, 2023. "Sustainable Digital Transformation in Healthcare: Advancing a Digital Vascular Health Innovation Solution," Sustainability, MDPI, vol. 15(13), pages 1-23, July.
    7. Tian, Yu-Xin & Zhang, Chuan, 2023. "An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data," International Journal of Production Economics, Elsevier, vol. 265(C).
    8. Mohammad Alamgir Hossain & Md. Maruf Hossan Chowdhury & Ilias O. Pappas & Bhimaraya Metri & Laurie Hughes & Yogesh K. Dwivedi, 2023. "Fake news on Facebook and their impact on supply chain disruption during COVID-19," Annals of Operations Research, Springer, vol. 327(2), pages 683-711, August.
    9. Indu Khurana & Daniel J. Lee, 2023. "Gender bias in high stakes pitching: an NLP approach," Small Business Economics, Springer, vol. 60(2), pages 485-502, February.
    10. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
    11. Tao Shu & Zhiyi Wang & Huading Jia & Wenjin Zhao & Jixian Zhou & Tao Peng, 2022. "Consumers’ Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China," IJERPH, MDPI, vol. 19(19), pages 1-19, October.
    12. Chen, Shiuann-Shuoh & Choubey, Bhaskar & Singh, Vinay, 2021. "A neural network based price sensitive recommender model to predict customer choices based on price effect," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    13. Weifeng Jia & Shuo Wang & Yongping Xie & Zifeng Chen & Kaixin Gong, 2022. "Disruptive technology identification of intelligent logistics robots in AIoT industry: Based on attributes and functions analysis," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 557-568, May.
    14. Wei Zhang & Linhui Sun & Xinping Wang & Anbo Wu, 2022. "The influence of AI word‐of‐mouth system on consumers' purchase behaviour: The mediating effect of risk perception," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 516-530, May.
    15. Jing Ge & Feng Wang & Hongxia Sun & Liuliu Fu & Mingwei Sun, 2020. "Research on the maturity of big data management capability of intelligent manufacturing enterprise," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 646-662, July.
    16. Just, Julian, 2024. "Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary," Technovation, Elsevier, vol. 129(C).
    17. Xueling Li & Yujie Long & Meixi Fan & Yong Chen, 2022. "Drilling down artificial intelligence in entrepreneurial management: A bibliometric perspective," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 379-396, May.
    18. Aavash Raj Pandey & Mahdi Seify & Udoka Okonta & Amin Hosseinian-Far, 2023. "Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire," IJERPH, MDPI, vol. 20(12), pages 1-11, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ddj:fseeai:y:2023:i:3:p:142-147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gianina Mihai (email available below). General contact details of provider: https://edirc.repec.org/data/fegalro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.