IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-032-06725-8_16.html

An AI-Powered Chatbot Using a Transformer-Based Language Model for Graduate Educational Schemes Recommendation

In: Technological Innovations for Sustainable Development

Author

Listed:
  • Sandireddy Vyshnavi Prasad Chowdary

    (Siddhartha Academy of Higher Education, Department of Computer Science and Engineering)

  • Atluri Sai Charan

    (Sutherland Global Services, Lead Data Analyst)

  • Ashutosh Satapathy

    (Siddhartha Academy of Higher Education, Department of Computer Science and Engineering)

  • Chinnam Sri Sai Prasanna

    (Siddhartha Academy of Higher Education, Department of Computer Science and Engineering)

  • Kokila Thottempudi

    (Siddhartha Academy of Higher Education, Department of Computer Science and Engineering)

Abstract

Availability of higher education is still the priority concern for Indian socio-economically underprivileged and rural students at 25%, according to the National University of Educational Planning and Administration (NUEPA) 2021–2022 annual report. The major hurdles are financial constraints and unavailability of information regarding government-funded education programs. An AI-based learning advice chatbot leveraging the Large Language Model Meta AI (LLaMA) has been proposed to address the issue. The model is founded on the LLaMA 3.1 transformer model specifically optimized for low-resource environments. The model was fine-tuned on a 1,200 proprietary educational dataset of scholarship and government program-oriented queries with a test accuracy of 94.39% with an 80%−20% train-test split. The 60–40% was employed as another split to verify its performance with the test accuracy at 92.97%. The chatbot provides user-specific recommendations for 24/7 availability, ease, and strong data security. Providing timely and accurate information will help reduce the financial and informational barrier, thereby increasing the equitable opportunity for people from low-income backgrounds and the community to access higher education.

Suggested Citation

  • Sandireddy Vyshnavi Prasad Chowdary & Atluri Sai Charan & Ashutosh Satapathy & Chinnam Sri Sai Prasanna & Kokila Thottempudi, 2025. "An AI-Powered Chatbot Using a Transformer-Based Language Model for Graduate Educational Schemes Recommendation," Lecture Notes in Information Systems and Organization, in: Badr-Eddine Boudriki Semlali & Ikram Ben Abdel Ouahab & Fabio Angeletti (ed.), Technological Innovations for Sustainable Development, pages 184-196, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-06725-8_16
    DOI: 10.1007/978-3-032-06725-8_16
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:lnichp:978-3-032-06725-8_16. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.