About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Computer Science and Education in Computer Science. 20th EAI International Conference, CSECS 2024, Sofia, Bulgaria, June 28–30, 2024, Proceedings

Research Article

Building a Chatbot to Adopt an Effective Learning Strategy for Graduate Courses in Computer Science

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-84312-9_19,
        author={Alex Elentukh and Shravan Motipally and Jacob Kustra and Toby March},
        title={Building a Chatbot to Adopt an Effective Learning Strategy for Graduate Courses in Computer Science},
        proceedings={Computer Science and Education in Computer Science. 20th EAI International Conference, CSECS 2024, Sofia, Bulgaria, June 28--30, 2024, Proceedings},
        proceedings_a={CSECS},
        year={2025},
        month={3},
        keywords={computer science education software engineering teaching and learning effectiveness language models chatbots prompt engineering retrieval augmented generation},
        doi={10.1007/978-3-031-84312-9_19}
    }
    
  • Alex Elentukh
    Shravan Motipally
    Jacob Kustra
    Toby March
    Year: 2025
    Building a Chatbot to Adopt an Effective Learning Strategy for Graduate Courses in Computer Science
    CSECS
    Springer
    DOI: 10.1007/978-3-031-84312-9_19
Alex Elentukh1,*, Shravan Motipally1, Jacob Kustra1, Toby March1
  • 1: Metropolitan College of Boston University, 1010 Commonwealth Ave., Boston
*Contact email: Elentukh@bu.edu

Abstract

Courses offered in a Computer Science program of a large university differ greatly in their structure. Such a variety is driven by the diversity of backgrounds of faculty members, as well as by the breadth and depth of topics covered. The pragmatic goal of our research is to help students to adopt an effective learning strategy while engaging in a graduate program of several dozen very different courses. As a first step, we established a database of frequently asked questions regarding class logistics. Building on this foundation we launched the chatbot to be readily available to all program registrants. This paper can serve as a step-by-step introduction to How-To-Build-A-Chatbot while dealing with challenges and successes encountered. A student is able to quickly browse through relevant questions and responses. Additionally, a student can actively participate in a conversation with the bot, select among five language models, adjust semantic matching, and switch on generative mode. We used Ragas framework to formally evaluate and measure the performance of the bot, while focusing on its ability to simulate the human-like interaction. The experimental nature of exchange with the bot served as a strong motivation for students to keep exploring and learning. The evolution from a list-bound bot design into a more fluid approach involving language technologies is not trivial. As a benefit, we were able to offer students the personalized and engaging learning experience.

Keywords
computer science education software engineering teaching and learning effectiveness language models chatbots prompt engineering retrieval augmented generation
Published
2025-03-14
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-84312-9_19
Copyright © 2024–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL