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Elevating Learning Experiences: Leveraging Large Language Models as Student-Facing Assistants in Discussion Forums

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Narges NorouziAuthors Info & Claims

SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2

Pages 1752 - 1753

https://doi.org/10.1145/3626253.3635609

Published: 15 March 2024 Publication History

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Abstract

Recent advancements in instruction-tuned large language models offer new potential for enhancing students' experiences in large-scale classes. Deploying LLMs as student-facing assistants, however, presents challenges. Key issues include integrating class-specific content into responses and applying effective pedagogical techniques. This study addresses these challenges through retrieval and prompting techniques, focusing on mitigating hallucinations in LLM-generated responses, a crucial concern in education. Furthermore, practical deployment brings further challenges related to student data privacy and computational constraints. This research strives to enhance the quality and relevance of LLM responses while addressing practical deployment issues, with an emphasis on creating a versatile system for diverse domains and teaching styles.

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Mittal MBailey APhelps VMiroyan MMitra CJain RNiousha RRanade GNorouzi NStone JYuen TShoop LRebelsky SPrather J(2025)Raising the Bar: Automating Consistent and Equitable Student Support with LLMsProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 210.1145/3641555.3705237(1549-1550)Online publication date: 18-Feb-2025
https://dl.acm.org/doi/10.1145/3641555.3705237
Miroyan MMitra CJain RRanade GNorouzi NStone JYuen TShoop LRebelsky SPrather J(2025)Analyzing Pedagogical Quality and Efficiency of LLM Responses with TA Feedback to Live Student QuestionsProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701965(770-776)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701965
Zamfirescu-Pereira JQi LHartmann BDeNero JNorouzi NStone JYuen TShoop LRebelsky SPrather J(2025)61A Bot Report: AI Assistants in CS1 Save Students Homework Time and Reduce Demands on Staff. (Now What?)Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701864(1309-1315)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701864

Index Terms

Elevating Learning Experiences: Leveraging Large Language Models as Student-Facing Assistants in Discussion Forums
1. Applied computing
  1. Education
    1. Computer-assisted instruction
    2. Interactive learning environments
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Discourse, dialogue and pragmatics
      2. Natural language generation

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Published In

SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2

March 2024

2007 pages

ISBN:9798400704246

DOI:10.1145/3626253

General Chairs:
Ben Stephenson
University of Calgary, Canada
,
Jeffrey A. Stone
Penn State University
,
Program Chairs:
Lina Battestilli
North Carolina State University, USA
,
Samuel A. Rebelsky
Grinnell College
,
Libby Shoop
Macalester College

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 15 March 2024

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SIGCSE 2024: The 55th ACM Technical Symposium on Computer Science Education

March 20 - 23, 2024

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Mittal MBailey APhelps VMiroyan MMitra CJain RNiousha RRanade GNorouzi NStone JYuen TShoop LRebelsky SPrather J(2025)Raising the Bar: Automating Consistent and Equitable Student Support with LLMsProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 210.1145/3641555.3705237(1549-1550)Online publication date: 18-Feb-2025
https://dl.acm.org/doi/10.1145/3641555.3705237
Miroyan MMitra CJain RRanade GNorouzi NStone JYuen TShoop LRebelsky SPrather J(2025)Analyzing Pedagogical Quality and Efficiency of LLM Responses with TA Feedback to Live Student QuestionsProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701965(770-776)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701965
Zamfirescu-Pereira JQi LHartmann BDeNero JNorouzi NStone JYuen TShoop LRebelsky SPrather J(2025)61A Bot Report: AI Assistants in CS1 Save Students Homework Time and Reduce Demands on Staff. (Now What?)Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 110.1145/3641554.3701864(1309-1315)Online publication date: 12-Feb-2025
https://dl.acm.org/doi/10.1145/3641554.3701864

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Teaching Assistants in MOOCs Forums: Omnipresent Interlocutors or Knowledge Facilitators

Untangling chaos in discussion forums: A temporal analysis of topic-relevant forum posts in MOOCs

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