Analyzing Pedagogical Quality and Efficiency of LLM Responses with TA Feedback to Live Student Questions
While Large Language Models (LLMs) have emerged as promising methods for automated student question-answering, guaranteeing consistent instructional effectiveness of the response remains a key challenge. Therefore, there is a need for fine-grained analysis of State-Of-The-Art (SOTA) LLM-powered educational assistants.
Authors
- Rishi K. Jain (ORCID: https://orcid.org/0009-0002-9890-2143)
- Gireeja Ranade (ORCID: https://orcid.org/0000-0002-6747-4492)
- Chancharik Mitra (ORCID: https://orcid.org/0009-0008-9826-7534)
- Narges Norouzi (ORCID: https://orcid.org/0000-0001-9861-7540)
- Mihran Miroyan (ORCID: https://orcid.org/0009-0001-6338-6671)
Institutions
- Carnegie Mellon University (US)
- University of California, Berkeley (US)
Publication Details
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1145/3641554.3701965
- Citations
- 5
- Primary Topic
- Advanced Text Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 12.40