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Article
2023
Knowledge-based and generative-AI-driven pedagogical conversational agents: A comparative study of grice’s cooperative principles and trust
Knowledge-based and generative-AI-driven pedagogical conversational agents: A comparative study of grice’s cooperative principles and trust
Abstract (English)
The emergence of generative language models (GLMs), such as OpenAI’s ChatGPT, is changing the way we communicate with computers and has a major impact on the educational landscape. While GLMs have great potential to support education, their use is not unproblematic, as they suffer from hallucinations and misinformation. In this paper, we investigate how a very limited amount of domain-specific data, from lecture slides and transcripts, can be used to build knowledge-based and generative educational chatbots. We found that knowledge-based chatbots allow full control over the system’s response but lack the verbosity and flexibility of GLMs. The answers provided by GLMs are more trustworthy and offer greater flexibility, but their correctness cannot be guaranteed. Adapting GLMs to domain-specific data trades flexibility for correctness.
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Big data and cognitive computing, 8 (2024), 1, 2.
https://doi.org/10.3390/bdcc8010002.
ISSN: 2504-2289
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@article{Wölfel2023,
url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16233},
doi = {10.3390/bdcc8010002},
author = {Wölfel, Matthias and Shirzad, Mehrnoush Barani and Reich, Andreas et al.},
title = {Knowledge-Based and Generative-AI-Driven Pedagogical Conversational Agents: A Comparative Study of Grice’s Cooperative Principles and Trust},
journal = {Big data and cognitive computing},
year = {2023},
volume = {8},
number = {1},
}