Safe Generative Chats in a WhatsApp Intelligent Tutoring System
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914859923275776 |
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| author | Levonian, Zachary Henkel, Owen |
| author_facet | Levonian, Zachary Henkel, Owen |
| contents | Large language models (LLMs) are flexible, personalizable, and available, which makes their use within Intelligent Tutoring Systems (ITSs) appealing. However, that flexibility creates risks: inaccuracies, harmful content, and non-curricular material. Ethically deploying LLM-backed ITS systems requires designing safeguards that ensure positive experiences for students. We describe the design of a conversational system integrated into an ITS, and our experience evaluating its safety with red-teaming, an in-classroom usability test, and field deployment. We present empirical data from more than 8,000 student conversations with this system, finding that GPT-3.5 rarely generates inappropriate messages. Comparatively more common is inappropriate messages from students, which prompts us to reason about safeguarding as a content moderation and classroom management problem. The student interaction behaviors we observe provide implications for designers - to focus on student inputs as a content moderation problem - and implications for researchers - to focus on subtle forms of bad content. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_04915 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safe Generative Chats in a WhatsApp Intelligent Tutoring System Levonian, Zachary Henkel, Owen Human-Computer Interaction Large language models (LLMs) are flexible, personalizable, and available, which makes their use within Intelligent Tutoring Systems (ITSs) appealing. However, that flexibility creates risks: inaccuracies, harmful content, and non-curricular material. Ethically deploying LLM-backed ITS systems requires designing safeguards that ensure positive experiences for students. We describe the design of a conversational system integrated into an ITS, and our experience evaluating its safety with red-teaming, an in-classroom usability test, and field deployment. We present empirical data from more than 8,000 student conversations with this system, finding that GPT-3.5 rarely generates inappropriate messages. Comparatively more common is inappropriate messages from students, which prompts us to reason about safeguarding as a content moderation and classroom management problem. The student interaction behaviors we observe provide implications for designers - to focus on student inputs as a content moderation problem - and implications for researchers - to focus on subtle forms of bad content. |
| title | Safe Generative Chats in a WhatsApp Intelligent Tutoring System |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.04915 |