Abstract
Social AI agents interact with members of a community, thereby changing the behavior of the community. For example, in online learning, an AI social assistant may connect learners and thereby enhance social interaction. These social AI assistants too need to explain themselves in order to enhance transparency and trust with the learners. We present a method of self-explanation that uses introspection over a self-model of an AI social assistant. The self-model is captured as a functional model that specifies how the methods of the agent use knowledge to achieve its tasks. The process of generating self-explanations uses Chain of Thought to reflect on the self-model and ChatGPT to provide explanations about its functioning. We evaluate the self-explanation of the AI social assistant for completeness and correctness.
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Notes
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OpenAI’s gpt3.5-turbo-instruct model has been used.
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Acknowledgements
This research has been supported by NSF Grants #2112532 and #2247790 to the National AI Institute for Adult Learning and Online Education. We thank members of the Design & Intelligence Laboratory for their contributions to this work.
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Basappa, R., Tekman, M., Lu, H., Faught, B., Kakar, S., Goel, A.K. (2024). Social AI Agents Too Need to Explain Themselves. In: Sifaleras, A., Lin, F. (eds) Generative Intelligence and Intelligent Tutoring Systems. ITS 2024. Lecture Notes in Computer Science, vol 14798. Springer, Cham. https://doi.org/10.1007/978-3-031-63028-6_29
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