Impacts of Anthropomorphizing Large Language Models in Learning Environments
This addresses the problem of emotional impacts on learners when using anthropomorphized AI in education, but it is incremental as it builds on existing media equation theory.
The paper examines the effects of anthropomorphizing large language models (LLMs) in learning environments, finding that learners often cannot distinguish LLM-based chatbots from human teachers, which influences their emotional responses.
Large Language Models (LLMs) are increasingly being used in learning environments to support teaching-be it as learning companions or as tutors. With our contribution, we aim to discuss the implications of the anthropomorphization of LLMs in learning environments on educational theory to build a foundation for more effective learning outcomes and understand their emotional impact on learners. According to the media equation, people tend to respond to media in the same way as they would respond to another person. A study conducted by the Georgia Institute of Technology showed that chatbots can be successfully implemented in learning environments. In this study, learners in selected online courses were unable to distinguish the chatbot from a "real" teacher. As LLM-based chatbots such as OpenAI's GPT series are increasingly used in educational tools, it is important to understand how the attribution processes to LLM-based chatbots in terms of anthropomorphization affect learners' emotions.