Impacts of Anthropomorphizing Large Language Models in Learning Environments

TL;DR

This paper explores how anthropomorphizing large language models in educational settings influences learners' emotional responses and educational outcomes, emphasizing the importance of understanding human-like perceptions of AI tutors.

Contribution

It provides a theoretical discussion on the implications of anthropomorphizing LLMs in learning environments, linking media equation theory with educational practice.

Findings

Learners often respond to LLMs as they would to human teachers.

Anthropomorphization affects learners' emotional engagement with AI tutors.

Understanding attribution processes can improve educational outcomes.

Abstract

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.