Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant

Building conversational agents that can conduct natural and prolonged conversations has been a major technical and design challenge, especially for community-facing conversational agents. We posit Mutual Theory of Mind as a theoretical framework to design for natural long-term human-AI interactions. From this perspective, we explore a community’s perception of a question-answering conversational agent through self-reported surveys and computational linguistic approach in the context of online education. We first examine long-term temporal changes in students’ perception of Jill Watson (JW), a virtual teaching assistant deployed in an online class discussion forum. We then explore the feasibility of inferring students’ perceptions of JW through linguistic features extracted from student-JW dialogues. We find that students’ perception of JW’s anthropomorphism and intelligence changed signifcantly over time. Regression analyses reveal that linguistic verbosity, readability, sentiment, diversity, and adaptability refect student perception of JW. We discuss implications for building adaptive community-facing conversational agents as long-term companions and designing to- wards Mutual Theory of Mind in human-AI interaction.