Exploring How Agent Voice Accents Shape Human-AI Collaboration in K-12 Group Learning

2026-06-10

AI Summary

This study investigates how varying accents—British, Indian, and African American—in generative AI voice agents influence collaborative behaviors, student perceptions, and interaction dynamics in K–12 group learning settings. Employing a mixed-methods between-subjects design, the research integrates large language model–driven voice agents, video analysis of small-group interactions, survey responses, and learning outcome assessments to reveal, for the first time, how accent shapes the social construction of AI roles and human collaboration mechanisms in group contexts. Findings indicate that agents with British accents are predominantly perceived as tools, whereas those with Indian or African American accents are more readily anthropomorphized as peers, significantly enhancing long-term trust, engagement, and reliance—thereby transcending the limitations of prior studies focused exclusively on one-on-one human–AI interactions.

Abstract

Collaboration is widely recognized as a cornerstone of 21st-century education, yet teachers still encounter persistent challenges in fostering productive peer interaction. LLM conversational peer agents introduce new possibilities for mediating in-person group work, raising questions about how persona design, particularly their voice characteristics, shapes learners' perceptions, trust, and interactional dynamics. While prior work has examined agent accent effects in one-to-one settings, little is known about how these effects manifest in groups. We conducted a between-subjects mixed-methods study with 33 teachers examining how a GenAI voice agent with different accents (British, Indian, and African American) influenced collaboration and agent perception. Across surveys, group interaction analyses, and artifacts, we find that accent shaped participants' mental models and the roles the agent assumed in group interaction. The British-accented agent was largely treated as a tool and engaged in detached, utility-based ways, whereas Indian- and African American-accented agents were more readily anthropomorphized and integrated as peers. These role expectations influenced trust, engagement, and reliance over time. This work advances understanding of how GenAI's sociolinguistic design features shape group dynamics in CSCL, with implications for designing culturally inclusive AI partners in group learning.

Problem

• human-AI collaboration
• voice accent
• group learning
• sociolinguistic design
• K-12 education

Innovation

• voice accent
• GenAI agent
• group learning
• anthropomorphism
• CSCL