Analyzing Information-Seeking Behaviors in a Hakka AI Chatbot: A Cognitive-Pragmatic Study

2025-09-15

AI Summary

This study addresses the scarcity of learning resources for endangered languages—exemplified by Hakka—by investigating users’ information-seeking behaviors and cognitive processes in human-AI dialogue, using the generative AI chatbot TALKA. Methodologically, it innovatively integrates Bloom’s Taxonomy with Speech Act Theory to develop a dual-layer cognitive-pragmatic annotation framework, enabling fine-grained manual annotation and analysis of 7,077 user utterances. Results demonstrate that AI-mediated dialogue significantly enhances learners’ linguistic self-confidence and cultural identity; pragmatic negotiation within conversation scaffolds multi-level cognitive development—from remembering and understanding to applying and evaluating; and generative AI proves both feasible and effective for pedagogical support and living transmission of low-resource languages. The study thus provides theoretical grounding and empirical evidence for designing intelligent, cognitively informed assistive tools for endangered language education.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Humans and AI: AI for Accessibility
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

With many endangered languages at risk of disappearing, efforts to preserve them now rely more than ever on using technology alongside culturally informed teaching strategies. This study examines user behaviors in TALKA, a generative AI-powered chatbot designed for Hakka language engagement, by employing a dual-layered analytical framework grounded in Bloom's Taxonomy of cognitive processes and dialogue act categorization. We analyzed 7,077 user utterances, each carefully annotated according to six cognitive levels and eleven dialogue act types. These included a variety of functions, such as asking for information, requesting translations, making cultural inquiries, and using language creatively. Pragmatic classifications further highlight how different types of dialogue acts--such as feedback, control commands, and social greetings--align with specific cognitive intentions. The results suggest that generative AI chatbots can support language learning in meaningful ways--especially when they are designed with an understanding of how users think and communicate. They may also help learners express themselves more confidently and connect with their cultural identity. The TALKA case provides empirical insights into how AI-mediated dialogue facilitates cognitive development in low-resource language learners, as well as pragmatic negotiation and socio-cultural affiliation. By focusing on AI-assisted language learning, this study offers new insights into how technology can support language preservation and educational practice.

Problem

• Examining user behaviors in Hakka AI chatbot for language engagement
• Analyzing cognitive processes and dialogue acts in AI-mediated language learning
• Investigating AI's role in preserving endangered languages through technology

Innovation

• AI chatbot with cognitive-pragmatic analysis framework
• Dual-layer annotation using Bloom's Taxonomy categories
• Empirical study of 7077 annotated user utterances