Potential Abstract:
In educational settings, conversations are often characterized by complexity and ambiguity, creating what researchers refer to as ill-structured conversations. These conversations, influenced by power dynamics and hegemonies, play a crucial role in shaping teaching and learning experiences. In this study, we propose a novel approach grounded in data science and artificial intelligence to unpack and analyze ill-structured conversations in educational contexts. By leveraging computational methods to analyze discourse patterns, sentiment analysis, and network structures within conversations, we aim to uncover underlying power structures and hegemonies that impact decision-making processes and outcomes.
Our research seeks to disrupt traditional power dynamics and challenge dominant narratives through the application of advanced technologies. By examining conversation data through a critical lens, we aim to empower educators, administrators, and policymakers to engage in more equitable and inclusive dialogue. Through this interdisciplinary approach, we hope to not only shed light on the complexities of ill-structured conversations but also provide practical insights for fostering more meaningful and transformative dialogues within educational settings.
Potential References:
- Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence
- Using asynchronous, online discussion forums to explore how life sciences students approach an ill-structured problem
- Unstructuring for insight: the legal profession in an age of AI and social change
- Human-centric cognitive decision support system for ill-structured problems
- Opportunities and challenges for AI-assisted qualitative data analysis: An example from collaborative problem-solving discourse data
