The field of human-AI interaction and knowledge production is undergoing a significant transformation, driven by the need for more nuanced and contextual understanding of AI's role in shaping human knowledge and decision-making. Recent research has highlighted the importance of rethinking traditional notions of ground truth, annotation quality, and evaluation metrics in AI-driven applications. There is a growing recognition of the need for more human-centered approaches to AI development, emphasizing the importance of transparency, accountability, and ethical considerations. The emergence of new frameworks and methodologies, such as the Situated Epistemic Infrastructures framework and the concept of Cyber Humanities, is enabling researchers to better analyze and address the complex relationships between humans, AI, and knowledge production. Noteworthy papers in this area include 'Beyond Agreement: Rethinking Ground Truth in Educational AI Annotation', which challenges traditional inter-rater reliability metrics, and 'Classifying Epistemic Relationships in Human-AI Interaction: An Exploratory Approach', which introduces a new framework for understanding human-AI epistemic relationships.