This study proposes a collaborative effort using AI tools as a solution to the difficulty of distinguishing between human-generated and deepfake content due to the proliferation of generative models. Using DeepFakeDeLiBot, a consultation-enhanced chatbot for deepfake text detection, we demonstrate that group-based problem-solving significantly improves the accuracy of machine-generated paragraph identification compared to individual efforts. While using DeepFakeDeLiBot does not significantly improve overall performance, it improves group dynamics through increased participation, consensus building, and increased frequency and diversity of inference-based utterances. Furthermore, participants who highly valued the effectiveness of group collaboration also benefited from DeepFakeDeLiBot's performance. This highlights the potential of consultation chatbots to foster interactive and productive group dynamics while ensuring the accuracy of collaborative deepfake text detection.