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The HIKMA Semi-Autonomous Conference represents the first landmark experiment in reimagining scholarly communication through an end-to-end integration of artificial intelligence into the academic publishing and presentation pipeline. This paper documents the design, implementation, and evaluation of the HIKMA framework, which spans dataset curation, automated manuscript generation, AI-assisted peer reviews, iterative revision, conference presentation, and archival dissemination. By combining generative language models, structured research workflows, and domain-specific safeguards, HIKMA demonstrates how AI can augment—not replace—traditional scholarly practices while ensuring intellectual property protection, transparency, and integrity. The conference serves as both a testbed and a proof-of-concept, offering insights into the opportunities and challenges of AI-enabled scholarship. In doing so, it advances the discourse on the future of knowledge production, raising critical questions about authorship, accountability, and the evolving role of human–AI collaboration in research ecosystems.