HIKMA: Human-Inspired Knowledge by Machine Agents through a Multi-Agent Framework for Semi-Autonomous Scientific Conferences

2025-10-24

AI Summary

This study addresses intellectual property, accountability, and academic integrity challenges arising from deep AI integration into scholarly communication. Methodologically, it proposes a human–AI collaborative, semi-automated scholarly workflow framework, integrating large language models, multi-agent coordination architectures, and domain-specific safety mechanisms to support end-to-end processes—including data curation, manuscript generation, peer-review assistance, intelligent revision, conference presentation, and archival deposition—while explicitly preserving human oversight and agency. Its primary contributions are threefold: (1) establishing principled guidelines for AI authorship attribution and responsibility allocation; (2) designing an auditable, transparent collaboration protocol; and (3) empirically validating the framework’s feasibility in real-world research settings—demonstrating significant efficiency gains without compromising academic integrity, traceability, or procedural rigor. Notably, it achieves the first fully closed-loop, semi-autonomous academic conference workflow grounded in accountable human–AI co-authorship.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Humanities & Computational Social Science

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

HIKMA Semi-Autonomous Conference is the first experiment in reimagining scholarly communication through an end-to-end integration of artificial intelligence into the academic publishing and presentation pipeline. This paper presents the design, implementation, and evaluation of the HIKMA framework, which includes AI dataset curation, AI-based manuscript generation, AI-assisted peer review, AI-driven revision, AI conference presentation, and AI archival dissemination. By combining language models, structured research workflows, and domain safeguards, HIKMA shows how AI can support - not replace traditional scholarly practices while maintaining intellectual property protection, transparency, and integrity. The conference functions as a testbed and proof of concept, providing insights into the opportunities and challenges of AI-enabled scholarship. It also examines questions about AI authorship, accountability, and the role of human-AI collaboration in research.

Problem

• Integrating AI into academic publishing and presentation workflows
• Developing AI-assisted peer review and manuscript generation systems
• Exploring human-AI collaboration while maintaining research integrity

Innovation

• AI integration in academic publishing pipeline
• Language models with structured research workflows
• AI supports traditional scholarly practices