From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration

Researchers propose an ontology-driven framework called CCAI (Contextual Collaboration AI Ontology) to document and trace human-AI interactions, converting ephemeral prompt-response exchanges into structured, queryable collaboration records. The framework addresses transparency and accountability gaps in AI-assisted workflows by explicitly modeling tasks, agent roles, resources, and constraints within a machine-interpretable vocabulary.

This research addresses a critical gap in how organizations document human-AI collaboration. Current generative AI workflows typically leave minimal audit trails—a prompt is submitted, an output emerges, and the surrounding context vanishes. This opacity creates problems for accountability, reproducibility, and trust, especially in regulated or high-stakes domains like software development, legal analysis, or medical decision-making. The CCAI ontology solves this by structuring collaboration metadata in a standardized format that can be queried and analyzed later.

The framework builds on established semantic web technologies (ontologies and SPARQL queries) to create what amounts to a comprehensive audit log for AI-assisted work. Rather than treating AI as a black box, the approach surfaces who was involved, what resources were consulted, which constraints applied, and how outputs connected to inputs. The case study demonstrates practical utility in software development, showing how the framework enhanced traceability across requirements analysis through testing phases.

For enterprise environments and regulated industries, this approach has substantial implications. Organizations increasingly deploy generative AI in knowledge work but lack systematic ways to document these interactions for compliance, quality assurance, or liability purposes. A standardized ontology could enable interoperability across tools and teams, making AI contributions visible to auditors and stakeholders. The work also signals growing maturity in AI governance—moving beyond simple output evaluation toward process-level transparency.

• →CCAI ontology converts opaque AI interactions into structured, queryable collaboration records linking prompts, outputs, and context
• →Framework enhances accountability and traceability by explicitly modeling tasks, roles, resources, and constraints in machine-interpretable format
• →Case study in software development shows practical utility across requirements analysis, design, implementation, and testing phases
• →Approach addresses critical transparency gaps in regulated or high-stakes workflows where AI audit trails are needed for compliance
• →Establishes design principles for future AI systems emphasizing collaborative context representation alongside output quality