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Research Papers

From Diagnosis to Action: An Evidence-Based Maturity Model for AI Integration in Translation

Research Paper

Gene, V., & Cannavina, V. (2026).

From diagnosis to action: An evidence-based maturity model for AI integration in translation. Ionian University, Intertranslations S.A., & RWS. In the Proceedings of NeTTIT (2026), Croatia.

Artificial Intelligence has rapidly transformed the language services industry, moving professional translation beyond traditional machine translation post-editing toward AI-enabled, hybrid localization workflows. Despite widespread adoption, organizations and language professionals often struggle to assess their current level of AI maturity and identify practical steps for sustainable implementation.

This research introduces an evidence-based AI Maturity Model specifically designed for the translation and localization industry. Drawing on a global survey of 400 language professionals conducted in 2025 and longitudinal comparisons with earlier industry studies, the paper identifies measurable patterns of AI adoption across technology integration, workflow design, skills development, quality assurance, ethics, and client collaboration.

The findings culminate in a practical four-stage maturity framework—Emerging, Developing, Strategic, and Transformative—that enables Language Service Providers (LSPs), translation teams, and independent linguists to benchmark their current AI capabilities, identify organizational gaps, and build structured roadmaps for responsible AI adoption.

Rather than viewing AI as a replacement for human expertise, the proposed model demonstrates how successful organizations evolve toward AI-augmented translation ecosystems where technology, governance, professional competencies, and client expectations develop together.

This research provides one of the first role-centric maturity models for AI integration in translation, offering both researchers and industry practitioners a practical framework for evaluating readiness, planning investments, and implementing AI responsibly.

Key Contributions

• Evidence-based AI maturity model built from global industry research
• Four clearly defined stages of AI integration
• Five measurable maturity dimensions covering technology, workflows, skills, ethics, and business alignment
• Practical roadmap for Language Service Providers and enterprise localization teams
• Actionable recommendations for responsible AI adoption in professional translation

Download the full research paper to explore the complete AI Maturity Model, methodology, survey findings, maturity assessment framework, and practical implementation guidance developed by researchers from Ionian University, Intertranslations S.A., and RWS.

Knowledge Graph-Mediated Translation (KGMT): A Theoretical Framework for Compliance-Aware Multilingual Communication in Regulated Domains

Research Paper

Gene, V., & Sosoni, V. (2026). Knowledge Graph-Mediated Translation (KGMT): A Theoretical Framework for Compliance-Aware Multilingual Communication in Regulated Domains. Ionian University. In the Proceedings of Convergence 2026. Surrey.

Large Language Models (LLMs) have significantly improved translation fluency, yet they continue to struggle with terminology-critical and compliance-sensitive content in regulated industries. This research introduces Knowledge Graph-Mediated Translation (KGMT), a theoretical framework that combines validated knowledge graphs with AI-assisted translation to deliver semantically grounded, terminology-consistent, and compliance-aware multilingual communication.

Drawing on principles from knowledge representation, translation studies, and computational linguistics, the paper argues that knowledge graphs provide the semantic grounding, regulatory traceability, and constraint validation required for high-risk domains such as cosmetics, pharmaceuticals, medical devices, and other regulated industries.

A three-condition experimental study comparing standard LLM translation, document-augmented translation, and KGMT demonstrates the potential of knowledge graph integration to improve regulatory awareness by enabling structured terminology management, compliance validation, and auditable multilingual workflows.

The paper establishes KGMT as a new architectural direction for AI-assisted translation, providing a theoretical foundation for future research into trustworthy, explainable, and compliance-aware localization systems.

Key Contributions

• Introduces the Knowledge Graph-Mediated Translation (KGMT) framework
• Combines AI translation with structured semantic knowledge
• Enables terminology consistency and regulatory provenance tracking
• Presents one of the first compliance-aware architectures for multilingual communication
• Provides a foundation for future AI-driven translation research in regulated industries

Download the complete paper to explore the theoretical framework, system architecture, experimental methodology, and future directions for Knowledge Graph-Mediated Translation.

Dual-Metric Compliance and Quality Evaluation of Knowledge Graph-Mediated Translation in Regulated Domains: An Enhanced Architectural Framework

Research Paper

Gene, V., & Sosoni, V. (2026). Dual-Metric Compliance and Quality Evaluation of Knowledge Graph-Mediated Translation in Regulated Domains: An Enhanced Architectural Framework. Ionian University. In the Proceedings of NeTTIT (2026), Croatia.

Building on the original Knowledge Graph-Mediated Translation (KGMT) framework, this second-phase study presents an enhanced architecture for evaluating AI-assisted translation in regulated industries through separate measurement of translation quality and regulatory compliance.

The research introduces three major innovations: an indexed source design containing controlled compliance violations, the Non-Compliance in Source (NCS) classification that distinguishes source-originated from translation-introduced errors, and an enhanced KGMT pipeline incorporating a domain-specific style guide, multilingual terminology resources, and linguistic normalization.

Using a controlled experimental comparison between standard LLM translation, document-augmented translation, and KGMT across English–Greek and English–French translation, the study demonstrates that KGMT achieved 100% source compliance detection recall, while baseline approaches failed to identify any compliance issues.

The findings show that knowledge graphs function as a deterministic stabilization layer for large language models, enabling auditable, regulation-aware translation workflows and providing a practical methodology for evaluating AI systems in compliance-critical environments.

This research advances KGMT from a theoretical concept to a validated evaluation framework, offering a reusable methodology for multilingual quality assurance across regulated industries.

Key Contributions

• Introduces the Non-Compliance in Source (NCS) evaluation methodology
• Achieves 100% compliance detection recall using Knowledge Graph-Mediated Translation
• Separates translation quality from regulatory compliance evaluation
• Demonstrates deterministic, auditable AI-assisted translation workflows
• Establishes a reusable framework for evaluating AI in regulated multilingual communication

Download the full paper to explore the enhanced KGMT architecture, experimental methodology, NCS evaluation framework, and empirical results demonstrating compliance-aware AI translation.

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