Journal of Applied Generative Artificial Intelligence

About JAGAI

The Journal of Applied Generative Artificial Intelligence (JAGAI) publishes peer-reviewed applied research on generative AI, including model development, evaluation, deployment, and governance.

JAGAI prioritizes work with measurable outcomes, transparent evaluation, and ethical considerations.

Aims & Scope

JAGAI focuses on generative systems used in real workflows. Submissions should detail methodology, evaluation design, safety considerations, and practical deployment insights.

Topics of Interest

• Generative models (text, image, audio, video, code) and applications
• Retrieval-augmented generation, prompting methods, agentic workflows
• Evaluation frameworks, benchmarks, and human-centered assessments
• Model alignment, safety, robustness, and risk reduction
• Bias analysis, fairness, and responsible generative AI practices
• Copyright, provenance, watermarking, and content authenticity
• Privacy-preserving generation and secure deployment architectures
• Generative AI in education, healthcare, business, and public services
• Human-AI collaboration and productivity in professional settings
• Governance, policy, and organizational adoption strategies
• Monitoring, incident response, and post-deployment auditing

Article Types

• Applied research articles
• Deployment and implementation studies
• Evaluation and benchmarking papers
• Responsible AI case studies and policy analyses
• Surveys and tutorials (peer-reviewed)

Recent Articles