Easy Journals for AI Research (2026)

AI research is produced by a diverse community — from academic researchers and PhD students to industry data scientists and applied ML engineers. Easy journals for AI research have clear, accessible submission processes, constructive peer review, and editorial teams familiar with the interdisciplinary nature of AI research. Keith Publications technology journals welcome AI researchers from all backgrounds and career stages.

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What to Look for When Choosing a Journal

Before submitting your research, evaluate journals on these key criteria:

Clear, detailed author guidelines with no ambiguity in formatting requirements
Responsive editorial support team to assist with submission queries
Straightforward online submission system with minimal technical barriers
Constructive peer review feedback to help improve your manuscript
Fair and transparent review criteria applied consistently to all submissions
Flexible on manuscript structure while maintaining scientific rigour

Recommended Journals — Cluster 2: Engineering, Technology & Applied Sciences

All journals below are published by Keith Publications — open access, peer-reviewed, and indexed for global discoverability.

Applied Sciences, Engineering, and Technology Journal (ASETJ)
Applied sciences, engineering disciplines, technology innovation, and interdisciplinary research bridging theory with real-world application.

Review: 2–4 weeks Open Access DOI (CrossRef) Google Scholar

Applied Sciences Engineering Research Technology Innovation Interdisciplinary Studies

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Columbia Journal of Engineering and Technology (CJET)
Electrical, mechanical, civil, computer, software, environmental, and industrial engineering with a focus on emerging technologies.

Review: 2–3 weeks Open Access DOI (CrossRef) Google Scholar

Electrical Engineering Mechanical Engineering Civil Engineering Computer Engineering

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Journal of Medical Technology and Innovation (JMTI)
Medical technology, biomedical engineering, health informatics, clinical innovation, and translational research at the medicine-technology interface.

Review: 2–4 weeks Open Access DOI (CrossRef) Google Scholar

Medical Technology Biomedical Engineering Health Informatics Clinical Innovation

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Fast Publication: All Keith Publications journals complete initial review within 2–5 business days and full peer review within 2–4 weeks. Articles are published online within 5–10 days of acceptance — making us one of the fastest open access publishers in the engineering and technology field.

What Makes an Engineering Journal Easy to Publish In?

An easy-to-publish engineering journal is not a low-quality journal — it is one with clear guidelines, responsive editorial support, a fair and transparent review process, and helpful feedback that makes revision achievable. Keith Publications engineering journals are designed to support authors from all backgrounds and career stages. Whether you are a first-time author, an early-career researcher, or an experienced engineer from a non-English-speaking country, our editorial team guides you through the process with clear communication, structured feedback, and a commitment to making publication accessible.

Tips for First-Time Engineering Journal Authors

If you are submitting your first engineering research paper, start by reading the journal's aims and scope carefully to ensure your topic fits. Download and follow the author guidelines template — formatting compliance is one of the most common reasons for desk rejection. Write a strong abstract (150–250 words) that clearly states your research problem, methodology, key results, and conclusion. Ensure your methodology section is reproducible and your figures and tables are clearly labelled. Submit a complete, proofread manuscript. At Keith Publications, our editors are committed to providing constructive, actionable feedback that helps you improve and publish successfully.

Tips to Maximise Your Acceptance Chances

Align your topic with the journal's scope — read the aims and scope carefully before submitting.
Write a compelling abstract — it's the first thing editors and reviewers read. Make it clear, concise, and keyword-rich.
Ensure strong methodology — clearly describe your research design, experimental setup, or computational approach.
Use current and relevant citations — include recent publications (within the last 5 years) alongside seminal engineering works.
Follow formatting guidelines exactly — non-compliant manuscripts are often desk-rejected without review.
Present results clearly — use tables, figures, and graphs to communicate quantitative findings effectively.
State practical implications — explain how your engineering or technology findings can be applied in practice.

Frequently Asked Questions

Which AI journals are easiest to publish in?

Keith Publications technology journals — CJET and ASETJ — are accessible and welcoming for AI researchers at all levels, with clear submission guidelines, responsive editorial support, and constructive peer review feedback.

Can data scientists without traditional academic backgrounds publish AI research?

Yes. Applied AI research from industry data scientists — demonstrating how machine learning methods solve real engineering or business problems — is within scope for publication at CJET and ASETJ, provided the methodology is documented clearly and the contribution is original.

What makes an AI paper easy to get through peer review?

A clear problem statement, well-documented AI architecture or methodology, reproducible experiments (with hyperparameters, dataset description, evaluation metrics), and an honest discussion of limitations and future work all make AI papers easier to review and accept.

Is applied AI research (not just new algorithms) accepted in easy engineering journals?

Yes. Applied AI research — demonstrating how existing AI techniques solve specific engineering challenges — is highly valued at Keith Publications and does not need to introduce a novel algorithm to be published successfully.

Related Research Guides

Explore other research journal guides in the Engineering, Technology & Applied Sciences cluster: