Overview

The KORA Journal of Applied AI & Multidisciplinary Studies (KJAI) was established to give applied and cross-domain artificial intelligence research a home of its own — work that is often too applied for a methods venue and too technical for a domain one.

The journal is published by The Korean Academy (KORA) and is managed by the Academic Publishing Division of The Korean Academy, based in India.

The division oversees the journal's editorial operations, peer review process, and academic development.

          KJAI brings together research from computer science, engineering, medicine and the health sciences, education, the environmental sciences, economics and finance, law and public policy, linguistics, and the humanities and social sciences — wherever artificial intelligence is being built, applied, evaluated or governed.
        

The journal publishes original research and review articles that present methodological advances, deployed systems and field studies, datasets and benchmarks, replication and negative results, and critical work on the human and societal consequences of applied AI.

The journal is published twice annually, in June and December, and accepts submissions from authors worldwide.

Mission & Vision

          Mission
          To advance the responsible application of artificial intelligence across disciplines by publishing rigorous, reproducible and useful research, and making it freely accessible to the global scholarly community.
        

          Vision
          To become a leading international journal recognised for excellence in cross-domain and interdisciplinary AI research — the venue where a clinician, an economist and a machine learning researcher can read each other's work and understand it.
        

KJAI is founded on the belief that most of what artificial intelligence will actually change happens outside the machine learning laboratory. The journal exists to connect method builders with the people who deploy their methods, and to give careful applied work a home regardless of where its author is based or how early they are in their career.

Two things follow from that. First, a paper is judged on whether its claims are supported, not on whether it beats a leaderboard — a well-executed negative result that closes off a plausible approach is a contribution. Second, an application paper must be legible to both audiences: enough method for a computer scientist to reproduce it, enough domain grounding for a specialist to trust it.

Aims & Scope

The KORA Journal of Applied AI & Multidisciplinary Studies (KJAI) publishes original research and review articles on artificial intelligence in application: the methods that make it work, the domains it is applied to, and the interdisciplinary questions that arise when the two meet.

The journal's focus is deliberately cross-domain. Work confined entirely to a theoretical corner of machine learning, with no line of sight to a use, is usually better placed elsewhere; so is domain work in which the AI component is incidental. What KJAI wants is the middle — and the papers that make the middle intelligible.

Applied AICross-Domain AIInterdisciplinary AI Machine LearningFoundation ModelsHuman-Centred AI Responsible AIAI for ScienceReproducibility

Core Research Areas

Artificial Intelligence Methods & Systems 인공지능 방법론

Machine learning, deep learning, representation learning and statistical learning theory
Natural language processing, large language models and multimodal foundation models
Computer vision, speech, signal and time-series modelling
Reinforcement learning, planning, search, optimisation and decision-making under uncertainty
Knowledge representation, graphs, reasoning and neuro-symbolic methods
Generative models, retrieval-augmented systems and agentic architectures
Efficient, on-device and edge AI; distributed, federated and privacy-preserving learning
Data engineering, MLOps, deployment, monitoring and lifecycle management
Evaluation, benchmarking, uncertainty quantification and reproducibility
And any related scope within this field

Cross-Domain & Applied AI 융합 응용

Health, medicine and the life sciences — diagnosis, imaging, clinical decision support, bioinformatics, drug discovery, public health
Education and learning sciences — intelligent tutoring, assessment, learning analytics, language learning and educational equity
Engineering, manufacturing and robotics — predictive maintenance, control, digital twins, autonomous systems, human–robot interaction
Environment, climate, agriculture and energy — remote sensing, forecasting, precision agriculture, sustainability and grid optimisation
Finance, economics, business and operations — risk, forecasting, fraud detection, supply chains and algorithmic decision-making
Law, governance and public administration — legal informatics, regulatory technology, public-sector AI and computational policy analysis
Language, culture and the humanities — computational linguistics, translation and interpreting technology, digital humanities and cultural heritage
Social sciences and computational social science — behaviour, networks, survey methodology and simulation
Security, forensics and critical infrastructure, where the work is defensive and disclosed responsibly
And any related scope within this field

Interdisciplinary, Human-Centred & Responsible AI 인간 중심 · 책임 있는 AI

Trustworthy AI — robustness, safety, reliability and failure analysis
Fairness, bias, auditing and the measurement of harm across populations
Explainability, interpretability and human understanding of model behaviour
Privacy, data protection, consent, provenance and data stewardship
Human–AI interaction, collaboration, cognitive science and the psychology of automation
AI ethics, law, governance, standards and regulatory compliance
Labour, economics and the organisational consequences of deployment
Research methodology — study design, statistics, replication, negative results and open science
AI for low-resource settings, languages and communities, and questions of access
And any related scope within this field

What we particularly want to see

Papers written for two audiences at once — a method a domain specialist can trust, and a domain problem a machine learning researcher can follow.
Deployment and field studies, including what broke. A system evaluated in the setting it was built for is worth more than one evaluated only on a held-out split.
Datasets, benchmarks and resources with documented provenance, licensing and limitations.
Replications, reproductions and negative results. These are reviewed and published on the same terms as anything else.
Work from low-resource settings and under-represented languages, regions and disciplines.

          What falls outside the scope
          Purely theoretical results with no path to application; domain studies in which the AI component is incidental or unexamined; benchmark increments with no analysis of why the numbers moved; product descriptions and marketing material; and any work whose primary purpose is to enable surveillance, deception, or harm to a person or group. Manuscripts using generative AI as an unexamined black box, with no evaluation of what it produced, are returned at screening.
        

Submissions are accepted in English. Manuscripts that fall between the three areas above, or that bring the methods of one to bear on another, are exactly what this journal is for.

Publishing Policy

The KORA Journal of Applied AI & Multidisciplinary Studies (KJAI) is an international peer-reviewed scholarly journal committed to maintaining high standards of academic integrity and editorial quality.

All submitted manuscripts undergo a rigorous double-blind peer review process conducted by independent expert reviewers selected for their expertise in the specific subject of the manuscript. Cross-domain submissions are normally sent to one methods reviewer and one domain reviewer, so that both halves of the paper are judged by someone qualified to judge them.

          The journal publishes two issues per year, in June and December. Each volume corresponds to one calendar year.
        

KJAI publishes only original scholarly work and does not accept commissioned or third-party content.

Editorial decisions are made independently by the editorial board and are based solely on academic merit, originality, methodological rigour, and relevance to the journal's scope.

The publisher supports the journal's production, dissemination, and preservation functions but does not influence editorial decisions or the peer review process.

Students, alumni and staff of The Korean Academy may submit manuscripts to the journal. Such manuscripts are handled by an independent editor with no connection to the author, who selects the reviewers and takes the decision alone. Acceptance remains subject to the outcome of peer review in every case — submission is not an entitlement to publication, and the connection to the publisher neither helps nor hinders a manuscript.

The journal adheres to internationally recognised standards of ethical scholarly publishing.

Reproducibility

Every research article is expected to include a code and data availability statement. Where code and data cannot be released — commercial restriction, patient confidentiality, licensing — the statement must say so and say why. Silence is not an acceptable answer, and a manuscript whose results could not in principle be checked by anyone is unlikely to survive review.

Authors are encouraged to deposit code and data in a public repository with a persistent identifier, and to report enough of the experimental setup — data splits, hyperparameters, hardware, random seeds, compute budget — that a competent reader could repeat the work.

ISSN Registration

KJAI has not yet been assigned an ISSN. An ISSN can only be applied for once a serial publication has issued its first number, so the journal's application will be submitted after Volume 1, Issue 1 is published in December 2026.

No ISSN is displayed anywhere on this site until one has been formally assigned. An ISSN is an identifier and not a mark of quality, approval, indexing or peer-review status, and the journal will present it as such.

Digital Object Identifier (DOI)

The journal does not currently register DOIs, and is not a member of any DOI registration agency. An application for DOI registration will be made in future, once the ISSN has been assigned, as an ISSN is normally required before a journal title can be registered.

          Until DOI registration is in place, each published article is identified by its volume, issue and page numbers together with a permanent article URL maintained by the publisher. DOIs will be assigned to articles published from that point onward, and the journal intends to register earlier articles retrospectively where possible.
        

Open Access

The KORA Journal of Applied AI & Multidisciplinary Studies (KJAI) is a fully open-access journal.

All published articles are freely available online immediately upon publication, without subscription or access fees. This ensures that research findings are accessible to the global scholarly community, practitioners, students, and the public.

          Copyright Information
          Copyright of published articles is held by The Korean Academy, the publisher of the journal.
        

Articles are made freely accessible to readers under the journal's open access policy, while copyright and publishing rights remain with the publisher in accordance with the journal's publishing agreement. Copyright in software and datasets released alongside an article stays with their authors under whatever licence they choose; the publishing agreement covers the article, not the artefacts.

Publication is funded by an Article Processing Charge of ₹2,000 for authors in India and USD 30 for international authors, invoiced only after a manuscript has been accepted. There is no submission fee, nothing is payable while a manuscript is under review, and a manuscript that is rejected is never charged for. The charge meets the real cost of running the journal — editorial administration, copy-editing and proofreading, typesetting and PDF production, hosting and preservation — none of which is passed on to readers. Full details, including waivers and refunds, are on the Article Processing Charges page.

Open access supports knowledge dissemination, research visibility, and scholarly impact — and keeps the journal readable by the practitioners whose problems it publishes work on.

Archiving

Preservation Policy

KJAI is committed to the long-term preservation and accessibility of published research.

          All published articles are stored and maintained through institutional and digital archiving systems managed by The Korean Academy to ensure permanent availability and accessibility independent of changes to journal management or hosting platforms.
        

Each article is preserved as an archival PDF with complete metadata in at least three locations, one of which is outside the publisher's own infrastructure.

Code and datasets are a harder problem. The journal does not host them, and a link to a personal repository is not preservation. Authors are asked to deposit artefacts in an archive that issues a persistent identifier — Zenodo, figshare, an institutional repository, or a recognised domain archive — and to cite that identifier in the article, so that what the paper depends on outlives the account that produced it.

This preservation strategy ensures that all digital assets remain safe, secure, and retrievable for future generations of scholars and researchers.