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About the Journal

Artificial Intelligence and Language Models (AILM) is an international, peer-reviewed, open-access scholarly journal published by Akira Cipta Solusi, Indonesia. Established in 2026, the journal commenced publication with Volume 1, Number 1 in June 2026. AILM is published exclusively in electronic format on a biannual basis, with regular issues released in June and December.

The journal serves as a scholarly platform for advancing theoretical, methodological, and applied research in artificial intelligence, natural language processing, and language model technologies. Its primary focus encompasses language-centric AI systems, particularly large language models, retrieval-augmented generation, model evaluation and benchmarking, robustness, reproducibility, multilingual and low-resource language processing, and the responsible development and deployment of AI technologies.

AILM welcomes original research articles, comprehensive reviews, and empirical studies from scholars, researchers, and industry professionals working in artificial intelligence, natural language processing, information retrieval, machine learning, knowledge representation, human–AI interaction, and related computational disciplines. The journal seeks to foster interdisciplinary scholarly exchange and advance the development of reliable, transparent, inclusive, and trustworthy AI systems through methodological rigor, reproducible experimentation, evidence-based evaluation, and responsible scientific innovation.

Focus and Scope

The focus of Artificial Intelligence and Language Models is the development, evaluation, and application of modern language-based AI systems. The journal gives special attention to research that moves beyond model construction alone and addresses the reliability, safety, evaluation, benchmarking, and deployment of AI systems in real-world contexts.

The journal welcomes submissions in, but not limited to, the following areas:

  • Language Models and LLM Systems
    Design, adaptation, instruction tuning, alignment, deployment, and evaluation of language models, including transformer-based models and large language model systems.

  • Evaluation and Benchmarking of Language Models
    Human evaluation, automated evaluation, LLM-as-a-judge, benchmark construction, meta-evaluation, reproducibility, reliability assessment, and alignment between human and model-based judgments.

  • Retrieval-Augmented Generation and Knowledge Integration
    RAG architectures, vector retrieval, sparse and hybrid retrieval, knowledge graphs, tool-augmented generation, citation-grounded generation, and knowledge-intensive question answering.

  • Robustness, Safety, and Adversarial Analysis
    Hallucination detection, adversarial prompting, robustness evaluation, safety-critical assessment, defense mechanisms, uncertainty, and trustworthy model behavior.

  • Responsible and Trustworthy AI
    Fairness, bias, transparency, accountability, interpretability, ethical deployment, data governance, and social implications of AI systems.

  • Multilingual and Low-Resource Language Processing
    AI and NLP methods for underrepresented languages, including Indonesian and regional languages, cross-lingual transfer, multilingual benchmarking, and inclusive AI development.

  • Human-AI Interaction and Applied Language Systems
    Real-world AI applications in education, healthcare, governance, libraries, law, digital humanities, public services, business, and industry, with attention to usability, impact, and reliability.

The journal accepts original research articles, empirical evaluation studies, benchmarking papers, systematic reviews, conceptual frameworks, technical reports, and applied case studies that contribute to the science and practice of language model-driven artificial intelligence.

Open Access Policy

Artificial Intelligence and Language Models is an open-access journal. All published articles are freely available online immediately upon publication. Readers may access, read, download, copy, distribute, print, search, and link to the full text of articles without subscription barriers, provided that proper credit is given to the authors and the journal.

The journal supports open access to scientific knowledge in order to promote wider dissemination, visibility, citation, collaboration, and public benefit from research.

Peer Review Process

All submitted manuscripts undergo an initial editorial screening to assess their relevance to the journal scope, originality, completeness, ethical compliance, and overall suitability for peer review. Manuscripts that pass the initial screening are reviewed by qualified reviewers with expertise in the relevant subject area.

The journal is committed to maintaining a transparent, fair, and rigorous peer review process. Editorial decisions are based on academic merit, methodological quality, originality, clarity, relevance, ethical compliance, and contribution to the field.

Publication Ethics

Artificial Intelligence and Language Models is committed to upholding academic integrity and publication ethics. Authors, reviewers, and editors are expected to follow ethical standards related to originality, authorship, citation practice, data integrity, conflicts of interest, plagiarism, duplicate publication, research misconduct, and responsible use of artificial intelligence tools.

Manuscripts may be checked for similarity, plagiarism, inappropriate citation practices, image manipulation, data irregularities, or other forms of academic misconduct. The journal reserves the right to reject, retract, correct, or issue an editorial notice for publications that violate ethical standards.

Copyright Notice and Licensing

Authors retain copyright of their published articles. Articles published in Artificial Intelligence and Language Models are distributed under the terms and conditions of the Creative Commons Attribution (CC BY) 4.0 license, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited.

By submitting a manuscript to the journal, authors agree that accepted and published articles will be made openly available under the journal’s open-access licensing policy.

Publication Frequency

AILM is published exclusively in electronic format on a biannual basis, with two regular issues released annually in June and December.

Publisher and Sponsorship Disclosure

Artificial Intelligence and Language Models is published by Akira Cipta Solusi.

The publisher supports the journal’s online publication, editorial workflow, platform management, and dissemination activities. Editorial decisions are made independently by the editorial team based on academic quality, relevance, originality, ethical compliance, and contribution to the field. The journal does not allow sponsorship, institutional affiliation, commercial interest, or financial consideration to influence editorial decisions.

Journal History

AILM commenced publication in June 2026 with its inaugural issue, Volume 1, Number 1. Since its establishment, the journal has served as an international platform for disseminating scholarly research in artificial intelligence and language model technologies.

Privacy Statement

The names, email addresses, affiliations, and other personal information entered in this journal site will be used exclusively for the stated purposes of this journal. Such information will not be made available for any other purpose or to any other party, except where required for editorial processing, peer review, publication, indexing, legal compliance, system administration, or publication ethics investigation.

Archiving and Digital Preservation

Artificial Intelligence and Language Models is committed to preserving published scholarly content and ensuring long-term accessibility. The journal will maintain digital copies of published articles through its online journal platform and may participate in recognized digital preservation services such as LOCKSS, CLOCKSS, the PKP Preservation Network, institutional repositories, or other archival systems.

Information regarding active participation in LOCKSS, CLOCKSS, PKP Preservation Network, or other preservation systems will be updated on the journal website once available.

Indexing and Visibility

The journal aims to improve the visibility and accessibility of published articles through indexing, metadata registration, search engine discoverability, and scholarly dissemination channels. Indexing information will be updated regularly on the journal website.

ISSN

e-ISSN: In process

Journal Aim

By integrating theoretical advances, empirical validation, evaluation methodology, and practical deployment, Artificial Intelligence and Language Models seeks to advance reliable, robust, transparent, and inclusive language model research. The journal aims to become a dedicated scholarly venue for research that strengthens the trustworthiness and real-world impact of artificial intelligence in an increasingly language-driven digital society.