About the Journal

Intelligent Learning and Management Transformation (ILMT) is a peer-reviewed, open-access academic journal that serves as an interdisciplinary platform for the dissemination of original research, theoretical developments, applied studies, and critical reviews in the fields of intelligent systems, learning sciences, educational technology, management innovation, digital transformation, and organizational intelligence.

The journal aims to bridge the gap between technology-driven learning environments and transformative management practices in both academic and industrial contexts. By fostering dialogue among scholars, practitioners, and policymakers, ILMT contributes to the advancement of knowledge in areas that integrate artificial intelligence (AI), machine learning (ML), organizational change, human resource development, and strategic management.

ILMT is published continuously online and adopts a double-blind peer-review policy to ensure high academic quality, fairness, and transparency. Each submission is evaluated by at least two to three expert reviewers who provide constructive feedback to help authors refine and improve their manuscripts.

The journal welcomes contributions from a wide range of disciplines including education, business management, information technology, data analytics, and cognitive sciences, reflecting its commitment to interdisciplinary research and cross-sectoral collaboration. ILMT particularly encourages studies that propose innovative models, frameworks, and evidence-based approaches that enhance learning effectiveness, management adaptability, and organizational transformation in the age of intelligence.

Aims and Scope

The principal aim of Intelligent Learning and Management Transformation is to promote a deeper understanding of how intelligent technologies and data-driven approaches are reshaping education, leadership, and organizational management. The journal seeks to serve as a scholarly hub for the exchange of ideas that inform theory, practice, and policy across diverse learning and managerial contexts.

Key Objectives

  • To publish original research articles, case studies, and review papers that explore emerging trends in intelligent learning systems and management transformation.

  • To support the integration of AI, machine learning, and big data analytics in educational and managerial decision-making.

  • To advance theoretical models that explain how intelligent technologies can enhance teaching, training, and strategic management.

  • To promote interdisciplinary research that combines insights from education, psychology, data science, business, and systems engineering.

  • To provide a credible academic platform for scholars, industry experts, and policymakers to discuss digital transformation in learning and management.

Scope of the Journal

The scope of ILMT includes, but is not limited to, the following thematic areas:

1. Intelligent Learning Systems

  • Adaptive and personalized learning environments

  • AI-based tutoring systems and e-learning innovations

  • Cognitive computing in education and training

  • Data analytics and learning behavior modeling

  • Digital pedagogy, gamification, and immersive learning

2. Management Transformation

  • Intelligent management models and decision-support systems

  • Organizational learning and knowledge management

  • Strategic transformation and digital governance

  • Human resource analytics and workforce intelligence

  • Innovation management, entrepreneurship, and change leadership

3. Cross-Disciplinary Themes

  • Human–AI collaboration and augmented intelligence

  • Ethics and accountability in intelligent systems

  • Management education and AI-driven pedagogy

  • Sustainable digital transformation in organizations

  • Data-driven public administration and policy innovation

Through the integration of these domains, ILMT aspires to redefine the conceptual and practical boundaries of both learning and management in the digital era.

Open Access Statement

Intelligent Learning and Management Transformation is committed to the principles of open access publishing, ensuring that all published articles are freely and permanently available online to the global academic community without any subscription or access fee.

This model enhances the visibility, accessibility, and impact of research by allowing readers, institutions, and organizations worldwide to read, download, copy, distribute, print, and link to full texts of articles, provided that the original work is properly cited.

By eliminating financial barriers, ILMT supports the democratization of knowledge, encouraging the exchange of scholarly ideas across national, disciplinary, and institutional boundaries. Authors benefit from greater citation potential and global exposure of their research, contributing to wider dissemination of academic innovations and discoveries.

Copyright and License

Authors publishing in Intelligent Learning and Management Transformation retain full copyright over their work. The journal operates under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

Under this license:

  • Others may copy, redistribute, remix, transform, and build upon the material for non-commercial purposes, provided appropriate credit is given to the original author(s) and source.

  • The material may not be used for commercial purposes without explicit written permission from the copyright holder.

This policy reflects ILMT’s commitment to ensuring that academic knowledge remains a public good while protecting authors’ intellectual property rights. Authors are encouraged to deposit their published articles in institutional or subject repositories, personal websites, or any non-commercial platforms, in accordance with the journal’s self-archiving policy.

Plagiarism Policy

ILMT upholds the highest standards of publication ethics and academic integrity. All manuscripts submitted to the journal are subjected to plagiarism detection using iThenticate software prior to peer review.

The editorial team evaluates the similarity index carefully to ensure that submitted work is original and properly cited. Manuscripts exhibiting significant overlap with previously published material or improper citation practices will be rejected immediately or returned to authors for revision, depending on the severity of the issue.

Any confirmed cases of plagiarism, self-plagiarism, data manipulation, or unethical publication behavior will result in withdrawal of the article, notification of the authors’ institution, and potential blacklisting of the author(s) from future submissions.

The journal strictly follows the Committee on Publication Ethics (COPE) guidelines to handle cases of misconduct, ensuring fairness, transparency, and accountability in the publication process.

Article Processing Charges (APCs)

To support open access publishing and cover the costs of editorial management, peer review, plagiarism screening, indexing, digital archiving, and website maintenance, the journal applies a modest Article Processing Charge (APC) of 3,000,000 Iranian Tomans for accepted manuscripts.

There are no submission fees or hidden costs. The APC is only payable after the manuscript has been accepted for publication following successful peer review.

Partial or full waivers may be considered for authors with limited institutional funding or those from developing regions, subject to approval by the Chief Editor. The journal believes in maintaining equitable access to publication opportunities regardless of authors’ financial resources.

Peer-Review Process

ILMT employs a double-blind anonymous peer-review process to ensure impartiality and scholarly rigor. Both authors and reviewers remain anonymous throughout the review process.

Stages of Review:

  1. Initial Screening: The Chief Editor and an Associate Editor conduct an initial evaluation of the manuscript for relevance to the journal’s aims and scope, quality of writing, ethical compliance, and adherence to formatting guidelines.

  2. Similarity Check: All submissions are screened through iThenticate for originality verification.

  3. Peer Review Assignment: Eligible manuscripts are assigned to two or three qualified reviewers with expertise in the manuscript’s subject area.

  4. Evaluation Criteria: Reviewers assess the manuscript’s originality, theoretical foundation, methodological rigor, contribution to knowledge, ethical standards, and clarity of presentation.

  5. Decision Process: Based on reviewers’ recommendations, the editorial team decides whether to:

    • Accept the manuscript,

    • Request minor or major revisions, or

    • Reject the submission.

  6. Revision Stage: Authors receive detailed feedback and are given sufficient time to address reviewers’ comments. The revised version is re-evaluated before final acceptance.

  7. Final Decision and Proofing: After acceptance, the article undergoes copyediting, layout design, and proofreading prior to online publication.

The typical review timeline is 6–8 weeks, depending on the complexity of the manuscript and reviewers’ availability. The journal is dedicated to maintaining an efficient, transparent, and fair review process that upholds academic excellence.

Archiving and Repository Policies

Intelligent Learning and Management Transformation is committed to long-term preservation and accessibility of all published articles. The journal ensures that every published manuscript remains permanently accessible online through its official website and trusted digital archiving systems.

Archiving Mechanisms

  • All published content is stored in multiple secure repositories and cloud-based backup systems to prevent data loss.

  • Metadata and DOIs (Digital Object Identifiers) are maintained to ensure persistent citation and retrieval.

  • The journal is preparing integration with national and international indexing and archiving databases, including Google Scholar, ISC, SID, and CrossRef.

Self-Archiving Policy

Authors are permitted and encouraged to:

  • Deposit pre-print (before peer review), post-print (after peer review but before typesetting), and final published versions of their manuscripts in institutional repositories, subject repositories, or personal websites.

  • Include proper citation and a link to the official version of the article on the journal’s website.

This policy aligns with the journal’s open-access philosophy, ensuring maximum dissemination, visibility, and impact of published research.