ABOUT THE JOURNAL

The primary aim of the Journal of Interdisciplinary AI Applications is to make visible the applications of artificial intelligence and data-driven technologies across different disciplines, to support original research in these areas, and to strengthen interdisciplinary knowledge production. The journal approaches artificial intelligence not merely as a technical or software-based development field, but as a multidimensional research ecosystem that influences decision-making processes, service design, institutional structures, sectoral transformation, and social relations.

Its editorial policy gives priority to application-oriented and problem-driven studies. Research submitted to the journal is expected to define a clear research problem, employ appropriate methods, present its scientific contribution explicitly, and, where possible, establish a meaningful connection with real-life, sectoral, institutional, or social contexts. Studies that merely offer general technology narratives, maintain only a weak relationship with artificial intelligence or data-driven systems, or fail to demonstrate their application context clearly are considered to have only limited alignment with the journal’s publishing priorities.

Special importance is given to the following themes within the journal’s scope:

• artificial intelligence applications
• machine learning and deep learning-based systems
• natural language processing and computer vision applications
• data science, data management, and data analytics studies
• data-driven decision-making, data quality, and data governance
• data mining, big data, and data visualization
• decision support systems and business intelligence
• information systems and management information systems
• digital transformation and intelligent organizations
• human–AI interaction
• ethical, explainable, and trustworthy artificial intelligence
• sector-oriented intelligent applications
• AI-enabled improvement of service processes
• artificial intelligence-supported analysis, forecasting, and optimization studies

The range of application domains covered by the journal is broad and explicitly includes the following areas:

• Health and medicine: Clinical decision support systems, medical image analysis, health informatics, patient monitoring systems, AI-supported diagnostic and predictive solutions, and intelligent systems aimed at improving healthcare service efficiency.
• Education: Adaptive learning systems, personalized educational technologies, learning analytics, AI-supported assessment and evaluation, student performance prediction, academic guidance, and advisory systems.
• Business and management: Enterprise analytics, human resources analytics, customer behavior analysis, operational optimization, strategic decision support, AI-supported management applications, and digital business models.
• Public administration: E-government applications, intelligent systems in public services, data-driven public decision processes, policy analytics, citizen-oriented digital public services, and smart governance solutions.
• Communication and media: Content analysis, sentiment analysis, media analytics, recommender systems, digital behavior analysis, AI-supported content processing, and misinformation/disorder detection.
• Law: AI ethics, algorithmic justice, accountability, data privacy, regulatory frameworks, legal technologies, and responsible artificial intelligence applications.
• Engineering: Intelligent manufacturing, robotics, quality control, sensor data analysis, predictive maintenance, automation systems, and smart engineering solutions in energy and infrastructure contexts.
• Sustainability: Energy efficiency, environmental monitoring, intelligent resource management, data and AI applications for sustainable development, and green digital transformation systems.
• Manufacturing: Industrial process optimization, production planning, quality analytics, operations management, smart factories, and AI applications focused on process improvement.
• Finance: Financial forecasting, risk analysis, fraud detection, customer segmentation, algorithmic decision support, and data-driven financial management systems.
• Agriculture: Smart agriculture, yield forecasting, environmental data analysis, precision agriculture technologies, resource-use optimization, and agricultural decision support systems.
• Social sciences: Technology acceptance, human behavior, digital society, the social impacts of artificial intelligence, institutional transformation, user experience, and human-centered intelligent systems research.

In addition to these fields, the journal explicitly welcomes original studies in data science, data management, data analytics, data analyst-oriented methods and practices, business intelligence, information systems, management information systems, digital transformation, systems management, intelligent systems management, and sector-oriented intelligent applications.

The journal may publish the following types of contributions:

• original research articles
• review articles
• application and case analysis studies
• short research papers
• methodological contributions
• technical notes
• editorials
• invited opinion papers
• field review papers

Scientific quality, methodological transparency, ethical compliance, interdisciplinary openness, and application value constitute the core principles of the journal’s editorial line.

The journal’s focus areas are defined within a broad yet coherent academic framework that encompasses the applications of artificial intelligence and data-driven systems across different disciplines. While diversity of fields is encouraged, each submission is expected to establish a strong and explicit scholarly connection with artificial intelligence, data, intelligent systems, or digital transformation.

The journal’s primary focus areas include:

• Artificial intelligence applications
• Machine learning
• Deep learning
• Natural language processing
• Computer vision
• Data science
• Data management
• Data analytics
• Data analyst-oriented methods and applications
• Data mining
• Big data systems
• Data visualization
• Business intelligence
• Decision support systems
• Information systems
• Management information systems
• Digital transformation
• Systems management
• Intelligent systems management
• Human–AI interaction
• Explainable artificial intelligence
• Ethical and trustworthy artificial intelligence
• Sector-oriented intelligent applications
• Intelligent healthcare systems
• Intelligent educational technologies
• Intelligent public services
• Industrial and manufacturing-oriented intelligent solutions
• Sustainability- and environment-oriented data/AI systems

The journal recognizes that the relationship between data and artificial intelligence cannot be reduced to technical modeling alone. Data collection, data cleaning, data governance, data quality, data architecture, data strategy, data-driven decision-making, and analytical maturity are also considered integral components of artificial intelligence applications. For this reason, studies examining the managerial, organizational, and applied dimensions of data-driven systems are regarded as valuable contributions within the journal’s scope.

The journal is published according to a regular schedule based on the principles of scientific quality and editorial sustainability. At the initial stage, the journal is planned to publish two issues per year. Each issue will consist of selected high-quality studies that fall within the aims and scope of the journal.

Maintaining a stable publication flow is among the journal’s main priorities in order to ensure academic credibility and long-term development. A balanced structure is sought among submission volume, peer-review capacity, and publication quality. Publication frequency may be reconsidered when necessary through editorial evaluation, and any possible changes will be announced publicly and transparently.

An online publication model is adopted as the primary mode of dissemination. In later stages, practices such as early view or article-based publication may also be considered in accordance with editorial policy.

The journal adopts an open-access publishing model. All published content is made available to readers without any subscription fee, access charge, or institutional membership requirement. Broad dissemination of scientific knowledge, stronger interdisciplinary exchange, and more effective use of research outputs in practical settings constitute the main foundations of the journal’s open-access policy.

Ensuring that academic knowledge produced in fields such as artificial intelligence, data science, and intelligent systems reaches beyond limited circles and becomes accessible to researchers, educators, industry professionals, decision-makers, entrepreneurs, and public institutions is regarded as highly important. In line with this understanding, the journal supports an open-access model that promotes scientific visibility, impact, and knowledge sharing.

Conditions for reuse and redistribution of content are regulated under the journal’s copyright and licensing policies.

The intellectual responsibility and scientific content of articles published in the journal belong to their authors. The copyright policy is designed to protect the academic contribution of authors while maintaining alignment with the principles of open-access publishing. By granting the journal the right of first publication, authors agree to the formal scholarly publication and record of their work.

The sharing, reuse, and distribution of published content are governed by the licensing model adopted by the journal. The selected open-license type will be clearly announced on the website and displayed visibly on the publication page of each article. Scientific use and dissemination are encouraged, provided that proper attribution is given.

Authors are responsible for obtaining the necessary permissions for any figures, tables, scales, visuals, datasets, or third-party materials used in their manuscripts. Content that may lead to copyright infringement will not be published. Academic integrity, legal compliance, and ethical responsibility form the basis of this policy.

The ownership and publishing structure of the Journal of Interdisciplinary AI Applications is carried out under BudunAI. The journal’s academic and editorial processes are structured on the basis of scientific quality, ethical publishing, editorial independence, and transparent management.

The Editor-in-Chief of the journal is Prof. Dr. Özel Sebetci. Editorial direction, academic evaluation principles, development of publication policies, and the scientific vision of the journal are conducted under the responsibility of the Editor-in-Chief. A clear distinction is maintained between the publisher and editorial decision-making processes. Decisions regarding acceptance, rejection, reviewer selection, revision evaluation, and publication are made solely on the basis of scientific criteria, ethical principles, and peer-review outcomes.

The journal’s management philosophy is based on the following principles:

• academic independence
• ethical publishing
• transparent editorial processes
• scientific quality assurance
• interdisciplinary openness
• international visibility

The editorial and institutional structure of the journal may be further developed over time; however, its core approach is to establish a reliable, reputable, and sustainable academic publishing platform.

The journal’s publication processes are conducted in accordance with the principles of institutional sustainability and academic responsibility. Technical infrastructure, digital publishing processes, editorial organization, and publication management are supported within the framework of available institutional resources. Any financial or institutional support structure is publicly stated in line with the principle of transparency.

Any form of support or institutional contribution remains independent from editorial decisions. Scientific quality, ethical compliance, and peer-review outcomes are the determining criteria in the evaluation and publication of manuscripts.

The journal clearly announces on its website whether it charges authors any submission, preliminary evaluation, or publication fees. If a fee policy exists, its scope, amount, and implementation procedures are explained transparently. If a no-fee publication model is adopted, that condition is also explicitly stated.