The Journal of AI Production and Consumption immediately brings two thoughts to mind. On the one hand, AI production is about the building of AI tools and AI consumption deals with the use of AI tools. On the other hand, AI production and consumption describe the impacts of AI on the general production and consumption of goods and services.
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Manuscripts are initially checked by editors and a decision is made either to send them for review or to desk reject. The journal uses two anonymous peer reviewers, ordinarily subject experts. The review focuses on the academic honesty of authors, the integrity of manuscripts, the nurturing of young academic writers by seasoned professors, and the generation of AI literature for universities and research agencies.
We invite the submission of original research, reviews, and case studies that investigate the dynamic interplay between Artificial Intelligence and a diverse range of disciplines. We welcome contributions that explore both the application of AI to advance these fields and the critical examination of its societal impact.
Topics of interest include, but are not limited to:
Before submission, please ensure your manuscript aligns with the journal's aims and scope. The Journal of AI Production and Consumption (jAIpc) publishes original, high-quality research that critically examines artificial intelligence through a dual lens: as a product itself and as a transformative force on global systems.
We welcome studies that investigate:
The journal encourages interdisciplinary work and particularly welcomes submissions that contextualize these themes within the African experience, engaging directly with global dialogues. For a detailed description, please see our [link to Aims & Scope page].
jAIpc accepts the following types of submissions:
Manuscripts should generally include the following elements in order:
Ensure this main document is fully anonymized for peer review. Remove all author names, affiliations, and identifying information from the file properties.
All manuscripts must be submitted electronically through our online submission system: [Link to Online Submission Portal].
You will be required to:
jAIpc employs a double-anonymous peer review process. The Editor-in-Chief conducts an initial screening for scope and quality. Suitable manuscripts are then sent to at least two independent expert reviewers. The review process typically takes 8–12 weeks. Authors will be notified of the editorial decision: Accept, Minor Revisions, Major Revisions, or Reject.
Our copyright policy is designed to maximize the impact of your research. Authors retain copyright to their work and are encouraged to share it widely, which enhances its visibility and reach. In return, the journal retains the exclusive right to formally publish and manage the definitive Version of Record.
For all editorial and submission queries, please contact the editorial office at: jaipc.editorial@[journaldomain].org
Two times a year: May and October.
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A non-refundable processing fee is required to support the publication of all accepted manuscripts. The fee amount is determined by the author's equity:
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These fees are subject to change and may be periodically reviewed without prior notice.
Payment details will be provided with the formal acceptance letter.
The dependence of farming households in Ghana’s Upper West Region on rainfed agriculture makes their livelihood climate vulnerable. Farmers in the area have relied on indigenous knowledge systems (IKS) for generations to adjust to changing climate. This includes observing animal behaviour, plant cycles, and performance of community rituals to guide farming decisions. Though these practices remain valuable, their reliability is reducing due largely to today’s unpredictable climate parameters. Artificial Intelligence (AI) tools and technologies provide forecasting, pest detection, and digital advisory services. Despite these promising prospects, the integration of AI with IKS is still limited. There is clear evidence that using hybrid technologies and approaches such as AI-driven forecasting, community participation, and using local language interfaces can improve resilience while respecting local culture. There are, however, challenges to integration, which include data and infrastructure gaps, digital literacy, and ethical concerns around data ownership and bias. Many opportunities lie in inclusive policies and community-driven frameworks, as well as farmer-centered designs. This review concludes that blending AI with IKS provides a practical and culturally grounded pathway for climate-smart agriculture. Success in this regard, however, depends on trust among stakeholders, equity, and strong institutional support.
Yakubu Dimmie Buduan