Aims and Scope

QAIRU Journal publishes original research and critical reviews in artificial intelligence and the computational sciences. The aim is to give work in these fields a route to publication that costs the author nothing, reaches readers without a paywall, and applies review that is anonymous on both sides and returns a substantive report rather than a verdict. The journal is published by Qazaq AI Research University, Astana, Republic of Kazakhstan, and publishes in English only.

The journal is method-led rather than topic-led. A manuscript is judged on what it establishes and how, not on the popularity of the problem it addresses. Negative and null results are publishable when the study design supports the conclusion.

Subject areas

The journal covers four areas, matching the laboratories of the university. The example topics below are indicative, not exhaustive; a manuscript that plainly belongs to one of these areas but is not listed is still in scope.

1. Artificial intelligence and machine learning

Foundational work on how learning systems are built, trained and evaluated.

• Learning theory: generalisation, optimisation, sample complexity, theoretical limits of a method
• Representation learning, self-supervised and transfer learning
• Model evaluation: benchmark design, measurement validity, statistical treatment of results, failure analysis
• Efficiency of training and inference: quantisation, distillation, sparsity, memory and energy cost
• Language and multimodal models: architecture, training data curation, alignment, evaluation of capability and of failure
• Uncertainty, calibration, robustness and interpretability of models

Manuscripts in this area are expected to state the baselines used, the number of runs behind each reported number, and the variance across them.

2. Robotics and intelligent systems

Work on systems that sense, decide and act in the physical world.

• Perception: sensor fusion, state estimation, mapping and localisation
• Motion planning under uncertainty, in dynamic or partially observed environments
• Control: learned controllers, model-predictive control, safety constraints and their verification
• Human-robot interaction, shared autonomy and teleoperation
• Embodied learning: simulation-to-reality transfer, data efficiency, learning from demonstration
• Field deployments, including hardware description, operating conditions and observed failure modes

Manuscripts reporting a physical system are expected to describe the hardware, the environment and the number of trials, and to report failures as well as successes.

3. Applied AI

Work in which an AI method is applied to a domain problem and the result is measured in that domain.

• Industry and manufacturing: quality inspection, predictive maintenance, process optimisation
• Healthcare: clinical decision support, diagnostic imaging, workflow and triage, with attention to validation and to patient safety
• Energy: forecasting, grid operation, monitoring of assets
• Education: assessment, tutoring systems, learning analytics
• Agriculture and remote sensing: yield estimation, disease detection, resource management
• Public services: document processing, service delivery, planning and resource allocation

Manuscripts in this area are expected to report validated deployment and measurable outcomes: what changed, by how much, measured against what baseline, over what period, and with what limitations. A demonstration that has not been evaluated against a defined comparison is not sufficient.

4. Computational methods and high-performance computing

Work on the numerical and computational machinery that scientific and AI workloads rest on.

• Numerical methods: discretisation, solvers, stability and error analysis
• Parallel and distributed algorithms, communication-avoiding methods, scalability studies
• Accelerator programming: GPU and other accelerators, memory hierarchy, kernel-level optimisation
• Scientific workflows, data management for large computational campaigns
• Simulation: model construction, verification and validation
• Reproducible computational experiments: environment capture, determinism, artefact description

Manuscripts reporting performance are expected to state the hardware, the software stack and versions, the problem sizes, and the measurement protocol.

What we look for

Editors and reviewers apply five criteria to every manuscript sent for review.

A manuscript that is technically correct but reports its results in a way that cannot be checked will be returned for revision.

Out of scope

The following are declined without review.

• Manuscripts with no methodological or empirical contribution
• Position pieces, opinion and commentary that are not supported by evidence
• Work outside the four subject areas above
• Manuscripts already published elsewhere, in whole or in substantial part
• Manuscripts under review at another journal at the same time
• Descriptions of a commercial product or service, where the purpose is promotion rather than a reproducible result

Authors who are unsure whether a manuscript fits may send the title and abstract to journal@qairu.edu.kz before submission. A pre-submission enquiry is answered as a scope opinion only and does not affect the later decision.

Audience

The journal is written for researchers, engineers and graduate students working in artificial intelligence, robotics, applied AI and scientific computing. Articles are expected to be readable by a specialist in a neighbouring subfield: domain-specific notation and terms should be defined at first use.

Related pages

• Editorial Policies — article types, word limits, decisions, appeals
• Author Guidelines — structure, templates, references, declarations
• Peer Review Process — how manuscripts are assessed
• Call for Papers — current invitations to submit

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Effective date: 2026-09-01 · Version: 1.0 · Questions: journal@qairu.edu.kz