Artificial Intelligence (AI) Usage Policy

Authors may use generative AI or AI-assisted technologies to support activities such as language improvement, literature organization, idea development, data exploration, coding support, visualization, or manuscript preparation. AI tools must not replace the authors' critical thinking, psychological reasoning, scholarly judgment, clinical interpretation, data analysis, or original contribution.

Authors are responsible for:

• verifying factual accuracy and checking references generated by AI;
• reviewing and substantially editing AI-assisted content;
• checking for bias, hallucination, fabricated citations, incorrect psychological claims, or misleading interpretations;
• protecting participant information, psychological records, personal data, copyrighted material, and unpublished research data;
• independently verifying AI-generated analyses, psychological interpretations, code, and research conclusions; and
• ensuring that the final manuscript represents the authors' own scholarly work.

Meaningful use of generative AI in manuscript preparation must be disclosed in a separate AI Declaration. The declaration should identify the tool used, its purpose, and the extent of human review and oversight.

Basic spelling, grammar, or punctuation checks do not normally require disclosure. If AI forms part of the research method, psychological assessment, behavioral analysis, psychometric modeling, qualitative analysis, experimental procedure, clinical decision-support research, or data analysis, its use must be described in sufficient detail in the Methods section.

AI tools, chatbots, language models, machine-learning systems, and other automated technologies must not be listed as authors or co-authors. Authorship requires human responsibility for the integrity of the work, approval of the final manuscript, accountability for its content, and the ability to respond to questions regarding the research.

Generative AI must not be used to create, manipulate, obscure, remove, or introduce features in clinical images, behavioral evidence, experimental images, psychological assessment figures, graphs, or other visual research evidence in a way that misrepresents the underlying data.

AI-generated illustrations that are purely explanatory or conceptual must be clearly identified and must not be presented as original empirical, diagnostic, clinical, or behavioral evidence.

An exception may apply when AI-assisted imaging, classification, segmentation, visualization, behavioral recognition, or pattern analysis is part of the research design or methodology. In such cases, authors must describe the tool, model/version, procedure, validation process where relevant, and its role in generating or interpreting research data in the Methods section.

Submitted manuscripts are confidential documents. Reviewers must not upload manuscripts, manuscript excerpts, participant information, psychological records, assessment results, unpublished datasets, supporting files, or review reports into public generative AI systems.

Peer review is a human scholarly responsibility. AI tools must not be used to replace independent scientific assessment, evaluate psychological validity autonomously, determine clinical significance, assess manuscript quality, or determine review recommendations. Reviewers remain personally responsible for the content and integrity of their reports.

Editors must not upload submitted manuscripts, participant information, psychological data, confidential correspondence, reviewer reports, or editorial decision letters into public generative AI systems.

AI must not replace human editorial judgment or be used to determine acceptance, revision, or rejection. Editors remain fully responsible for scientific evaluation, ethical assessment, communication, and final publication decisions.

Psychological research frequently involves sensitive information relating to mental health, cognition, emotions, personality, behavior, interpersonal relationships, clinical history, counseling, trauma, educational records, or digital activity. Such information must not be entered into public AI systems unless lawful authorization, ethical approval, appropriate safeguards, and applicable consent or data-use permissions are in place.

• Personally identifiable participant information must be protected.
• De-identification or anonymization must be applied where appropriate.
• Institutional, legal, contractual, and ethical restrictions on psychological data processing must be respected.
• Clinical, counseling, diagnostic, or assessment records must not be uploaded to public AI platforms without appropriate authorization.
• AI-assisted analysis must not expose participants to avoidable privacy, stigma, reputational, psychological, or discriminatory risks.
• Data-governance limitations should be stated clearly where they affect reproducibility or sharing.

When artificial intelligence, machine learning, natural language processing, automated psychological assessment, predictive analytics, behavioral classification, or decision-support systems are central to the research, authors must provide sufficient methodological detail to allow scientific evaluation.

• Identify the model, algorithm, software, or platform used.
• Describe training, validation, testing, and external validation procedures where applicable.
• Explain dataset composition, participant characteristics, inclusion and exclusion criteria, and preprocessing procedures.
• Report relevant performance measures, uncertainty, sensitivity, specificity, or other appropriate metrics.
• Describe steps taken to assess overfitting, data leakage, calibration, fairness, and generalizability.
• Report relevant psychological, clinical, cultural, and population limitations.
• Explain human oversight and how model outputs were interpreted.
• Where applicable, discuss potential harms arising from false classifications, biased predictions, inappropriate labeling, or unsupported psychological conclusions.

AI systems used in psychological research may reproduce or amplify biases present in assessment data, clinical records, behavioral datasets, cultural assumptions, demographic distributions, linguistic data, or historical diagnostic practices.

Authors should evaluate relevant differences in model performance across populations where scientifically appropriate and should avoid unsupported claims of universal applicability. Potential effects related to age, gender, language, culture, disability, socioeconomic context, ethnicity, or other relevant participant characteristics should be discussed where they materially affect interpretation.

AI-generated outputs must not be treated as substitutes for professional psychological judgment, clinical assessment, ethical reasoning, appropriate supervision, or other qualified human decision-making.

Authors must avoid overclaiming diagnostic value, predictive accuracy, treatment relevance, behavioral meaning, clinical utility, or generalizability when such claims are not supported by appropriate evidence. Any recommendation derived from AI-assisted psychological research remains subject to human interpretation, professional standards, and scholarly accountability.