RESEARCHER’S INFORMATION PORTFOLIO IN THE ERA OF ARTIFICIAL INTELLIGENCE

Key findings from this study

• The authors report that researcher adoption of AI technologies increased from 57 percent to 84 percent in 2025.
• The framework establishes that scientific journals prohibit AI systems from serving as authors while permitting their use for editing, literature search, and data analysis.
• The review identifies that machine learning algorithms enable detection of hidden patterns and correlations inaccessible through traditional analytical methods.

Overview

This article examines how generative artificial intelligence technologies are reshaping the researcher's information portfolio and reconfiguring research methodologies in the digital transformation era. The authors address the comprehensive toolkit now available to researchers, encompassing AI-enabled platforms for information search, data analysis, pattern recognition, and result visualization. The work evaluates both the affordances and risks associated with integrating generative AI into research workflows, including efficiency gains in processing large datasets alongside ethical concerns about bias, result falsification, and superficiality. The analysis incorporates emerging publication ethics guidelines from scientific journals regarding acceptable AI use, including restrictions on AI authorship and requirements for methodological transparency. The article synthesizes current perspectives on how AI transitions from experimental tool to core component of research infrastructure.

Methods and approach

The authors conduct an analytical synthesis drawing on international publication ethics documents and guidelines from journals including Organizational Psychology, which articulates standards for AI tool disclosure and author responsibility. The approach maps both opportunities and risks of AI integration across research activities. The framework addresses tools for automating literature search, machine learning algorithms for large-scale data analysis, and pattern detection capabilities. The evaluation incorporates statistics on researcher adoption rates, noting growth from 57 percent to 84 percent in 2025. The analysis distinguishes permissible AI applications such as text editing, literature search, data collection, and analysis from prohibited uses including AI co-authorship. The authors develop a conceptual model positioning AI as a co-worker within research processes while identifying methodological disclosure requirements, verification responsibilities, and ethical boundaries.

Results

The authors identify a fundamental transformation in research practice wherein AI tools become essential infrastructure rather than supplementary aids. Scientific journals now require researchers to disclose all AI tools used in methodology sections, specifying software versions, query parameters, and detailed application procedures. Publication ethics frameworks prohibit AI systems and chatbots from serving as authors or co-authors while permitting their use for editing, literature search, and data processing, provided researchers independently verify output accuracy.

The framework distinguishes key advantages including accelerated information retrieval, identification of hidden patterns and correlations through machine learning algorithms that traditional methods cannot readily detect, and time optimization in result analysis and interpretation. Concurrent risks encompass ethical uncertainties about permissible AI use in research contexts, potential bias in generated information, result falsification, and analytical superficiality. The analysis notes that each new AI tool introduces both enhanced capabilities and novel problems, with no exhaustive enumeration possible given rapid technological evolution. The researcher's information portfolio now necessarily incorporates diverse instruments, algorithmic methods, data resources, and ethical considerations specific to telecommunication technology deployment in research activities.

Implications

The integration of generative AI into research practice necessitates continuous monitoring of technological developments and ongoing revision of methodological standards. Publication venues increasingly adopt explicit policies governing AI disclosure, shifting responsibility to researchers for validating AI-generated outputs and documenting all computational tools employed. This transparency requirement reflects recognition that AI capabilities enable new forms of pattern detection while introducing verification burdens absent from traditional analytical methods. The co-worker positioning of AI within research workflows demands clarification of acceptable use boundaries, particularly as adoption rates accelerate.

The ethical landscape remains unsettled, with journals attempting to establish norms while fundamental questions about AI permissibility persist. The requirement for detailed methodological documentation of AI application including prompt criteria and version specifications indicates movement toward standardized reporting practices. However, the proliferation of AI tools outpaces consensus development on appropriate deployment parameters. Researchers must navigate efficiency gains against risks of bias propagation, superficial analysis, and result distortion. The article positions AI integration as irreversible yet requiring ongoing governance evolution to address emerging challenges in research integrity, authorship attribution, and analytical rigor as generative technologies become foundational to scientific infrastructure.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.