Year: 2026
Type: article
Abstract: Academic integrity in student research should be regarded as a principle governing the entire research process, encompassing the use of sources, method selection, data collection and analysis, and the presentation of findings. The aim of the article is to identify the main risks of academic integrity violations in student marketing research and to substantiate mechanisms for preventing them at different stages of the research process. The study employs the analysis and synthesis of scholarly publications, thematic classification of risks, and comparative analysis of approaches to promoting academic integrity. The main risks of academic integrity violations are systematized according to the stages of student research. Problems associated with improper citation, the use of unverified sources, and the reproduction of claims whose accuracy has not been established are identified. The risks of empirical data distortion, unjustified exclusion of observations, and overgeneralization of findings are examined. The relationship between the quality of the source base, the reliability of empirical data, and the validity of research conclusions is established. Particular attention is paid to the use of generative artificial intelligence, including the risks of fabricated references, unsupported claims, and misinterpretation of findings. The study substantiates the value of completing research tasks in stages, with interim assessment of the results. It proposes verifying data provenance, documenting data collection and processing procedures, and justifying the criteria used to exclude observations. The role of instructor feedback in identifying methodological errors and developing students’ research competencies is established. An approach to regulating the use of generative artificial intelligence is formulated, specifying permitted operations, requiring verification of generated content, and ensuring transparent disclosure of its use. The findings can be used to develop methodological guidelines, assignments, and assessment criteria for student marketing research, taking into account requirements for citation, empirical data verification, and the use of generative artificial intelligence.
Source: Academic Visions
Authors: Oksana Penkova, Nataliia Pacheva
Institution: Uman National University
Language: Ukrainian
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7224095437