POTAPOVA S., AMBROSENKO N., LYSAK O. METHODOLOGY OF A DIFFERENTIATED APPROACH TO THE SEARCH FOR EDUCATIONAL AND SCIENTIFIC INFORMATION: A CRITERION-ALGORITHMIC MODEL FOR CHOOSING BETWEEN SEARCH ENGINES, AI TOOLS AND EBS. LIFELONG EDUCATION: The 21st Century.
2026. Vol. 14. No. 3. DOI: 10.15393/j5.art.2026.13087


Vol. 14. No. 3.

Lifelong learning in the modern world: the research and design methodology
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METHODOLOGY OF A DIFFERENTIATED APPROACH TO THE SEARCH FOR EDUCATIONAL AND SCIENTIFIC INFORMATION: A CRITERION-ALGORITHMIC MODEL FOR CHOOSING BETWEEN SEARCH ENGINES, AI TOOLS AND EBS

POTAPOVA Svetlana O.
PhD in Biology, Associate professor of the Department of Psychology, Pedagogy and Human Ecology
Krasnoyarsk State Agrarian University
(Krasnoyarsk, Russian Federation)
sveta_p@kgau.ru
AMBROSENKO Nikolai D.
PhD in Technical Sciences, Associate professor, Associate professor of the Department of Information Technology and Mathematical Support of Information Systems
Krasnoyarsk State Agrarian University
(Krasnoyarsk, Russian Federation)
nikolai.ambrosenko@yandex.ru
LYSAK Olga A.
Head of the Scientific Library Department
Krasnoyarsk State Agrarian University
(Krasnoyarsk, Russian Federation)
irlibkgau@mail.ru
Keywords:
professional education methodology
information search
information literacy
digital didactics
criterion-algorithmic model
differentiated approach
electronic library systems
EBS
generative artificial intelligence
search engines.		 Abstract: the article is devoted to the problem of the imbalance in the use of search engines (PS), generative AI and electronic library systems (EBS) by students, where the priority of speed over reliability leads to an unformulated conscious information search activity. The aim is to develop a criterion-algorithmic model of differentiated choice between these resources. Materials and methods are based on the system-activity approach and the ACRL Framework. The methods of theoretical analysis, comparative evaluation of resource paradigms, theoretical modeling and scenario construction are used. The empirical basis is a survey of >1400 students and an analysis of modern research. Research results based on the operationalization of the key concepts of the ACRL Frame-work, a system of five criteria has been developed for the comparative analysis of resources: relia-bility, depth, accuracy, legitimacy and level of information processing. The analysis made it possible to create a criterion-based evaluation matrix that identified the strengths and weaknesses of each type of resource. Based on it, an algorithmic model of differentiated choice is proposed in the form of a flowchart that consistently compares the parameters of an educational and scientific task (goal / stage, required level of reliability, type of content sought) with the profile of the optimal resource. The model is iterative in nature and is implemented through four key application scenarios (initial familiarization, official data search, text processing and generation, deep scientific search), forming students' conscious information retrieval activities. The model serves as a practical tool for developing students' ability to make informed resource choices. It helps to reduce cognitive load, minimize the risks of using unreliable sources and develop a reflective research position. The authors consider the model as a starting point for discussion and see the prospect of its further testing in the educational process, organizing a pedagogical experiment and developing comprehensive methodological support for integration into higher education in order to develop digital culture and conscious information retrieval activities.
Paper submitted on: 02/04/2026; Accepted on: 06/18/2026; Published online on: 09/26/2026.

References

  1. Goryunova L. N. Review of contemporary studies of information retrieval. Vestnik Sankt-Peterburgskogo universiteta. Seriya 12. Psikhologiya. Sotsiologiya. Pedagogika [Vestnik of Saint Petersburg University. Series 12. Psychology. Sociology. Pedagogy]. 2009. No. 1-1. P. 206–213. (In Russ.)
  2. Toisteva O. S. System-activity approach: essential characteristics and principles of implementation. Pedagogicheskoe obrazovanie v Rossii [Pedagogical Education in Russia]. 2013. No. 2. P. 198–202. (In Russ.)
  3. Erkenov M. S. Basic principles of construction and functioning of information retrieval systems on the Internet. Vestnik Rostovskogo gosudarstvennogo universiteta putei soobshcheniya [Vestnik of Rostov State Transport University]. 2007. No. 2 (26). P. 69–77. (In Russ.)
  4. Chernov I. G., Agafonova A. A. Information retrieval systems in education. Sovremennye innovatsionnye tekhnologii podgotovki inzhenernykh kadrov dlya gornoi promyshlennosti i transporta [Modern Innovative Technologies for Training Engineering Personnel for Mining Industry and Transport]. 2016. No. 1 (3). P. 548–554. (In Russ.)
  5. Gendina N. I., Kosolapova E. V., Edinak A. Yu. Artificial intelligence and humanitarian knowledge: neural networks in university educational practice. Vestnik Moskovskogo gosudarstvennogo universiteta kul'tury i iskusstv [Vestnik of Moscow State University of Culture and Arts]. 2025. No. 4 (126). P. 167–182. DOI: 10.24412/1997-0803-2025-4126-167-182 (In Russ.)
  6. Gladkova L. N., Kukuev E. A., Krezhevskikh O. V., Frolenkova A. L. Research competencies of students in the era of artificial intelligence. Perspektivy nauki i obrazovaniya [Perspectives of Science and Education]. 2025. No. 3. P. 55–71. DOI: 10.32744/pse.2025.3.4 (In Russ.)
  7. Ambrosenko N. D., Lysak O. A., Potapova S. O. Assessment of survey results on the differences between neural networks and search engines as an indicator of students’ information literacy. Nauka i obrazovanie 2024 [Science and Education 2024: Proceedings of the X International Scientific and Practical Conference, September 25, 2024]. Moscow, 2024. P. 6–7. (In Russ.)
  8. Potapova S. O., Ambrosenko N. D. Study of students’ information behavior in the context of searching for educational materials. Obrazovanie. Innovatsii. Kachestvo [Education. Innovation. Quality]: Proceedings of the VI International Scientific and Methodological Conference. Kursk, January 29, 2025. Kursk, 2025. P. 156–162. (In Russ.)
  9. Georgas H. Google vs. the Library: Student Preferences and Perceptions When Doing Research Using Google and a Federated Search Tool. Portal: Libraries and the Academy. 2013. Vol. 13. No. 2. P. 165–185.
  10. Potapova S. O., Ambrosenko N. D., Lysak O. A. Study of conditions and approaches to the formation of supra-disciplinary information competence of university students in the context of using electronic library systems. Russki zhournal obrazovania i psichologii [Russian Journal of Education and Psychology]. 2024. Vol. 15. No. 6-1. P. 112–134. DOI: 10.12731/2658-4034-2024-15-6-622 (In Russ.)
  11. Gendina N. I., Kosolapova E. V. Neural networks for the development of personal intelligence: new didactic solutions for developing information-analytical competencies of university students. Kul'tura: teoriya i praktika [Culture: Theory and Practice]. 2025. No. 5 (66). P. 1‒12. (In Russ.)
  12. Gladkova L. N., Ravedovskaya U. Yu., Semenovskikh T. V. et al. Qualitative study of students’ cognitive preferences in the use of artificial intelligence. Nauka dlya obrazovaniia [Science for Education Today]. 2025. Vol. 15. No. 4. P. 136–157. DOI: 10.15293/2658-6762.2504.06 (In Russ.)
  13. Ivanova A. E., Tarasova K. V., Talov D. P. Between interest and skill: how students perceive and use artificial intelligence. Vysshee obrazovanie v Rossii [Higher Education in Russia]. 2025. Vol. 34. No. 8–9. P. 9–32. DOI: 10.31992/0869-3617-2025-34-8-9-9-32 (In Russ.)
  14. Association of College & Research Libraries (ACRL). Framework for Information Literacy for Higher Education Chicago: ACRL, 2016. [Electronic resource]. Electron. dan. URL: https://www.ala.org/acrl/standards/ilframework (date of access 23.01.2026).

 


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DOI: http://dx.doi.org/10.15393/j5.art.2026.13087