Philosophy, Prompts, Zoomers, and Generation Alpha

This article examines the impact of generative artificial intelligence on the educational strategies of modern students (Gen Z, Zillennials, and Generation Alpha) through the lens of teaching philosophy and related disciplines. Today’s information-communication landscape and rapidly evolving sociocultural reality necessitate the constant updating of knowledge regarding the latest high-tech advancements. The study argues that the wholesale shift of students toward interacting with Large Language Models (LLMs) is transforming their cognitive habits and linguistic culture.

The author identifies a paradox: alongside a decline in spontaneous oral communication skills, a need has emerged to develop a new form of textual literacy—prompt engineering. The study analyzes the semantic divergence between the terms «AI» and «ИИ» (the Russian equivalent), as well as their origins and widespread usage. It proposes practical methodological solutions, a diagnostic assessment rubric, and specific scenarios for implementing prompt sessions for humanities students across Russian universities, aimed at enhancing their methodological reflection.

Prompt engineering is positioned not merely as a technical skill, but as a fundamental 21st-century competency in the humanities—one that is already essential for effective work and may soon become extremely difficult or even impossible to do without. Incorporating prompting as a key element of instruction enables the creation of simulated, instructor-led interactive and communicative scenarios; this opens up innovative avenues for human-AI interaction while helping to mitigate the persistent challenge of limited face-to-face engagement with large student groups.

The article develops a specialized rubric with specific criteria designed to evaluate not the final AI-generated text, but the logic of the dialogue (log file or chat history), thereby demonstrating the student’s methodological reflection. It also proposes a scale for converting scores into an academic result—such as a «pass,» eligibility for a test or exam, or a final grade—to serve as a supplement to other course requirements.

The didactic potential of prompt sessions makes it possible to transform AI from a tool for academic dishonesty (plagiarism) into a catalyst for cognitive activity. The task facing today’s higher education instructor is to move beyond passive techno-skepticism and spearhead the transformation of the haphazard, superficial AI usage typical of Gen Z and the subsequent Generation Alpha into methodologically sound, in-depth research work.

This work is based on the teaching of philosophy, but it is not limited to that field alone. Such prompts are frequently used in foreign language instruction and can be effectively applied to teaching subjects across the humanities, technical fields, and natural sciences.

1. Давлатова, М. А., & Сперанская, М. В. (2026). Промпт-инжиниринг как ключевая компетенция в образовании: сущность, особенности и подходы к оцениванию. Высшее образование в России, 35(2), 53–73. https://elibrary.ru/hgzdxp

2. Ивахненко, Е. Н., & Ковальзон, М. М. (2024). Будет ли присуждена искусственному интеллекту Нобелевская премия в 2040 году? Информационное общество, 2, 2–10. https://elibrary.ru/qkwwly

3. Ивахненко, Е. Н., & Никольский, В. С. (2023). Chat GPT в высшем образовании и науке: угроза или ценный ресурс? Высшее образование в России, 32(4), 9–22. https://elibrary.ru/tzhihu

4. Ивахненко, Е. Н. (2009). Аутопойезис информационных объектов. Информационное общество, (1), 34–41. https://elibrary.ru/kuygib

5. Титова, С. В., & Темурян, К. Т. (2026). Формулировка промптов в языковом обучении: дидактические принципы и методические стратегии. Вестник МГПУ. Серия Филология. Теория языка. Языковое образование, 1(61), 172–191.

6. Титова, С. В., & Харламенко, И. В. (2025). Структура профессиональной компетенции педагога иностранных языков в области использования искусственного интеллекта. Язык и культура, (69), 220–246. https://elibrary.ru/eceboi

7. Титова, С. В., & Чикризова, К. В. (2025). Разработка и использование обучающих материалов на основе ИИ в вузах: правовые аспекты. Высшее образование в России, 34(6), 91–111. https://elibrary.ru/vznbea

8. Харламенко, И. В., & Воног, В. В. (2020). Обратная связь как форма контроля в техногенной образовательной среде. Информатика и образование, 5(314), 44–9. https://elibrary.ru/axtnrd

9. Federiakin, D., Molerov, D., Zlatkin-Troitschanskaia, O., & Maur, A. (2024). Prompt engineering as a new 21st century skill. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1366434.

10. Jurafsky, D., & Martin, J. H. (2025). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models (Third edition draft). Stanford University2.