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Specifics of future teacher’s AI literacy formation: Assessment of value-ethical reflection
2 State University of Education
Introduction. The increase in the mass inconsistent use of artificial intelligence technologies in the field of education determines the problem of studying the process of nurturing future teachers’ artificial intelligence literacy (AI-literacy). The article provides an overview of this problem. The purpose of the study is to identify the specifics of nurturing future teachers’ AI literacy based on the assessment of value-ethical reflection
Materials and Methods. The research methodology includes systematic, qualitative, axiological, and cognitive-technological approaches. The set of methods included the analysis and generalization of theoretical research, measuring the value awareness of the use of AI technologies by future teachers - a survey of 200 education students (157 students from State University of Education , 43 students from Tambov State University named after G.R. Derzhavin University), as well as an analysis of the results of the participant observation and didactic experiment (self-assessment matrix, achievement tests, analysis of products and a reflective essay) conducted on the basis of Tambov State University named after G.R. Derzhavin (sample included 30 students majoring in Education).
Results. The study substantiates the essence of AI literacy of education students. Its ethical component is defined as mandatory for a teacher; the components of the ethical component of AI literacy, its cognitive, activity-based and value-reflective criteria and indicators are identified. Three levels of assessment (elementary, basic and creative) have been established, ensuring the measuring the effectiveness of education students’ value-ethical reflection as a condition for the formation of AI literacy. The features and principles nurturing the ethical component of AI literacy of a future teacher based on the value-based understanding of the use of AI technologies are revealed. The principle of integration of axiological and technological strategies is defined as the basis for the ethically conscious and responsible use of AI technologies in teaching. A measuring instrument for evaluating the ethical component of AI literacy has been developed. It is revealed that students demonstrate insufficient ethical component of AI literacy, manifested in the inability to distinguish between acceptable and unacceptable practices of using AI technologies, misunderstanding the consequences of unethical use of AI tools. The results of the study show the importance of awareness about the risks, mastering the skills and values of using AI technologies by a future teacher, and confirm the need for value-ethical reflection in the process of nurturing the AI literacy of future teachers by evaluating options for ethical navigation scenarios in AI technologies.
Conclusions. The contribution of the study is to clarify the structure of future teachers’ AI literacy. The transition from the role of an "AI user" to the role of a "teacher-researcher who knows, establishes and relies on ethical norms in the application of AI" requires the inclusion of value-ethical reflection as a regulated cognitive assessment in the educational process of future teachers (through university teachers’ guidance, self-assessment, and auto-didactics). A value-reflective assessment of the use of AI technologies is recommended for implementation in teacher education programs in the context of the digital transformation of education. The prospects are also related to the adaptation of diagnostic and didactic strategies for different degree programmes.
AI technologies; Conscious application of AI; AI literacy; Ethical component of AI literacy; Value-ethical reflection; Nurturing AI literacy; Future teacher
- Southworth J., Migliaccio K., Glover J., Glover J., Reed D., McCarty C., Brendemuhl J., Thomas A. Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 2023, vol. 4, pp. 100127. DOI: https://doi.org/10.1016/j.caeai.2023.100127
- Jin Y., Martinez-Maldonado R., Gašević D., Yan L. GLAT: The generative AI literacy assessment test. Computers and Education: Artificial Intelligence, 2025, vol. 9, pp. 100436. DOI: https://doi.org/10.1016/j.caeai.2025.100436
- Markus A., Carolus A., Wienrich C. Objective measurement of AI literacy: Development and validation of the AI competency objective scale (AICOS). Computers and Education: Artificial Intelligence, 2025, vol. 9, pp. 100485. DOI: https://doi.org/10.1016/j.caeai.2025.100485
- Ma M., Ng D. T. K., Liu Z., Wong G. K. W. Fostering responsible AI literacy: A systematic review of K-12 AI ethics education. Computers and Education: Artificial Intelligence, 2025, vol. 8, pp. 100422. DOI: https://doi.org/10.1016/j.caeai.2025.100422
- Su J., Zhong Y., Ng D. T. K. A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 2022, vol. 3, pp 100065. DOI: https://doi.org/10.1016/j.caeai.2022.100065
- Ilyushin L. S., Torpasheva N. A. Artificial intelligence technologies as a resource for transforming educational practices. Yaroslavl Pedagogical Bulletin, 2024, no. 3, pp. 62-71. (In Russian) URL: https://elibrary.ru/adwmmg
- Chopik O. A. Artificial intelligence as a factor in transforming the subjective position of students in higher education. Higher Education in Russia, 2025, no. 8-9. pp. 54-73. (In Russian) URL: https://elibrary.ru/gectxe DOI: https://doi.org/10.31992/0869-3617-2025-34-8-9-54-73
- Ivanova A. E., Tarasova K. V., Talov D. P. Between interest and skill: how students perceive and use AI. Higher Education in Russia, 2025, vol. 34 (8-9), pp. 9-32. (In Russian) URL: https://elibrary.ru/byqmrj DOI: https://doi.org/10.31992/0869-3617-2025-34-8-9-9-32
- Zemtsov D. I., Gruzdev I. A. “Digital centaur”: human-ai collaborative learning in universities. Higher Education in Russia, 2025, vol. 34 (10), pp. 47-62. (In Russian) URL: https://elibrary.ru/qczdyp DOI: https://doi.org/10.31992/0869-3617-2025-34-10-47-62
10.Dodson T. M., Thompson-Hairston K., Reed J. M. Nursing students' AI literacy and ethical understanding of AI in nursing education. Teaching and Learning in Nursing, 2025, vol. 20 (4), pp. 390-394. DOI: https://doi.org/10.1016/j.teln.2025.07.004
11.Fan Y., Tang L., Le H., Shen K., Tan S., Zhao Y., Shen Y., Li X., Gašević D. Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 2024, vol. 56 (2), pp. 489-530. DOI: https://doi.org/10.1111/bjet.13544
12.Michel-Villarreal R., Vilalta-Perdomo E., Salinas-Navarro D. E., Thierry-Aguilera R., Gerardou F. S. Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education Sciences, 2023, vol. 13 (9), pp. 856. DOI: https://doi.org/10.3390/educsci13090856
13.Lim E. M. The effects of pre-service early childhood teachers' digital literacy and self-efficacy on their perception of AI education for young children. Education and Information Technologies, 2023, vol. 28 (10), pp. 12969-12995. DOI: https://doi.org/10.1007/s10639-023-11724-6
14.Pei B., Lu J., Jing X. Empowering preservice teachers’ AI literacy: Current understanding, influential factors, and strategies for improvement. Computers and Education: Artificial Intelligence, 2025, vol. 8, pp. 100406. DOI: https://doi.org/10.1016/j.caeai.2025.100406
15.Ivakhnenko E. N., Nikolsky V. S. ChatGPT in higher education and science: A threat or a valuable resource? Higher Education in Russia, 2023, vol. 32 (4), pp. 9-22. (In Russian) URL: https://elibrary.ru/tzhihu DOI: https://doi.org/10.31992/0869-3617-2023-32-4-9-22
16.Tikhonova N. V., Pomortseva N. P. Final qualification paper in university in the context of artificial intelligence proliferation: University students’ perspective. Higher Education in Russia, 2025, vol. 34 (6). pp. 112-135. (In Russian) URL: https://elibrary.ru/oijjfw DOI: https://doi.org/10.31992/0869-3617-2025-34-6-112-135
17.Titova S. V., Chikrizova K. V. Design and implementation of ai-driven educational resources in higher education: A legal perspective. Higher Education in Russia, 2025, vol. 34 (6), pp. 91-111. (In Russian) URL: https://elibrary.ru/vznbea DOI: https://doi.org/10.31992/0869-3617-2025-34-6-91-111
18.Tomlinson E., Schoch M., Macfarlane S., Aryal S., Kumar F., Bunker N., McDonall J. A course-wide approach to building generative artificial intelligence literacy across an undergraduate nursing curriculum. Nurse Educator, 2025, vol. 50 (2), pp. 113-115. DOI: https://doi.org/10.1097/NNE.0000000000001803
19.Tikhonova N. V., Sabirova D. R. Teacher ai literacy: A theoretical conceptualisation. The Education and Science Journal, 2025, vol. 27 (6), pp. 180-206. (In Russian) URL: https://elibrary.ru/reymvt DOI: https://doi.org/10.17853/1994-5639-2025-6-180-206
20.Pinski M., Benlian A. AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects. Computers in Human Behavior: Artificial Humans, 2024, vol. 2 (1), pp. 100062. DOI: https://doi.org/10.1016/j.chbah.2024.100062
21.Yang T., Cheon J., Cho M. H., Huang M. undergraduate students’ perspectives of generative AI ethics. International Journal of Educational Technology in Higher Education, 2025, vol. 22 (1), pp. 35. DOI: https://doi.org/10.1186/s41239-025-00533-1
22.Lintner T. A systematic review of AI literacy scales. NPJ Science of Learning, 2024, vol. 9, pp. 50. DOI: https://doi.org/10.1038/s41539-024-00264-4
23.Anders A. D., Speltz E. D. Developing generative AI literacies through self-regulated learning: A human-centered approach. Computers and Education: Artificial Intelligence. 2025, vol. 9, pp. 100482. DOI: https://doi.org/10.1016/j.caeai.2025.100482
24.Aldemir T., Bicer A., Kilinc S., Moon J., Kwok M. Exploring emergent AI-TPACK competencies in a two-week AI literacy module for preservice teachers. Teaching and Teacher Education, 2025, vol. 168, pp. 105231. DOI: https://doi.org/10.1016/j.tate.2025.105231
25.Bozkurt A. Why generative AI literacy, why now and why it matters in the educational landscape? Kings, queens and GenAI dragons. Open Praxis, 2024, vol. 16 (3), pp. 283-290. DOI: https://doi.org/10.55982/openpraxis.16.3.739
26.Hwang Y., Lee J. H. Exploring students’ experiences and perceptions of human-AI collaboration in digital content making. International Journal of Educational Technology in Higher Education, 2025, vol. 22, pp. 44. DOI: https://doi.org/10.1186/s41239-025-00542-0
27.LaFlamme K. A. Scaffolding AI literacy: An instructional model for academic librarianship. Journal of Academic Librarianship, 2025, vol. 51 (3), pp. 103041. DOI: https://doi.org/10.1016/j.acalib.2025.103041
28.Sysoev P. V. А modern teacher's competence in the field of artificial intelligence: Structure and content. Higher Education in Russia, 2025, vol. 34 (6). pp. 58-79. (In Russian) URL: https://elibrary.ru/zjmqfd DOI: https://doi.org/10.31992/0869-3617-2025-34-6-58-79
29.Garashkina N. V., Druzhinina A. A. Cognitive engagement involvement as a basis for designing the educational process in the preparation of students of pedagogical directions. Higher Education in Russia, 2023, vol. 32 (1), pp. 93-109. (In Russian) URL: https://elibrary.ru/jdktod DOI: https://doi.org/10.31992/0869-3617-2023-32-1-93-109
30.Chiu T. K. F., Xia Q., Zhou X., Chai C. S., Cheng M. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 2023, vol. 4, pp. 100118. DOI: https://doi.org/10.1016/j.caeai.2022.100118
31.Wong M. Y. Beyond asking ‘should’ and ‘why’ questions: Contextualised questioning techniques for moral discussions in moral education classes. Journal of Moral Education, 2021, vol. 50 (3), pp. 368-383. DOI: https://doi.org/10.1080/03057240.2020.1713066
32.Basak T., Cerit B. Comparing two teaching methods on Nursing students ethical decision-making level. Clinical Simulation in Nursing, 2019, vol. 29, pp. 15-23. DOI: https://doi.org/10.1016/j.ecns.2019.02.003
