13.00.00 - Педагогические науки
GENERATIVE ARTIFICIAL INTELLIGENCE IN FORMATIVE ASSESSMENT: TRANSFORMING FEEDBACK, STUDENT AGENCY, AND LEARNING OUTCOMES
- Том 10 № 31 (2026): Social Science and Innovation — 2026-yil oktabr soni
- 2026-11-01
- Открытый доступ
Ключевые слова:
generative artificial intelligence; formative assessment; feedback literacy; student agency; assessment validity; pedagogical designАннотация
Generative artificial intelligence offers rapid feedback, rubric drafting, and explanatory dialogue, yet speed is not equivalent to formative quality. This conceptual review integrates established formative assessment theory with contemporary guidance on generative AI to propose a Feedback–Interpretation–Action (FIA) cycle for classroom use. The framework distinguishes feedback accuracy, students' interpretation of comments, and demonstrable revision or transfer. It specifies a three-layer checking procedure—disciplinary verification, pedagogical calibration, and fairness review—and offers an illustrative protocol for written assignments. The article argues that teachers should use AI to expand opportunities for formative dialogue while retaining final judgment over consequential assessment. Because no primary student data were collected, the paper presents propositions and a research agenda rather than measured effects. Its main contribution is an auditable instructional design that evaluates whether AI feedback actually enables learners to close a learning gap.
Библиографические ссылки
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102
Sadler, D. R. (1989). Formative assessment and instructional systems. Instructional Science, 18, 119–144. https://doi.org/10.1007/BF00117714
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
Saranya, E., Sethuraman, P., Olimjonov, H., Nagendram, S., Ganesh, M., & Sapaev, I. (2026, August). Deep Reinforcement Learning for Cloud Resource Allocation and Energy-Efficient Data Center Operations. In 2026 International Conference on Next-Gen Intelligent Computing and Applications (ICONICA) (Vol. 1, pp. 1-11). IEEE.
Kannan, N., Kollinal, R. K., Olimjonov, H., Ahammad, S. H., Kanagaraju, M., & Sapaev, I. (2026, August). Intelligent Ensemble Learning for Climate-Aware Agricultural Planning and Resource Allocation. In 2026 International Conference on Next-Gen Intelligent Computing and Applications (ICONICA) (Vol. 1, pp. 1-10). IEEE.
Guru, K. V., Rajan, C., Olimjonov, H., Lakshmi, N. V., Joseph, A. V., & Nazarova, S. (2026, August). Capsule Network-Based Historical Document Digitization for Intelligent Knowledge Organization and Digital Archiving. In 2026 International Conference on Next-Gen Intelligent Computing and Applications (ICONICA) (Vol. 1, pp. 1-9). IEEE.
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