Keywords
backward design
authentic assessment
extended assessment
ASUI approach
academic integrity
How to Cite
Abstract
The rapid development of generative artificial intelligence causes the fundamental paradigm shift of evaluation in the educational process. This calls into question the validity and objectivity of traditional methods of testing knowledge. This research analyzes the adaptation of the classical backward design methodology by G. Wiggins and D. McTighe as a strategic tool for redesigning academic disciplines in response to the challenges of digitalization. Transitioning from content-oriented teaching to outcome-centered design is advisable, based on the prevalence of forming "enduring understandings" and complex competencies that cannot be imitated through reproductive knowledge or algorithmic responses. Particular attention is paid to the synergy of understanding and self-knowledge (explanation, interpretation, application, perspective, empathy) in the knowledge control system. This allows us to differentiate human intelligence from the cognitive patterns of large language models. It is proposed utilizing three innovative assessment strategies: the introduction of "stepped" tasks to visualize the architecture of thinking, the use of authentic assessment in real professional scenarios, and the concept of extended or augmented assessment, where artificial intelligence acts as an operated cognitive partner. Practical cases of transition to methods of "logic defense," simulation training, and assessment of the process instead of the result are proposed based on a comparative analysis of international practices and the latest Recommendations of the Ministry of Education and Science of Ukraine (2025). These cases were demonstrated using examples from STEM disciplines, humanities, medicine, and law. The necessity of abandoning the concept of "punitive detection" in favor of a strategy of "transparent use" of technologies is crucial. For shaping tasks an approach called ASUI (Application, Situation, Uniqueness, Interaction) is proposed. The successful adaptation of higher education depends on the formation of a new culture of academic integrity. AI literacy is becoming an integral part of the professional training of teachers and students. Pedagogical design has to focus on the development of critical thinking, the ability to verify information, and the ethical use of innovative tools.
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East Carolina University. (n. d.). Backwards design. Teaching Toolkit. https://teachingtoolkit.ecu.edu/backwards-design/ (in English).
Goff, L., & Dennehy, T. (2025, October 24). Case studies in learning and teaching. Toolkit for the Ethical Use of GenAI in Learning and Teaching. University College Cork. https://www.ucc.ie/en/ethical-use-of-generative-ai-toolkit/case-studies-in-learning-and-teaching/ (in English).
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Lorteau, S., & Sarro, D. (2025). Artificial intelligence in legal education: A scoping review. The Law Teacher. https://doi.org/10.2139/ssrn.5762982 (in English).
McTighe, J. (2023). AI and understanding by design. McTighe & Associates Consulting. https://jaymctighe.com/ai-and-ubd/ (in English).
Melliti, M. (2024). Using genre analysis to detect AI-generated academic texts. Diá-logos, 16(29), 9–27. https://doi.org/10.61604/dl.v16i29.377 (in English).
Michigan State University. (2025). Guidelines for the use of generative artificial intelligence (Generative AI) tools. https://ai.msu.edu/guidelines (in English).
Millin, T., Millin, M., & Pearce, J. (2025). Asynchronous video-based scaffolding of English academic writing skills for distance tertiary students: An innovative approach to supporting postgraduate students in hybrid courses. Video Journal of Education and Pedagogy, 10(1). https://doi.org/10.1163/23644583-bja10062 (in English).
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Reihanian, I., Hou, Y., & Sun, Q. (2026). From pilots to practices: A scoping review of GenAI-enabled personalization in computer science education. AI, 7(1), 6. https://doi.org/10.3390/ai7010006 (in English).
Ritholz, E. (2025). Introducing the AI-Teaching Assistant (AITA) framework. Educational Technology, Hostos Community College. https://commons.hostos.cuny.edu/edtech/ai/aita/ (in English).
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Sangwa, S., Mutabazi, P., & Muvunyi, J. B. (2025). AI-enabled framework for program and course design in higher education [v1]. Preprints. https://doi.org/10.20944/preprints202512.0682.v1 (in English).
Shaban, S., & Magzoub, M. E. (2025). Artificial Intelligence in medical education and assessment: The next step in the IT Revolution. F1000Research, 14, 1360. https://f1000research.com/articles/14-1360 (in English).
Stapleton-Corcoran, E. (2023). Backward design. Center for the Advancement of Teaching Excellence, University of Illinois Chicago. https://teaching.uic.edu/cate-teaching-guides/syllabus-course-design/backward-design/ (in English).
Tao, Z., Werry, I. P., Zeng, Z., & Miao, Y. (2024). The role and value of generative AI in medical education and training. International Journal of Information Technology (IntJIT), 29(1), 1–12. https://intjit.org/cms/journal/volume/29/1/291_4.pdf (in English).
University College Cork. (2025). Case studies in learning and teaching. Toolkit for the Ethical Use of GenAI in Learning and Teaching. https://www.ucc.ie/en/ethical-use-of-generative-ai-toolkit/case-studies-in-learning-and-teaching/ (in English).
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University of Toronto. (n. d.). Backward design. Digital Learning Innovation. https://onlinelearning.utoronto.ca/backward-design/ (in English).
Utomo, P. S., & Yan, J. (2025). The use of GenAI in medical education in China and Indonesia: A comparative literature review. Education for Health, (38). https://doi.org/10.62694/efh.2025.404 (in English).
Wang, H., Dang, A., Wu, Z., & Mac, S. (2024). Generative AI in higher education: Seeing ChatGPT through universities’ policies, resources, and guidelines. Computers and Education: Artificial Intelligence, (7), 100326. https://doi.org/10.1016/j.caeai.2024.100326 (in English).
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Wiggins, G., & McTighe, J. (2005). Understanding by design (2nd Ed.). Association for Supervision and Curriculum Development (ASCD) (in English).
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