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Open AccessArticle
A Conceptual Model of Hybrid Intelligent Assessment Systems for Higher Education
by
Slavko Rakic
Slavko Rakic 1,2,*
,
Janika Leoste
Janika Leoste 1,3
,
Einar Kivisalu
Einar Kivisalu 1,2,
Jaanus Pöial
Jaanus Pöial 1,2
and
Voldemar Tomusk
Voldemar Tomusk 1,2
1
IT College, Tallinn University of Technology, 19086 Tallinn, Estonia
2
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
3
School of Educational Sciences, Tallinn University, 10120 Tallinn, Estonia
*
Author to whom correspondence should be addressed.
Educ. Sci. 2026, 16(7), 1109; https://doi.org/10.3390/educsci16071109 (registering DOI)
Submission received: 1 May 2026
/
Revised: 17 June 2026
/
Accepted: 9 July 2026
/
Published: 10 July 2026
Abstract
Rapidly expanding use of Generative Artificial Intelligence (GenAI) in higher education creates both opportunities and challenges for learning assessment. While GenAI can provide adaptive feedback and personalization, its pedagogical integration remains underdeveloped and often disconnected from established theories of learning and participatory design processes. This paper addresses this gap by proposing an integrative conceptual model of Hybrid Intelligent Assessment Systems (HIAS), which combines AI capabilities with human oversight to enable transparent, ethical, and pedagogically aligned assessment. HIAS is structured through three interdependent layers of adoption: a pedagogical layer, aligning AI-supported assessment with self-regulated learning and the development of knowledge, skills, and attitudes; a governance layer, ensuring transparency, fairness, and human-in-the-loop validation; and a technological layer, enabling scalable integration within digital learning environments. The study is situated in Estonia, a digitally advanced context with system-level AI integration through the national AI Leap initiative. To complement the conceptual model, an empirical study was conducted across three major Estonian universities, involving 66 professors and researchers and 153 students. In addition, a small-scale pilot implementation was conducted in a design thinking course to explore the practical feasibility of a course-specific HIAS-based AI assistant. The findings reveal a consistent pattern: while both groups demonstrate a broadly positive orientation toward AI, students approach AI primarily as an efficiency-driven learning tool, whereas academic staff emphasize pedagogical control, ethical considerations, and responsible use. Across both groups, AI literacy remains uneven, particularly in critical evaluation and structured application. These findings expose a critical gap between rapid AI adoption and insufficient pedagogical integration. In response, HIAS is proposed as a structured, human-centered framework that supports teachers in designing AI-enhanced learning environments and students in developing critical, self-regulated, and responsible use of AI.
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MDPI and ACS Style
Rakic, S.; Leoste, J.; Kivisalu, E.; Pöial, J.; Tomusk, V.
A Conceptual Model of Hybrid Intelligent Assessment Systems for Higher Education. Educ. Sci. 2026, 16, 1109.
https://doi.org/10.3390/educsci16071109
AMA Style
Rakic S, Leoste J, Kivisalu E, Pöial J, Tomusk V.
A Conceptual Model of Hybrid Intelligent Assessment Systems for Higher Education. Education Sciences. 2026; 16(7):1109.
https://doi.org/10.3390/educsci16071109
Chicago/Turabian Style
Rakic, Slavko, Janika Leoste, Einar Kivisalu, Jaanus Pöial, and Voldemar Tomusk.
2026. "A Conceptual Model of Hybrid Intelligent Assessment Systems for Higher Education" Education Sciences 16, no. 7: 1109.
https://doi.org/10.3390/educsci16071109
APA Style
Rakic, S., Leoste, J., Kivisalu, E., Pöial, J., & Tomusk, V.
(2026). A Conceptual Model of Hybrid Intelligent Assessment Systems for Higher Education. Education Sciences, 16(7), 1109.
https://doi.org/10.3390/educsci16071109
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