Hybridizing Educational Assessment: a Theoretical Model from Judicial Science

Contemporary artificial intelligence (AI) systems are being integrated into decision-making processes across domains including legal systems and educational practices, combining automation with human oversight. This paper examines the potential for adapting hybrid judicial decision-making models to educational assessment, specifically student evaluation processes. The proposed model draws from judicial systems where algorithms generate initial judgment drafts that are subsequently reviewed by judges. Applied to education, this framework involves automated systems performing initial evaluations based on standardized data, with educators reviewing and refining results. This hybrid approach may offer immediate feedback, reduced educator workloads, enhanced objectivity, and personalized assessments while maintaining human authority for final decisions and potentially ensuring ethical outcomes.