BEYOND THE ALGORITHM: EXPLORING STUDENT ACCEPTANCE OF AI GRADING THROUGH AN ADAPTED TAM FRAMEWORK

Artificial Intelligence (AI) has become increasingly prevalent in education, from completing student assignments to analyzing research and grading tasks using large language models (LLMs).AI-assisted grading systems introduce new complexities regarding trust, fairness, and learning outcomes.This study presents findings from an experiment in a business course.Students participated in an assignment evaluated under both traditional instructor and AI-supported grading.Survey data assessed perceived fairness, satisfaction, and trust.Results revealed that students in the AI + human oversight condition rated fairness higher and exhibited greater trust in the system.This led to the development of the Technology Acceptance Model for AI Grading (TAM-AIG), which includes constructs such as perceived fairness, transparency, trust, and human oversight.These findings inform the ethical and practical implementation of AI in assessment practices.