A No-Code Solution for Streamlining Student Feedback and Assessment Processes

Abstract

This study investigates the efficiency of AI grading compared to traditional human grading methods in educational assessment processes. Utilizing the Baccalytics plat form as a case study, the research examines the impact of AI-driven grading on grading time, accuracy, and overall workflow automation. The study employs a controlled ex perimental design, with human graders constituting the control group and the Bacca lytics AI test grader representing the experimental group. Results indicate a significant improvement in grading efficiency with the AI grader, as evidenced by substantially lower grading times and comparable accuracy to human graders. Furthermore, cor relations between various variables, such as character count and grading time, shed light on the factors influencing grading processes. Despite the promising findings, limitations are acknowledged, including teacher awareness of timing and the need for ongoing training and support. Recommendations for future research include explor ing Baccalytics’s scalability, addressing equity gaps in access to personalized learning experiences, and investigating its impact on student-teacher interactions and feedback processes. The study contributes to understanding the potential of AI-driven grading systems in enhancing educational practices and improving learning outcomes.