Teacher-AI Interaction in the Selection of Target Texts

Even if the importance of feedback in educational training is widely recognized, delivering timely and effective feedback is not always sustainable, especially in educational settings. To address this challenge, this paper presents the use of an Artificial Agent in both the correction process and online feedback delivery. This approach aims to facilitate the delivery of recursive feedback, thus enhancing the overall learning process. The research design investigates three key phases of the assessment process: test preparation, test evaluation and analysis, and recursive feedback delivery. This paper focuses on the second phase, specifically how the artificial agent and the human agent interact in the correction of student assessments, and details the procedure used to select target texts. The evaluation procedure has been developed and tested. The trial was carried out in the 2023/24 academic year using the open answers submitted by 263 first-year students enrolled in the Master’s Degree course in Primary Education at the University of Macerata. The adopted methods allowed for reliable data on approximately 80% of the student submissions. Additionally, the system highlights which texts still require further evaluation and indicates the uncertainty of different blocks, identifying those with more reliable evaluations. Although there is still a long way to go and many developments are possible, the results obtained so far are promising for the adoption of models based on recursive interaction between artificial agent and human agent to make the widespread use of feedback in daily university practice more sustainable