AI-powered teaching assistants: Enhancing educator efficiency with NLP-based automated feedback systems

Chandrakant, Soni Maitrik (2025) AI-powered teaching assistants: Enhancing educator efficiency with NLP-based automated feedback systems. International Journal of Science and Research Archive, 14 (3). 009-018. ISSN 2582-8185

Further demands in education grading and feedback delivery have led to the development of AI teaching assistants using Natural Language Processing (NLP) systems. Automated systems support grading efficiency through analysis of student work, which provides instant, consistent, and useful feedback to students. AI evaluation software supports educators to manage workload more effectively while preserving high assessment standards. Evaluating written responses with NLP tools enables teachers to examine grammatical elements, structural organization, and content organization to achieve better student comprehension. Student learning performance and outcomes improve because AI-powered teaching assistants supply customized feedback that aligns with students' personalized learning requirements. This research investigates NLP-based grading technologies by examining their benefits, constraints,nts, and conceivably ethical issues. Educational institutions use these tools in their facilities to generate comprehensive assessments regarding their impact on instructor workload and student performance alongside student evaluation processes and faculty members. The gathered data indicates that artificial intelligence grading tools improve conventional assessment methods through their flexible and efficient grading systems. An uncorrected understanding of the limitations and biased behavior of NLP models continues to represent the main issues in the field.