In conclusion, this paper proposes a system to improve the evaluation of learning in higher education and evaluate it through artificial intelligence. The teaching device provides real-time feedback, improves evaluation evaluation efficiency, and enables human–computer interaction, and it is computed with artificial intelligence and natural language processing. It first gathered quantitative and qualitative data, as well as evaluation data, from educators and learners to measure its efficacy compared to traditional evaluation. The results of the experimental studies show that AI-supported assessments are more precise, offer more relevant feedback, are faster, and present more stimulating for learners when done by manual grading. Additionally, case studies conducted in Georgia State University and Coursera have shown that it is impossible for the current level examples of manual grading feedback to produce the same results and benefits as AI-driven feedback tools. Therefore, automated evaluation can also guarantee teachers have more time to encourage student motivation and self-regulated learning, hence altering the pedagogical procedures of digital knowledge.