MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123807 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy Vishwavidyapeeth; Malla Reddy University; Malla Reddy Engineering College For Women; and Malla Reddy College Of Engineering And Technology, Medchal-Malkajgiri, Telangana, on Dec. 9, 2025, for 'explainable ai interface for educational feedback systems.'
Inventor(s) include M Krishna Kanth; Mr Lakshmi Rahul Reddy; Mr. Thummapudi Venkata Seshu Kiran; Dr. M. Chalapathi Rao; Mr. Ayub Baig; and Kkoushil Reddy.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "Education is another field where the use of AI-driven solutions is getting more popular in order to assess student performance, track their learning, and provide personal feedback. Although they are effective in analyzing extensive amounts of data and determining learning patterns, the results that are delivered by most of them are hard to interpret by students and educators. The lack of transparency in the feedback also leads to the inability of the learners to comprehend why some specific suggestions are given, and the instructors to change teaching strategies in accordance with the output of AI. A explainable AI interface provides the solution to this gap as this interface renders feedback in a way combined with only clear and interpretable feedback and aligned with the teacherly adjustments. The framework interprets assignments, quizzes and behavioral patterns and performance indicators to create an insight into learning progress. The interface is able to present more than a shuttered score or an automated comment that leaves everyone, however, the structured account that supports the inferences that were drawn. This involves pointing out certain sections of the work of a student, illustrating line of reasoning, displaying performance trends, and providing practical suggestions that are related to learning objectives. Teachers get side-by-side explanations that reveal what drives student predicaments so that they can narrow down teaching and personalize interventions. It means that the interface modifies the explanation depending on the grade level, taught topic and concept complexity that is evaluated. In the long run, models will develop their interpretative abilities based on interaction information and teacher feedbacks. The explainable AI interface can foster greater learning, establish trust and foster more informed decision-making by students within educational settings due to its combination of transparency and personalized insights."
Disclaimer: Curated by HT Syndication.
