View Computer Science directory

Faculty Member

Eshita Zaman

Biography

Eshita Zaman earned her Ph.D. in Computer Science from Iowa State University. After graduation, she began her career as a Lecturer in the Computer Science department at Utah Valley University (UVU). Her research focuses on model checking and verification, particularly in probabilistic model checking and the verification of probabilistic hyperproperties. She completed her Master's degree in Computer Science from North Dakota State University and her Bachelor's degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology.

Teaching

CS 1420

Accelerated Introduction to Programming, Fall 2025

CS 1400

Fundamentals of Programming, Fall 2025

CS 1410

Object Oriented Programming, Fall 2025

CS 1400

Fundamentals of Programming, Summer 2025

CS 1420

Accelerated Introduction to Programming, Spring 2025

CS 1400

Fundamentals of Programming, Spring 2025

CS 1400

Fundamentals of Programming, Spring 2025

CS 1410

Object Oriented Programming, Spring 2025

CS 1410

Object Oriented Programming, Spring 2025

Presentations

Zaman, Eshita (Presenter & Author), Ratul, Qudrat E Alahy (Co-Author), Das, Saikat (Co-Author), Memari, Majid (Co-Author), i-ETC, "The Role of AI in Transforming Education_ A Systematic Review of Trends", Array, UVU, Orem, UT. (May 9, 2025)

Scholarly/Creative Works

Ratul, Qudrat E Alahy , Zaman, Eshita , Das, Saikat , Memari, Majid , (2025) "The Role of AI in Transforming Education_ A Systematic Review of Trends" . IEEE. https://doi.org/10.1109/ietc64455.2025.11039329
Zaman, Eshita , Ciardo, Gianfranco , Ábrahám, Erika , Bonakdarpour, Borzoo , (2022) "HyperPCTL Model Checking by Probabilistic Decomposition" . Springer International Publishing. https://doi.org/10.1007/978-3-031-07727-2_12

Awards

CET One time fund for student project

CET, UVU - September 25, 2025

I propose to develop an AI-assisted autograding agent for one of the core CS courses, CS 1410_ Object-Oriented Programming. The system will run student programs in a secure sandbox, evaluate them against instructor-designed tests (unit, hidden stress, mutation), and transform failures into concise, human-readable hints—designed to guide rather than spoil. Through Canvas integration, students will receive both grades and feedback within minutes, enabling rapid iteration while concepts remain fresh. The immediate beneficiaries are 200–250 students each semester, many of whom are first-generation learners who may hesitate to seek help in office hours but respond positively to timely, low-stakes feedback.