Rimal, B., & Ballabh, S., & Telang, N. K. (2025, June), WIP: AI-Driven Personalized Learning for an Introductory Computing Course  Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2--57370

\bibitem{asee_peer_57370}
  Rimal, B., \& Ballabh, S., \& Telang, N. K. (2025, June), \emph{WIP: AI-Driven Personalized Learning for an Introductory Computing Course}
  Paper presented at 2025 ASEE Annual Conference \& Exposition , Montreal, Quebec, Canada .
  10.18260/1-2--57370

Bishweshwor Rimal, Sneha Ballabh, and Nina Kamath Telang.  "WIP: AI-Driven Personalized Learning for an Introductory Computing Course".  2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada , 2025, June.  ASEE Conferences, 2025.  https://peer.asee.org/57370 Internet.  23 Aug, 2025

\bibitem{asee_peer_57370}
  Bishweshwor Rimal, Sneha Ballabh, and Nina Kamath Telang.
  "WIP: AI-Driven Personalized Learning for an Introductory Computing Course".
  \emph{2025 ASEE Annual Conference \& Exposition , Montreal, Quebec, Canada , 2025, June}.
  ASEE Conferences, 2025.
  https://peer.asee.org/57370 Internet.  23 Aug, 2025

@INPROCEEDINGS{asee_peer_57370
author = "Bishweshwor Rimal, Sneha Ballabh, and Nina Kamath Telang"
title = "WIP: AI-Driven Personalized Learning for an Introductory Computing Course"
booktitle = "2025 ASEE Annual Conference \& Exposition "
year = "2025"
month = "June"
address = "Montreal, Quebec, Canada "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/57370}
number = {10.18260/1-2--57370}
}

TY  - CPAPER
AB  - In this work in progress paper, we present the design and implementation of AI-driven applications that create personalized learning experiences for first-year engineering students in an introductory computing course. This foundational course is crucial for Computer Engineering students, covering essential topics from computer system fundamentals to assembly language programming. While there are no prerequisites, students with some prior computer programming experience tend to be better prepared. To address this disparity, we have developed a range of comprehensive resources, including notes, solved problems, videos, and sample programs, which are beneficial for all students. However, to further enhance student mastery, we propose the use of self-assessment and personalized tutoring.
Our project leverages generative AI to develop customized question banks based on course materials within the Canvas learning management system. This approach provides a personalized learning experience tailored to each student’s needs. The key innovation is to ensure that the generated questions are strictly aligned with the course content, avoiding the use of external internet sources to maintain relevance and accuracy.
Genux, an innovative startup founded by one of our co-authors, has developed 'agentic apps,' which are AI-driven applications that generate context-aware interactive graphical user interfaces (GUIs) for user interaction. These apps use artificial intelligence to create user-friendly interfaces, featuring dynamic question formats, tables of contents, and suggestions for additional questions.
Our project has yielded two advanced tools: Textbook and Modules. Textbook serves as an AI-powered book expert, allowing students to ask questions and receive detailed explanations about the course's textbook content, making the learning process more interactive and personalized. Modules, which are provided to students weekly through our learning management system, offer interactive questions and exercises based on textbook chapters, reinforcing understanding and retention. Both tools are built on the Genux platform, enabling the creation of dynamic user interfaces generated by AI agents.
In this paper, we discuss the structure of this AI-driven tool in a required first-year computing course, the level of usage and reported usefulness by students, and the impact of this tool on student performance in this course.


AU  - Bishweshwor Rimal
AU  - Sneha Ballabh
AU  - Nina Kamath Telang
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - WIP: AI-Driven Personalized Learning for an Introductory Computing Course
UR  - https://peer.asee.org/57370
DO  - 10.18260/1-2--57370
ER  -