Zhilin Zhang


About Me

Human-Computer Interaction; Social Computing; Human-Centered AI; Learning Technologies

I am a 5-year BS-MS student in Computer Science at the University of Illinois at Urbana-Champaign (UIUC), co-advised by Professor Lawrence Angrave and Professor Karrie Karahalios.

My research interests are in Human-Computer Interaction and Education, with a focus on studying how people interact with complex algorithmic systems, investigating societal issues such as misinformation, bias, privacy concerns caused by AI, and designing new technologies and mechanisms to address these problems. I am motivated to provide people with a more informed, fair, secure, accessible, satisfying, and engaging experience when interacting with intelligent systems.

Please feel free to contact me at: zhilinz2@illinois.edu

Latest Updates

  • [12/2020]: A NEW PAPER ACCEPTED AT CHI 2021
    Our paper "Attitudes Surrounding an Imperfect AI Autograder" was accepted by CHI 2021. Congratulations to all my amazing collaborators!

    Our ASEE 2020 paper "Improving Student Accessibility, Equity, Course Performance, and Lab Skills: How Introduction of ClassTranscribe is Changing Engineering Education" has won the "Best Diversity, Equity & Inclusion Paper Honorable Mention". Congratulations to all my wonderful co-authors!

    I started to work as a Teaching Assistant for CS410: Text Information Systems (Text Retrieval and Mining), with Professor ChengXiang Zhai.

    I gave a lightning talk "ClassTranscribe: Addressing the COVID Challenge and Promoting Better Equity in Education" at CRA 2020.

  • [05/2020]: UPGRADED FROM BS TO MS
    I finished my BS (with thesis) in Computer Science at UIUC. I will continue to spend one year on MS (with thesis) in Computer Science at UIUC (expected graduation: 08/2021).




ClassTranscribe is a Microsoft sponsored e-learning platform. I analyzed behavioral data of 1,895 users totaling more than five million interactions with the system. We published the results at SIGCSE and ASEE. My on-going research is to further investigate students’ attitudes and behaviors towards searching and learner sourcing.

AI Autograder
AI Autograder

I used a grounded-theory approach to code the qualitative data from a 22-participant interview study. The results provided insights into students’ folk theories, interaction strategies, perceived accuracy, satisfaction and fairness towards the AI grading system. The paper has been accepted by CHI 2021.


I led the design and implementation of a 20-participant user study on an integrated friend-sourcing tool that helps visually impaired people with inaccessible web tasks. The user study investigated users’ attitudes towards the tool, regarding friend-sourcing vs. crowd-sourcing, paid vs unpaid, privacy concerns, and impacts on their relationship.

On-Device Interaction Mining
On-Device Interaction Mining

I designed and developed an Android app using the Android Accessibility Service to automatically record users’ interactions with other apps on the phone. The logged data include gestures, screenshots, and view hierarchies. We are using the data to mine user habits and generate task shortcuts.

Privacy Probe
Privacy Probe

In this study, I used the ZIPT system to collect and analyze data from 20 participants on their opinions regarding privacy concerns over popular mobile apps (Facebook, Amazon, Chase, etc.) The results shed light on mobile users’ privacy concerns beyond their personally identifiable information (PII).

Ninja World
Ninja World

A VR game on Oculus, created with Unity. I implemented several attacking modes of the Ninja. I also ran user studies and enhanced the user experience on decreasing dizziness, improving prompts and controlling gestures, and enriching the experience of the boss fight.



(* = Equal Contribution)

(On students’ searching behaviors in online video-based learning)

In Submission at ASEE 2021

Zhilin Zhang*, Bhavya*, Ruihua Sui, Chirantan Mahipal, Yun Huang, Rob Kooper, Lawrence Angrave

(On students’ attitudes and behaviors towards a UDL-based e-learning tool)

In Submission at SIGCSE 2021

Zhilin Zhang, Lawrence Angrave, Patrick Lin, Ian Ludden

(On surveying the needs of students with disabilities in engineering courses)

In Submission at ASEE 2021

Zhilin Zhang*, Kusum Vanwani*, Jenny Amos*, Lawrence Angrave*, Hongye Liu*

(On a friend-sourcing approach to aid people with visual impairments)

In Submission at CSCW 2021

Zhuohao Zhang, Zhilin Zhang, Haolin Yuan, Natã Barbosa, Sauvik Das, Yang Wang

Attitudes Surrounding an Imperfect AI Autograder

CHI 2021 - Full Paper [pdf]

Silas Hsu*, Tiffany Wenting Li*, Zhilin Zhang, Max Fowler, Craig Zilles, Karrie Karahalios

Improving Student Accessibility, Equity, Course Performance, and Lab Skills: How Introduction of ClassTranscribe is Changing Engineering Education

Lawrence Angrave, Karin Jensen, Zhilin Zhang, Chirantan Mahipal, David Mussulman, Christopher Schmitz, Robert Baird, Hongye Liu, Ruihua Sui, Maryalice Wu, and Rob Kooper

Who Benefits? Positive Learner Outcomes from Behavioral Analytics of Online Lecture Video Viewing using ClassTranscribe

SIGCSE 2020 - Full Paper [pdf]

Lawrence Angrave, Zhilin Zhang, Genevieve Henricks-Lepp, Chirantan Mahipal

What Benefits? Exploring the Influences of Student Behaviors in an Online Video Class

UIUC - Undergraduate Senior Thesis

Zhilin Zhang

ClassTranscribe: Addressing the COVID Challenge and Promoting Better Equity in Education

CRA 2020 - Lightning Talk [video]

Zhilin Zhang, Lawrence Angrave


Honors & Awards

Best Diversity, Equity & Inclusion Paper Honorable Mention

American Society for Engineering Education


the National Engineering Honor Society

Dean’s List

Grainger College of Engineering, UIUC


Contact Me

I am now actively applying for Ph.D. programs. Please feel free to contact me if you are interested in knowing more about me!