I am a senior majoring in Computer Science at UC Irvine and a researcher. My work asks why LLM-based systems fail and when their outputs can be trusted, applied to automated grading, feedback systems, and learning analytics. I aim to build AI that makes education more accessible, fair, and effective, at the intersection of artificial intelligence, learning science, and data-driven educational tools.
Vedant Khatri
B.S. Computer Science, University of California, Irvine, expected June 2027 · GPA 3.775
Researching AI in education: automated grading, feedback systems, and learning analytics.
khatriv1@uci.edu · GitHub · LinkedIn · Irvine, CA
About
Publications
Peer-reviewed conference proceedings
Xu, Z., Khatri, V., Dai, Y., Liu, X., Li, S., Zhang, X., & Yu, R. (2026). Enhancing LLM-Based Data Annotation with Error Decomposition. In Proceedings of LAK '26: 16th International Learning Analytics and Knowledge Conference (pp. 325–335). Best Paper Nominee. doi:10.1145/3785022.3785070
Khatri, V., Cusimano, A., Swiecki, Z., Xu, Z., Liu, X., & Yu, R. (2026). From Diagnosis to Redesign: Using Quantitative Ethnography to Improve Multi-Agent LLM Essay Scoring. Accepted as a full paper at the International Conference on Quantitative Ethnography (ICQE 2026). arXiv:2608.22566
Peer-reviewed journal articles
Khatri, V., Sinha, A., & Kumari, R. (2022). A study of the impact of e-learning on the health of school going adolescents in Bareilly city: A cross-sectional study. Indian Journal of Forensic and Community Medicine, 9(3), 112–116.
Sinha, A., Khatri, V., & Nath, B. (2022). Relevance of health education to e-learning-associated problems among the school-going adolescents in Bareilly city: An interventional study. Journal of Family Medicine and Primary Care, 11, 6863–6868.
In preparation
Large Language Models for Educational Data Annotation at Scale. Manuscript in preparation.
Research
Data Science Research Assistant
- Research on language models for educational data annotation, resulting in a paper accepted to LAK 2026.
- Ran LLM annotation experiments across four datasets (Bloom, MathDial, Uptake, and GUG), five prompting strategies, and two model versions.
- Contributed to the framework separating task-inherent ambiguity from model-based annotation error.
- Building an LLM pipeline that classifies course assignments into 12 types, refined over several prompt and codebook versions and evaluated against 300 human-labeled samples.
Research Collaborator
- Developed the codebook and qualitative coding scheme for analyzing an LLM-based clinical-reasoning tutoring system, working with Yann Hicke.
- Coded real tutor-learner transcripts, showing that coding the tutor's moves alone cannot separate effective from ineffective tutoring, a finding that reshaped the study's direction.
- Identified corpus-level data-quality issues that would have corrupted automated coding.
Research Collaborator
- Integrated PromptHive, which helps instructors generate questions through prompt engineering, with LLMRespondent, which estimates question difficulty using synthetic LLM respondents and Item Response Theory calibration, to build a difficulty filter for generated questions.
- Investigated why these difficulty estimates are unreliable: a single LLM produces a narrow proficiency distribution and can confidently misrate questions.
Undergraduate Researcher
- Received UROP (Undergraduate Research Opportunities Program) recognition for research on the effects of e-learning on students, presented at the UROP Symposium 2024.
Research Assistant
- Conducted cross-sectional and interventional studies examining adolescent health, including data collection and statistical analysis.
Teaching
Course Manager
- Run the operational side of one of UC Irvine's largest introductory computer science courses, with roughly 175 to 340 students per term.
- Train and coordinate 3–4 TAs and graders each term.
- Manage roster syncing, attendance sheets, retake-ticket eligibility and delivery, secure per-student credentials for in-lab exams, and grading for lab exams and zyBooks assignments.
- Operate and extend the course's Python automation pipeline (Canvas, zyBooks, and Google Sheets APIs), adding features on request such as lecture-attendance tracking and automated retake-ticket delivery.
Learning Assistant
- Supported the C++ portion of the introductory sequence while serving concurrently as Course Manager for ICS 31, with office hours, one-on-one debugging support, lab sections, and exam and assignment materials.
Grader
- Graded programming assignments, projects, and exams, and held office hours.
Learning Assistant
- Supported intermediate Python instruction, with weekly office hours, lab sections, and assignment and exam materials.
Learning Assistant
- Supported introductory Python instruction in the same course I later managed, with the same weekly office-hour and lab duties.
Service & Leadership
Co-authored a paper presented at the 16th International Learning Analytics and Knowledge Conference.
Conference volunteer, assisting with operations and attendee support.
Advocated for international students, addressing concerns and organizing transition support events.
Head Boy (2022–2023) and House Captain (2020–2022), leading career fairs, cultural events, mentorship programs, and academic workshops for 800+ participants.
Skills
Python, C++, SQL · Pandas, NumPy, scikit-learn, TensorFlow, Keras · Flask · Git/GitHub, Jupyter, OpenCV, Linux, Bash
Awards & Funding
SURF (Summer Undergraduate Research Fellowship), UC Irvine. A competitively funded summer research fellowship.
UROP (Undergraduate Research Opportunities Program) recognition, UC Irvine.
Dean's Honor List, UC Irvine.
LAK 2026 Best Paper Nominee.