activity
20162026
most citedPersonalized Education in the AI Era: What to Expect Next?

335 citations · 428 across the 63 of their papers we have counts for

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13 papers · 1 filter

cs.LG2026

CATTO: Balancing Preferences and Confidence in Language Models

Nisarg Parikh, Ananya Sai, Pannaga Shivaswamy +2

Large language models (LLMs) often make accurate next token predictions but their confidence in these predictions can be poorly calibrated: high-confidence predictions are frequent…

cs.LG2026

KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks

Zhangqi Duan, Nigel Fernandez, Andrew Lan

Open-ended tasks, such as coding problems that are common in computer science education, provide detailed insights into student knowledge. However, training large language models (…

cs.LG2025

Pattern-based Knowledge Component Extraction from Student Code Using Representation Learning

Muntasir Hoq, Griffin Pitts, Tirth Bhatt +4

Personalized instruction aims to provide learners with support that adapts to their individual knowledge and progress toward learning objectives. Discovering and tracing Knowledge…

cs.LG2025

LookAlike: Consistent Distractor Generation in Math MCQs

Nisarg Parikh, Nigel Fernandez, Alexander Scarlatos +2

Large language models (LLMs) are increasingly used to generate distractors for multiple-choice questions (MCQs), especially in domains like math education. However, existing approa…

cs.LG2023

A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing

Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee +3

In this paper, we take a preliminary step towards solving the problem of causal discovery in knowledge tracing, i.e., finding the underlying causal relationship among different ski…

cs.LG20224 cited

Process-BERT: A Framework for Representation Learning on Educational Process Data

Alexander Scarlatos, Christopher Brinton, Andrew Lan

Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn.…