Papers with personalized learning
PathBuilder: A Quality-Controlled LLM System for Personalized Learning Pathways (2026.acl-demo)
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Jasper Meynard Arana, John Andrew Mañacop, John Allen Manacop, Roy Andrew Garcia, Keith Rick Piniera, Kristine Ann M. Carandang, Ethan Robert Casin, Christian Alis, Christopher Monterola
| Challenge: | Large language models (LLMs) enable scalable content generation for personalized learning, but reliability and pedagogical alignment remain open challenges. |
| Approach: | They propose a web-based system that integrates expert-validated assessment, retrieval-augmented generation (RAG), and an LLM-as-a-Judge validation loop within a closed instructional pipeline. |
| Outcome: | The proposed system achieves a gain of 37.9 percentage points and a large effect size in a real-world deployment with 179 registered users. |
FreeTalky: Don’t Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue (2022.lrec-1)
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| Challenge: | FreeTalky is a deep learning-based foreign language learning platform for people who experience anxiety dealing with foreign languages. |
| Approach: | They propose a deep learning-based foreign language learning platform called FreeTalky . it employs a humanoid robot NAO and various deep learning models . |
| Outcome: | The proposed system provides personalized learning based on persona dialogue and grammar error correction, and also helps alleviate xenoglossophobia by replacing the real human in the conversation with a NAO robot, through human evaluation. |
Few-shot Personalization of LLMs with Mis-aligned Responses (2025.naacl-long)
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| Challenge: | Existing approaches have limited successes in personalizing large language models due to the lack of personalized learning or the reliance on shared personal data. |
| Approach: | They propose a few-shot personalization of large language models with mis-aligned responses using LLMs by learning a set of personalized prompts for each user based on user profile and examples of previous opinions. |
| Outcome: | The proposed method significantly improves performance across benchmarks compared to best-performing baselines. |
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation (2023.emnlp-main)
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| Challenge: | Existing approaches for distilling large language models into smaller, more efficient student models are based on educational science principles such as knowledge tracing and personalized learning. |
| Approach: | They propose a method for distilling large language models into smaller, more efficient student models that are aligned with educational science principles such as knowledge tracing and personalized learning. |
| Outcome: | The proposed approach outperforms LLMs on three benchmarks while employing significantly fewer parameters. |
Tracing Mathematical Proficiency Through Problem-Solving Processes (2026.findings-acl)
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| Challenge: | Knowledge Tracing (KT) models a learner's evolving knowledge state over time, but lacks the rich information embedded in students' problem-solving processes. |
| Approach: | They propose a framework that uses a teacher-student-teacher pipeline to extract students’ Mathematical Proficiency (MP) as intermediate representation. |
| Outcome: | The proposed framework improves the prediction performance of existing KT methods and provides interpretable explanations by explicitly modeling students’ mathematical proficiency. |