🏷️ open-source
16 pages tagged with open-source(15 articles, 1 concepts)
📄 OATutor: An Open-source Adaptive Tutoring System and Curated Content Library for Learning Sciences Research
> OATutor (Open Adaptive Tutor) is the first open-source adaptive tutoring system built on Intelligent Tutoring System (ITS) principles, developed at UC Berkeley's CAHL Lab. It combines an MIT-license…
📄 Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework
> **An open, executable module library for engineering-grounded AI (EGAI) in power systems education lowers the entry barrier for newcomers, with a progressive difficulty ladder from DNN templates to …
📄 IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems
Presents a 24,795-example multilingual instruction dataset for teaching LLMs to deliver educational content grounded in Indian Knowledge Systems. Spans seven languages and bridges a gap in non-Western…
📄 Agentic AI in Education: A Scoping Review of Research Landscape, Capabilities, and the Frontier Agent Paradigm
Published in *Computers and Education: Artificial Intelligence*, accepted 27 July 2026. 📄 doi:10.1016/j.caeai.2026.100653…
📄 MathBuddy: Affective Math Tutoring
> **MathBuddy: Affective Math Tutoring** — EMNLP 2025 Demo. An emotionally aware LLM-powered mathematics tutor that dynamically models student emotions from both conversational text and facial express…
2026-07-29 · math-education, affective-computing, intelligent-tutoring, k-12, pedagogical-llm-training
📄 EduQwen: Pedagogical RL
> **EduQwen: Pedagogical RL** — A multi-stage optimization strategy combining reinforcement learning (DAPO) and supervised fine-tuning (SFT) to enhance the pedagogical knowledge of open-source LLMs, p…
📄 MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education
MedGame transforms static clinical cases into structured, executable storytelling games for medical education, moving beyond the localized question-answering and single-turn feedback that characterize…
📄 Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy
Multimodal large language models (MLLMs) are increasingly used to interpret visualizations, yet most evaluations remain chart-centric and offer limited insight into **scientific visualization (SciVis)…
📄 AiAWE: An Open-Source LLM Automated Writing Evaluation System Using LoRA-Adapted Instruction-Tuned Models
Gayed presents **AiAWE**, an open-source [[automated-grading|automated writing evaluation]] (AWE) system that scores argumentative essays using a LoRA-adapted instruction-tuned [[llm|large language mo…
📄 The Environmental Cost of LLMs in AIED: Reporting and Practices
> **Sabrina C. Eimler, Lukas Erle, Daniel Flood, Aditi Haiman, Luca Häckert, André Helgert, Lachlan McGinness, Büsra Yapici**…
🏷️ DOT Framework Survey: Practitioner Beliefs and Behaviors in AI-Enhanced Education
A 2026 cross-sectional survey (n=72) by Gibson, Azukas, and Knezek examined how higher education practitioners think about and use AI in teaching, grounded in the **DOT Framework** — a synthesis of [[…
📄 StanBKT: Rethinking Parameter Estimation in Bayesian Knowledge Tracing
StanBKT introduces an open-source Python package for Bayesian Knowledge Tracing (BKT) that moves beyond traditional expectation-maximization (EM) point estimates to full Bayesian inference via Stan. T…
📄 What Makes Words Hard? Sakura at BEA 2026 Shared Task on Vocabulary Difficulty Prediction
🔗 [Code](https://github.com/adno/vocabulary-difficulty) This paper presents two complementary approaches to predicting vocabulary difficulty for language learners, achieving state-of-the-art results …
📄 LaTA: A Drop-in, FERPA-Compliant Local-LLM Autograder for Upper-Division STEM Coursework
> LaTA: A Drop-in, FERPA-Compliant Local-LLM Autograder for Upper-Division STEM Coursework **Rodríguez (2026)** — Oregon State University. Submitted to Computers & Education.…
📄 AcademiClaw: When Students Set Challenges for AI Agents
> **Yu, Lu, Si et al. (77 authors, 2026)** — Shanghai Jiao Tong University, SII, GAIR. Open-source benchmark.…
📄 Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation
> Schleifer, Ariely & Klebanov (2026) investigate a critical gap in [[automated-grading]]: **how scoring quality degrades for mid-range student responses**. Most ASAS evaluations focus on clearly corr…