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Found 8 papers, 1 papers with code
Decomposed Inductive Procedure Learning: Learning Academic Tasks with Human-Like Data Efficiency
Human learning relies on specialization -- distinct cognitive mechanisms working together to enable rapid learning.
An Integrated Platform for Studying Learning with Intelligent Tutoring Systems: CTAT+TutorShop
This platform has been used to develop and conduct an estimated 147 research studies which have run in a wide variety of laboratory and real-world educational settings, including K-12 and higher education, and have addressed a wide range of research questions.
AI2T: Building Trustable AI Tutors by Interactively Teaching a Self-Aware Learning Agent
As AI2T learns it can accurately estimate its certainty of performing correctly on unseen problem steps using STAND: a self-aware precondition learning algorithm that outperforms state-of-the-art methods like XGBoost.
What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use
Prompting LLMs for complex tasks (e. g., building a trip advisor chatbot) needs humans to clearly articulate customized requirements (e. g., "start the response with a tl;dr").
STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning
STAND is a data-efficient and computationally efficient machine learning approach that produces better classification accuracy than popular approaches like XGBoost on small-data tabular classification problems like learning rule preconditions from interactive training.
Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming
The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (referred to as "pAIr programming") where an AI system collaborates with a human programmer.
Decomposed Inductive Procedure Learning
Recent advances in machine learning have made it possible to train artificially intelligent agents that perform with super-human accuracy on a great diversity of complex tasks.
Learning Cognitive Models using Neural Networks
Secondly, for domains where a cognitive model is available, we show that representations learned through CogRL can be used to get accurate estimates of skill difficulty and learning rate parameters without using any student performance data.
