AI2T: Building Trustable AI Tutors by Interactively Teaching a Self-Aware Learning Agent
AI2T is an interactively teachable AI for authoring intelligent tutoring systems (ITSs). Authors tutor AI2T by providing a few step-by-step solutions and then grading AI2T's own problem-solving attempts. From just 20-30 minutes of interactive training, AI2T can induce robust rules for step-by-step solution tracking (i.e., model-tracing). 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. Our user study shows that authors can use STAND's certainty heuristic to estimate when AI2T has been trained on enough diverse problems to induce correct and complete model-tracing programs. AI2T-induced programs are more reliable than hallucination-prone LLMs and prior authoring-by-tutoring approaches. With its self-aware induction of hierarchical rules, AI2T offers a path toward trustable data-efficient authoring-by-tutoring for complex ITSs that normally require as many as 200-300 hours of programming per hour of instruction.
Code (0)
등록된 구현이 없습니다.
Tasks
HallucinationSimilar Papers 제목 키워드 기반
Apprentice Tutor Builder: A Platform For Users to Create and Personalize Intelligent Tutors
Intelligent tutoring systems (ITS) are effective for improving students' learning outcomes. However, their development is often complex, time-consuming, and requires specialized programming and tutor design knowledge, th…
AI AgentIs your multimodal large language model a good science tutor?
Multimodal large language models (MLLMs) demonstrate impressive performance on scientific reasoning tasks (e.g., ScienceQA). However, most existing benchmarks focus narrowly on the accuracy of the final answer while igno…
Language ModelingLanguage ModellingLarge Language ModelMultimodal Large Language ModelInvestigating the Impact of Personalized AI Tutors on Language Learning Performance
Driven by the global shift towards online learning prompted by the COVID 19 pandemic, Artificial Intelligence has emerged as a pivotal player in the field of education. Intelligent Tutoring Systems offer a new method of …
CogEvo-Edu: Cognitive Evolution Educational Multi-Agent Collaborative System
Large language models (LLMs) are increasingly deployed as conversational tutors in STEM education, yet most systems still rely on a single LLM with a static retrieval-augmented generation (RAG) pipeline over course mater…
Decision MakingA Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain
Traditional machine learning algorithms use data from databases that are mutable, and therefore the data cannot be fully trusted. Also, the machine learning process is difficult to automate. This paper proposes building …
BIG-bench Machine Learning