arXiv AI
Aug 11
CircuitReason-1k: Benchmarking Long-Horizon Visual-to-Symbolic Reasoning inElectrical Circuits
arXiv:2608. 09374v1 Announce Type: new Abstract: Electrical circuit analysis requires more than recognizing components in an image.
CircuTutor: Transforming Static Circuit Problems into Intelligent and Dynamic Tutoring
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arXiv AI
Sep 15
Enhancing Large Language Model-Based Systems for End-to-End Circuit Analysis Problem Solving
The paper introduces an enhanced end‑to‑end circuit analysis framework built on Gemini 2.5 Pro, targeting engineering education. It addresses two key failure modes—circuit‑recognition hallucinations and reasoning‑process hallucinations—by adding a YOLO detector for source polarity re‑identification and an ngspice verification loop for iterative refinement. The resulting pipeline achieves 97.59 % accuracy on 83 undergraduate problems, markedly outperforming the baseline Gemini model and demonstrating significant gains across varied diagram styles and textbooks.
arXiv Machine Learning
Oct 22
Sketch2Sim: an end-to-end modular framework for hand-drawn circuit analysis
The paper introduces Sketch2Sim, an end‑to‑end modular framework that transforms hand‑drawn circuit diagrams into SPICE‑compatible netlists and automatically computes nodal voltages and currents. It employs a three‑stage pipeline: dual YOLO detectors for components and text, a CRNN‑CTC OCR module for symbol value extraction, and image‑processing techniques (thresholding, skeletonization, DBSCAN) to identify nodes and map components. Experiments on a curated dataset of 1,000 RLC circuits show robust performance across diverse handwriting styles, lighting, and clutter, and the system includes a user interface for rapid digitization and simulation.
arXiv AI
Oct 27
Making Reasoning Visible: Configuration-Aware Diagnostic Modeling for Adaptive Scaffolding in Structured Reasoning Tasks
arXiv:2610.35609v1 Announce Type: cross Abstract: Modeling the latent, multi-step reasoning processes of learners is a core challenge for adaptive tutoring systems, which often rely on simple right/w...
arXiv AI
Aug 10
Curriculum as Code: An AI-Assisted Architecture for Instructional Design in STEM Education
arXiv:2608. 07364v1 Announce Type: new Abstract: Contribution: This paper presents a six-phase AI-assisted instructional design architecture based on the Curriculum as Code paradigm, integrating Generative AI with LaTeX and Python to automate the creation of reproducible, visually consistent, and technically precise materials for STEM education.
arXiv Machine Learning
Jul 3
A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA
arXiv:2504. 03711v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI)-driven electronic design automation (EDA) techniques have been extensively explored for VLSI circuit design applications.
arXiv Machine Learning
Sep 2
PEARL: Training Socratic Tutors with Pedagogically Aligned Reinforcement Learning
PEARL is a framework that trains Socratic tutoring agents using pedagogically aligned reinforcement learning. It introduces a controllable student simulator to model diverse cognitive states, a reward model that jointly evaluates pedagogical quality and correctness, and a stable multi‑objective RL approach to balance competing tutoring goals. Experiments demonstrate that PEARL competes with both open‑source tutoring systems and leading proprietary LLMs.
arXiv AI
Aug 14
AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models
arXiv:2608. 13472v1 Announce Type: cross Abstract: Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition.
arXiv Computation and Language
Sep 2
A Dataset for Modeling Iterative Problem-Solving
The paper introduces CodeInsight, a large-scale dataset of over 3 million code submissions from 3,286 undergraduate students in two introductory C++ courses, capturing test‑case outcomes, timestamps, and source code. It presents a benchmark that evaluates various modeling approaches—including a Recurrent State Space Model and an LLM‑based predictor—on their ability to predict iterative problem‑solving dynamics such as performance changes and error persistence. The study finds that the RSSM outperforms other models on most courses, while the LLM generates full submissions but with lower predictive accuracy, suggesting it functions more as a generative solver than a behavior predictor.
arXiv Computation and Language
Sep 21
CoLearn: An Agentic Tutor that Learns its Learner in a Human--AI Co-Learning Loop
CoLearn is an interactive, agentic tutoring system that learns about each learner through a persistent memory of mastery and misconceptions, updated with a Bayesian Knowledge Tracing model that uses a large language model as an observation function. It generates personalized questions targeting the learner’s weakest topics and recurring misconceptions, and provides a live evidence view for progress visualization and blind A/B comparison. In blind A/B tests, learners preferred questions conditioned on this memory 68‑69% of the time, and simulations show the agent’s belief converges toward the learner’s true mastery.
arXiv AI
Sep 10
Tracing Mathematical Proficiency Through Problem-Solving Processes
The paper introduces Knowledge Tracing Leveraging Problem‑Solving Process (KT‑PSP), a method that incorporates students’ problem‑solving steps to model mathematical proficiency more comprehensively than traditional knowledge tracing. It presents the KT‑PSP‑25 dataset and a new framework, StatusKT, which uses a teacher‑student‑teacher LLM pipeline to extract proficiency indicators, generate responses, and evaluate mastery. Experiments show that StatusKT improves prediction accuracy and offers interpretable explanations by explicitly modeling proficiency.
arXiv AI
Oct 1
Examining Variation in How Guided AI Tutors Resolve Student Impasses
The study analyzes 20,462 student turns from 1,260 sessions with a guided LLM chemistry tutor, identifying 6,630 impasse turns categorized as conceptual errors, expressed uncertainty, or help‑seeking. Three tutoring conditions—baseline, no‑direct‑answer, and guided—were simulated, revealing that the baseline tutor often gave direct answers, the no‑direct‑answer tutor always asked follow‑up questions, and the guided tutor varied its responses based on context. Impasse trajectories showed that each additional impasse turn reduced the likelihood of recovery, while addressing errors became increasingly beneficial compared to repeated scripted questioning.