Homa Khosravian | Searcharxiv

arXiv2026

A Quantum Optimization Algorithm for Optimal Electric Vehicle Charging Station Placement for Intercity Trips

Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure for long-distance travel. Identifying optimal charging station locations in large transportation networks is an NP-hard combinatorial optimization problem. This paper applies Grover Adaptive Search (GAS) to improve the efficiency of solving the Charging Station Location Problem (CSLP). The proposed method achieves a quadratic improvement in computational complexity over classical exact methods, such as branch and bound. This paper develops a quantum subroutine that encodes the CSLP constraints by marking feasible solutions with objective value below a given threshold, and integrates this subroutine within the GAS procedure. The approach is demonstrated on a 7-node transportation network in central Illinois, with an analysis of success probability and sensitivity to algorithm parameters.

quant-ph↗

arXiv2025

PAPPL: Personalized AI-Powered Progressive Learning Platform

Engineering education has historically been constrained by rigid, standardized frameworks, often neglecting students' diverse learning needs and interests. While significant advancements have been made in online and personalized education within K-12 and foundational sciences, engineering education at both undergraduate and graduate levels continues to lag in adopting similar innovations. Traditional evaluation methods, such as exams and homework assignments, frequently overlook individual student requirements, impeding personalized educational experiences. To address these limitations, this paper introduces the Personalized AI-Powered Progressive Learning (PAPPL) platform, an advanced Intelligent Tutoring System (ITS) designed specifically for engineering education. It highlights the development of a scalable, data-driven tutoring environment leveraging cutting-edge AI technology to enhance personalized learning across diverse academic disciplines, particularly in STEM fields. PAPPL integrates core ITS components including the expert module, student module, tutor module, and user interface, and utilizes GPT-4o, a sophisticated large language model (LLM), to deliver context-sensitive and pedagogically sound hints based on students' interactions. The system uniquely records student attempts, detects recurring misconceptions, and generates progressively targeted feedback, providing personalized assistance that adapts dynamically to each student's learning profile. Additionally, PAPPL offers instructors detailed analytics, empowering evidence-based adjustments to teaching strategies. This study provides a fundamental framework for the progression of Generative ITSs scalable to all education levels, delivering important perspectives on personalized progressive learning and the wider possibilities of Generative AI in the field of education.

cs.CY↗