Aug 2026· International Journal of Sciences and Innovation Engineering· 0 citations
TL;DR
EduMind is introduced, a unified tutoring and assessment platform designed around a dual-track evaluation model that demonstrates how assessment and tutoring can be unified into a seamless workflow, and remained operationally stable throughout all testing phases.
Abstract
Modern academic institutions face a fundamental gap: instruction is designed for the average student, leaving individuals with specific weaknesses without any targeted support mechanism. Standardised course pipelines and identical assessments for all students have consistently failed to close this instructional gap. Advances across Artificial Intelligence (AI), Natural Language Processing (NLP), and Generative AI now make it feasible to construct learning environments that actively evolves as each student progresses [1].
This paper introduces EduMind, a unified tutoring and assessment platform designed around a dual-track evaluation model. Closed- form questions are evaluated using fixed-logic scoring for consistent results, while open-ended answers are assessed by computing meaning-level correspondence with expert reference responses. The combined output enables fine-grained identification of both proficient and deficient knowledge areas at the topic level [2][3].
EduMind's embedded Generative Al module transforms evaluation data directly into targeted instructional content, addressing identified gaps with structured explanations and study material packaged into downloadable PDF reports. The system demonstrates how assessment and tutoring can be unified into a seamless workflow, and remained operationally stable throughout all testing phases.
: Traditional educational assessment systems often prioritize grading over learning, falling short in automating the evaluation of complex coding and subjective responses. This introduces inconsistencies and slows the feedback cycle. This paper introduces InsightEval, an AI-powered system designed to transform static assessments into dynamic tools for continuous learning. It automates the evaluation of multiple-choice, coding, and subjective questions by integrating technologies like Judge0 for code execution and Large Language Models for natural language grading. The system’s core innovation is a 'Feedback -to-Improvement Loop,' which analyzes incorrect answers using NLP to identify conceptual gaps and then generates personalized micro-quizzes for targeted reinforcement. It also visualizes topic interconnections through Concept Linkage Maps, helping learners and teachers track conceptual mastery. InsightEval delivers an interactive, feedback-driven experience that promotes deeper understanding and continuous academic growth.
S. A, B. Reddy, Eric Varghese et al.· Proceedings of the 1st Inter...· 0 citations
Michael, a syllabus-aware AI teaching assistant designed to scaffold reasoning through structured, hint-first dialogue aligned with course progression, rather than providing direct solutions, is introduced, suggesting that curriculum-aligned constraints and hint-first scaffolding can support instructional integration without displacing pedagogical goals.
Or Peretz, Roei Zerahia· International Journal of Inf...· 0 citations
The proliferation of large language models (LLMs) and generative artificial intelligence has catalyzed an unprecedented pedagogical paradigm shift within higher education. This study investigates the adoption, efficacy, and cognitive impact of these technological tools among undergraduate cohorts in engineering and life sciences. Recognizing that baseline familiarity with AI does not inherently translate into advanced operational competency or prompt engineering literacy, this study evaluates the deployment of both standard LLMs and retrieval-augmented generation (RAG)-based customized assistants. The investigation employs a rigorous dual-phase methodology: an exploratory assessment of technology acceptance using standard ChatGPT, followed by a tightly controlled quasi-experiment evaluating the impact of domain-specific “GPT Custom” mentors on academic performance on complex engineering tasks. The empirical results demonstrate that customized AI assistants significantly improve final academic outcomes, yielding average grade increases of more than 15% on data-intensive analytical assignments. Furthermore, the deployment of customized assistants notably reduced grade variability among students, indicating a homogenization of academic performance that effectively levels the learning environment without compromising rigor. This improvement was statistically amplified when students utilized premium, high-capacity versions of the models for extensive synthesis tasks. Ultimately, the data indicate that while AI offers robust adaptive scaffolding, its efficacy depends on users’ critical-thinking capacities, underscoring the urgent need for educational frameworks that cultivate prompt-engineering literacy and responsible human-AI collaboration.
Jorge Cruz-Ángeles, Mariana E. Elizondo-García, Genaro Zavala et al.· International Journal of Eng...· 0 citations
The results suggest that learner- and curriculum-aware alignment may matter more for effective tutoring than model category alone, and that such alignment is both measurable and improvable.
Benjamin Barlog, Hudson Craig, Ze-Dong Peng· IEEE International Conferenc...· 0 citations
A simulation-based textual analysis of prompt design evaluates a frontier large language model as a tutor across 60 scripted sessions on a single topic and point to dynamic, dialogue-aware prompting alongside explicit SRL scaffolding.
Kendall Hartley, Fabiola Sáez-Delgado, Javier Mella-Norambuena· Future Internet· 0 citations
A novel approach to Intelligent Tutoring Systems (ITS) is presented by integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enable dynamic personalization in educational contexts by implementing a three-layered architecture combining semantic retrieval mechanisms with generative AI capabilities.
Kuyoro Afolashade, N. Uchenna, Akinwunmi Damilare· British journal of computer,...· 0 citations
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