Concept Maps are semantic graph summary representations of relations between concepts in text. They are particularly beneficial for students with difficulty in reading comprehension, such as those with special educational needs and disabilities. Currently, the field of concept map extraction from text is outdated, relying on old baselines, limited datasets, and limited performances with F1 scores below 20%. We propose a novel neuro-symbolic pipeline and a GPT3.5-based method for automated concept map extraction from text evaluated over the WIKI dataset. The pipeline is a robust, modularized, and open-source architecture, the first to use semantic and neural techniques for automatic concept map extraction while also using a preliminary summarization component to reduce processing time and optimize computational resources. Furthermore, we investigate the large language model in zero-shot, one-shot, and decomposed prompting for concept map generation. Our approaches achieve state-of-the-art results in METEOR metrics, with F1 scores of 25.7 and 28.5, respectively, and in ROUGE-2 recall, with respective scores of 24.3 and 24.3. This contribution advances the task of automated concept map extraction from text, opening doors to wider applications such as education and speech-language therapy. The code is openly available.

Education is poised for a transformative shift with the advent of neurosymbolic artificial intelligence (NAI), which will redefine how we support deeply adaptive and personalized learning experiences. The integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), a significant and popular form of NAI, presents a promising avenue for advancing personalized instruction via neurosymbolic educational agents. By leveraging structured knowledge, these agents can provide individualized learning experiences that align with specific learner preferences and desired learning paths, while also mitigating biases inherent in traditional AI systems. NAI-powered education systems will be capable of interpreting complex human concepts and contexts while employing advanced problem-solving strategies, all grounded in established pedagogical frameworks. In this paper, we propose a system that leverages the unique affordances of KGs, LLMs, and pedagogical agents – embodied characters designed to enhance learning – as critical components of a hybrid NAI architecture. We discuss the rationale for our system design and the preliminary findings of our work. We conclude that education in the era of NAI will make learning more accessible, equitable, and aligned with real-world skills. This is an era that will explore a new depth of understanding in educational tools.

Abstract With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations. Resource: https://tibonto.github.io/educor/ .

In our position paper on a technology-enhanced smart learning environment, we propose the innovative combination of a knowledge graph representing what one has to learn and a learning path defining in which order things are going to be learned.In this way, we aim to identify students' weak spots or knowledge gaps in order to individually assist them in reaching their goals.Based on the performance of different learning paths, one might further identify the characteristics of a learning system that leads to successful students.In addition, by studying assessments and the different ways a particular problem can be solved, new methods for a multi-dimensional classification of assessments can be developed.The theoretical findings on learning paths in combination with the classification of assessments will inform the design and development of a smart learning environment.By combining a knowledge graph with different learning paths and the corresponding practical assessments we enable the creation of a smart learning tool.While the proposed approach can be applied to different educational domains and should lead to more effective learning environments fostering deep learning in schools as well as in professional settings, in this paper we focus on the domain of mathematics in primary and high schools as the main use case.