Adaptive learning, which requires the in-depth understanding of students' learning processes and rational planning of learning resources, plays a crucial role in intelligent education. However, how to effectively model these two processes and seamlessly integrate them poses significant implementation challenges for adaptive learning. As core learning resources, exercises have the potential to diagnose students' knowledge states during the learning processes and provide personalized learning recommendations to strengthen students' knowledge, thereby serving as a bridge to boost student-oriented adaptive learning. Therefore, we introduce a novel task called Knowledge-aware Exercise Generative Recommendation (KEGR). It aims to dynamically infer students' knowledge states from their past exercise responses and customizably generate new exercises. To achieve KEGR, we propose an adaptive multi-agent cooperation framework, called ExeGen, inspired by the excellent reasoning and generative capabilities of LLM-based AI agents. Specifically, ExeGen coordinates four specialized agents for supervision, knowledge state perception, exercise generation, and quality refinement through an adaptive loop workflow pipeline. More importantly, we devise two enhancement mechanisms in ExeGen: 1) A human-simulated knowledge perception mechanism mimics students' cognitive processes and generates interpretable knowledge state descriptions via demonstration-based In-Context Learning (ICL). In this mechanism, a dualmatching strategy is further designed to retrieve highly relevant demonstrations for reliable ICL reasoning.

Adaptive learning, which requires the in-depth understanding of students' learning processes and rational planning of learning resources, plays a crucial role in intelligent education. However, how to effectively model these two processes and seamlessly integrate them poses significant implementation challenges for adaptive learning. As core learning resources, exercises have the potential to diagnose students' knowledge states during the learning processes and provide personalized learning recommendations to strengthen students' knowledge, thereby serving as a bridge to boost student-oriented adaptive learning. Therefore, we introduce a novel task called Knowledge-aware Exercise Generative Recommendation (KEGR). It aims to dynamically infer students' knowledge states from their past exercise responses and customizably generate new exercises. To achieve KEGR, we propose an adaptive multi-agent cooperation framework, called ExeGen, inspired by the excellent reasoning and generative capabilities of LLM-based AI agents. Specifically, ExeGen coordinates four specialized agents for supervision, knowledge state perception, exercise generation, and quality refinement through an adaptive loop workflow pipeline. More importantly, we devise two enhancement mechanisms in ExeGen: 1) A human-simulated knowledge perception mechanism mimics students' cognitive processes and generates interpretable knowledge state descriptions via demonstration-based In-Context Learning (ICL). In this mechanism, a dual-matching strategy is further designed to retrieve highly relevant demonstrations for reliable ICL reasoning.

This paper analyzes Large Language Models (LLMs) with regard to their programming exercise generation capabilities. Through a survey study, we defined the state of the art, extracted their strengths and weaknesses and finally proposed an evaluation matrix, helping researchers and educators to decide which LLM is the best fitting for the programming exercise generation use case. We also found that multiple LLMs are capable of producing useful programming exercises. Nevertheless, there exist challenges like the ease with which LLMs might solve exercises generated by LLMs. This paper contributes to the ongoing discourse on the integration of LLMs in education.

We discuss in detail the challenges of building a Language learning games are key tools to vitalize language learning tool for BIL, such as the lack of endangered languages (Thomason, 2015; Xu et al., written and phonetical resources, ethical concerns 2022; Neubig et al., 2020). LARA (Akhlaghi et al., on available treebanks and databases used for exercise 2019), a multi language learning assistant, is an generation, and provide some suggestions example that has been key to support actions related on steps forward. We managed to build a minimal to endangered languages protection (Rayner proof of concept course for Guajajara language divided and Wilmoth, 2023; Bédi et al., 2022; Zuckermann in two sections. We employed dependency et al., 2021). Despite the necessity of language treebanks and a lexical database on BIL as source learning tools to vitalize endangered languages, for exercise generation. The main contribution of they are typically restricted to high-resource languages, this work is to present a case of study on building a such as english, and require significant language learning tool for BIL and, we hope, it will effort to be extended to languages with few spoken serve as an starting point for the development of and written resources.

Adaptive learning aims to provide customized educational activities (e.g., exercises) to address individual learning needs. However, manual construction and delivery of such activities is a laborious process. Thus, in this paper, we study a novel task of adaptive and personalized exercise generation for online language learning. To this end, we combine a knowledge tracing model that estimates each student's evolving knowledge states from their learning history and a controlled text generation model that generates exercise sentences based on the student's current estimated knowledge state and instructor requirements of desired properties (e.g., domain knowledge and difficulty). We train and evaluate our model on real-world learner interaction data from Duolingo and demonstrate that LMs guided by student states can generate superior exercises. Then, we discuss the potential use of our model in educational applications using various simulations. These simulations show that our model can adapt to students' individual abilities and can facilitate their learning efficiency by personalizing learning sequences.

The field of CPS draws from several areas in computer science, electrical engineering, and other engineering disciplines, including computer architecture, embedded systems, programming languages, software engineering, real-time systems, operating systems and networking, formal methods, algorithms, computation theory, control theory, signal processing, robotics, sensors and actuators, and computer security. Similarly, over the past 14 years, we have had students from computer science, electrical and computer engineering, mechanical engineering, civil engineering, and even bioengineering. Integrating this bewildering diversity of subject areas into a coherent whole for students with such a wide breadth of backgrounds has been a challenge we had to overcome. One approach would have been to not attempt such an integration. Instead, we could have opted for a collection of courses that together cover all the key areas in CPS.