Using Artificial Intelligence to improve teaching and learning benefits greater adaptivity and scalability in education. Knowledge Tracing (KT) is recognized for student modeling task due to its superior performance and application potential in education. To this end, we conceptualize and investigate counterfactual explanation as the connection from XAI for KT to education. Counterfactual explanations offer actionable recourse, are inherently causal and local, and easy for educational stakeholders to understand who are often non-experts. We propose KTCF, a counterfactual explanation generation method for KT that accounts for knowledge concept relationships, and a post-processing scheme that converts a counterfactual explanation into a sequence of educational instructions. We experiment on a large-scale educational dataset and show our KTCF method achieves superior and robust performance over existing methods, with improvements ranging from 5.7% to 34% across metrics. Additionally, we provide a qualitative evaluation of our post-processing scheme, demonstrating that the resulting educational instructions help in reducing large study burden. We show that counterfactuals have the potential to advance the responsible and practical use of AI in education. Future works on XAI for KT may benefit from educationally grounded conceptualization and developing stakeholder-centered methods.

The use of Large Language Models (LLMs) in police opera- tions is growing, yet an evaluation framework tailored to po- lice operations remains absent. While LLM’s responses may not always be legally “incorrect”, their unverified use still can lead to severe issues such as unlawful arrests and improper evidence collection. To address this, we propose PAS (Po- lice Action Scenarios), a systematic framework covering the entire evaluation process. Applying this framework, we con- structed a novel QA dataset from over 8,000 official docu- ments and established key metrics validated through statis- tical analysis with police expert judgements. Experimental results show that commercial LLMs struggle with our new police-related tasks, particularly in providing fact-based rec- ommendations. This study highlights the necessity of an ex- pandable evaluation framework to ensure reliable AI-driven police operations. We release our data and prompt template.

Real-time processing of deep learning models on conventional systems, such as CPUs and GPUs, is highly challenging due to memory bottlenecks. This is exacerbated in Large Language Models (LLMs), where the majority of executions are dominated by General Matrix Multiplication (GEMM) operations, which are relatively more memory-intensive than convolution operations. Processing-in-Memory (PIM), which provides high internal bandwidth, can be a promising alternative for LLM serving. However, since current PIM systems do not fully replace traditional memory, data transfer between the host and PIM-side memory is essential. Therefore, minimizing the transfer cost between the host and PIM is crucial for serving LLMs efficiently on the PIM. In this paper, we propose PIM-LLM, an end-to-end framework that accelerates LLMs using an efficient tiled GEMM library and several key target-aware optimizations on real-world PIM systems. We first propose PGEMMlib, which provides optimized tiling techniques for PIM, considering architecture specific characteristics to minimize unnecessary data transfer overhead and maximize parallelism. In addition, Tile-Selector explores optimized parameters and techniques for different GEMM shapes and available resources of PIM systems using an analytical model. To accelerate LLMs using PGEMMlib, we integrate it into the TVM deep learning compiler framework. We further optimize the LLM execution by applying several key optimizations: Build-time memory layout adjustment, PIM resource pooling, CPU/PIM cooperation support, and QKV generation fusion. Evaluation shows that PIM-LLM achieves significant performance gains of up to 45.75x over the TVM baseline for several well-known LLMs. We strongly believe that this work provides key insights for efficient LLM serving on real PIM devices.

Recent advances in large reasoning models (LRMs) have demonstrated impressive capabilities in highly structured domains such as mathematics and programming. However, their application to education-where effective reasoning must be pedagogically meaningful, context-sensitive, and responsive to real student needs-remains relatively unexplored. Existing large language models (LLMs) often struggle to deliver instructional coherence, formative feedback, or simulate sophisticated teacher decision-making, limiting their practical utility in educational settings. To fill this gap, we present Pedagogy-R1, a comprehensive pedagogical reasoning framework designed to adapt LLMs for authentic classroom tasks. Our approach features three key innovations: (1) a distillation-based training pipeline that uses pedagogically filtered outputs for instruction tuning, (2) the Well-balanced Educational Benchmark (WBEB), which systematically evaluates models across five dimensions-subject knowledge, pedagogical knowledge, knowledge tracing, essay scoring, and real-world teacher decision-making-and (3) the Chain-of-Pedagogy (CoP) prompting strategy, employed both to generate pedagogically enriched training data and to elicit teacher-like reasoning during inference. We conduct a mixed-methods evaluation, combining fine-grained quantitative analyses of model performance with qualitative insights into the model's pedagogical reasoning patterns.

This paper presents novel techniques for enhancing the performance of knowledge tracing (KT) models by focusing on the crucial factor of question and concept difficulty level. Despite the acknowledged significance of difficulty, previous KT research has yet to exploit its potential for model optimization and has struggled to predict difficulty from unseen data. To address these problems, we propose a difficulty-centered contrastive learning method for KT models and a Large Language Model (LLM)-based framework for difficulty prediction. These innovative methods seek to improve the performance of KT models and provide accurate difficulty estimates for unseen data. Our ablation study demonstrates the efficacy of these techniques by demonstrating enhanced KT model performance. Nonetheless, the complex relationship between language and difficulty merits further investigation.

The rapid development of information and communication technologies has demonstrated their potential to enable people with physical disabilities (PPD) to improve their livelihoods and reduce social exclusion in China. Only a few studies have been conducted to investigate how online social support boosts PPD’s sense of belonging and to determine which type of social support they need. We followed the sense of belonging theory and social support theory to review the related literature and understand the mechanisms by which PPD may use mobile applications to receive support in the online environment. Both personal interviews and questionnaire surveys were conducted. We collected data from 337 PPD from the China Disabled Person’s Federation. Results of the statistical analysis revealed that both emotional and informational support are significantly related to sense of belonging. Furthermore, sense of belonging affects social connection and social well-being. The results of the moderation analysis showed female and male PPD received different kinds of support. The findings offer insights for disability service institutions interested in better enablement of PPD.

Advancements in artificial intelligence (AI) have stimulated the development of educational AI tools (EAIT). EAITs intelligently assist teachers in formulating better pedagogical decisions or actions for their students. However, teachers are hardly integrating EAITs, and little is known about their perceptions of EAITs. This study seeks to identify human factors that encourage or restrict teachers’ acceptance of EAITs. We propose a revised technology acceptance model incorporating teachers’ pedagogical beliefs and perceived trust in EAITs. Survey data were collected from 215 teachers in South Korea and analyzed using structural equation modeling. The results indicate that teachers with constructivist beliefs are more likely to integrate EAITs than teachers with transmissive orientations. Furthermore, perceived usefulness, perceived ease of use, and perceived trust in EAITs are determinants to be considered when explaining teachers’ acceptance of EAITs. Among them, the most influential determinant of predicting their acceptance was found to be how easily the EAIT is constructed. Significant implications for researchers and stakeholders regarding the development and integration of EAITs are discussed.

With the rapid technological change of society with Artificial Intelligence, elementary schools' goal should be to prepare the next generations according to competencies. We propose an AI curriculum to cultivate students' AI literacy to answer the question of ‘why and what to teach’ on AI. The proposed AI curriculum focuses on achieving AI literacy based on three competencies: AI Knowledge, AI Skill, and AI Attitude. We anticipate that the proposed curriculum will equip students with core competencies for the future with AI.

The constraint in sharing the same physical learning environment with students in distance learning poses difficulties to teachers. A significant teacher-student interaction without observing students' academic status is undesirable in the constructivist view on education. To remedy teachers' hardships in estimating students' knowledge state, we propose a Student Knowledge Prediction Framework that models and explains student's knowledge state for teachers. The knowledge state of a student is modeled to predict the future mastery level on a knowledge concept. The proposed framework is integrated into an e-learning application as a measure of automated feedback. We verified the applicability of the assessment framework through an expert survey. We anticipate that the proposed framework will achieve active teacher-student interaction by informing student knowledge state to teachers in distance learning.

The purpose of this study was to determine the most important factor that determines young Chinese social network service (SNS) users’ sustainable electronic word-of-mouth (e-WOM) behaviour in Korea. This study focuses on users’ online flow experience to demonstrate sustainable e-WOM behaviour to develop a new customer base for Korean movie distributors and considers the acculturation effects in the flow theory model. We followed the flow theory approach for developing the measures of constructs and examined previous related studies to predict the relationships among the variables. Data were collected from 185 Chinese moviegoers attending Korean universities. Using SmartPLS 2.0 for statistical analysis, we found positive relationships between each pair of variables. Our research findings can offer insights to marketers who are interested in understanding the online behaviour of Chinese students in Korean universities and in establishing strategies to build relationships with them.

This article describes the development of learning widget on m-learning and e-learning environments. A widget is a small, simple and useful application supporting user-oriented contents. The user may select and install widgets that are convenient as well as an auto-updating application including weather or calendar. These widgets are especially more useful, because they are able to be installed on a mobile device, a website or a desktop computer. If we take advantage of widgets for education, we may use this learning tool for delivering and pulling learning contents, essences of lessons or word learning. To that end, we developed an effective learning widget and then verified its usability, usefulness and effectiveness for m-learning and e-learning. That is, we evaluated the learning widget with a heuristic evaluation method. We identified 72 interface problems by using a set of 10 usability criteria or heuristics. In addition, we considered how to design the learning widget with consideration given to devices on m-learning and e-learning. Moreover, we experimented by conducting a pilot test with 34 students, a field test with 60 teachers and technology acceptance model (TAM) analysis with 15 teachers. We verified the effectiveness and usefulness of learning with a questionnaire, a quiz and TAM, where the subjects, after using the learning widget in real learning activities, rated the widget's efficacy. The result shows that the learning widget is useful for m-learning and e-learning environments.