With the advancements of large language models (LLMs), intelligent tutoring systems have witnessed significant progress. The extensive knowledge and reasoning capabilities of LLMs enable intelligent tutoring systems to generate more helpful tutoring dialogues with scaffolding instructions. However, these systems fail to provide scaffolds that align with the personalized needs of students due to the lack of attention to the long-term learning process of students. Meanwhile, the pursuit of more suitable scaffolds through complex reasoning may result in additional computational overhead. To address these issues, we propose LEAP, a Long-term Educational Adaptive Planning system that can model students' long-term learning process. Specifically, LEAP plans for scaffolds through collaboration of direct planning and thoughtful reasoning to improve efficiency and captures students' long-term learning progress through cognitive state extraction. Then we propose LEAD, a Long-term Educational Archive Dataset to alleviate the lack of data and validate the effectiveness of LEAP, which is constructed through real-world students' reactions and simulation of the teacher-student interactions. Experiments on several datasets demonstrate the effectiveness of LEAP.

Cognitive Diagnosis Models (CDMs) are designed to assess students' cognitive states by analyzing their performance across a series of exercises. However, existing CDMs often struggle with diagnosing infrequent students and exercises due to a lack of rich prior knowledge. With the advancement in large language models (LLMs), which possess extensive domain knowledge, their integration into cognitive diagnosis presents a promising opportunity. Despite this potential, integrating LLMs with CDMs poses significant challenges. LLMs are not well-suited for capturing the fine-grained collaborative interactions between students and exercises, and the disparity between the semantic space of LLMs and the behavioral space of CDMs hinders effective integration. To address these issues, we propose a novel Knowledge-enhanced Cognitive Diagnosis (KCD) framework, which is a model-agnostic framework utilizing LLMs to enhance CDMs and compatible with various CDM architectures. The KCD framework operates in two stages: LLM Diagnosis and Cognitive Level Alignment. In the LLM Diagnosis stage, both students and exercises are diagnosed to achieve comprehensive and detailed modeling. In the Cognitive Level Alignment stage, we bridge the gap between the CDMs' behavioral space and the LLMs' semantic space using contrastive learning and mask-reconstruction approaches. Experiments on several real-world datasets demonstrate the effectiveness of our proposed framework.

Recommender systems primarily utilize user–item interactions (i.e., collaborative information) and auxiliary textual information (i.e., semantic information) to infer user preferences and provide recommendations. With the advancement in large language models (LLMs), attempts have been made to incorporate their remarkable language comprehension capabilities into recommendation tasks. However, existing LLM4Rec methods face challenges in seamlessly integrating both collaborative and semantic information, as there is an inherent gap between these two types of data. Moreover, these methods struggle to capture the fine-grained distinctions in user preferences, which are essential in recommendation tasks, due to the loss design of LLMs. To address these issues, we propose a multi-stage prompt-tuning method for leveraging pre-trained LLMs in various recommendation tasks, named SCRec. Specifically, SCRec leverages Semantic and Collaborative information as supervision signals in two distinct stages: the semantic prompt-tuning stage and the collaborative prompt-tuning stage. This method breaks down user and item representations into semantic and collaborative perspectives, enabling a pre-trained LLM to first deduce the qualitative preferences of users over items from semantic information, and then generate quantitative recommendations from collaborative information. In addition, we propose a meta-mapping approach to provide personalized mapping functions for encoding collaborative information and integrate a novel numeric-informed head based on MSE loss for LLM in the second stage, which helps to better capture fine-grained distinctions in user preferences. Experiments on three public datasets for rating prediction and top-N recommendation tasks demonstrate that our method surpasses both conventional and LLM-based techniques, showing the strength of sequentially merging semantic and collaborative information in recommendation tasks.

Knowledge tracing (KT) is a fundamental task in intelligent education aimed at tracking students’ knowledge status and predicting their performance on new questions. The primary challenge in KT is accurately inferring a high-quality representation of students’ knowledge state that effectively captures their understanding of questions. However, existing methods are typically developed under the assumption that students’ behaviors directly reflect their knowledge state, which may not hold true especially in online learning scenarios. Abnormal behaviors exhibited by students, such as guessing and plagiarism, can introduce biases into the data, making it difficult to accurately assess students’ true knowledge state. To address this limitation, we propose a novel DebiAsed Cognition rEpresentation (DACE) modeling approach. This approach introduces a novel adversarial training strategy based on information bottleneck theory to obtain a debiased knowledge state representation that retains only the most reliable information for accurately predicting students’ performance on new questions. Moreover, we design a novel contrastive learning module through embedding-based augmentation to further enhance the robustness and generalizability of the learned knowledge state representation. We conduct extensive experiments on three public KT datasets and the newly released dataset BaiPy to demonstrate the superiority of our model over strong baselines, particularly when confronted with biased data. Our code and datasets are available at https://github.com/lvXiangwei/DACE.git .

The focus of our work is on diagnostic tasks in personalized learning, such as cognitive diagnosis and knowledge tracing. The goal of these tasks is to assess students' latent proficiency on knowledge concepts through analyzing their historical learning records. However, existing research has been limited to single-course scenarios; cross-course studies have not been explored due to a lack of dataset. We address this issue by constructing PTADisc, a Diverse, Immense, Student-centered dataset that emphasizes its sufficient Cross-course information for personalized learning. PTADisc includes 74 courses, 1,530,100 students, 4,054 concepts, 225,615 problems, and over 680 million student response logs. Based on PTADisc, we developed a model-agnostic Cross-Course Learner Modeling Framework (CCLMF) which utilizes relationships between students' proficiency across courses to alleviate the difficulty of diagnosing student knowledge state in cold-start scenarios. CCLMF uses a meta network to generate personalized mapping functions between courses. The experimental results on PTADisc verify the effectiveness of CCLMF with an average improvement of 4.2% on AUC. We also report the performance of baseline models for cognitive diagnosis and knowledge tracing over PTADisc, demonstrating that our dataset supports a wide scope of research in personalized learning. Additionally, PTADisc contains valuable programming logs and student-group information that are worth exploring in the future.