As online learning platforms continue to expand, the integration of large language models (LLMs) into course recommendation systems has shown significant potential, leveraging their semantic reasoning capabilities and inherent world knowledge. Current approaches typically adopt a small-model retrieval followed by an LLM-based re-ranking strategy. However, these methods face notable limitations. They often do not fully utilize the built-in knowledge of LLMs to enhance the initial retrieval phase and lack sufficient adaptation to the unique characteristics of educational contexts, limiting their effectiveness in personalized course recommendations. In addition, issues such as hallucinations and inconsistencies in the decoding of LLM output pose challenges to the reliability of these systems.To address these challenges, we propose CourseLRec, an innovative course recommendation framework that combines the semantic reasoning capabilities of LLMs with the efficiency of sequence modeling techniques. CourseLRec introduces three key innovations: LLM-Embedding for data augmentation of course titles and types, combined with Course-Aware Fusion to dynamically balance user interaction patterns and course content semantics; an Education-CoT Prompt to effectively integrate domain knowledge with learners’ progressive learning trajectories; and LoRA fine-tuning alongside a Context-Enhanced Token-to-Course Mapping module to enhance computational efficiency and semantic modeling. Comprehensive experiments on MOOCCourse and MOOCCube datasets demonstrate that CourseLRec outperforms state-of-the-art models across multiple evaluation metrics, highlighting its effectiveness in course recommendation tasks.

Incorporating a critiquing component into recommender applications facilitates the enhancement of user perception. Typically, critique-able recommender systems adapt the model parameters and update the recommendation list in real-time through the analysis of user critiquing keyphrases in the inference phase. The current critiquing methods necessitate the designation of a dedicated recommendation model to estimate user relevance to the critiquing keyphrase during the training phase preceding the recommendations update. This paradigm restricts the applicable scenarios and reduces the potential for keyphrase exploitation. Furthermore, these approaches ignore the issue of catastrophic forgetting caused by continuous modification of model parameters in multi-step critiquing. Thus, we present a general Representative Items Sampling Framework for Critiquing on Knowledge Graph Recommendation (RISC) implemented as a plug-in, which offers a new paradigm for critiquing in mainstream recommendation scenarios. RISC leverages the knowledge graph to sample important representative items as a hinge to expand and convey information from user critiquing, indirectly estimating the relevance of the user to the critiquing keyphrase. Consequently, the necessity for specialized user-keyphrase correlation modules is eliminated with respect to a variety of knowledge graph recommendation models. Moreover, we propose a Weight Experience Replay (WER) approach based on KG to mitigate catastrophic forgetting by reinforcing the user's prior preferences during the inference phase. Our extensive experimental findings on three real-world datasets and three knowledge graph recommendation methods illustrate that RISC with WER can be effectively integrated into knowledge graph recommendation models to efficiently utilize user critiquing for refining recommendations and mitigate catastrophic forgetting.

BACKGROUND: Alzheimer's disease and related dementias (ADRD) affect over 55 million globally. Current clinical trials suffer from low recruitment rates, a challenge potentially addressable via natural language processing (NLP) technologies for researchers to effectively identify eligible clinical trial participants. OBJECTIVE: This study investigates the sociotechnical feasibility of NLP-driven tools for ADRD research prescreening and analyzes the tools' cognitive complexity's effect on usability to identify cognitive support strategies. METHODS: A randomized experiment was conducted with 60 clinical research staff using three prescreening tools (Criteria2Query, Informatics for Integrating Biology and the Bedside [i2b2], and Leaf). Cognitive task analysis was employed to analyze the usability of each tool using the Health Information Technology Usability Evaluation Scale. Data analysis involved calculating descriptive statistics, interrater agreement via intraclass correlation coefficient, cognitive complexity, and Generalized Estimating Equations models. RESULTS: Leaf scored highest for usability followed by Criteria2Query and i2b2. Cognitive complexity was found to be affected by age, computer literacy, and number of criteria, but was not significantly associated with usability. DISCUSSION: Adopting NLP for ADRD prescreening demands careful task delegation, comprehensive training, precise translation of eligibility criteria, and increased research accessibility. The study highlights the relevance of these factors in enhancing NLP-driven tools' usability and efficacy in clinical research prescreening. CONCLUSION: User-modifiable NLP-driven prescreening tools were favorably received, with system type, evaluation sequence, and user's computer literacy influencing usability more than cognitive complexity. The study emphasizes NLP's potential in improving recruitment for clinical trials, endorsing a mixed-methods approach for future system evaluation and enhancements.