Ngoc Luyen Le

Ngoc Luyen Le, Marie-Hélène Abel, Bertrand Laforge

Collaborations with Generative AI often begin with a short prompt and end with an opaque output, leaving implicit who was involved, what task was being pursued, which resources were used, and which constraints should have shaped the process. This limited contextual explicitness hinders trust, traceability, and accountability, particularly when Generative AI is embedded in information-intensive workflows such as search, querying, and profile management. This paper introduces From Prompts to Context, an ontology-driven framework for representing Human-Generative AI collaboration. Its core component, the Contextual Collaboration AI Ontology (CCAI), models key elements of collaboration - including tasks, agent roles, resources, and constraints - as a shared machine-interpretable vocabulary. By combining populated CCAI instances with SPARQL-based context retrieval in operational workflows, the framework turns otherwise ephemeral prompt-response interactions into structured and queryable collaboration traces linking prompts, outputs, and their surrounding context. The approach is illustrated through a case study involving a software development team building a competency-based education feature for viewing and updating learner competency profiles. The case study shows how the framework can support the representation and documentation of collaboration episodes across requirements analysis, design, implementation, and testing. Within this setting, the results indicate that explicit collaboration modelling helps make task context more explicit, improves the traceability of AI-generated contributions, and supports more transparent and accountable Human-Generative AI practices. We conclude by outlining design principles for future Human-Generative AI systems that emphasise not only output quality, but also the explicit representation of the collaborative context in which outputs are produced.

Ngoc Luyen Le, Marie-Hélène Abel, Bertrand Laforge

Linking learning resources to a structured competency framework is key to enabling competency-based search and curriculum analytics in Learning Management Systems (LMS). However, manual tagging is labor-intensive, and fully automatic methods often lack transparency. In this paper, we present an end-to-end alignment pipeline that uses a large language model (LLM) as a constrained, evidence-producing tagger. LMS resources -both instructional content and assessments -are first segmented into meaningful pedagogical fragments. For each fragment, a small set of candidate competencies is retrieved from structured competency profiles enriched with graph-based context. The LLM then selects the most relevant competencies from this set and provides supporting evidence spans from the fragment text. These predictions are refined using the structure of the competency graph and aggregated at the resource level. We evaluate our approach on a dataset built from the Computer Science department's competency referential at the Université de Technologie de Compiègne (UTC), covering 22 competencies across multiple course materials. Our LLM+BM25+Graph (LBG) pipeline achieves strong results, with a micro-F1 of 0.57 and macro-F1 of 0.50 at the fragment level, 0.51 macro-F1 at the resource level, and an MRR of 0.82outperforming zero-shot and few-shot LLM variants, retrieval/similarity baselines, and supervised classifiers -while also producing more mechanically traceable evidence spans to support human auditing and educational analysis.

Ngoc Luyen Le, Marie-Hélène Abel, Bertrand Laforge

Early-warning models built from Learning Management System (LMS) logs aim to predict end-of-course outcomes early enough to enable timely learner support. However, reported "early" performance is often inflated by temporal leakage. This occurs when the pipeline uses information that would not yet be available at the time of prediction. We formalize cutoff-based early outcome prediction under a temporal availability constraint and introduce LEAP (Leakage-Excluded Early-Availability Protocol), which enforces cutoff-first truncation prior to joins and aggregation and audits feature provenance to prevent post-cutoff evidence from entering the benchmark. We instantiate LEAP on the public Open University Learning Analytics Dataset (OULAD) as a multi-step protocol for leakage-controlled evaluation across weekly cutoffs. Using several standard learning methods, we evaluate performance using ROC-AUC, PR-AUC, Brier score, and F1@0.5. Results show improving performance as the observation window expands, with a marked gain around week~3; Random Forest performs best at the earliest cutoffs, while Gradient Boosting dominates thereafter. Leakage ablations further show that temporal violations, especially through assessment information, can inflate apparent "early" performance.

Mohammed Oussama Seddini, Mohamed Ez-Zaouia, Ngoc Luyen Le et al.

Mixed Reality (MR) offers immersive and multimodal opportunities for education but remains difficult for teachers to author without technical expertise. We propose MRGEN, a conceptual framework for LLM-powered authoring tools to support teachers in creating MR learning activities that work on mobile devices (tablets and smartphones). MRGEN articulates three axes: Learning Objectives, MR Modality, and GAI Assistance. To validate our framework, we implemented a prototype based on the open-source MIXAP authoring platform and conducted a user study with 24 participants. Results show that LLM-powered authoring reduced task duration by 36% on average, and that over 90% of participants found the AI support helpful for brainstorming, structuring, and aligning content with their learning goals. These findings yielded very promising results for future AI-assisted MR authoring tools.

Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou

Knowledge graphs have proven to be effective for modeling entities and their relationships through the use of ontologies. The recent emergence in interest for using knowledge graphs as a form of information modeling has led to their increased adoption in recommender systems. By incorporating users and items into the knowledge graph, these systems can better capture the implicit connections between them and provide more accurate recommendations. In this paper, we investigate and propose the construction of a personalized recommender system via knowledge graphs embedding applied to the vehicle purchase/sale domain. The results of our experimentation demonstrate the efficacy of the proposed method in providing relevant recommendations that are consistent with individual users.

Ngoc Luyen Le, Marie-Hélène Abel, Bertrand Laforge

Authoritative competency frameworks such as ESCO, ROME, and O*NET are essential for aligning education with labor market needs, yet their technical complexity and structural heterogeneity hinder practical adoption by educators. This paper introduces SkillGraph-Service, an interoperable microservice designed to bridge this gap by unifying these resources into a provenance-preserving Knowledge Graph (KG). Adopting a KG-first, LLM-fallback architecture, the system combines symbolic rigor with sub-symbolic flexibility. It implements a lightweight hybrid retrieval engine (fusing SQLite FTS5 and HNSW vector search) to handle the vocabulary mismatch in educator queries, and utilizes Large Language Models (LLMs) strictly for constrained ranking and audience-aware explanation. Empirical evaluation on a multilingual dataset reveals that the proposed hybrid strategy achieves superior retrieval effectiveness (nDCG@5>0.94) with sub-200 ms latency, rendering computationally expensive cross-encoder re-ranking may be unnecessary for this domain. Furthermore, an analysis of generated explanations highlights a trade-off between fluency and faithfulness: while JSON-constrained LLMs ensure high citation precision, deterministic templates remain the most reliable method for maximizing evidence coverage. The resulting architecture offers a practical, scalable, and auditable solution for integrating complex skill data into digital learning ecosystems.

Ngoc Luyen Le, Marie-Hélène Abel, Philippe Gouspillou

In today's data-rich environment, recommender systems play a crucial role in decision support systems. They provide to users personalized recommendations and explanations about these recommendations. Embedding-based models, despite their widespread use, often suffer from a lack of interpretability, which can undermine trust and user engagement. This paper presents an approach that combines embedding-based and semantic-based models to generate post-hoc explanations in recommender systems, leveraging ontology-based knowledge graphs to improve interpretability and explainability. By organizing data within a structured framework, ontologies enable the modeling of intricate relationships between entities, which is essential for generating explanations. By combining embedding-based and semantic based models for post-hoc explanations in recommender systems, the framework we defined aims at producing meaningful and easy-to-understand explanations, enhancing user trust and satisfaction, and potentially promoting the adoption of recommender systems across the e-commerce sector.

Ngoc Luyen Le, Marie-Hélène Abel

Group decision-making is becoming increasingly common in areas such as education, dining, travel, and finance, where collaborative choices must balance diverse individual preferences. While conventional recommender systems are effective in personalization, they fall short in group settings due to their inability to manage conflicting preferences, contextual factors, and multiple evaluation criteria. This study presents the development of a Context-Aware Multi-Criteria Group Recommender System (CA-MCGRS) designed to address these challenges by integrating contextual factors and multiple criteria to enhance recommendation accuracy. By leveraging a Multi-Head Attention mechanism, our model dynamically weighs the importance of different features. Experiments conducted on an educational dataset with varied ratings and contextual variables demonstrate that CA-MCGRS consistently outperforms other approaches across four scenarios. Our findings underscore the importance of incorporating context and multi-criteria evaluations to improve group recommendations, offering valuable insights for developing more effective group recommender systems.

Ngoc Luyen Le, Marie-Hélène Abel

Recent advancements in language models and pre-trained language models like BERT and RoBERTa have revolutionized natural language processing, enabling a deeper understanding of human-like language. In this paper, we explore enhancing recommender systems using textual embeddings from pre-trained language models to address the limitations of traditional recommender systems that rely solely on explicit features from users, items, and user-item interactions. By transforming structured data into natural language representations, we generate high-dimensional embeddings that capture deeper semantic relationships between users, items, and contexts. Our experiments demonstrate that this approach significantly improves recommendation accuracy and relevance, resulting in more personalized and context-aware recommendations. The findings underscore the potential of PLMs to enhance the effectiveness of recommender systems.

Ngoc Luyen Le, Marie-Hélène Abel

Group recommender systems aim to generate recommendations that align with the collective preferences of a group, introducing challenges that differ significantly from those in individual recommendation scenarios. This paper presents Joint Group Profiling and Recommendation via Deep Neural Network-based Multi-Task Learning, a framework that unifies group profiling and recommendation tasks within a single model. By jointly learning these tasks, the model develops a deeper understanding of group dynamics, leading to improved recommendation accuracy. The shared representations between the two tasks facilitate the discovery of latent features essential to both, resulting in richer and more informative group embeddings. To further enhance performance, an attention mechanism is integrated to dynamically evaluate the relevance of different group features and item attributes, ensuring the model prioritizes the most impactful information. Experiments and evaluations on real-world datasets demonstrate that our multi-task learning approach consistently outperforms baseline models in terms of accuracy, validating its effectiveness and robustness.

Ngoc Luyen Le, Gildas Tagny Ngompé

In this article, we present the BTransformer18 model, a deep learning architecture designed for multi-label relation extraction in French texts. Our approach combines the contextual representation capabilities of pre-trained language models from the BERT family - such as BERT, RoBERTa, and their French counterparts CamemBERT and FlauBERT - with the power of Transformer encoders to capture long-term dependencies between tokens. Experiments conducted on the dataset from the TextMine'25 challenge show that our model achieves superior performance, particularly when using CamemBERT-Large, with a macro F1 score of 0.654, surpassing the results obtained with FlauBERT-Large. These results demonstrate the effectiveness of our approach for the automatic extraction of complex relations in intelligence reports.