Publications by Jibril Albachir Frej

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes

Antoine Bosselut, Jibril Albachir Frej, Paola Mejia Domenzain, Luca Mouchel, Tatjana Nazaretsky, Seyed Parsa Neshaei, Thiemo Wambsganss

Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input ...

2024

GELEX: Generative AI-Hybrid System for Example-Based Learning

Jibril Albachir Frej, Aybars Yazici

Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To a ...

Association for Computing Machinery2024

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

Vinitra Swamy, Jibril Albachir Frej, Paola Mejia Domenzain, Luca Zunino, Tommaso Martorella, Elena Grazia Gado

Intelligent Tutoring Systems (ITS) enhance personalized learning by predicting student answers to provide immediate and customized instruction. However, recent research has primarily focused on the correctness of the answer rather than the student's perfor ...

2024

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

Martin Jaggi, Mary-Anne Hartley, Vinitra Swamy, Jibril Albachir Frej, Thierry Bossy, Thijs Vogels, Malika Satayeva

Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space. Multimodal (MM) models aim to extract the synergistic predictive potential of multiple data types to create a shared feature space with aligned sema ...

2023

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Jibril Albachir Frej, Tatjana Nazaretsky

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners ...

2023

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Jibril Albachir Frej

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes

GELEX: Generative AI-Hybrid System for Example-Based Learning

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Graph Chatbot

Chat with Graph Search

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

GELEX: Generative AI-Hybrid System for Example-Based Learning

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks