Publications de Vinitra Swamy | EPFL Graph Search

A Human-Centric Approach to Explainable AI for Personalized Education

Deep neural networks form the backbone of artificial intelligence (AI) research, with potential to transform the human experience in areas ranging from autonomous driving to personal assistants, healthcare to education. However, their integration into the ...

EPFL2025

The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions

Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Jibril Albachir Frej, Tatjana Nazaretsky

In recent decades, we have witnessed the democratization of AI-powered Educational Technology (AI-EdTech). However, despite the increased accessibility and evolving technological capabilities, its adoption is accompanied by significant challenges, predomin ...

Elsevier BV2025

iLLuMinaTE: An LLM-XAI Framework Leveraging Social Science Explanation Theories Towards Actionable Student Performance Feedback

Tanja Christina Käser Jacober, Vinitra Swamy

Recent advances in eXplainable AI (XAI) for education have highlighted a critical challenge: ensuring that explanations for state-of-the-art models are understandable for non-technical users such as educators and students. In response, we introduce iLLuMin ...

Association for the Advancement of Artificial Intelligence2025

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

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

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

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Julian Thomas Blackwell, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Isadora Alves de Salles

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

2024

AI or Human? Evaluating Student Feedback Perceptions in Higher Education

Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Jibril Albachir Frej, Tatjana Nazaretsky

Feedback plays a crucial role in learning by helping individuals understand and improve their performance. Yet, providing timely, personalized feedback in higher education presents a challenge due to the large and diverse student population, often resultin ...

Springer Nature Switzerland2024

MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks

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

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

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

Tanja Christina Käser Jacober, Vinitra Swamy, Jibril Albachir Frej

Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...

2023

Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design

Tanja Christina Käser Jacober, Vinitra Swamy, Mirko Marras, Sijia Du

Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle th ...

2023

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Tanja Christina Käser Jacober, Natasa Krco, Vinitra Swamy, Mirko Marras, Bahar Radmehr

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

2022

FATED 2022: Fairness, Accountability, and Transparency in Educational Data

Vinitra Swamy

The increasing impact of machine learning and algorithmic decision making on education has brought about growing opportunities and concerns. Evidence has shown that these technologies can perpetuate and even magnify existing educational and social inequiti ...

2022

Meta Transfer Learning for Early Success Prediction in MOOCs

Tanja Christina Käser Jacober, Vinitra Swamy, Mirko Marras

Despite the increasing popularity of massive open online courses (MOOCs), many suffer from high dropout and low success rates. Early prediction of student success for targeted intervention is therefore essential to ensure no student is left behind in a cou ...

ACM2022

Ripple: Concept-Based Interpretation for Raw Time Series Models in Education

Tanja Christina Käser Jacober, Vinitra Swamy, Mirko Marras, Jibril Albachir Frej

Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. However, extr ...

2022

Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling

Tanja Christina Käser Jacober, Vinitra Swamy, Thiemo Wambsganss

Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in di ...

2022

Interpreting Language Models Through Knowledge Graph Extraction

Martin Jaggi, Angeliki Romanou, Vinitra Swamy

Transformer-based language models trained on large text corpora have enjoyed immense popularity in the natural language processing community and are commonly used as a starting point for downstream tasks. While these models are undeniably useful, it is a c ...

2021