Publications by 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

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

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

Vinitra Swamy, Tanja Christina Käser Jacober

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

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

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

Vinitra Swamy, Paola Mejia Domenzain, Tanja Christina Käser Jacober, Julian Thomas Blackwell, 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

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

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

Martin Jaggi, Mary-Anne Hartley, Vinitra Swamy, Thierry Bossy, Tanja Christina Käser Jacober, Thijs Vogels, 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

Vinitra Swamy, Tanja Christina Käser Jacober, 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

Vinitra Swamy, Tanja Christina Käser Jacober, 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

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

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