123 citations · 145 across the 17 of their papers we have counts for
18 papers
Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration
Marta Knežević, Seyed Parsa Neshaei, Jibril Frej +2
Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex systems. Large language mode…
SCRIBE: Structured Chain Reasoning for Interactive Behaviour Explanations using Tool Calling
Fares Fawzi, Vinitra Swamy, Dominik Glandorf +2
Language models can be used to provide interactive, personalized student feedback in educational settings. However, real-world deployment faces three key challenges: privacy concer…
Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
Shang-Chi Tsai, Yun-Nung Chen
With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…
A Human-Centric Approach to Explainable AI for Personalized Education
Vinitra Swamy
Deep neural networks form the backbone of artificial intelligence research, with potential to transform the human experience in areas ranging from autonomous driving to personal as…
iLLuMinaTE: An LLM-XAI Framework Leveraging Social Science Explanation Theories Towards Actionable Student Performance Feedback
Vinitra Swamy, Davide Romano, Bhargav Srinivasa Desikan +2
Recent advances in eXplainable AI (XAI) for education have highlighted a critical challenge: ensuring that explanations for state-of-the-art AI models are understandable for non-te…
Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection
Isadora Salles, Paola Mejia-Domenzain, Vinitra Swamy +2
Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectivenes…
Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning
Elena Grazia Gado, Tommaso Martorella, Luca Zunino +4
Intelligent Tutoring Systems (ITS) enhance personalized learning by predicting student answers to provide immediate and customized instruction. However, recent research has primari…
Intrinsic User-Centric Interpretability through Global Mixture of Experts
Vinitra Swamy, Syrielle Montariol, Julian Blackwell +3
In human-centric settings like education or healthcare, model accuracy and model explainability are key factors for user adoption. Towards these two goals, intrinsically interpreta…
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
Zeming Chen, Alejandro Hernández Cano, Angelika Romanou +17
Large language models (LLMs) can potentially democratize access to medical knowledge. While many efforts have been made to harness and improve LLMs' medical knowledge and reasoning…
Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance
Thiemo Wambsganss, Xiaotian Su, Vinitra Swamy +3
Large Language Models (LLMs) are increasingly utilized in educational tasks such as providing writing suggestions to students. Despite their potential, LLMs are known to harbor inh…
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks
Vinitra Swamy, Malika Satayeva, Jibril Frej +5
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 potenti…
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
Vinitra Swamy, Jibril Frej, Tanja Käser
Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems. As models get larger, more ubiquitous, and pervas…
Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design
Vinitra Swamy, Sijia Du, Mirko Marras +1
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,…
RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education
Mohammad Asadi, Vinitra Swamy, Jibril Frej +3
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, design…
Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling
Thiemo Wambsganss, Vinitra Swamy, Roman Rietsche +1
Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases i…
Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs
Vinitra Swamy, Bahar Radmehr, Natasa Krco +2
Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainab…
Meta Transfer Learning for Early Success Prediction in MOOCs
Vinitra Swamy, Mirko Marras, Tanja Käser
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 inte…
Interpreting Language Models Through Knowledge Graph Extraction
Vinitra Swamy, Angelika Romanou, Martin Jaggi
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 poi…