36 citations · 61 across the 9 of their papers we have counts for
11 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…
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…
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
Beatriz Borges, Negar Foroutan, Deniz Bayazit +87
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they a…
Course Recommender Systems Need to Consider the Job Market
Jibril Frej, Anna Dai, Syrielle Montariol +2
Current course recommender systems primarily leverage learner-course interactions, course content, learner preferences, and supplementary course details like instructor, institutio…
Towards Modeling Learner Performance with Large Language Models
Seyed Parsa Neshaei, Richard Lee Davis, Adam Hazimeh +3
Recent work exploring the capabilities of pre-trained large language models (LLMs) has demonstrated their ability to act as general pattern machines by completing complex token seq…
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…
Let Me Teach You: Pedagogical Foundations of Feedback for Language Models
Beatriz Borges, Niket Tandon, Tanja Käser +1
Natural Language Feedback (NLF) is an increasingly popular mechanism for aligning Large Language Models (LLMs) to human preferences. Despite the diversity of the information it can…
Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling
Jade Maï Cock, Muhammad Bilal, Richard Davis +2
Algorithms deployed in education can shape the learning experience and success of a student. It is therefore important to understand whether and how such algorithms might create in…
Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning
Roberta Galici, Tanja Käser, Gianni Fenu +1
Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is o…
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,…