activity
20192026
most citedPredicting Engagement in Video Lectures

14 citations · 41 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CL2026

Sell Me This Stock: Unsafe Recommendation Drift in LLM Agents

Zekun Wu, Adriano Koshiyama, Sahan Bulathwela +1

People increasingly use LLM agents for multi-turn financial recommendations, where the agent pulls market data through tools and tracks user preferences across turns. When tool out…

cs.CY2025

TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models

Sahan Bulathwela, Daniel Van Niekerk, Jarrod Shipton +3

Personalised education is one of the domains that can greatly benefit from the most recent advances in Artificial Intelligence (AI) and Large Language Models (LLM). However, it is…

cs.CL2024

Stereotype Detection in LLMs: A Multiclass, Explainable, and Benchmark-Driven Approach

Zekun Wu, Sahan Bulathwela, Maria Perez-Ortiz +1

Stereotype detection is a challenging and subjective task, as certain statements, such as "Black people like to play basketball," may not appear overtly toxic but still reinforce r…

cs.CY2023★ 2 cited

A Toolbox for Modelling Engagement with Educational Videos

Yuxiang Qiu, Karim Djemili, Denis Elezi +5

With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future. Th…

cs.CY2021★ 4 cited

An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All

Maria Perez-Ortiz, Erik Novak, Sahan Bulathwela +1

Artifical Intelligence (AI) in Education has great potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of ne…

cs.IR2021★ 4 cited

PEEK: A Large Dataset of Learner Engagement with Educational Videos

Sahan Bulathwela, Maria Perez-Ortiz, Erik Novak +2

Educational recommenders have received much less attention in comparison to e-commerce and entertainment-related recommenders, even though efficient intelligent tutors have great p…

cs.CY2020★ 9 cited

VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement

Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz +1

With the emergence of e-learning and personalised education, the production and distribution of digital educational resources have boomed. Video lectures have now become one of the…

cs.CY2020★ 14 cited

Predicting Engagement in Video Lectures

Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani +2

The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of iden…

cs.IR2019★ 8 cited

Towards an Integrative Educational Recommender for Lifelong Learners

Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz +1

One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities betwee…

cs.AI2019

TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources

Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz +1

The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality educa…