October 2026 - I'm excited to share that my PhD student, Abubakir Siedahmed, published a paper with Angela Duckworth and her colleagues in Nature Human Behavior showing that longer load times lead to lower engagement and learning outcomes. This study used data from the ASSISTments and CodeCamp platforms. The team tracked hundreds of thousands of clickstream data points to find that delays as short as 1-2 seconds impaired student learning. Students faced with these delays spent less time on the assignments, completed fewer problems, and earned lower scores. Here's the official paper link, and here's a full-text link to the paper.

July 2026 - This past year, my student Eamon Worden released a new dataset called FoundationalASSIST, developed through a collaboration between my lab at WPI, the ASSISTments Foundation, and Shashank Sonkar at UCF. Access the paper here: https://arxiv.org/abs/2602.00070. Access to the dataset here: https://huggingface.co/datasets/ASSISTments/FoundationalASSIST.  We’d love to see how the community uses it and are happy to answer questions.

Neil Heffernan is an active researcher in the fields of (1) artificial intelligence and education, (2) educational data mining and (3) learning analytics. In order to support research in these fields, Dr. Heffernan created the E-TRIALS Testbed, a tool that allows ASSISTments to be used as a platform to do science and support evidence-based practice.

Most recently, the focus of my research is using large language models (or AI) to support math teachers on their students' open-ended answers. Open-ended answers represent a shift in math education and common core learning standards. By asking the student to describe their thinking in words, teachers can better gauge student understanding beyond a right or wrong answer. The problem is that teachers don't often have the time to read every single written answer or provide immediate feedback to their students. Much of my research is focused on validating AI scoring and feedback to students, and providing teachers with advanced insights into student thinking. 

For over a decade Neil Heffernan has been teaching one undergraduate AI class (Introduction to Artificial Intelligence: CS 4341) and one graduate course in his area (rotating between the three course listed below). 

He rotates through these three graduate classes, teaching one each year:

• Special Topics: Large Language Models for Education (CS 525) *New* To be taught 2026-27)

• Artificial Intelligence for Adaptive Educational Technology (CS 568). (Last taught Fall 2024, next expected to be taught in 2027-28) 

• User Modeling, which focuses on educational data mining (CS 565). (Last taught Fall 2025, next expected to be taught in 2028-29)

• Special Topics: Online Learning Infrastructure (CS 525) (Last taught 2023, next expected to be taught in Fall 2029-30) 

He teaches at the undergraduate level this class:

• Introduction to Artificial Intelligence (CS 4341) (last taught Fall 2019, A Term).