Dr Saad M. Khan is presenting at the e-Assessment Question Virtual Conference

Dr Saad M. Khan will be presenting at the e-Assessment Question Conference 2020

Dr Saad M. Khan, Senior Director, AI and Machine Learning at ACTNext is presenting CPSX – An AI-driven Approach to Measuring Collaboration Skills Using Online GamesPSX at the e-Assessment Question Virtual Conference

This presentation is part of the Innovation in Assessment webinar on 25 June at 11am (BST).

Find out more about the conference programme here.

About the presentation

Games are everywhere, in fact it may be hard to keep up with whatever new game your friends or your kids may be playing. My team and I at ACTNext have been doing work on collaborative problem solving in our own video game. Using AI and Machine Learning techniques we are developing non-invasive methods of measuring social-emotional skills in an ecologically valid manner. Specifically, we will look at how team dynamics and team member task interactions are manifested as observable behaviors, and how these behaviors can be analyzed via passive collection such as video, audio, and eye tracking data streams. These data are merged with self- and peer-report measures to provide a holistic representation of individuals’ collaboration skills as they manifest in team settings. I believe such innovations can help meet a growing need in K-12 education of measuring soft skills.


About the Presenter

Dr Saad M. Khan will be presenting at the e-Assessment Question Conference 2020

Dr. Saad M. Khan is Sr. Director, AI and Machine Learning at ACTNext, ACT. With expertise in computer vision and machine learning, his interests span a spectrum of multidisciplinary research that includes NLP, multimodal analytics, psychometrics, educational games and simulations. At ACTNext, he leads research in the development of AI and machine learning based next generation learning and assessment systems. Prior to joining ACTNext, Khan was at ETS and SRI International where he spearheaded research on multimodal analytics in educational assessment and led design and development of intelligent training/learning systems that can adapt to both changing pedagogical objectives and learner behavior. He has served as principal investigator (PI) on programs in immersive training, human performance assessment, and automated target recognition awarded by IES and DARPA, among others. His work on using AI to mitigate learning gaps in underserved populations won the Gates Foundation funded Algorithms for Change award in 2018. Khan has authored over 35 publications and holds five issued patents in machine learning. He received a Ph.D. in computer science from the University of Central Florida in 2008 and is a senior member of IEEE and chaired the Signal Processing Chapter of IEEE’s Princeton Section.