Invited Speakers

Re-thinking Education in the Face of AI

Time: TBD

Location: TBD

William Swartout

Chief Science Officer, USC Institute for Creative TechnologiesCo-Director, USC Center for Generative AI and SocietyResearch Professor, Computer Science

Abstract: When ChatGPT was released in the fall of 2022, the education community panicked. Suddenly there existed a highly capable AI that was facile with language. Teachers feared students would use Gen AI to cheat and write their essays for them, and the press and internet were rife with articles proclaiming the Death of the Term Paper. Banning AI was problematic since preventing students from using AI while in school would not prepare them for the world into which they would graduate, and the detectors that purported to tell if text was written by an AI or a human had significant false positive and false negative error rates. Working with faculty from the USC undergraduate writing program we developed a writing tool called ABE that takes a different approach. In ABE, we use generative AI to help students brainstorm about a topic, and then when they are finished with their essays (which they write themselves) we use generative AI again, not as a writer, but as a reader, to read their essays and offer critiques, answering questions such as: Does the essay have a good hook? Is there adequate support for the claims? Are there other points of view that should be considered, but weren't? Surveys have shown that students have received ABE very positively and have found it helpful in their writing.
Stepping back a bit, I believe Gen AI is going to force us to reconsider how we teach across a very broad spectrum of intellectual domains. While each domain presents its own challenges, I believe that our experience with ABE is an exemplar of how Gen AI can be integrated into instructional design to actually improve students' critical thinking skills rather than detract from them.
Bio: William Swartout is chief science officer at the USC Institute for Creative Technologies, providing overall direction to the institute’s research programs. He is also co-Director of the Center for Generative AI and Society and a research professor in the Computer Science Department at the USC Viterbi School of Engineering.
Swartout has been involved in cutting edge research and development of artificial intelligence systems throughout his career. In 2009, Swartout received the Robert Engelmore Award from the Association for the Advancement of Artificial Intelligence (AAAI) for seminal contributions to knowledge-based systems and explanation, groundbreaking research on virtual human technologies and their applications, and outstanding service to the artificial intelligence community. Swartout is a Fellow of the AAAI, has served on their Board of Councilors and is past chair of the Special Interest Group on Artificial Intelligence (SIGART) of the Association for Computing Machinery (ACM).
He has served as a member of the Air Force Scientific Advisory Board, the Board on Army Science and Technology of the National Academies and the JFCOM Transformation Advisory Group. Prior to helping found the ICT in 1999, Swartout was the Director of the Intelligent Systems Division at the USC Information Sciences Institute.
His particular research interests include virtual humans, natural language processing, particularly explanation and text generation, knowledge acquisition, knowledge representation, and intelligent computer based education. He received his Ph.D. and M.S. in computer science from MIT and his bachelor’s degree from Stanford University.

Explainable Neural Text Classification

Time: TBD

Location: TBD

Diana Inkpen

ProfessorSchool of Electrical Engineering and Computer Science,University of Ottawa, Canada

Abstract: Advances in Large Language Models (LLMs) allow us to develop highly accurate neural text classifiers. One of their major disadvantages is their lack of explainability, due to their black box nature. I am looking into neural text classifiers that are explainable, in order to open their black box architecture, at least partially. Explainability can come at the level of the classification model or at the level of the decision made for each new test data. The explanations need to look into what was learnt from the training data (unless there is no training or minimal training) and also into the pre-trained model (LLM) that was used as a basis for the classifier. To explain the individual decisions for each test data, one step is to calculate feature importance with methods such as LIME, SHAP, or Integrated Gradients. More useful full-text explanations can be generated via customized prompting, or via joint leaning of classes and explanations during training. I will show results for two case studies: applications to legal text mining and to mental health text mining. The evaluation of the generated explanations is done via automatic measures, as well as with human judges, in order to see if they find the explanations relevant and useful.
Bio: Diana Inkpen is a Professor at the University of Ottawa, in the School of Electrical Engineering and Computer Science. She received her Ph.D. in Computer Science from the University of Toronto, Canada, and her M.Sc. and B.Eng. in Computer Science and Engineering from the Technical University of Cluj-Napoca, Romania. Her research is in applications of Natural Language Processing and Deep Learning. She is the editor-in-chief of the Computational Intelligence journal and the associate editor for the Natural Language Engineering journal. She published a book on Natural Language Processing for Social Media (Morgan and Claypool Publishers, Synthesis Lectures on Human Language Technologies, the third edition appeared in 2020), 11 book chapters, more than 45 journal articles, and more than 150 conference papers. She has received many research grants, the majority of which include intensive industrial collaborations.

Generative AI for Adaptive Military Training: Combining Environmental Simulation with Intelligent Tutoring

Time: TBD

Location: TBD

Keith Brawner

Research Program Manager of the Institute for Creative Technologies UARCSenior Research and Artificial Intelligence Lead for AFC-DEVCOM Soldier Center

Abstract: This talk explores the transformative potential of generative AI in revolutionizing military training through adaptive, competency-based learning systems. By combining bottom-up environmental simulation with top-down pedagogical design, we demonstrate how AI can create dynamic, personalized training experiences that adapt in real-time to individual and team performance. by utilizing generative AI across multiple dimensions: from procedurally generating hyper-realistic terrain using satellite imagery and neural rendering techniques to dynamically crafting scenarios that maintain optimal learning difficulty (guided by the 85% success rate principle). Intelligent agents serve as both adversaries and instructors, providing tailored challenges and pedagogical feedback. These systems integrate seamlessly with frameworks like the Generalized Intelligent Framework for Tutoring (GIFT), enabling real-time assessment and adaptive instruction through AI-generated computational gateways.
This talk highlights key technical advancements, including real-time data capture, synthetic data generation for continuous improvement, and cross-platform delivery mechanisms from mobile devices to full-scale simulations. It also discusses challenges in maintaining psychological fidelity, simulating complex multi-domain operations (land/air/sea), and incorporating real-world changes into training scenarios. While focused on military applications, the principles discussed are broadly applicable to any domain requiring scalable, adaptive, multi-form generative AI simulations.

Bio: Dr. Keith Brawner is the Program Manager of the Institute for Creative Technologies University Affiliated Research Center (ICT UARC) for the Undersecretary of Defense for Research and Engineering, as managed by the US Army DEVCOM Soldier Center, where he is also the lead for AI initiatives. He is a research leader in intelligent tutoring systems with over 100 publications in research/application of simulation and training technologies. While he manages the ICT at the University of Southern California, he works at the SGT First Class Paul Ray Smith Simulation and Training Technology facility in Orlando, Florida. He has worked on, or contributed to, systems which train land navigation, marksmanship, hovercraft operation, destroyer decision making, physics, UAV route planning, calling for indirect fires, submarine harbor navigation, sonar system operation, helicopter flight, counterinsurgency, and so many others. His current research focus is on enhancing simulations, the realism within simulations, the behaviors of simulated characters, and the creation of human behavior change. He earned a BS in Electrical Engineering from the University of Central Florida. He earned a MS, and PhD in Computer Engineering while working full-time for the Navy (Naval Air Warfare Center Training Systems Division) and Army (Army Research Laboratory), respectively. He is the recipient of the National Training and Simulation Association Modeling and Simulation Award (2nd highest honor), the NTSA Top (10) Under 40, and the UCF Alumni 30 Under 30 Award. He’s energetic.

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