UMBC Center for AI ]]>
Join Dr. Mariann Hawken, director of UMBC's Instructional Technology, and Josh Abrams, Instructional Design Specialist, for an exploration of Ultra’s new AI Conversations, a Socratic questioning exercise powered by AI to support the responsible use of AI. In this session, discover how AI Conversations can be integrated into your courses to enhance student learning and engagement. When defined by the instructor for complexity and personality, the AI Conversation allows students to explore a topic and gain new perspectives.
AI Conversations will be available in the September release and future updates to this tool in late fall will include roleplaying and debate exercises.
Get more information and register for the event here. ]]>Can AI help align assessments to Bloom's Taxonomy?
The UMBC Language Technology Seminar Series (LaTeSS – pronounced lattice) showcases talks from experts researching various language technologies, including but not limited to natural language processing, computational linguistics, speech processing, and digital humanities. UMBC people can join the group here.
3-4 pm EDT Tuesday, 10 Sept. 2024, ITE 325b at UMBC and online via WebEx
AI is being integrated into virtually every computer system we use, but often in ways that mean we cannot see the decisions AI makes for us. If we don't see a decision, we cannot notice whether we agree with it, and what we don't notice, we cannot change. For example, using an AI summarization system means trusting that it has captured all the aspects of a document that are relevant to you. If the task is high stakes, then the only way to check is to read the original document, but that significantly decreases the value of the summary. In this talk, I will present the concept of AI resilient interfaces: systems that use AI while giving users the information they need to notice and change its decisions. I will walk through two examples of novel systems that are more AI resilient than the typical solution to the problem for (1) SQL generation and (2) faster reading. I will conclude with thoughts on the potential and pitfalls of designing with AI resilience in mind.
Jonathan K. Kummerfeld is a Senior Lecturer (i.e., research tenure-track Assistant Professor) in the School of Computer Science at the University of Sydney. He is currently also a DECRAfellow, and collaborates with a range of academics across the world, including on DARPA-funded projects on AI agents that communicate. He completed his Ph.D. at the University of California, Berkeley, and was previously a postdoc at the University of Michigan, and a visiting scholar at Harvard. Jonathan’s research focuses on interactions between people and NLP systems, developing more effective algorithms, workflows, and systems for collaboration. He has been on the program committee for over 50 conferences and workshops. He currently serves as Co-CTO of ACL Rolling Review (a peer review system) and is a standing reviewer for the Computational Linguistics journal and the Transactions of the Association for Computational Linguistics journal.
CLARK is a digital repository aimed at advancing cybersecurity education by delivering high-quality curriculum resources to educators throughout the United States. We will explore the design science methodology that guided its development, emphasizing how these principles maintain the repository’s usability and relevance for users. Additionally, the presentation will address the integration of artificial intelligence into CLARK and how its extensive dataset of curriculum content has facilitated this process. Attendees will learn how CLARK meets the changing demands of cybersecurity education and contributes to shaping the next generation of cyber professionals.
Dr. Blair Taylor is the Director of the Center for Interdisciplinary and Innovation Cybersecurity (Cyber4All) and a Professor in the Department of Computer and Information Sciences at Towson University. She is a national expert in cybersecurity education and curriculum development. She worked with NSA’s College of Cyber as a subject matter expert on long-term strategies to increase the pipeline of qualified students and build the nation’s cyber workforce.
Paige Zaleppa is a Ph.D. student in Information Technology at Towson University, where she is studying the intersection of cybersecurity education and artificial intelligence. She has led the development of the CLARK project since late 2019 and has been an adjunct faculty member at Towson University since 2021. She is passionate about connecting educators and students with educational resources that can be utilized to create the next generation of cyber heroes.
Support for this event was provided in part by NSF under SFS grant DGE-1753681. The UMBC Cyber Defense Lab meets biweekly, Fridays 12-1pm. All meetings are open to the public.
The UMBC Training Centers will hold an AI Technical Meetup and Networking event from 5:00 PM to 7:00 PM EDT on Thursday, August 29, at their facility in Columbia, MD.
Speakers Ommo Clark, Siddhant Gupta, and Devon Slonakerwill share their experiences with the AI technology currently shaping the industry. Topics will include
An evaluation of the state-of-the-art Deepfake Detection software and a discussion of which companies have the most effective detection products;
The local installation and effectiveness of various Small Language Models (SLMs) with a focus on Speech to Text and Text to Speech models;
A demonstration of using APIs and Anthropic Claude models to build your own AI application;
A demonstration of the mid-August release of GPT-4o’s Advanced Voice Mode and powerful tools to enhance their AI-driven applications efficiently.
Register for this free event here.
UMBC Professors Lujie Chen (PI) and Shimei Pan (Co-Pi) received an NSF award of over $365,000 from the NSF Innovations in Graduate Education program in collaboration with the University of Central Florida. The project will augment, refine, and pilot Caselet, a scalable case-based practice tool, by leveraging AI, machine learning, and data analytics approaches, including large language models (LLMs).
The new effort will support the development of data science problem-solving skills in cognitive (the knowledge and skills themselves) and metacognitive domains (the skills for learning how to learn). It addresses the rapidly changing landscape of education in computing and data-intensive courses regarding what and how we teach. The project will augment and refine the Caselet practice tool in three dimensions to support scalable deployment and adoption through an iterative design and test framework.
Caselet will be extended with new features and piloted and tested by up to 1000 students drawn from three graduate programs over at UMBC. The work will focus on three tasks to address scale-up challenges. The first task will explore the approach to help scale up the authoring of Caselet using Large Language Models. The second task aims to scale up the cognitive skills assessment in data science problem solving using machine learning models to track students’ skill mastery at a refined level of precision. The third task will focus on the scalable assessment of metacognitive competencies related to data science problem-solving through multichannel multimodal data collection in controlled lab environments and course-based and self-paced settings.