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Goel, Ashok K.

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    A virtual coach for question asking and enabling learning by reflection in startup engineering
    (Georgia Institute of Technology, 2020-12) Goel, Ashok K. ; Hong, Sung Jae ; Kuthalam, Mukundan ; Arcalgud, Arup ; Gulati, Siddharth ; Howe, James ; Karnati, Nikhita ; Mardis, Aaron ; Ro, Jae ; McGreggor, Keith ; College of Computing ; School of Interactive Computing
    The Socratic method of teaching engages learners in extended conversations and encourages learning through answering questions, making arguments, and reflecting on the evolving conversation. This method can be a powerful instrument of learning by reflection, especially in domains in which the right answers to open questions are not known in advance such as entrepreneurship. In this paper, we describe an initial experiment in developing AI technology for simulating the Socratic method of teaching in learning about entrepreneurship. When a would-be entrepreneurs creates a business model on the Business Model Canvas (BMC), the AI agent named Errol uses semantic and lexical analysis of the entries on the BMC to ask questions of the students. By attempting to categorize and correct the errors that novices typically make, Errol seeks to accelerate the process by which a novice can start creating more expert-like business models
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    Using Spatial Structure in the Associative Retrieval of 2-D Line Drawings
    (Georgia Institute of Technology, 2002) Yaner, Patrick W. ; Goel, Ashok K. ; College of Computing
    We consider the problem of associative image retrieval, focusing on retrieval of 2-D line drawings by example. We represent 2-D line drawings as semantic networks of spatial elements and relations among them. We describe a process for retrieving the drawings based on a structural analogy between the query and the stored images. We then present several methods of retrieving the drawings: the first family methods uses logical unification and resolution to accomplish the matching; the second family of methods heuristically prunes the stored drawings and then does the resolution and unification on the remaining drawings; two more methods treat the retrieval problem as a constraint satisfaction problem and use common CSP techniques for solving it; and the last two methods combine the heuristic step of the second method with the CSP technique of the third and fourth. We report on experimental results that compare the performance of these methods on computer-based libraries of drawings. A surprising result of our work is that for the fastest of these methods two stage retrieval appeared to offer no benefit over one stage retrieval.
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    Blended Learning in Practice: A Guide for Practitioners and Researchers
    ( 2019-04-11) Aiello, Brittany ; Goel, Ashok K. ; Kadel, Robert S. ; Margulieux, Lauren ; Center for 21st Century Universities ; Georgia Institute of Technology. School of Interactive Computing ; Strada Education Network ; Georgia State University. Dept. of Learning Sciences
    A Guide for Practitioners and Researchers (MIT Press) A guide to both theory and practice of blended learning offering rigorous research, case studies, and methods for the assessment of educational effectiveness. Blended learning combines traditional in-person learning with technology-enabled education. Its pedagogical aim is to merge the scale, asynchrony, and flexibility of online learning with the benefits of the traditional classroom―content-rich instruction and the development of learning relationships. This book offers a guide to both theory and practice of blended learning, offering rigorous research, case studies, and methods for the assessment of educational effectiveness. The contributors to this volume adopt a range of approaches to blended learning and different models of implementation and offer guidelines for both researchers and instructors, considering such issues as research design and data collection. In these courses, instructors addressed problems they had noted in traditional classrooms, attempting to enhance student engagement, include more active learning strategies, approximate real-world problem solving, and reach non-majors. The volume offers a cross-section of approaches from one institution, Georgia Tech, to provide both depth and breadth. It examines the methodologies of implementation in a variety of courses, ranging from a first-year composition class that incorporated the video game Assassin's Creed II to a research methods class for psychology and computer science students. Blended Learning will be an essential resource for educators, researchers, administrators, and policy makers.
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    National AI Institute for Adult Learning and Online Education: Vision, Goals and Plans
    ( 2022-03-10) Goel, Ashok K. ; GVU Center ; Georgia Institute of Technology. School of Interactive Computing ; Georgia Institute of Technology. Center for 21st Century Universities
    NSF has recently established a National AI Institute for Adult Learning and Online Education (AI-ALOE) headquartered at Georgia Tech. The AI-ALOE Institute seeks to address the societal challenges of lifelong learning, workforce development, and reskilling and upskilling of millions of American workers. Online education offers an affordable medium for taking education to workers where they live. AI-ALOE will develop new AI technologies that enhance cognitive engagement, teacher presence, social interaction, and self-directed learning in online education, and thereby improve its quality for adult learners in STEM disciplines. In addition to these use-inspired AI techniques, AI-ALOE will conduct foundational AI research on personalization of learning at scale, interactive machine teaching, mutual theory of mind, and participatory design of sociotechnical systems for responsible AI. I will describe AI-ALOE’s vision, goals and plans, using examples from my research laboratory for illustration.
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    Jill Watson: A Virtual Teaching Assistant for Online Education
    (Georgia Institute of Technology, 2016) Goel, Ashok K. ; Polepeddi, Lalith ; College of Computing ; School of Interactive Computing ; Georgia Institute of Technology. Design & Intelligence Laboratory
    MOOCs are rapidly proliferating. However, for many MOOCs, the effectiveness of learning is questionable and student retention is low. One recommendation for improving the learning and the retention is to enhance the interaction between the teacher and the students. However, the number of teachers required to provide learning assistance to all students enrolled in all MOOCs is prohibitively high. One strategy for improving interactivity in MOOCs is to use virtual teaching assistants to augment and amplify interaction with human teachers. We describe the use of a virtual teaching assistant called Jill Watson (JW) for the Georgia Tech OMSCS 7637 class on Knowledge-Based Artificial Intelligence. JW has been operating on the online discussion forums of different offerings of the KBAI class since Spring 2016. By now some 750 students have interacted with different versions of JW. In the latest, Spring 2017 offering of the KBAI class, JW autonomously responded to student introductions, posted weekly announcements, and answered routine, frequently asked questions. In this article, we describe the motivations, background, and evolution of the virtual question-answering teaching assistant.
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    An adaptive approach to qualitative modeling in design
    (Georgia Institute of Technology, 1993) Goel, Ashok K. ; Georgia Institute of Technology. Office of Sponsored Programs ; Georgia Institute of Technology. College of Computing ; Georgia Institute of Technology. Office of Sponsored Programs
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    Model-Based Reconfiguration of Schema-Based Reactive Control Architectures
    (Georgia Institute of Technology, 1997) Chen, Zhong ; Goel, Ashok K. ; Rowland, Paul ; Stroulia, Eleni ; College of Computing ; Mobile Robot Laboratory ; Institute for Robotics and Intelligent Machines
    Reactive methods of control get caught in local minima. Fortunately schema-based reactive control systems have built-in redundancy that enables multiple configurations with different modes. We describe a model-based method that exploits this redundancy, and, under certain conditions, reconfigures schema-based reactive control systems trapped in behavioral cycles due to the presence of local minima. The qualitative model specifies the functions and modes of the perceptual and motor schemas, and represents the reactive architecture as a structure-behavior-function model. The model-based method monitors the reactive processing, detects failures in the form of behavioral cycles, analyzes the processing trace, identifies potential modifications, and reconfigures the reactive architecture. We report on experiments with a simulated robot navigating a complex space.
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    Thinking in Pictures as a Cognitive Account of Autism
    (Georgia Institute of Technology, 2010) Kunda, Maithilee ; Goel, Ashok K. ; GVU Center ; Georgia Institute of Technology. School of Interactive Computing
    We analyze the hypothesis that some individuals on the autism spectrum may use visual mental representations and processes to perform certain tasks that typically developing individuals perform verbally. We present a framework for interpreting empirical evidence related to this “Thinking in Pictures” hypothesis and then provide comprehensive reviews of data from several different cognitive tasks, including the /n/-back task, serial recall, dual task studies, Raven’s Progressive Matrices, semantic processing, false belief tasks, visual search and attention, spatial recall, and visual recall. We also discuss the relationships between the Thinking in Pictures hypothesis and other cognitive theories of autism including Mindblindness, Executive Dysfunction, Weak Central Coherence, and Enhanced Perceptual Functioning.
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    SoD-TEAM: Teleological reasoning in adaptive software design
    (Georgia Institute of Technology, 8/31/2012) Goel, Ashok K. ; Rugaber, Spencer ; Office of Sponsored Programs ; College of Computing ; School of Interactive Computing
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    Reasoning About Function in Reflective Systems
    (Georgia Institute of Technology, 1993) Stroulia, Eleni ; Goel, Ashok K. ; College of Computing
    Functional models have been extensively investigated in the context of several problem-solving tasks such as device diagnosis and design. In this paper, we view problem solvers themselves as devices, use functional models to represent how they work, and subsequently employ these models for performance-driven reflective reasoning and learning. We represent the functioning of a problem solver as a structure-behavior-function model that specifies how the knowledge and reasoning of the problem solver results in the achievement of its goals. We view performance-driven learning as the task of redesigning the knowledge and reasoning of the problem solver. We use the structure-behavior-function model of the problem solver to monitor its reasoning, reflectively assign blame when it fails, and redesign its knowledge and reasoning. This paper describes an architecture for reflective model-based reasoning that is capable of a broad range of learning tasks. It also illustrates reflective model-based learning using examples from the Autognostic system, a reflective path planner.