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Applied Software Tools for Supporting Children with Intellectual Disabilities
Title
Applied Software Tools for Supporting Children with Intellectual Disabilities.
Creator
Abualsamid, Ahmad, Hughes, Charles, Dieker, Lisa, Sims, Valerie, Wiegand, Rudolf, University of Central Florida
Abstract / Description
We explored the level of technology utilization in supporting children with cognitive disabilities at schools, speech clinics, and with assistive communication at home. Anecdotal evidence, literature research, and our own survey of special needs educators in Central Florida reveal that use of technology is minimal in classrooms for students with special needs even when scientific research has shown the effectiveness of video modeling in teaching children with special needs new skills and...
Show moreWe explored the level of technology utilization in supporting children with cognitive disabilities at schools, speech clinics, and with assistive communication at home. Anecdotal evidence, literature research, and our own survey of special needs educators in Central Florida reveal that use of technology is minimal in classrooms for students with special needs even when scientific research has shown the effectiveness of video modeling in teaching children with special needs new skills and behaviors. Research also shows that speech and language therapists utilize a manual approach to elicit and analyze language samples from children with special needs. While technology is utilized in augmentative and alternative communication, many caregivers utilize paper-based picture exchange systems, storyboards, and daily schedules when assisting their children with their communication needs. We developed and validated three software frameworks to aid language therapists, teachers, and caregivers in supporting children with cognitive disabilities and related special needs. The Analysis of Social Discourse Framework proposes that language therapists use social media discourse instead of direct elicitation of language samples. The framework presents an easy-to-use approach to analyzing language samples based on natural language processing. We validated the framework by analyzing public social discourse from three unrelated sources. The Applied Interventions for eXceptional-needs (AIX) framework allows classroom teachers to implement and track interventions using easy-to-use smartphone applications. We validated the framework by conducting a sixteen-week pilot case study in a school for students with special needs in Central Florida. The Language Enhancements for eXceptioanl Youth (LEXY) framework allows for the development of a new class of augmentative and alternative communication tools that are based on conversational chatbots that assist children with special needs while utilizing a model of the world curated by their caregivers. We validated the framework by simulating an interaction between a prototype chatbot that we developed, a child with special needs, and the child's caregiver.
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Date Issued
2018
Identifier
CFE0006964, ucf:52908
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006964
EPISODIC MEMORY MODEL FOR EMBODIED CONVERSATIONAL AGENTS
Title
EPISODIC MEMORY MODEL FOR EMBODIED CONVERSATIONAL AGENTS.
Creator
Elvir, Miguel, Gonzalez, Avelino, University of Central Florida
Abstract / Description
Embodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into...
Show moreEmbodied Conversational Agents (ECA) form part of a range of virtual characters whose intended purpose include engaging in natural conversations with human users. While works in literature are ripe with descriptions of attempts at producing viable ECA architectures, few authors have addressed the role of episodic memory models in conversational agents. This form of memory, which provides a sense of autobiographic record-keeping in humans, has only recently been peripherally integrated into dialog management tools for ECAs. In our work, we propose to take a closer look at the shared characteristics of episodic memory models in recent examples from the field. Additionally, we propose several enhancements to these existing models through a unified episodic memory model for ECAÂÂ's. As part of our research into episodic memory models, we present a process for determining the prevalent contexts in the conversations obtained from the aforementioned interactions. The process presented demonstrates the use of statistical and machine learning services, as well as Natural Language Processing techniques to extract relevant snippets from conversations. Finally, mechanisms to store, retrieve, and recall episodes from previous conversations are discussed. A primary contribution of this research is in the context of contemporary memory models for conversational agents and cognitive architectures. To the best of our knowledge, this is the first attempt at providing a comparative summary of existing works. As implementations of ECAs become more complex and encompass more realistic conversation engines, we expect that episodic memory models will continue to evolve and further enhance the naturalness of conversations.
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Date Issued
2010
Identifier
CFE0003353, ucf:48443
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003353