Knowledge Based Artificial Augmentation Technology: The Next Generation of Scholarly Writing Academic Tools

Presentation was too large to upload.  In addition, Martin La Pierre is my dissertation student for whom I am his chair.  This presentation can be confirmed by Mansureh Kebritchi, Ph.D. University Research Chair Center for Educational and Instructional Technology Research (CEITR) as she was in attendance at the convention.

Association for Educational and Communications Technology (AECT)
Dr. Dale Crowe, Mr. Martin Lapierre (dissertation student)
Presentation Date: 
Wednesday, October 19, 2016
Event or Conference: 
Learning In Las Vegas
Presentation Type: 
Paper Presentation
Boyer's Domain: 
Presentation Location: 
3000 Paradise Road
Las Vegas, NV 89109
United States
Associated Awards: 
UOPX Center Research Fellowship
Instructional Designers, in partnership with information technology researchers, recognize that Artificial Intelligence (AI) is moving beyond the realm of science fiction. Knowledge-based systems are becoming a reality. AI knowledge-based applications are currently being used in the United States health care system. Given the nature of knowledge-based systems it is plausible to portal health care (AI) knowledge-based technology to educational tool based applications. The primary focus of the research was to take one aspect of current academic tools (scholarly writing) and to examine the strengths and weaknesses of each, in addition to exploring the potential benefits and practicality of migration to knowledge-based applications, and developing a plan and model for developing a comprehensive Scholarly Writing Software. A knowledge-based system (KBS) has the potential to interact with a word processing program (e.g. Microsoft Word) beyond what is currently available to provide real time suggestions and recommendations to improve scholarly writing, including syntactic and sematic recognition. It also possible to program any of the writing style rules from APA (American Psychological Association), into knowledge based scholarly writing program. It is hoped that the results of this study may further assist in the developmental research and testing that may lead to a knowledge based computer/human interaction program. Instructional Designers/Technologists, in partnership with information technology researchers, are at the cusp of moving into the next level of technology enhancement, knowledge based applications. Corporations including IBM (Watson), Microsoft (Microsoft Knowledge Base) and Google (Google Knowledge Vault/Now) are beginning to offer access to instructional designers and programmers the inherent capabilities to create applications and instructional models with Natural Language Processing (NLP) abilities. NLP is the ability of a computer program to understand human speech as it is spoken. NLP is a component of artificial intelligence (AI). The population for this study consisted of software developers/engineers familiar with traditional and knowledge-based software. In addition, instructional designers/developers were included as part of the population. The sample size was 15 participants. Participants were individually interviewed with semi structured interview questions. Participants provided insight into the following research questions: RQ1 What is the strengths and weaknesses of current academic writing software? RQ2 What are the contributing factors for developing a successful AI knowledge-based scholarly writing software? RQ3 What are some unique challenges facing instructional designers and information technology developers in producing knowledge-based scholarly writing software? RQ4 What is the required components (technical, conceptual, etc.) for a knowledge based application that can identify syntactic and semantic recognition? RQ5 What is a practical plan for developing a scholarly writing software program? Data analysis consisted of documentation from the individual interviews. In addition, prior to data entry into NVivo participants were asked to member check their responses. Data was entered after the transcription of digital recording or hand written field notes. NVivo was used to assist in organizing data by classifying, coding and sorting. Using an inductive approach the data was analyzed for categories, themes and similarities. After identification of the categories and themes a feasibility model was developed for development of scholarly writing software (SWS).