Abstract: A formal formulation of the Task and work quantification system of Taskoids for A.I assisted in teaching using AWS Sumerian and Lex. We define the taskoids TAv1.0 and TAv2.0, the 2.0 version being animated. Taskoids are viewed as intention machine evolution with an EdTech framework for work-study based MOOC courses to assist in machine evolution and SaaS-based automation. Keywords: AWS Sumerian, Lex, Taskoid, ppt presentations, document storage, AWS s3, intents, AWS Lambda, Automation of Teaching. What: We define a Taskoid for a SaaS-based design of two versions of a Teaching Assistant with a conversational UI to a slide-based talk and a question and answer session. We define a formal framework for the Taskoid definition. How: The formal system is defined in a symbolic algebraic and DOM format with an axiomatic proof. The code is implemented in python using AWS CLI and Lex JSON objects. Why: The increased need for classroom and online education has led to scalability concerns and cost of education, addressed by the automation of teaching especially in the graduate curriculum in the Udacity learning model, of automated slide talks and QnA sessions. Automated grading has already been incorporated in the Udacity Model. Thus graduate MOOC courses can easily be assembled just in time using the slide presentations, QnA service and suggested readings with automated grading and feedback software. So What: The promise of free graduate education and work-study positions to move the graduate student community beyond the low-income status is finally possible within the Udacity Learning Model. Automated components with Taskoids enable easier formulation of MOOC content and presentation, and low code blueprints called Taskoids, allow for production coding in course work, allowing work-study positions of true resource usage at 40 hours per week, allowing for compensation on par with the industry internships. Hence implementing True EdTech models.
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[v1] 2019-08-24 14:10:03
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