The Design and Use of Conversational Intelligent Tutoring Systems and Computer Simulation for the Use of Students of Technology Entrepreneurship

Document Type

Conference Proceeding

Publication Date



Entrepreneurship is complex and dynamic. It involves continuously pursuing novel or better products and business models amidst constraints, uncertainty, and constant change among ecosystem participants (or "agents"). Entrepreneurship education, therefore, needs to be non-linear. Yet, traditional teaching methods in entrepreneurship came from business management education practices: lectures, case studies, and group discussions-mostly ineffective because entrepreneurship is more dynamic and non-linear. Recent entrepreneurial experiential learning attempts include starting and running a business and using computer simulations to reduce time and cost. There are opportunities to introduce non-linear and more human-like approaches to the learning interface, and these are some of the aims of intelligent tutor systems (ITS). This study proposes using a conversational ITS (CITS) as the learning experience interface for a technology entrepreneurship program to teach students various concepts. Conversations, through natural language, will take advantage of recent developments in large language models (LLMs) and related conversational agents and Al assistants such as ChatGPT. At the heart of the learning tool is a suite of computer simulation environments specifically for technology entrepreneurship, with the choice of technology entrepreneurship forcing novelty and relative market uncertainty in product offerings. The design and selection of technologies will follow evaluation frameworks on the effectiveness of entrepreneurship teaching simulation environments: fidelity, verification, and validity. The expected output will be a simulation environment resulting from multiple design-build-implement iterations. The CITS and the simulation core engine shall interface with a Learning Management System (LMS). The study will also generate insights after simulation sessions with domain experts and students through educational data mining (EDM) of the resulting logs.

This document is currently not available here.