Date Approved

5-16-2023

Embargo Period

5-17-2023

Document Type

Thesis

Degree Name

M.S. Electrical and Computer Engineering

Department

Electrical and Computer Engineering

College

Henry M. Rowan College of Engineering

Advisor

Ying (Gina) Tang, Ph.D.

Committee Member 1

Ben Wu, Ph.D.

Committee Member 2

Cheng Zhu, Ph.D.

Keywords

Action Graph, Adaptive Game, Artificial Intelligence, Directed Graph, Educational Game, Personalized System

Subject(s)

Computer-assisted instruction--Computer programs

Disciplines

Computer Engineering | Electrical and Computer Engineering

Abstract

Traditional education systems are based on the one-size-fits-all approach, which lacks personalization, engagement, and flexibility necessary to meet the diverse needs and learning styles of students. This encouraged researchers to focus on exploring automated, personalized instructional systems to enhance students’ learning experiences. Motivated by this remark, this thesis proposes a personalized instructional system using a graph method to enhance a player’s learning process by preventing frustration and avoiding a monotonous experience. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated with our system would both detect player errors and provide personalized assistance to direct a player in the direction of a correct solution. To verify system performance, this research presents comparison testing on a group of students engaging in the game both with and without AI. Students who played the AI-assisted game showed an average 20% decrease in time needed and an average 58% decrease in actions taken to complete the game.

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