Date of Award

Spring 5-22-2023

Document Type

Thesis (Undergraduate)

Department or Program

Cognitive Science

First Advisor

Lorie Loeb

Abstract

This thesis is concerned with utilizing artificial intelligence and machine learning (AI/ML) techniques and cognitive theories of feedback to enhance learning outcomes in the field of user interface and user experience (UI/UX) design. The capabilities of AI/ML have expanded immensely over the past several years, and it is now being effectively used in software programs like Grammarly, a tool that provides intelligent feedback on writing skills including grammar, tone, and clarity. Grammarly has been uniquely successful as a feedback tool because it relies on lessons from cognitive science regarding student feedback and learning outcomes. Currently, there is no comparable software available for UI/UX, making it a uniquely untapped area for effective learning tools. The question that this thesis attempts to answer, therefore, is: How can the successes of Grammarly and established cognitive feedback principles inform the design of an AI/ML-based feedback tool for UI/UX design? To answer that question, this thesis explores previous work on AI/ML techniques, cognitive feedback theories, structural similarities between grammar and design, and design heuristics in order to ultimately define the theoretical groundwork for a “Grammarly for UI/UX design.”

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