Digital learning innovation: Engineering students’ learning motivation for AI digital scaffolding

Sung, Jung Sun

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https://hdl.handle.net/2142/129675 Copy

Description

Title

Digital learning innovation: Engineering students’ learning motivation for AI digital scaffolding

Author(s)

Sung, Jung Sun

Issue Date

2025-04-01

Director of Research (if dissertation) or Advisor (if thesis)

Huang, Wen-Hao David

Doctoral Committee Chair(s)

Huang, Wen-Hao David

Committee Member(s)

• Hood, Denice Ward
• Oh, Eunjung Grace
• Paquette, Luc

Department of Study

Educ Policy, Orgzn & Leadrshp

Discipline

Educ Policy, Orgzn & Leadrshp

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• AI-scaffolding
• Motivation
• ARCS Model
• Engineering

Abstract

This research intends to understand how the advanced features of newly emerging AI motivate college students as scaffolding activities in the STEM learning environment. Scaffolds are proven to be an effective way of teaching critical thinking and problem-solving skills for STEM education. Technology-enhanced scaffolding activities need more attention because they can provide individualized and self-regulated learning processes to students with different needs. In terms of digital technology, Artificial Intelligence (AI) has the potential for supplementing human capabilities and our power to learn. Recently with the advent of the Large Language Model, we are faced with revolutionary changes that may have unprecedented impacts on the way we learn. This new brand of AI is still in its infancy. However, as an emerging technology, comprehending what this technology offers to students’ learning through motivational scaffolding activities requires insightful observation. This study compared advanced AI’s novel features (interaction, generative, and analysis) as motivational tactics to conventional methods of scaffold activities with the goal of increasing students’ critical thinking and problem-solving skills. In this study, we apply a mixed methods design approach grounded in the Attention Relevance Confidence Satisfaction (ARCS) model, modified to accommodate the unique features of AI. This study was designed to answer which factors of motivation were mostly influenced by these features and how these factors differently impact students’ performance and motivations. Study findings showed that generative AI features are influenced differently based on the type of problem. In the process of solving computational problems, ‘Relevance’ was found to be the most influential factor. Participants were motivated by how AI provided them with practical and innovative methods of solving real-world problems and interdisciplinary approaches. However, participants lacked the experience and understanding to connect these methods to the existing knowledge and previously acquired scientific concepts. Meanwhile, for conducting research and creating essay problems, the ‘Confidence’ factor was the most significantly influenced by AI features. This was because participants felt confident about using AI in the same way as they would in daily life and they already had a firm foundation of knowledge about the topic through the conventional scaffolding activity. Furthermore, findings from this study include students’ perceptions of using AI for engineering learning and AI-generated information/solutions. Students perceive AI as a supplementary working tool rather than a learning tool as they have limited experience with AI encouraging their critical thinking process and accessing AI-generated information. Also, the results provided guidelines for designing further optimized strategies for the design of AI-based scaffolds in STEM education and its research directions by developing field-specific scaffolding activities and expanding the ‘Relevance’ category.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129675

Copyright and License Information

Copyright 2025 Jung Sun Sung

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