AI Research Fellows 24-25
The following overviews outline the AI research areas our fellows will explore and publish.
Yu-Hui Ching and Yu-Chang Hsu
Pre-service Teachers’ Perceptions of Using GenAI Tools as Critical Peers in Online Learning
This study will promote AI literacy modules for future teachers in EdTech 202, Teaching and Learning in a Digital Age. Through a learning community and rapid iteration process, Ching and Hsu aim to enhance students’ self-improvement and critical thinking skills by using GenAI tools as critical peers in assignments, improving their ability to receive, evaluate, and reflect on AI feedback. The study seeks to deepen pre-service teachers’ understanding of AI and demonstrate how AI tools can be used to develop critical thinking about their work.
Tiffany Hitesman with Ti Macklin
What has age got to do with it?: Nontraditional Student Perceptions and Use of Artificial Intelligence
Nontraditional student experiences with AI systems in online writing courses have not been sufficiently researched, especially in comparison to their more traditional student counterparts. By focusing on this overlooked demographic, this study seeks to give voice to nontraditional students and understand their perceptions, concerns, and prior knowledge about AI in the context of online writing courses.
Andy Hung and Brett Shelton
Fostering Doctoral Success: Multi-Agent System Feedback for Dissertation Proposals
Doctoral students need help completing dissertations which is sometimes due to timely feedback and challenges faced by faculty members. The solution in this research is to design a multi-agent system that provides the feedback to students on their dissertation proposals. Using AI tools to build a domain expert, a methodology expert, and a writing expert, these agents collaborate to provide student feedback. Success will be measured by the quality of student work through rubrics and surveys.
Steven Hyde and Gundy Kaupins
Martin-Gale: AI-Powered Case Generation and Grading
Case studies are often outdated, lacking diversity, and ineffective at simulating real-world processes. Using AI case generators and providing feedback from an AI grader can improve student performance. By using open-source language models, case studies will become AI chatbots. This research will be assessed through quantitative and qualitative methods of learner perceptions.
Karen Krier with Karen Nicholas and Yu-Hui Ching
Students’ Perspectives on Utilizing Poe for Research in an Online International Business Management Course
Non-traditional learners in online courses need community, engagement, and research skill development. By customizing the learning experience through an AI chat tool, students in this course will learn research skills and gain asynchronous support. This research will be an exploratory case study and will use semi-structured interviews and document analysis of chat scripts to measure outcomes.
Ross Perkins
An Investigation of Artificial Intelligence Tools in the Teaching and Learning Research Methods
Research methods faculty have concerns about using AI tools due to both data security and student reliance on AI. Through a mixed-methods study, this research will explore how faculty are currently thinking about this dilemma and learn what practices they use to integrate AI into their research methods courses.
Margaret Sass
Community-Centric AI Project Development
The focus of this research project is for students to design a community-based project that leverages ChatGPT and other AI tools to solve specific challenges faced by small and/or local communities. Students will be required to critically assess the ethical considerations involved in applying AI to these community projects. My research delves deeper into AI education—how to teach students to use AI tools both effectively and ethically, particularly within smaller, localized communities. By engaging in this fully online course, students will not only develop essential AI skills but also contribute meaningfully to community development through the thoughtful application of AI technology. Online students will first identify challenges within local communities and then provide the necessary AI education and tools to meet those needs.
Brian Stone
AI as artificial person, statistical model, or copilot: How mental models of AI affect use of the technology
Mental models of what generative AI is and how it works can alter how students use AI in their learning, in ways that may enhance or hinder learning, especially in online environments. This research will involve experimental and survey research to see how manipulating mental models of AI may affect how AI is used by online students, as well as identifying individual differences in how students use AI.
Online Education Research Symposium
Boise State University’s online faculty are leading research efforts in online education. The upcoming Online Education Research Symposium on October 11, 2024 (10:30 am-12:30 pm Mountain Time) will showcase four important funded projects from the 2023-2024 eCampus Center Research Fellowship program. These projects, which focus on online educational research, seek to improve student success by examining the impact of accessibility, artificial intelligence, machine learning, and ePortfolios on credit for prior learning. The Online Education Research Symposium is a unique opportunity to engage with online faculty’s cutting-edge research. We invite everyone – all Boise State students, staff, and faculty – to participate in this virtual event and enriching journey of discovery and learning. Take advantage of the chance to gain insights into the latest advancements in online education research and contribute to the ongoing discussion about the future of online learning.
Save the date and register for the event today!
Research Fellows 23-24
Baker Lawley
Clinical Associate Professor, Interdisciplinary Professional Studies and Bachelor of Applied Science
Lawley is exploring student learning and sentiment after completing their ePortfolios and how this high-impact strategy contributes to a transformative experience. This research focuses on credit for prior learning students, particularly those who are more nontraditional.
Patrick Lowenthal
Professor in Educational Technology and Innovation Faculty Associate, eCampus Center
Lowenthal’s innovative study focuses on faculty perceptions of accessibility tools like Ally. By combining analytics with qualitative data, he seeks to understand faculty needs, provide professional development, and enhance online course accessibility at Boise State. This research may pave the way for a more inclusive online educational environment.
Andy Hung and Kerry Rice
Professors in Educational Technology
Hung and Rice will leverage analytics, machine learning, and AI to obtain profound insights into online student engagement trends. By pinpointing when online students disengage, they plan to design personalized interventions for positive outcomes. Their research stands to bring about transformative changes in identifying and responding to online student needs and learning patterns.
Brian Stone
Associate Professor, Department of Psychology
Stone seeks to innovate instructional practices across disciplines by exploring the frontier of AI-supported student learning in online courses. Aligning with Bloom’s taxonomy, he will create pedagogical strategies that instructors can apply at various levels. Stone’s research promises to culminate in an open educational resource, offering an invaluable asset to educators everywhere.