Midwestern Association of Graduate Schools Annual Meeting

Presenters: Lily Compton and Kristin Terrill

Abstract: Graduate education is a foundation for a career in academia, industry, or both. Capstone projects (e.g., dissertations) are not an endpoint but one of many digital footprints that contribute to students’ future reputations and careers. Digital publishing enhances the visibility of students’ scholarly works through institutional and global repositories; however, with visibility comes additional scrutiny. Graduate students must learn to navigate complicated issues like copyright, ethics and integrity, shared authorship, digital accessibility, and generative artificial intelligence to ensure that their scholarly works are solid building blocks for lifelong careers.  

This presentation introduces a proposed open educational resource (OER) handbook that compiles the knowledge graduate students and early career researchers need to: 

1. Establish and protect professional credibility 

2. Report and publish results with integrity 

3. Prepare and complete electronic theses and dissertations that meet institutional, state, and federal guidelines. 

This first-of-its-kind handbook aims to streamline crucial information from dispersed sources (e.g. the Graduate College, the Office of the Vice President for Research, the Office of University Counsel, and the University Library) into a single central repository for general research publication guidance and standards. Faculty who teach research courses and mentor students will be encouraged to adopt this OER since it functions as a scaffold for systematic understanding of academic research and dissemination practices during the formative stages of their graduate students’ research trajectories. 

We wish to thank Megan O’Donnell (ISU University Library) for her contributions to this project.

Presenters: Kristin Terrill, Lily Compton, and William Graves

Abstract: Frameworks for conducting systematic reviews of literature, such as meta-analyses, research syntheses, narrative reviews, etc., have been widely adopted and refined as methodological approaches. However, approaches to developing literature reviews that provide background for research articles, theses, and dissertations are less well-defined. Graduate students and early career researchers often find the process of reviewing relevant literature to establish the context and motivation for their research daunting and time-consuming. They may rely on informal recommendations or their own intuition to lay the groundwork for their research writing, and risk delaying publication or producing incomplete, unconvincing arguments resulting in laborious revisions and resubmissions.   

The emergence of generative artificial intelligence (gen AI) tools for supporting literature review presents a promising avenue for enhancing novice researchers’ literature review processes and improving their written products. In this presentation, we propose a framework for Artificial Intelligence – Facilitated Literature Review (AI-FLR) that establishes high-level priorities for trustworthy handling of literature in research writing and proposes practical steps for integrating generative AI into a non-systematic literature review procedure while maintaining the prominence of human judgment and research integrity. First, we analyze selected frameworks to identify strengths and gaps related to the needs of novice researchers’ use of gen AI and the related concerns of research and scholarly communities. Then, we utilize related elements to create the AI-FLR framework that guides the steps for using gen AI to reduce time-consuming mechanical and repetitive tasks while augmenting the creative tasks that require interpretation and authentic idea creation or development.  Lastly, we discuss the intersections between our AI-FLR framework within graduate education and elements of Trustworthy AITM (Deloitte, 2022) and how we can promote shared consensus among stakeholders, e.g., graduate student and committee members or researchers and principal investigators.