This study critically examines the redesign of an undergraduate research methods course using the Quality Matters (QM) 7th Edition rubric as a framework for both quality assurance and pedagogical enhancement. Using an autoethnographic approach, the analysis draws on reflective journaling, peer feedback, and course content reviews to trace an iterative, three-phase redesign process: self-assessment, targeted professional development, and course reconstruction. The redesign prioritized QM standards related to alignment of learning objectives, learner experiences, and accessibility, while integrating AI tools such as automated feedback systems and generative writing aids. These tools were evaluated by tracking student participation in course activities and through reflective assignments. The findings reveal strategies for balancing adherence to quality standards with active participation and student-centered learning. They also highlight technology-driven approaches that promote flexibility and inclusivity for diverse student learners. Overall, this study demonstrates how reflective, data-informed course design can bridge quality assurance and pedagogical innovation. It offers practical guidance for integrating emerging technologies within established instructional standards.

Authors:
Mercedes Valadez, California State University, Sacramento,United States

About the Presenter(s)
Dr. Mercedes Valadez is an Associate Professor in the Division of Criminal Justice at California State University, Sacramento.

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