Educational Techonology in Undergraduated Research Training at an HSI
General description of the project
This proposal presents a new course-based undergraduate research (CURE) model currently in development for a second-year course in a two-year student success sequence at a Hispanic-Serving Institution (HSI) that is also a Research I university. The course is intentionally designed to provide culturally relevant support, mentoring, and research strategies—integrating educational technology and AI tools—to advance the next generation of scholars, particularly students historically underrepresented in research pathways. The first-year course centers on foundational college success skills and helping students build connections to their majors. The second-year course—launching in the upcoming term—deepens students’ understanding of how research is embedded in their field and how the ability to interpret and apply research strengthens their academic and professional expertise.
The syllabus and course activities introduce students to key components of research design, literature reviews, research methodologies, and theoretical frameworks commonly used in research. Students engage in weekly research training sessions, participate in critical dialogue with leading scholars, and connect with peer and near-peer mentors to support their development as emerging scholars. The course also incorporates ethical AI literacy throughout each phase of the research process, guiding students in using AI tools responsibly for source identification, information organization, and literature synthesis. Although the course will be in progress at the time of the conference, the presentation will focus on the development process and extensive planning, including culturally informed design, pilot testing, and alignment with institutional priorities around research engagement and responsible AI use. The model is highly cost-effective, leveraging existing technologies, campus expertise, and reusable instructional modules. Lessons learned in planning include the need for explicit AI-usage guidelines, strong scaffolding for research tasks, and assessments that capture student thinking processes, not just outputs.
Technologies
The course integrates generative AI tools (e.g., ChatGPT and other institution-approved models), citation management systems (Zotero/Mendeley), and academic databases. These technologies support the full research cycle—from initial topic exploration to identifying credible sources, synthesizing literature, and organizing research findings. AI is introduced as a tool for organizing information and structuring ideas, but students are trained to validate all AI-generated content and the reliance on peer-reviewed scholarship is emphasized.
To support the course goals, we developed AI prompt strategies, verification workflows, rubrics tied to ethical use, and activities that require students to compare AI-generated suggestions with scholarly evidence. These design choices reinforce the course’s emphasis on research transparency, critical evaluation, and disciplinary relevance, while ensuring student access to emerging technologies.
Explain project results
The course is structured to advance institutional and student success outcomes, particularly for students at an HSI where culturally relevant research training is essential for supporting diverse scholars. Students gain early exposure to research design, methodological reasoning, and theoretical frameworks—skills that support upper-division coursework and foster deeper engagement within their academic disciplines. Weekly training sessions, scholar dialogues, and peer/near-peer mentoring promote students’ academic confidence, research competence, and emerging scholarly identity.
The culminating project, a student research presentation, is intentionally designed to synthesize students’ research knowledge and strengthen their sense of belonging in academic spaces—particularly critical at institutions striving to broaden participation in research. Combined with ethical AI training, the course equips students with transferable digital literacy skills that prepare them for academic, professional, and civic research demands. For the institution, the course offers a scalable, evidence-based model for embedding research education into the undergraduate experience while addressing the urgent need for ethical and responsible AI literacy across a large student body.
Why it should be considered best practice?
This project represents a best practice because it integrates ethical AI use with foundational research training in a structured, developmental sequence from the first to second year that is tailored to an HSI Research 1 context. It establishes a holistic approach where students build academic identity, learn core research concepts, engage with experts, and receive mentoring—while simultaneously learning how to use emerging technologies responsibly.
Its cost-effective design, modular structure, and built-in education research plan make the course highly replicable across departments and institutions. Because the research content is grounded in transferable skills—research design, literature review, methodology, theoretical frameworks—this model is relevant for all majors and adaptable to multiple disciplinary contexts.
Highlights of your proposed presentation
This presentation introduces a two-year course sequence that links first-year success skills to second-year research literacy through a culturally relevant, HSI-informed design. The model integrates instruction in research design, methodologies, theoretical frameworks, and literature reviews; weekly research training and dialogues with leading scholars; and peer and near-peer mentoring to support the development of students’ scholarly identities. Ethical AI-use frameworks are embedded throughout all activities, alongside a comprehensive education research plan that includes surveys, rubric-guided evaluations, qualitative reflections, and analytics on AI usage. The session will also share strategies for scaling this model across majors and institutional contexts.
The Evaluation Committee will evaluate submitted proposals based on the following criteria. Each area will be rated on a scale from 1 to 5 (1= non-satisfactory; 5 =outstanding), for a maximum of 45 points.