RCN-UBE: hbcu faculty collaborative network for the integration of artificial intelligence and machine learning into teaching general biology

This National Science Foundation (NSF) Biological Sciences (CFDA 47.074) program Project Grant award of $250,000 provides funding to Florida A & M University (FAMU) for the "RCN-UBE: HBCU FACULTY COLLABORATIVE NETWORK FOR THE INTEGRATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING INTO TEACHING GENERAL BIOLOGY" initiative. The project aims to enhance student success and enrich the general biology learning experience at HBCU institutions by integrating artificial intelligence (AI) and machine learning (ML) into the curriculum. Key objectives include preparing biology students for data-intensive STEM careers, improving student performance and engagement in biology courses through active, technology-driven pedagogy, and advancing AI/ML-based teaching and research capabilities at HBCUs. Over a 3-year project period from Oct 2024 to Sep 2027, the award will support 12 HBCU biology faculty from FAMU, Jackson State University, Grambling State University, and Alcorn State University in developing and evaluating new AI/ML-integrated biology course modules. The project seeks to create a replicable model of AI/ML-integrated pedagogy that can be adopted by other minority-serving institutions.

Abstract

At historically black colleges and universities (hbcus), biology is the most frequently chosen major among science disciplines and many stem majors also require a general biology course. These courses often include traditional lectures and lab activities, which can lead to low success rates and diminished enthusiasm among students. This reliance on passive learning fails to engage even well-prepared students effectively. In addition, hbcus are behind other institutions regarding integration of important cutting-edge skills, such as artificial intelligence (AI) or machine learning (ML), which are required to prepare today's stem workforce. To assist in addressing these challenges, this network aims to enhance student success and enrich the overall learning experience by integrating AI/ML into the general biology curriculum. By doing so, the network will generate new knowledge that will enhance stem education and learning environments at florida a & m university (famu), jackson state university (JSU), grambling state university (GSU), and alcorn state university (ASU), while also benefiting other minority-serving institutions and preparing stem students for the modern workforce. The goals of this initiative include: 1) preparing biology students for careers in data-intensive fields such as biotechnology and BIO-data science by equipping them with relevant AI/ML skills; 2) enhancing the learning experience and performance in biology courses through active, technology-driven pedagogy; 3) improving overall success rates in general biology at hbcus and developing a replicable model of AI/ML-integrated pedagogy that can be adopted by other institutions, and; 4) advancing AI/ML-based teaching and research at hbcus, thereby broadening faculty and institutional capabilities in these cutting-edge technologies. To achieve these goals, 12 experienced biology faculty members from famu, JSU, GSU, and ASU will partner in workshops to learn basics of research with AI/ML and to develop modules to incorporate AI/ML applications into general biology courses. Courses infused with AI/ML will be compared to traditional sections to assess their impact on student performance and engagement. Network members and selected students from the courses will meet periodically to share results from the courses, develop new modules, and share research outcomes. This project is being jointly funded by the directorate for biological sciences, division of biological infrastructure, and the directorate for stem education, division of undergraduate education as part of their efforts to address the challenges posed in vision and change in undergraduate biology education: a call to action (http://visionandchange/finalreport/).? This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are planned for this award.