Localizing AI Innovation: How CoreEthic AI Empowered an HBCU Social Sciences Department
When CoreEthicAI began its collaboration with a Social Sciences Department of a mid‑sized HBCU, we encountered a familiar tension: campus leaders were eager to “do AI,” yet faculty worried that a broad, top‑down initiative would overwhelm already overtaxed professors, administrators and students. We proposed a different path that is rooted in the conviction that the AI revolution in higher education succeeds not through monolithic mandates from above, but by empowering individual departments to adapt tools and workflows to their own scholarly practices.
We unfolded our programming over a single semester. First, we convened a listening tour: where we sat down with department faculty, admin, and students to map their existing workflows, data challenges, and pedagogical goals. We discovered that their discipline prized narrative nuance and critical interpretation as much as statistical rigor, that students thrived on project‑based learning, and that any AI adoption would have to honor both. Armed with these insights, we co‑designed a phased program of activities. The kickoff “AI in Social Inquiry” symposium invited faculty and students into hands‑on small groups, where they used open-source interview transcripts to develop and compare machine‑generated themes with their own codes. By anchoring the demonstration in their own data and using familiar methods we showed how AI could amplify their existing research processes and develop new and profound insights!
Next came an intensive, department‑scoped “ML Boot Camp.” We delivered concise tutorials on Python fundamentals, regression and classification with scikit‑learn, and network analysis for studying social ties. Afternoons ran a “Research Clinic,” where instructors brought their own questions on AI and their own research to ensure relevance and immediate application. Aware that ethical concerns often stall AI adoption, we guided the creation of a departmental AI Ethics Working Group and introduced a component of our CARB Model framework. Over two facilitated sessions, faculty, graduate students, and administrators drafted targeted guidelines: clear attribution of AI‑assisted analysis in student papers; permissible uses of generative tools for literature reviews; and mandatory consent for any predictive‑analytics intervention. These principles will be woven into all departmental course syllabi’s, signifying a department‑owned standard rather than an external university requirement.
To reinforce momentum, we inaugurated a monthly “AI in Social Sciences Research Brown Bags,” hosted in the department. During the brown‑bag lunches, visiting scholars within CoreEthicAI networks in academia presented on timely topics including on AI’s role in historical text mining, and a hands-on breakout called “Curriculum Translation”. In the breakout faculty sketched concrete lesson plans and discussed assessment redesigns on the spot, embedding AI insights generated from our tool kits. Ultimately, the path to meaningful AI integration in higher education runs through department‑level innovation. By tailoring activities to disciplinary conventions, co‑creating ethical guardrails with faculty, and embedding AI tools in everyday research and teaching, we brought every stakeholder on board. This grassroots approach not only met accreditation and equity goals but also ignited a culture of data‑driven inquiry. It revealed that when departments own the process, AI becomes a powerful ally in supporting faculty and departmental goals and most importantly preparing students for the challenges to come.
