CUNY SPH AI Committee

Understanding Student Attitudes Toward AI to Develop an Ethical AI Literacy Module for CUNY SPH: Student Module for AI Readiness Training (CUNY SMART)

The rapid expansion of generative AI in academia and public health presents both opportunities and challenges for CUNY students, who currently lack structured support regarding responsible, ethical, and equity-centered use. CUNY SMART ultimately provides a practical, innovative, ethics-centered educational module to deepen students' understanding of AI and create a replicable model for CUNY.

We will rigorously assess student needs by conducting and analyzing focus groups with SPH students to understand their current AI usage, confidence, risks, benefits, and support gaps. Findings from will inform the creation of a comprehensive AI Literacy and Responsible Use Module tailored for CUNY student development and coursework. This project addresses three priority areas: AI Education & Literacy, AI in Courses, and Addressing Ethical & Social Implications of AI.

To ensure broad applicability we will collaborate with the CUNY SPH AI Working Group (members with dual appointments at The Graduate Center and City College), The GC's Open Working Group on AI (cross-CUNY), and a Staff Research Scientist at Google (industry insight).

Specific Aims

Aim 1: Conduct focus groups and interviews with students to examine SPH students’ current experiences, practices, and comfort levels regarding AI tools in academic and professional contexts.

Aim 2: Conduct a Framework Analysis to identify student knowledge gaps, ethical concerns, misconceptions, and preferences for learning about AI use in public health.

Aim 3: With FG data and scoping work, develop a robust AI Literacy and Responsible Use Module that addresses student-identified needs and integrates principles of AI governance, data ethics, academic integrity, and public health equity.

Project Design: This qualitative study uses focus groups to explore how SPH students understand and use AI.

Sampling and Recruitment: We will recruit MPH students from all SPH departments using email announcements, and outreach to instructors and student groups. The study will include six to eight focus groups, with the final sample size determined by thematic saturation.
Focus Groups: 10 focus groups of 6 participants for 60 minutes. After informed consent, the facilitator will guide a discussion that explores how students currently use AI in their coursework and professional roles; the benefits and challenges they encounter; and their level of understanding of AI concepts (see attached guide).
Data Management and Analysis: Sessions will be audio-recorded and transcribed. Transcripts will be reviewed for accuracy, de-identified, and imported into Google Sheets for a Framework Analysis to look at identified themes of interest.

Deliverables: The primary deliverable of this project is a comprehensive educational module on AI literacy and responsible use. The module will teach students how to evaluate AI-generated content for accuracy, completeness, and bias and will provide practical examples of inaccurate or misleading outputs to strengthen critical evaluation skills. Additionally, results and best practices resulting from our research will be shared back through CUNY-specific and public health conferences.

Feasibility: Our team is fully assembled and prepared: We lead the AI Working Group which meets bi-weekly. Further, we have identified a group of 3 MPH students who are interested in conducting this work as their capstone project. The funding requested will enhance the project's rigor and ensure its completion by June 30.

Understanding Student Attitudes Toward AI to Develop an Ethical AI Literacy Module for CUNY SPH: Student Module for AI Readiness Training (CUNY SMART)

Project Design: This qualitative study uses focus groups to explore how SPH students understand and use AI.

Sampling and Recruitment: We will recruit MPH students from all SPH departments using email announcements, and outreach to instructors and student groups. The study will include six to eight focus groups, with the final sample size determined by thematic saturation.

Data Management and Analysis: Sessions will be audio-recorded and transcribed. Transcripts will be reviewed for accuracy, de-identified, and imported into Google Sheets for a Framework Analysis to look at identified themes of interest.