1. Assess AI Literacy Needs

The project will first establish a baseline understanding of AI literacy across the interdisciplinary GES community.

Using the Five-Stage AI Literacy Continuum, the project will examine how community members currently engage with generative AI: from limited or no engagement, to uncritical use, informed use, critical evaluation, and improvement-oriented practice.

The assessment will identify:

• Current AI tools and tasks people use

• Areas where community members feel confident or uncertain

• Barriers to AI use, including mistrust, ethical concerns, privacy, access, and uncertainty about appropriate use

• Training needs specific to genetic engineering and society

The Continuum describes patterns of AI engagement. Someone may show stronger AI literacy in one discipline or task but rely on AI more uncritically in another. This makes context-specific assessment important for an interdisciplinary community like GES.

2. Develop GES-Specific Training

Based on the community’s needs, the project will develop hands-on training modules that connect generative AI literacy to real GES research and engagement activities.

Planned applications include:

• Analyzing public and stakeholder discourse around gene editing

• Summarizing regulatory and policy documents

• Synthesizing scientific and interdisciplinary literature

• Developing plain-language science communication

• Exploring coding and data-analysis workflows

Each example will go beyond demonstrating what an AI tool can produce. Training will emphasize when AI is appropriate, how to verify its outputs, how to recognize limitations, and where human expertise remains necessary.

The project will also create open Google Colab examples and reusable instructional resources so that community members can practice these skills.

3. Move Beyond AI Adoption Toward Critical Use

A central objective is to help participants move beyond using AI toward informed and critical engagement.

The Liu and Levy Continuum identifies two common extremes: avoiding AI because of fear, mistrust, ethical concerns, or lack of access, and using AI uncritically by accepting its outputs without sufficient verification. The project is designed to support movement away from both extremes.

The training will emphasize the transition toward:

Informed Use: selecting appropriate AI applications, understanding limitations, verifying facts and sources, recognizing potential bias, and applying human judgment.

Critical Evaluation: examining assumptions, uncertainty, bias, disciplinary fit, and the possible consequences of errors. 

The paper identifies Stages 2–3 as a recommended higher-education baseline, with Stage 2 representing responsible use and Stage 3 representing more advanced analytical judgment. Stage 4, Improvement, extends this further through workflow design, prompt engineering, evaluation protocols, customization, and AI development

4. Deliver Interactive Workshops

The project will translate the training materials into hands-on workshops for GES affiliates and students.

Workshops will allow participants to practice AI-assisted tasks while also examining the quality, limitations, and implications of the resulting outputs. Participants will be encouraged to compare approaches, verify information, consider disciplinary context, and reflect on the role of human judgment.

5. Evaluate Progress and Build Reusable Resources

The project will evaluate how participants’ AI-literacy practices develop and use feedback to refine the training materials.

The project will then produce:

• An evaluated training model

• Open Google Colab tutorials and code examples

• Workshop materials

• Responsible-use guidance

• An open, citable resource repository

• A roadmap for future development and external support