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The SUNY AI for the Public Good Fellows
Recognizing that artificial intelligence is a significant element of the information landscape that students navigate, SUNY revised its General Education Framework to include AI as part of the Information Literacy core competency. Starting in Fall 2026, AI literacy will be an official component of the core competency for all incoming undergraduate students.
To support the work of updating courses and developing learning activities, the SUNY AI for the Public Good Fellows will offer resources and consultations on AI literacy and the ethical use of AI for faculty, instructional designers, and librarians across SUNY.
Chancellor King Announces Second Cohort of SUNY AI for the Public Good Fellows
Updates
The 2025-2026 AI for Public Good Fellows developed resources for the support of general education, AI course policies, and assignment development. Faculty may use these materials to incorporate ethical approaches to artificial intelligences in courses across disciplines.
Syllabus Resources
These resources offer a framework for developing syllabus policies.
Pedagogy
These resources offer pedagogical resources including frameworks for approaching the incorporation of AI into the curriculum and various assignments.
Classroom Activities and Assessments
Events
2025-26 Academic Affairs Fellows General Education Support Webinars
The SUNY Academic Affairs Fellows Programs offered webinars through the 2025-26 academic year to support campuses, as well as faculty, staff and administrators, on a wide range of topics related to AI literacy, civic discourse, DEISJ, and sustainability.
The AI for the Public Good Fellows offered six webinars on AI literacy.
SUNY Updated Information Literacy General Education Overview, 11/17/25
Integrating SUNY Updated Information literacy General Education in Courses, 12/08/25
Back to Fundamentals: Using, creating, and sharing information ethically in the age of AI, 02/23/26
AI Across the Sciences: Connecting Teaching, Learning, and Research, 03/23/26
AI and the Evolving Research Lifecycle: Multidisciplinary Perspectives, 03/30/26
AI and Civic Engagement, 04/21/26
These webinars and related materials are available in the SUNY Open Access Repository (SOAR).
For webinars offered by the other fellows programs, check out their webpages.
Resources for Campuses
Artificial intelligence (AI) poses seismic opportunities and threats to the ways in which faculty teach and students learn. The resources provided below are intended to assist faculty in reflecting upon their course goals to develop course policies and pedagogies that promote responsible use of AI, including the promotion of AI literacy within the broader context of information literacy.
To suggest a resource to be added to this list or to offer feedback about an identified resource, please fill out the Resource Idea Form. These resources are still under development, with pedagogy resources to be added later in 2026.
★ = Resources created by the Fellows
AI Ethics
AI Ethics
Many of the conversations and controversies surrounding AI come down to ethical principles. We invite faculty to review the resources below related to the ethics of AI, including policies, SUNY legal guidance, academic integrity, authorship, sustainability, privacy, bias, opportunity, and more.
Recommended Reading
| Resource Title |
Description |
| AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps (2024) |
Gao, D. K., Haverly, A., Mittal, S., Wu, J., & Chen, J. (2024). AI ethics: A bibliometric analysis, critical issues, and key gaps. International Journal of Business Analytics (IJBAN), 11(1), 1-19. https://doi.org/10.4018/IJBAN.338367 |
| Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration (2023) |
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277–304. https://doi.org/10.1080/15228053.2023.2233814 |
| Guidelines for Ethical Use and Acknowledgement of Large Language Models in Academic Writing (2024) |
Mann, S. P., Vazirani, A.A., Aboy, M., Earp, B. D., Minssen, T., Cohen, I. G., & Savulescu, J. (2024). Guidelines for ethical use and acknowledgement of large language models in academic writing. Nat Mach Intell, 6, 1272–1274. https://doi.org/10.1038/s42256-024-00922-7 |
| The Ethics of AI Ethics: An Evaluation of Guidelines (2020) |
Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds & Machines, 30, 99–120. https://doi.org/10.1007/s11023-020-09517-8 |
| The Global Landscape of AI Ethics Guidelines (2019) |
Jobin, A., Ienca, M. & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nat Mach Intell, 1, 389–399. https://doi.org/10.1038/s42256-019-0088-2 |
| The Post-Plagiarism University (2025) |
Shirky, C. (2025, November 3). The post-plagiarism university. The Chronicle of Higher Education. https://www.chronicle.com/article/the-post-plagiarism-university |
| AI Risk Management Framework (NIST) |
This resource from NIST provides a comprehensive framework for identifying, assessing, and managing risks associated with artificial intelligence systems. It addresses issues such as bias, transparency, accountability, and reliability, making it useful for teaching responsible AI development and use. |
| The Use of Generative AI Tools in Higher Education: Ethical and Pedagogical Principles (2025) |
Nguyen, K.V. (2025). The use of generative AI tools in higher education: Ethical and pedagogical principles. J Acad Ethics, 23, 1435–1455. https://doi.org/10.1007/s10805-025-09607-1 |
| Using Artificial Intelligence for Scholarly Writing (2025) |
Oermann, M. H., Owens, J. K., Carter-Templeton, H., Peterson, G., & Bailey, H. E. (2025). Using artificial intelligence for scholarly writing. American Journal of Nursing 125(11), 52-55. https://doi.org/10.1097/AJN.0000000000000179 |
| Worldwide AI Ethics: A Review of 200 Guidelines and Recommendations for AI Governance (2023) |
Corrêa, N. K., Galvão, C., Santos, J. W., Del Pino, C., Pinto, E.P., Barbosa, C., Massmann, D., Mambrini, R., Galvão, L., Terem, E., & de Oliveira, N. (2023). Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance. Patterns, 4(10). https://doi.org/10.1016/j.patter.2023.100857 |
Pedagogy
Pedagogy
Approaching AI literacy through the discipline of information literacy calls for more than instruction in AI tool use; the AI Fellows emphasize approaches that encourage students to develop critical understanding of how these systems function and the discernment to determine when, where, why, and if their use is appropriate. The materials below include frameworks, reflective tools, lesson plans, and other resources for strategically integrating AI literacy into the classroom and the curriculum. In cases where AI is used to create outputs, the process - i.e., the human inputs - becomes the focus of assessment, with an emphasis on iteration and metacognition.
| Resource Title |
Description |
| ★ SUNY AI Fellows for the Public Good: AI Curriculum and Resource Mapping (SUNY General Education Conference Pre-Workshop, April 2026) |
The SUNY AI Fellows for the Public Good developed worksheets that campuses may use to critically map AI literacy within 1) a single course; 2) across a program curriculum; or 3) across a campus (resource/support assessment). These materials were first shared with attendees of the SUNY General Education Conference at Saratoga Springs, April 2026. |
| ★ SUNYLA SILC: Advancing AI Literacy |
A report by the SUNYLA Information Literacy Committee (SILC) Advancing AI Literacy Working Group, co-chaired by AI Fellow Lauren deLaubell and Ken Fujiuchi, regarding frameworks and student learning outcomes related to AI literacy, as embedded within information literacy broadly. Learning activities are in development and will be available later in 2026. |
| Artificial Intelligence (AI) Literacy and Pedagogy |
This curated library guide provides a comprehensive collection of frameworks, pedagogical approaches, and SUNY-specific resources designed to help faculty integrate artificial intelligence literacy and generative tools into their courses. |
| A Model to Enhance Students’ AI Literacy |
A guideline to help educators seamlessly integrate AI into the curriculum, and ensure all students are prepared for the AI-driven future. Includes learning objectives, recommended tools, and sample tasks for incorporating AI literacy at three levels, from foundational up to advanced. |
Classroom Activities and Assessments
| Resource Title |
Description |
| ★ Adaptable AI Literacy Toolkit |
A flexible toolkit that provides instructors with customizable resources to teach core AI literacy concepts across disciplines. It includes frameworks, activities, and guidance for helping students understand AI capabilities, limitations, and responsible use. |
| ★ First Year Writing and AI Use Lessons |
A collection of lesson plans designed to help first-year writing students critically engage with generative AI tools in the writing process. The materials emphasize ethical use and source evaluation in addition to reflective writing practives. |
| ★ Ethical AI Tool Use in Writing: A Case Studies Approach |
This lesson uses fictional case studies to help first-year writing students analyze ethical gray areas in generative AI use. Includes both a PPT and PDF of the case studies. |
| ★ Gen AI Intro Science Activities |
Introductory, hands-on activities that introduce students to generative AI concepts within a scientific context. These exercises help learners explore how AI can support scientific inquiry, data interpretation, and hypothesis generation while reinforcing critical thinking. |
| ★ Geography and GIS Machine Learning Activities |
Applied machine learning activities focused on geographic and environmental data, including a bird identification use case. The resource supports experiential learning by connecting AI techniques to real-world spatial analysis and ecological applications. |
| ★ Fostering Writing-to-Learn Skills with Critical AI Literacy (Stony Brook course) |
Sample course modules created by AI Fellow Shyam Sharma and his colleagues at Stony Brook. Online activities guide students through the ethical integration of AI tools into the writing process, emphasizing agency, transparency, and the use of AI as a support for critical thinking rather than a replacement for it. |
| ★ DesignWriteStudio |
Sample course created by AI Fellow Steven Schneider for SUNY Polytechnic Institute, exploring intensive use of AI in a collaborative authorship model to produce durable, machine-readable knowledge artifacts within wikis. |
| SUNY AI Community for Educators Resources (ACE) |
Hosted by the SUNY AI Community for Educators (ACE), this repository offers a diverse array of peer-reviewed lesson plans and classroom activities across various disciplines, specifically designed by and for faculty to facilitate the practical application of generative AI in higher education. |
| Blooms stAIrcase Activities |
A table of AI activity prompts that encourage AI literacy across a variety of disciplines. Each activity is mapped to a corresponding discipline and Meskell and Baker's Blooms stAIrcase framework. |
| Artificial Intelligence in Learning & Teaching |
Maintained by the Stony Brook University Center for Excellence in Learning and Teaching (CELT), this comprehensive resource hub offers faculty practical strategies for navigating generative AI, including guidance on syllabus statements, assessment redesign, and an interactive student course focused on ethical writing-to-learn skills. Includes a student module request form to request student modules. |
| AI Pedagogy Project |
Developed by metaLAB at Harvard, this is a curated collection of open-access assignments and introductory guides to help educators across all disciplines lead critical, hands-on explorations of the capabilities and ethical limitations of generative AI. |
| CRAFT Stanford: Empowering Students with AI Literacy |
Developed by the Stanford Graduate School of Education, Classroom-Ready Resources About AI For Teaching (CRAFT) offers a robust collection of co-designed, multidisciplinary materials intended to help students explore and critique artificial intelligence. These free, adaptable resources range from quick activities to full lesson plans, allowing educators to seamlessly integrate AI literacy into various subject areas including art, math, and history. |
| TextGenEd: Teaching with Text Generation Technologies |
Peer-reviewed, open-access collection of 34 undergraduate-level assignments, focused on literacy, creative exploration, ethical considerations, and professional writing. |
| The Peer & AI Review + Reflection (PAIRR) Packet |
The Peer & AI Review + Reflection (PAIRR) Packet offers a structured five-part curricular intervention that guides students through collaborative peer review and critical engagement with AI feedback. Educators can utilize these open-licensed materials, including rubrics and reflection assignments, to help students navigate language equity and the ethical nuances of generative AI in the writing process. |
| Assessment Design and Generative AI (University of Toronto) |
This University of Toronto Canvas course page offers comprehensive strategies for adapting course assessments in response to generative AI, categorized by whether the instructor chooses to prohibit, allow, or actively invite its use. It provides practical pedagogical advice on fostering student communication, redesigning prompts to emphasize local course context, and using AI for critical reflection and fact-checking activities. |
| Generative AI Literacy: A Guide for Higher Education |
This resource provides guidance for developing generative AI literacy in higher education, including how AI systems function, their limitations, and strategies for responsible and ethical use in academic work. It supports faculty in designing assignments and discussions that promote critical engagement with AI tools. |
| Transforming Education with Generative AI and Social Annotation |
A Hypothesis case study highlighting how faculty, including those at SUNY New Paltz, utilize social annotation to foster critical thinking and ethical engagement with generative AI by requiring students to collaboratively analyze and critique AI-produced content directly within course texts. |
Student Resources
| Resource Title |
Description |
| ★ Artifacts of Outputs Generated with AI Assistance |
This collection of AI artifacts curated by AI Fellow Steven Schneider showcases a diverse range of AI-assisted outputs, including policy initiatives, literacy frameworks, and technical analyses designed to demonstrate the practical and ethical applications of generative tools in academic and civic contexts. |
| Library AI Guide (SUNY Oneonta) |
Overview on AI use and issues for faculty and students. Includes general definitions, ethical considerations, methods for citing AI use, and other guidance. |
Recommended Reading
