AI in Teaching and Learning at Stanford: Course and Curriculum Seed Grants

AI in Teaching and Learning at Stanford: Course and Curriculum Seed Grants is sponsored by Stanford University (VPUE's AI Meets Education at Stanford (AIMES) initiative and GSE's Stanford Accelerator for Learning). These grants provide funds and support to faculty and lecturers to plan and implement changes to courses and curricula that meaningfully address or integrate AI.

Instructors and student collaborators are encouraged to experiment with new teaching methods and learning experiences.

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AI in Teaching and Learning at Stanford: Course and Curriculum Seed Grants provide funds and support to faculty and lecturers to plan and implement changes to courses and curricula that meaningfully address or integrate AI.

Instructors and student collaborators are encouraged to experiment with new teaching methods, learning experiences, disciplinary perspectives, and ways of assessing learning in undergraduate and graduate courses—grounded in both creativity and instructional rigor. The grant program is led by VPUE’s AI Meets Education at Stanford (AIMES) initiative and is part of a joint effort between VPUE and GSE’s Stanford Accelerator for Learning .

Funding & Types of Grants Seed grants between $10,000 and $100,000 for planning or implementing course and curriculum changes that address or integrate AI in Stanford courses: Planning—Up to $10,000: a group of faculty, instructors, and students in a program or department may propose a plan to convene, discuss, and plan addressing curricular aspects of generative AI.

Course implementation—Up to $25,000: an individual instructor or teaching team within a program or department may propose to make coordinated, substantial changes related to generative AI in a single course.

Curriculum implementation—Up to $75,000 for implementation in a group of three courses, or $100,000 for four or more courses: a group of instructors may propose to make coordinated, substantial changes related to generative AI in a cluster of three or more courses.

Proposals may include (but are not limited to): Convening groups of instructors and students in departments and programs to discuss and make plans for addressing curricular aspects of generative AI.

Redesigning assignments and assessments, in one course or across several sequential courses, to scaffold the development of independent critical thinking and problem-solving skills without, before, or alongside the development of effective use of generative AI.

Creating and implementing modules in one or several related courses that introduce and advance AI literacy and inquiry skills appropriate for the discipline, with increasing complexity as students progress.

Integrating experiential learning (e.g., projects that engage students in structured and contextual learning and reflection, such as in labs, studios, makerspaces, museums, archives, and community or other field sites) that addresses discipline-specific AI challenges into one or a series of courses.

Building shared approaches, tools, and materials to support teaching AI-related topics across courses and contexts, such as AI and ethics or AI in research.

The Stanford Center for Teaching and Learning (CTL) provides expertise, coordination, and support for educational design and for understanding student learning and the effectiveness of seed grant projects, as follows, based on project needs and goals: Curriculum and pedagogical design consultations Frameworks and tools to assist with course and curriculum design and planning Evidence-based teaching and learning practices Facilitating curriculum discussions or workshops (e.g., in the department or program) Methods for analyzing and understanding the educational effectiveness of the project Academic technology expertise and consultative support for navigating Stanford technology policy, approval processes, support services, and so on Training for graduate students involved in implementation grants on teaching methods related to generative AI and on analyzing educational effectiveness A learning community cohort for all grant recipients; the community will progress together through a Canvas course called Critical AI Literacy for Instructors over several weeks, and meet to follow up on applying content to their projects Support instructors with resources, in the form of expertise, assistance, and staff time Catalyze coordinated approaches to teaching and learning in the age of AI Enhance learning with and without AI in ways that are effective, clear, relevant, and legible for students Advance and share curricular approaches to AI across Stanford schools, departments, and programs Faculty—Academic council faculty who teach undergraduate or graduate courses.

Lecturers and Academic Staff—Full-time academic staff who teach undergraduate or graduate courses, including lecturers, may apply with approval from their supervisor. Students—Undergraduate and graduate students are welcome as team members working with faculty, lecturers, and academic staff.

Faculty and academic staff applicants are encouraged to add students; if students are not identified at the proposal stage, they may join teams later. All grant recipients must: Draw on the supports listed above to align projects with evidence-based educational practices. Participate in 3-4 meetings of seed grant recipients.

Create a short interim report and a final report. Respond promptly to communication about the grants and use funds within the grant period for their intended purposes, while following applicable institutional policies. Share results and insights from grants via convenings and other media or formats of your choice (subject to stipulations about data sharing from IRB and SDOC).

Course or curriculum implementation grant recipients must also gather and analyze evidence of educational effectiveness. All grant recipients will be advised in their award notifications whether they need to submit information about their projects to the Institutional Review Board (IRB) and the Student Data Oversight Committee (SDOC); if so notified, they must do so before project activities begin.

While Course and Curriculum Seed Grants are not intended to support in-depth research, certain forms and uses of student data are nonetheless governed by institutional IRB and SDOC policies. The application form will ask you for information about team members, elements of a project narrative, and a budget.

Please open these Google Documents to preview the questions and read the budget guidance: Course and Curriculum Seed Grant Proposal Questions Course and Curriculum Seed Grant Budget Guidance and Instructions Clarity of planning goals and activities. Potential to result in implementation activities. For implementation grants: Clarity and relevance of the educational challenge related to generative AI.

Educational soundness of the plans to address the challenges. Coherence and quality of the plans to analyze the educational effectiveness of the proposed changes. Potential for positive impact on students and learning.

How can the funds be used? Please read detailed financial guidelines here .

Planning grants up to $10,000 each: costs of convening (e.g., facilities, catering) travel costs for guest speakers student workers for logistic help with meetings May not be used for salary or supplemental pay support Planning grant recipients in 2026-27 will be eligible to apply for implementation grants the following year Course or curriculum implementation grants: Up to $25,000 for a single course; up to $75,000 or $100,000 for a cluster of three, or four or more, courses.

Supplemental pay for instructors doing additional work to incorporate AI-related changes into a course (up to $10,000 for an individual). Instructor effort can be included if the amount of work is too great to be done as supplemental work and relief of a course is necessary (up to $20,000); departments can use the savings to fund replacement teaching.

TAs or CAs specifically working on implementing AI-related changes while the revised course is first taught. Hourly student workers (graduate and/or undergraduate) outside of the term when the revised course is being taught, e.g. to help prepare and refine revised course materials and approaches.

Relevant and necessary software licenses that support the course or curricular changes and that cannot be carried out with existing Stanford licensed software (review of tools may be required). Development of new AI tools, such as a custom-built software application. We encourage applicants with such proposals to consider the Innovation with Evidence grant program instead.

Costs of teaching courses (i.e., faculty or lecturer salaries). TAs/CAs already allocated by the department for ordinary teaching duties. Learn More About This Opportunity