AI Interdisciplinary Institute Course Development Grants is sponsored by University of Maryland. Supports development of new AI-focused courses at the University of Maryland.

AIM Course Development Grants | AIM UMD AIM Course Development Grants AIM Course Development Grants In Spring 2024, UMD launched the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM), bringing together AI experts across campus to focus on responsible, ethical development and use of the technology to advance public good in industry, government and society.

Given the rapid pace of AI development, a core part of AIM’s mission is to reimagine learning in the face of these drastic changes through the introduction of four new interdisciplinary programs, including Bachelor of Science and Bachelor of Arts degrees in Artificial Intelligence. Students across all majors will learn the principles of AI and how they apply to their field of study.

To continue to build our programs and offer a rich array of courses to University of Maryland students, AIM is pleased to announce the availability of course development funds for courses that fit into one of three tracks: AI + X; New AI Course Development; and Pathway to AI.

Each track this year has different requirements and we encourage those interested to read carefully each of the track requirements to see which best fits their idea.

December 1, 2025: Course Development Grant Application Opens ( Application Here ) February 16, 2026: Course Development Grant Applications Due On or around April 28, 2026 : Recipients notified of decisions Interested faculty may only be PI or co-PI on one course development grant.

Track 1: AI + X (Total Award up to $10,000) The intention of an AI + X course is to develop a catalog of courses that demonstrate a tangible application of AI within specific major, disciplines, and interdisciplines; build a spectrum of courses allowing students to develop progressively deeper AI skills within their major(s); and equip students with hands-on skills or critical understanding to use AI tools for data analysis, modeling, or creation of artifacts relevant to their educational and career paths.

Track 2: New AI Course Development (Total Award up to $10,000). This track would support the development of new courses that directly align with and strengthen one of the Institute's existing pillars (accessibility, sustainability, learning, and social justice). The course should address an identified gap in the current curriculum.

The New AI Course Development grant is different from AI + X in that it centers a societal problem and draws from multiple disciplines, theories, methodologies, etc. to address it. Track 3: Pathway to AI (Total Award up to $50,000).

The Pathway to AI course would broaden the university-wide understanding of what AI is, how it works, and its past, present and future (potential) impact; create an accessible AI “on-ramp” for all students regardless of discipline; and be suitable for a broad undergraduate audience with potential eligibility for general education credit.

We are looking for a team of 3 faculty (at least 1 PTK faculty member and 1 TTK faculty member) to develop such a foundational interdisciplinary AI course. Applications will be accepted through Qualtrics . If you are considering applying for a course development grant this cycle but are unsure which track best fits your idea, please email aim@umd.

edu . We’re happy to have a discussion, as best we can, to assist you in the decision making process. Course development grant awardees will receive notification of their award on or around April 28, 2026.

Each awardee will receive an official letter indicating the award amount and disbursement timeline. Different from last year, awards will be disbursed prior to June, 2026. Awardees are required to submit a syllabus no later than December 2026 and participate in a Spring 2027 Research and Learning Showcase.

By accepting the course development grant award, recipients acknowledge and accept the submission of their materials by December 2026. Use of funds: Funds may be used for faculty summer salary, graduate student support, course material acquisition, software licensing, development of open educational resources (OER), or stipends for guest speakers. Funds may not be used for faculty academic year salary or course buyouts.

If you have any questions about how funds can be used, please email aim@umd. edu . In the inaugural year, 7 proposals for AI-focused courses were awarded grants for the 2025-2026 academic year.

Though the tracks have changed for this year's grant cycle, these courses still embody AIM's interdisciplinary mission and vision. An AI + X course is defined by its focus on applying or interrogating AI techniques within a specific major, discipline, or interdiscipline. The course’s technical depth, learning objectives, and activities should be tailored to the intended student level.

Proposals are encouraged for courses at any level from 100-level to advanced graduate seminars. We especially encourage applications for courses that are interdisciplinary in their approach to AI.

The intention of an AI + X course is to develop a catalog of courses that demonstrate a tangible application of AI within specific major, disciplines, and interdisciplines; build a spectrum of courses allowing students to develop progressively deeper AI skills within their major(s); and equip students with hands-on skills or critical understanding to use AI tools for data analysis, modeling, or creation of artifacts relevant to their educational and career paths.

Courses that intersect with AIM’s focus areas, including accessibility, sustainability, social justice, and learning will be given special consideration.

AIM construes these research areas broadly, with accessibility including at least disability, aging, neurodiversity and mental health; sustainability including climate, food security, agriculture, aquaculture, and their civic, public health and business impacts; social justice including race, gender, digital inequality, policy, and social and historical relations of power; and learning including the creation, dissemination and acquisition of knowledge across people, teams, and organizations.

Below are potential expectations and guidelines for different levels of course proposals. At all levels, we encourage hands-on activities showcasing the AI tools. Introductory level courses (100-200 level): may focus on developing AI literacy in various disciplines across campus.

These courses would likely have no prerequisites and would serve as a foundation for students who have limited to no experience with AI and its intersection with their major of interest. Upper level courses (300-400 level): may focus on deeper application of AI and problem-solving within the discipline.

These courses would create scaffolding from previous coursework (not necessarily in AI) and could AI methodologies as they pertain to the discipline. Graduate level courses: may focus on advanced applications, research methodologies, and creation of new knowledge. These courses would encourage students to build or critically analyze/evaluate complex AI models relevant to their particular discipline/area of study.

A 3-4 page description of the course including (this doesn’t have to be a wholly thought out syllabus week-by-week): The unit/department that will offer the course (and indicate if the course could be cross-listed); Course description (be sure to explain how this course would help students in X major or field of study enrich their ability to either progress through the major or be career ready); How the course intersects with an AIM pillar (accessibility, sustainability, social justice, and learning); Sample readings and assignments; Potential tools or platforms that will be used in the course.

Letter of Support from the department chair or unit head indicating their willingness to offer the course in the next 1-2 years. The letter should outline a (a) plan for offering the course in the future and how it would/could be integrated into the departments curriculum; (b) if it would be offered on a recurring basis; and (c) if it will be offered on a recurring basis how often it would/could be offered.

Scope of Potential Impact: The proposal clearly articulates how the course will deeply and meaningfully integrate AI into the specific discipline (AI + X) and its potential to enhance the university’s AI course offerings. The proposal indicates the breadth and/or depth of the work—this may be represented by the number of students (seats) or academic programs that will be impacted or benefit from the course.

Curricular Innovation: The proposal approaches the topic of AI from its disciplinary/interdisciplinary perspective in ways that are innovative in terms of form, content, assignments, and/or outcomes. Feasibility and Sustainability: This assesses the practical and logistical strength of the proposal. The proposal clearly articulates achievable specific objectives and provides a clear benchmark for measuring progress and results.

Included in this are potential risks that have been considered and planned for, especially if involving community engagement. The proposal should also include information on the institutional support needed to become a lasting part of the curriculum and be offered on a recurring/regular basis. Scalability and Adaptability: The proposal addresses how the course could be taught and adapted beyond the proposing instructor.

It details how the course may serve as a model for other sections of the same course or other courses. Transformative and Inclusive Pedagogy: The proposal should address how the course will implement alternative assessments to quizzes/exams/etc. and go beyond only a lecture style course. The proposal should describe not just a list of topics but the holistic course experience as it aligns with the teaching and assessment methods.

Relevance to AIM pillar: The proposal should address the course's connection to one of the AIM pillars. New AI Course Development This track supports the development of new courses that directly align with and strengthen one of the Institute's existing pillars (accessibility, sustainability, learning, and social justice). The course should address an identified gap in the current curriculum.

The New AI Course Development grant is different from AI + X in that it centers a societal problem and draws from multiple disciplines, theories, methodologies, etc. to address it. Undergraduate and graduate courses will be considered for the new AI course development grant.

If a course will primarily serve graduate students with the possibility of serving academic mature undergraduate students, it will be evaluated alongside other graduate course proposals. Courses that intersect with AIM’s focus areas, including accessibility, sustainability, social justice, and learning will be given special consideration.

AIM construes these research areas broadly, with accessibility including at least disability, aging, neurodiversity and mental health; sustainability including climate, food security, agriculture, aquaculture, and their civic, public health and business impacts; social justice including race, gender, digital inequality, policy, and social and historical relations of power; and learning including the creation, dissemination and acquisition of knowledge across people, teams, and organizations.

Accessibility: Courses could explore the intersections of AI and: assistive/adaptive technologies; designing inclusive digital spaces; or bias related to disability, aging, neurodiversity, mental health, etc. Sustainability: Courses could explore AI applications for climate modeling, precision agriculture, food insecurity, aquaculture, and supply chain optimization.

Social Justice: Courses could evaluate algorithmic bias, digital inequality, the use of AI in policy and law, and how AI intersects with social and historical relations of power. Learning: Courses could explore the future of education (k-12 and higher education) with AI; Ai as a tool to enhance epistemology and pedagogy; and AI’s impact on learning, broadly speaking.

A 3-4 page description of the course including (this doesn’t have to be a wholly thought out syllabus week-by-week): The unit/department that will offer the course (and indicate if the course could be cross-listed); How the course intersects with an AIM pillar (accessibility, sustainability, social justice, and learning); Sample readings and assignments; Potential tools or platforms that will be used in the course.

Letter of Support from the department chair or unit head indicating their willingness to offer the course in the next 1-2 years. The letter should outline a (a) plan for offering the course in the future and how it would/could be integrated into the departments curriculum; (b) if it would be offered on a recurring basis; and (c) if it will be offered on a recurring basis how often it would/could be offered.

Scope of Potential Impact: The proposal clearly articulates how the course will deeply and meaningfully engage a broad, interdisciplinary audience of students from multiple colleges/departments/etc. The proposal clearly articulates the course's potential to enhance the university’s AI course offerings.

The proposal indicates the breadth and/or depth of the work—this may be represented by the number of students (seats) or academic programs that will be impacted or benefit from the course. Curricular Innovation: The proposal approaches the topic of AI from its disciplinary/interdisciplinary perspective in ways that are innovative in terms of form, content, assignments, and/or outcomes.

Feasibility and Sustainability: This assesses the practical and logistical strength of the proposal. The proposal clearly articulates achievable specific objectives and provides a clear benchmark for measuring progress and results. Included in this are potential risks that have been considered and planned for, especially if involving community engagement.

The proposal should also include information on the institutional support needed to become a lasting part of the curriculum and be offered on a recurring/regular basis. Scalability and Adaptability: The proposal addresses how the course could be taught and adapted beyond the proposing instructor. It details how the course may serve as a model for other sections of the same course or other courses.

The proposal should clearly articulate how the course could be updated as technology evolves, especially given the rapid-development of AI. Transformative Pedagogy: The proposal should address how the course will implement project-based learning; case-study based learning, or workshops/live-labs. The proposal should describe not just a list of topics but the holistic course experience.

Relevance to AIM pillar: The course provides a deep, meaningful, and thoughtful exploration of an AIM pillar with rigor from multiple perspectives. Artificial Intelligence is transforming every sector of society and every field of academic inquiry. To prepare our students to be informed, critical, and innovative leaders in an AI-driven world, AIM is offering a Pathway to AI course development grant, this year.

We AIM to prepare students at the University of Maryland to engage with this technology in an informed way and through an inquisitive, innovative, and culturally aware lens. We are looking for a team of 3 faculty (at least 1 PTK faculty member and 1 TTK faculty member) to develop such a foundational interdisciplinary AI course.

This course would be an accessible pathway to the concepts, applications, and societal implications of AI for all undergraduate students, regardless of their major or prior technical background. The Pathway to AI course would: Broaden the university-wide understanding of what AI is, how it works, and its past, present, and future impact. Create an accessible AI “on-ramp” for all students regardless of discipline.

Be suitable for a broad undergraduate audience.

A 4-5 page description of the course including (this doesn’t have to be a wholly thought out syllabus week-by-week): A description of the team and how it will benefit not only the development of the course but students; The unit/department that will offer the course (and indicate if the course could be cross-listed); How the course intersects with an AIM pillar (accessibility, sustainability, social justice, and learning); Sample readings and assignments; Potential tools or platforms that will be used in the course.

A budget describing how you will use the funding. This can include stipends for faculty, stipends for graduate student research assistants who can help with developing the course, projected honoraria for guest speakers, etc. The budget should indicate what portion of the funds will be used to prepare for the course and what portion will be used once the course is running.

Letters of Support from unit heads (chairs, dean, etc) of each faculty member on the team indicating their support for offering the course in the next 1-2 years with the same faculty team.

* The letter should outline a (a) plan for offering the course in the future and how it would/could be integrated into the departments curriculum; (b) if it would be offered on a recurring basis; and (c) if it will be offered on a recurring basis how often it would/could be offered. * Additional funding may be available to incentivize the same team teaching this as a team taught course in AY 2027-2028.

Scope of Potential Impact (Breadth): The course proposal showcases it would serve a broad and diverse undergraduate population from across academic disciplines/majors/programs. The proposal clearly outlines how the course will help build students' AI literacy on campus.

Curricular Innovation: The proposal approaches the topic of AI from innovative ways in terms of form, content, assignments, and/or outcomes, making AI concepts understandable and engaging for those with no prior knowledge or technical experience. Feasibility and Sustainability: This assesses the practical and logistical strength of the proposal.

The proposal clearly articulates achievable specific objectives and provides a clear benchmark for measuring progress and results. Included in this are potential risks that have been considered and planned for, especially if involving community engagement. The proposal should also include information on the institutional support needed to become a lasting part of the curriculum and be offered on a recurring/regular basis.

Scalability and Adaptability: The proposal addresses how the course could be taught and adapted beyond the proposing instructors. It details how the course may serve as a model for other sections of the same course or other courses. The proposal should clearly articulate how the course could be updated as technology evolves, especially given the rapid-development of AI.

Transformative Pedagogy: The proposal should address how the course will implement project-based learning; case-study based learning, or workshops/live-labs. The proposal should describe not just a list of topics but the holistic course experience. Team Composition and Collaboration: The intention of this grant is to foster collaboration and recognize the expertise of all contributing PIs.

The role of each faculty member should be clearly defined, substantive, and essential to the success of the course. Frequently Asked Questions (FAQ) Can I submit a proposal for a Course Development Grant and a Research SEED Award? Absolutely!

We strongly encourage people to submit proposals for both. Can proposals be for co-taught courses? Yes.

If you are applying for a co-taught course, indicate the reasons for doing so as well as how the department offering the course will support the collaboration. Does the PI need to be the instructor of record for the course the proposal is for? No. If there is a strong rationale for the PI to be another person, the proposal will still be considered.

Are you open to funding courses that do not intersect with AIM focus areas or General Education requirements? We would consider strong proposals for courses that are interdisciplinary in their approach but do not intersect with AIM focus areas or general education requirements; however, we are interested in courses that do. Am I required to have a guarantee for matching funds to be considered for a course development grant?

No. Course development grant applicants are not required to submit a guarantee of matching funds or have matching funds to be considered. If I need to adjust my budget after my course development grant application is accepted, am I able to do so? Yes.

If the course development grant required a budget and was accepted, you are able to submit a readjustment to the budget as long as the fundamental premise of the proposal does not change. We understand planned expenses change and are committed to working with awardees should unexpected circumstances occur. How am I able to use the course development grant funds?

Funds may be used for faculty summer salary, graduate student support, course material acquisition, software licensing, development of open educational resources (OER), or stipends for guest speakers. Funds may not be used for faculty academic year salary or course buyouts.