• December 1, 2025: Course Development Grant Application Opens
• 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 this grant cycle. Applicants will be required to provide their information, any co-PI information, as well as the appropriate Worktag number for fund disbursement. If you are not sure what the appropriate Worktag number is, please contact your department, college, or units business manager. This will help us to expedite fund disbursement upon being awarded a grant.

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.

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):
  • Proposed course title;
  • 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;
  • Learning outcomes;
  • 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.

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):
  • Proposed course title;
  • The unit/department that will offer the course (and indicate if the course could be cross-listed);
  • Course description;
  • How the course intersects with an AIM pillar (accessibility, sustainability, social justice, and learning);
  • Sample readings and assignments;
  • Learning outcomes;
  • 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.

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 4-5 page description of the course including (this doesn’t have to be a wholly thought out syllabus week-by-week):
  • Proposed course title;
  • 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);
  • Course description
  • How the course intersects with an AIM pillar (accessibility, sustainability, social justice, and learning);
  • Sample readings and assignments;
  • Learning outcomes;
  • 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 assistance 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.