Provost's Learning Innovation Grants

Funding innovative teaching that advances student learning at RIT.

The Provost's Learning Innovation Grants (PLIG) program supports faculty in exploring, developing, and implementing innovative approaches to teaching and learning. Whether investigating a new instructional model, redesigning a course, integrating emerging technologies, or refining an existing teaching practice, PLIG provides funding for projects that have the potential to positively impact student learning and success while contributing to a culture of instructional innovation across the university.

PLIG offers multiple funding opportunities designed to support projects of varying scope—from targeted course improvements to comprehensive course redesign initiatives

Funding: Up to $10,000, with a $2,000 limit for faculty summer stipend(s).

The Innovation Grant supports faculty pursuing significant teaching and learning innovations that extend beyond routine course revisions. Projects should investigate new approaches that have the potential to improve student learning, engagement, or success while contributing to evidence-informed teaching practices at RIT.

Projects may include:

  • Innovative instructional strategies
  • New approaches to assessment and feedback
  • Emerging educational technologies
  • Interdisciplinary learning experiences
  • Experiential or community-engaged learning
  • New instructional models
  • Other innovative approaches that improve teaching and learning

Deliverable Requirements:

  • Project proposal and budget (provided via the application).
  • A final summary report of the project’s implementation, assessment, outcomes, and lessons learned.
  • Participate in a project debrief meeting with the Innovation Grant review committee and designated CTL staff. 
  • Presentation of the project findings at RIT’s Teaching & Learning Conference.

Effective Date Range:

  • May to November of the following year.

Funding: Up to $5,000, with a $2,000 limit for faculty summer stipend(s).

The Focus Grant supports course redesign projects aligned with strategic institutional priorities. Focus areas may evolve over time to address emerging opportunities and institutional needs.

Current Focus Area:

AI Course Revision
Artificial intelligence (AI) is transforming how we teach and learn across different academic disciplines and professional fields. When approached thoughtfully, AI can expand our teaching and learning capacity such as helping students grasp complex ideas, explore creative possibilities, and analyze information on a scale that was once unimaginable. Even when AI is not directly used or discussed within a course, its presence can affect the way students are preparing for class or completing their assignments. 

The goal of this focus grant is to support RIT instructors in thoughtfully revising course activities and assessments to account for the presence of AI. This program invites faculty across disciplines to experiment with innovative teaching practices that prepare students to learn in the age of AI in ethical, creative, and critical ways. 

Applicants are asked to frame their proposals within one of three pedagogical categories (i.e., project types): 

  • AI as a Learning Tool
  • AI Literacy & Ethics
  • Assignments & Assessments Revisions

Choosing a category helps applicants clarify the central purpose of their course revision: whether they aim to enhance student learning with AI tools, foster critical understanding of AI and its ethical and social implications, or redesign assignments and assessments to address the realities of an AI-infused academic environment.  

Requirements:

  • Course redesign proposal and budget (provided via the application).
  • Consultation meeting with CTL instructional design team.
  • A final summary report of the project’s implementation, assessment, outcomes, and lessons learned.
  • Poster presentation of project findings at RIT’s Teaching & Learning Conference.
  • Sharing course materials or practices with colleagues.

Effective Date Range:

  • May to March of the following year.

Funding: $1,500

The PLIG Micro Grant provides rapid funding for faculty interested in implementing smaller-scale improvements to a course. These grants are intended for focused course enhancements that can be completed with minimal administrative burden, while still improving the student learning experience.

Projects may include:

  • Active learning enhancements
  • Assignment redesign
  • Course accessibility improvements
  • Small-scale AI integration
  • Technology-supported teaching improvements
  • Learning activity redesign
  • Other targeted instructional improvements

Micro Grants are designed to encourage experimentation and provide faculty with an accessible entry point into instructional innovation.

Requirements:

  • Brief proposal and simplified budget (provided via the application).
  • Optional consultation meeting with Center for Teaching and Learning (CTL).
  • Brief reflection on and copy of the revised syllabus.
  • Dissemination through a lightning talk, roundtable, showcase, or other approved informal format at RIT’s Teaching and Learning Conference.

Effective Date Range:

  • May to March of the following year.

Use of Funds

Funding will vary depending on the grant type and the resources required for implementation. Examples of how grant funds may be used, include:

  • Course release, with department head/chair approval (reasonable, actual replacement costs for full-time, tenured, or tenure-track faculty members removed from teaching).
  • Summer salary for faculty time (total can not exceed $2,000).
  • Funding for student workers (graduate or undergraduate), teaching assistants, and related materials.
  • Development of new technology-based learning tools and/or environments.
  • Technologies or equipment required by the project that are not normally provided by the department/college. (Note: Any equipment or other materials purchased with grant funds are the property of your department and revert to the department after your project is completed.)
  • Resources for research design and consultation, data collection and aggregation, instrument development and/or purchase, secure data storage, data analysis, and report generation.
  • Travel to support research activity and/or meet with potential funding sources.

Eligibility

  • Applicant shall be a full-time faculty member for the entirety of the award effective date range and will not be on official leave for any of this period.
  • Applicants shall not have been a recipient of the grant in the preceding year.
  • Proposed projects involving human subject research must obtain IRB approval.
  • Proposed projects must be in alignment of one of the defined grant types and must not be repeat applications.

Criteria

Applications for this grant will be evaluated against the Provost’s Learning Innovation Grants rubric. The rubric assesses the potential success and impact of each proposed project across six qualitative dimensions. 

In addition to the rubric, each funding opportunity has the following additional selection criteria:

Innovation Grants

  • Prior Grant Participation: Applicants may not have received an Innovation Grant in the preceding year.
  • Additional Funding: Preference will be given to proposals that leverage matched funds or other financial support from additional sources.
  • Dissemination and Impact: Preference will be given to proposals that include a clear plan for sharing project outcomes, findings, and lessons learned with colleagues and contributing to evidence-informed teaching and learning practices at RIT.

AI Course Revision Focus Grants

  • Prior Grant Participation: Preference may be given to first-time applicants.
  • Use of RIT-Supported AI Tools: Preference will be given to proposals that incorporate RIT-supported AI tools in meaningful ways that support the project’s goals.
  • Dissemination and Impact: Preference will be given to proposals that include a clear plan for sharing project outcomes, findings, and lessons learned with colleagues.

Microgrants

  • Prior Grant Participation: Preference will be given to first-time applicants and faculty who have not previously received the grant.
  • AI Related Projects: Priority will be given to proposals that address the designated AI focus area.

Application

The call for grant proposals is now closed.

Next Call for Grant Proposals
The call for grant proposals will open in the Fall semester. Stay tuned for the announcement and submission guidelines.

How to Apply
When the call for grant proposals opens, detailed instructions and submission form will be available on this page. Interested applicants will be able to submit their proposals online.

Stay Informed.
To receive the latest updates, including the opening of the call for grant proposals, please watch for emails from the Center for Teaching and Learning (ctl@rit.edu).

Grant Cycles

Innovation Grants
Effective Date Range: May 2027 to November 2028

AI Course Revision Focus Grants
Effective Date Range: May 2027 to March 2028

Microgrants
Effective Date Range: May 2027 to March 2028

Effective Date Range: April 1, 2026-November 30, 2027
Deliverables:
August 22, 2026: Full Project Plan Report (template)
January 9, 2027: Preliminary Findings Report (template)
May 12, 2027: PLIG Poster Showcase session at RIT's Teaching and Learning Conference
August 21, 2027: Final Report and Budget (template)

Effective Date Range: April 1, 2025-November 30, 2026
Deliverables:
August 22, 2025: Full Project Plan Report (Report Template)
January 9, 2026: Preliminary Findings Report (Report Template)
May 13, 2026: PLIG Poster Showcase at CTL's Summer Institute for Teaching and Learning 
August 21, 2026: Final Findings and Budget Report (Report Template)

Effective Date Range: April 1, 2024 – November 30, 2025
Deliverables:
August 23, 2024: Full Project Plan Report (Report Template)
January 10, 2025: Preliminary Findings Report (Report Template)
May 2025: PLIG Showcase at CTL's Summer Institute for Teaching and Learning
August 25, 2025: Final Report & Budget Report (Report Template)

Effective Date Range: May 1, 2023 - November 30, 2024
Deliverables:
August 25, 2023: Full Project Plan
January 12, 2024: Preliminary Findings report
May 2024: PLIG Showcase at CTL's Summer Institute for Teaching and Learning
August 23, 2024: Final Report & Budget

Grant Recipients

Jessamy Comer, COLA (PI)
Let’s Play! Introducing Gamification to a Psychology Course in Behaviorism (Active Learning)

Marissa Tirone, CAD (PI)
Juan Noguera, CAD (Co-I)
Design on Display: An Industrial Design Reference Library (Active Learning)

Susan Quatro, COLA (PI)
Active Learning with Creativity (Active Learning)

Travis Meyer, KGCOE (PI)
Asynchronous/Hybrid CAD Instruction for Enhanced Active Learning (Active Learning)

Dean Ganskop, GCCIS (PI)
Nick Snyder, GCCIS (Co-I) 
Hiring Student Experts to Improve Active Learning (Active Learning)

Edward Brown, KGCOE (PI)
Developing a DC Motor Control Systems Laboratory Module for Biomedical Engineers (Active Learning)

Raphael Abrahao, COS (PI)
An educational platform for quantum cryptography (Exploration)

Ruth Book, SOIS (PI)
Corinne Black, SOIS (Co-I)
Emma Duncan, SOIS (Co-I)
Upgrading Ungrading: Exploring Student-Centered Approaches to Alternative Assessment (Exploration)

Thomas Kinsman, GCCIS (PI)
Micro-AI and Tiny ML (Exploration)

Karuna Koppula, KGCOE (PI)
Nicole Hill, KGCOE (Co-I)
Obioma Uche, KGCOE (Co-I)
Jairo Diaz, KGCOE (Co-I)
Program-wide AI Implementation: An Exploration of Responsible Integration of AI use for Enhancing Student Learning. (Exploration)

Kierstin Muroski, NTID (PI)
Learning in Informal Research Spaces (Exploration)

Nickesia Gordon, COLA (PI)
Using AI Voice Recorders in Qualitative Research Data Gathering and Analysis: COMM 402 (Generative AI)

Zack Butler, GCCIS (PI)
Using GenAI to develop broken-code exercises to train novice programmers (Generative AI)

Elizabeth Reeves O’Connor, COLA (PI)
Shaun Foster, CAD (Co-I)
Finding Your Voice in an AI World (Generative AI)

Jake Adams, GCCIS (PI)
Sten Mckinzie, GCCIS (Co-I)
Justus Robertson, GCCIS (Co-I)
Yiqin Zhao, GCCIS (Co-I)
Andrew Wheeland, GCCIS (Co-I)
Travis Stodter, GCCIS (Co-I)
JP Takats, GCCIS (Co-I)
Sean Boyle, GCCIS (Co-I)
Empowering Game Design Education with Professional AI Tool Access (Generative AI)

Daniel DeLuna, CAD (PI)
Creation of Custom Motion Design Tools via Agentic AI Agents (Generative AI)

Zhiqiang Tao, GCCIS (PI)
Annemarie Ross, NTID (Co-I)
Developing GenAI-Assisted Accessibility for Deaf and Hard-of-Hearing Students in Instructional Laboratories (Generative AI)

Justus Robertson, GCCIS (PI)
Yiqin Zhao, GCCIS (Co-I)
John-Paul Takats, GCCIS (Co-I)
Evaluating Scaffolded AI Tutoring for Programming and Debugging in Computing Education (Generative AI)

Laurie O'Brien, CAD (PI)
AI for Creative Professionals: Generative Tools for Photography, Design, and Video (Generative AI)

Bartosz Krawczyk, COS (PI)
TutorBot++ for Machine Learning / Computer Vision: A Plug-In Extension for Rubric-Aligned Support in Mathematical Derivations and Coding Workflows (Generative AI)

Hinda Mandell, COLA (PI)
Fashioning a ‘FreeShop’ Thrift Closet at Pittsford Community Library through the course VISL-390 Visual Activism (TAD)

Ihab Mardini, CAD (PI)
Design for Humanitarian Solutions: A 3D Printed Water Filter Pilot For Underserved Communities (TAD)

Jesse O'Brien, CAD (PI)
3D Game Animation (TAD)

Susan Lakin, CAD (PI)
Location-Based AR for Public History: Co-designing community stories for social impact (TAD)

Contact

For all inquiries related to the Provost’s Learning Innovation Grants program, please email plig@rit.edu.