California Education Learning Lab's AI FAST Challenge: Funding for Accelerated Study and Transformation

California Education Learning Lab's AI FAST Challenge: Funding for Accelerated Study and Transformation is sponsored by California Education Learning Lab. This program supports research and projects using generative AI for teaching and learning, specifically in areas like generative AI-assisted chip design to prepare students for careers in the semiconductor industry.

AI FAST Challenge: Funding for Accelerated Study and Transformation – California Education Learning Lab Protected: AI FAST Challenge: Funding for Accelerated Study and Transformation Funding for Accelerated Study and Transformation Looking for Quantum FAST collaborators? Looking for Quantum FAST collaborators?

Check out the Quantum FAST Networking Sheet to find potential partners and others interested in advancing California's quantum ecosystem. If you would like to share your information with others, please email Learning Lab at quantumca@calearninglab. org for access to the Quantum FAST Networking Sign-Up Form.

The AI FAST Challenge is now closed. You can view the award announcement page HERE and for more detailed descriptions of awarded projects, please visit our search project page HERE . Please join our community to find out about future fund opportunities.

—————————————————————————————————————————————————————————————————————————————————————— Like Learning Lab’s AI Grand Challenge , the AI FAST Challenge seeks to incentivize faculty across the California Community Colleges, and California State University and University of California campuses to lead the constructive development of AI use, understanding, and capability to enhance teaching and learning and close equity gaps among students.

For AI FAST Challenge, Learning Lab intends to award grants between $25,000 and $200,000 over an accelerated 12 – 18 month implementation cycle to support nimble, innovative research and development of AI teaching and learning projects by individual faculty or institutions (up to $150,000), or cohorts of institutions with shared curricular or policy goals (up to $200,000).

Stay updated on upcoming events by signing up for our Listserv and explore our website for more information. For questions, contact us at info@calearninglab. org .

** Note on the Budget Template : If you will be applying for a FAST grant with compensation through a PSA please contact Learning Lab for a custom budget template. Looking for Quantum FAST collaborators? Check out the Quantum FAST Networking Sheet to find potential partners and others interested in advancing California's quantum ecosystem.

If you would like to share your information with others, please email Learning Lab at quantumca@calearninglab. org for access to the Quantum FAST Networking Sign-Up Form. Whether the focus is generative AI or AI generally, we invite submissions in four tracks: Track 1: Essential Disciplinary Knowledge, Understanding, and Skills.

In a world where AI may be able to surpass human performance in specific tasks, what core knowledge, understanding, or skills should higher ed (continue to) invest in, and how does this vary by discipline or CTE field? What foundational sequences of learning or skills still matter?

How can the foundational and fundamental be better taught and demonstrated leveraging AI, and how can this further our goals for more equitable student opportunity and success? What must students know or be able to do no matter how capable AI gets and what new skills will they need to effectively leverage AI, including mechatronic or robotic AI, in their field? Track 2: Optimizing Teaching, Learning, and Student Advancement.

AI is positioned to play a transformative role in course development, pedagogy, assessment, and opportunities for equitable participation and advancement. How can AI accelerate, optimize, or personalize the teaching, learning, or student advancement cycles? What learning or success gains should we aim for, and how can we ensure that learning is not just occurring, but thriving and enduring?

Track 3: Accuracy, Authenticity, and Assessment. Generative AI makes mistakes and so do humans. Generative AI lies and so do humans.

How do you assess the accuracy or authenticity of answers from AI or humans? How do you learn from mistakes? How does that differ in disciplines dependent on correct or objective answers vs. variable or subjective answers?

Are students and faculty comfortable with AIs that judge their work, and should they be? How can we develop the tools and know-how to judge AI? Propose something that doesn’t fit neatly in any of the tracks above but fits into this larger narrative of AI Teaching and Learning.

We encourage you to think creatively! For more details and examples, see the full RFP. To apply for a Learning Lab AI FAST Challenge Grant, projects must: Be led by a faculty member or administrator of a UC, CSU, or a California community college .

Have two letters of recommendation. One of these letters must be from the employer the applicant reports to.

If applying as an individual for funds through a PSA rather than an institutional grant, the letter of recommendation from your employer (UC, CSU, or California Community College) must include: 1) permission for the applicant to perform Learning Lab AI FAST Challenge work outside their normal faculty position/workload (if a full-time employee); and 2) permission for the applicant to receive this grant through a Professional Services Agreement.

The grant application process consists of up to three stages: Statement of Intent (includes eligibility acknowledgement, applicant information, proposed project track and a brief project description, team member information, and institutional information) Proposal and Letters of Recommendation ; and A written response to evaluator questions , if any. Applications will be accepted on a rolling basis through Learning Lab’s Grant Portal.

All applicants must submit their Statement of Intent at least three weeks prior to submitting a proposal. The Learning Lab will contact applicants if there are eligibility or RFP alignment concerns at this stage. If there are no eligibility issues, applicants may proceed to proposal submission, after which their proposal will be assigned to two Selection Committee evaluators for review.

Evaluators will have one month to review the proposal, after which they may optionally send applicants questions. If applicants receive evaluator questions, they must submit their response to the Grant Portal within two weeks. After reviewing responses, evaluators will make an “award” or “no award at this time” recommendation to the Learning Lab.

All materials received will be acknowledged by the Grant Portal. If any materials are missing or if the applicant is found to be ineligible, Learning Lab will contact the applicant directly. Th e submission process will take approximately 11 weeks from Statement of Intent to Decision Notification.

Projects can commence when all signatures are obtained after award negotiation (typically four weeks after decision). Additionally, grantees are required to submit quarterly or biannual reports and invoices and attend three project check-ins (at launch, mid-point, and at close). The grant will close when the final invoice and evaluation are received.

Statements of Intent- First Day to Submit June 7, 2024, 8:00 AM PT Statements of Intent- Last Day to Submit October 10, 2024 5:00 PM PT Proposal- First Day to Submit June 28, 2024 8:00 AM PT Proposal- Last Day to Submit October 31, 2024 5:00 PM PT Learning Lab will assign two AI Challenge Selectin Committee members to review submitted AI FAST proposals, according to a published rubric.

Awards and final award amounts are contingent on successful negotiation of a grant agreement or professional services agreement. Learning Lab may take into account whether other similar projects have been awarded under this program in order to avoid duplication. Learning Lab will also take into account institutional, disciplinary, and geographic diversity in making awards.

Submitted proposals will be assessed for eligibility and evaluated using a rubric and scoring system. Scoring will factor into the decision to make an award, but will not be the only factor as noted above. Learning Lab may use AI in the evaluation process, but all proposals will be rated and scored by humans.

The scoring rubric and a more detailed description of Learning Lab’s AI use will be available on Learning Lab’s AI FAST grants webpage by June 14, 2024. Use of AI by the Selection Committee in the evaluation of proposals: Given the nature of the AI Challenge to test/utilize effective and ethical use of AI, there is incentive for Selection Committee members to use AI in their proposal review process.

Our Selection Committee has discussed how best to approach this in an ethical and fair way, and has agreed upon the following guidelines: All proposals will be assessed by each committee member assigned to review proposals independent of any use of AI. Selection Committee members are allowed to use AI to support or improve their narrative feedback summary that will be shared with applicants.

If Committee members opt to use AI in this way, they will input only their own writing into the AI and will strip any identifying information about the applicant before doing so. With permission from the applicant, for select applications some Selection Committee members may input an application and the rubric into an AI tool (most likely an LLM) to get an “alternate take.

” This may, for instance, allow them to see if they have scored the application fairly or missed anything in their scoring process. This will only be done if applicants have indicated within Submittable at the time of proposal submission that they will allow this.

Applicants should carefully consider the pros and cons before making this decision and feel comfortable saying no if they do not want to risk data leakage or risk that their application might be used for training by the AI tool. Committee members will remove personally identifiable information from applications before uploading it to an AI tool.

✔ Demonstrate knowledge about the students and/or faculty groups your project intends to impact, including historically underrepresented student groups [1] and other underserved student groups [2] . ✔ Include any relevant data and disaggregated data that will help us understand the student and/or faculty groups you intend to impact. ✔ Use asset-based language [3] to describe your student/faculty populations.

✔ Be ambitious but realistic . ✔ Be specific about your goals and the impact or outcomes you are hoping to achieve with the project and connect how or why your approach may lead to fulfilling the goals and outcomes of your project. ✔ Highlight the research base that supports your proposed work and demonstrate fluency as pertains to your proposed project’s area.

Do not take for granted the same knowledge level for all proposal evaluators. ✔ Be specific about how the project will work in practice . ✔ Given the difficulties of faculty recruitment and collaboration, if applicable to your project, please include the project’s proven strategies for recruitment and fostering inclusion .

✔ If applicable to your project, demonstrate authentic, well-balanced collaboration [4] among partner institutions and/or departments, and identify structures and/or processes to help sustain collaborative efforts. ✔ Think about how sustainability might work and what you need for ongoing sustainability. ✔ Demonstrate that leadership and/or faculty peers support your approach/your proposal.

✔ Double-check your timeline . If your project involves data collection from students or faculty, have you consulted with your IRB and Institutional Research departments to ensure your timeline is realistic? Have you accounted for the time it will take to make new course materials accessible?

✔ Double-check your budget . Is your budget realistic? For larger or more complex projects, have you accounted for the administrative work required to support the project?

✔ For innovative efforts, not all things may go according to plan. Do you have the structures in place to learn, iterate/pivot, resolve conflict and disagreement, and find a path forward? ✔ If you use AI in the production of the proposal , describe how you used AI .

Remember that you are responsible for the accuracy of citations and other representations in your project’s proposal, whether AI assisted or not. ✔ Please review the vendor selection guidance (see “Resources” tab) and demonstrate proper due diligence with selection.

[1] This refers to groups for which demographic data collected by higher education institutions demonstrates persistent patterns of exclusion or marginalization in higher education generally or STEM fields specifically.

There is no universal definition for this term but it frequently includes students who are Black/African American, Latine, American Indian, Alaskan Native, Pacific Islander, some Asian American subgroups, and women in some STEM fields. [2] U nderrepresented student groups are distinct from underserved student groups.

While “underrepresented” refers to student group presence within higher education as tracked by the specific demographic data categories selected by higher education institutions, ”underserved” refers to inequitable access to resources and assistance.

Underserved student groups may variably and intersectionally include first generation college students; students with disabilities; neurodivergent students; unhoused students or students from under-resourced socio-economic backgrounds; and LGBTQ+ students, in addition to the student groups listed above as underrepresented.

[3] Asset-based Language: Language that focuses on student (or faculty) strengths, agency, and opportunities rather than deficits. For example, an asset-based framework would lead with the ambition and persistence demonstrated by students from under-resourced communities who enroll in the course rather than use the label “underprepared students” without context or nuance.

There are several online resources that provide examples and guidance on using asset-based language, including the Here to Here Language Guide, Cal State East Bay’s Inclusive Language Guide, and the UC Davis Development and alumni guide Using Inclusive Language. [4] Authentic Collaboration: Learning Lab funded projects are inherently collaborative, but achieving authentic collaboration requires intentionality.

Projects seeking Learning Lab funding should demonstrate that power and responsibility is equitably distributed and that carrying out the vision for the project is a collective process. Awareness of the conditions/dynamics that can lead to power struggles, conflicts, and other dysfunctions may lead to creative opportunities for collaborative partnership.

Additionally, the project budget should be well-balanced and reflect the values of collaboration that the proposal describes by funding project team members and institutions fairly. The documents within this section are intended to be helpful resources as your project team develops a proposal. There is no requirement for their use ( with the exception of the Budget Template, which will be available by May 19th ).

** Note on the Budget Template : If you will be applying for a FAST grant with compensation through a PSA please contact Learning Lab for a custom budget template.

Assessment Plan Template Implementation Plan Template Logic Model Template Logic Model Sample Open Educational Resources Guidance Accessible Resources Guidance Outcomes and Impact Guidance Ethics Guidance Vendor Selection Considerations FAQ Budget Template Expanded Budget Template Budget Sample AI FAST Rubric AI RFP Q&A Webinar Recording 5/17/24