During the Minimum Viable Product (MVP) Phase participants will build upon their prototypes from the previous phase and deliver MVPs. An MVP refers to a development approach where a product is built with the minimum features necessary to satisfy the early adopters or users and gather valuable feedback for further development. During this phase, significant progress must be made to the prototypes to be considered for the Challenge MVP awards and follow-on funding opportunities.
At the conclusion of the MVP Phase, participating teams will deliver a demonstration of their MVP and provide other relevant information during a 7-minute presentation. The judges select the winning teams, and each team will receive a prize as defined on the prizes page.
The Nittany AI Alliance is pursuing additional funding beyond the Challenge prizes. It is our goal to extend the Challenge experience by providing ongoing funding in the form of project funding and paid internships. These additional funding sources will be evaluated under a separate set of criteria. So, while a team may not be awarded one of the MVP prizes, it could still receive funding by way of the other forms mentioned.
Important dates for the MVP Phase include:
- Monday, April 1, 2024, by noon (ET) — Deadline to submit MVP for internal review
- Friday, April 19, 2024*, from noon to 8:00 p.m. (ET) — MVP live pitches and the Nittany AI Student Society celebration event and announcement of winners.
* date subject to change to coincide with Blue & White game weekend
The pitch, lasting no longer than seven minutes, should be focused on demonstrating the MVP, detailing how AI enables the solution, and detailing the potential benefits and impact of the solution in one or more the following areas:
- AI for Good — Providing a societal good that has the potential to improve the lives of others,
- Improving Penn State operations — Improve student success and/or the student experience at Penn State.
These reviewers will include:
- subject-matter experts related to the challenges the solution addresses
- technical experts fluent in AI/ML capabilities
- industry representatives
- key decision makers from Penn State
Teams should have representatives present that can answer questions from each of the reviewers.
MVP Documentation Requirements
The supporting documentation in this round should not exceed five pages in length and should focus on supporting the criteria described below. Screenshots and graphics can be referenced in the text and included in an appendix so that these are considered outside the five-page maximum.
A one-page section that includes:
- title of project
- name, campus, and college of each team member
- name, email, and phone number of the primary point of contact
A one-page section that provides (often in bullet points):
- project title
- problem statement
- solution description
- benefits and potential impact of the solution
Overview of Project Idea
Documentation can include an overview of the problem your solution addresses and how it is being addressed. Many reviewers in this phase will not have participated as reviewers in the Prototype Phase, so this may be the first time they are seeing the MVP. Please be sure to provide enough information to ensure they understand the problem and the impact/benefit your MVP provides. Try and quantify the impact of your solution to the extent that you can.
Documentation can include a technical description of the approach the team used to achieve its goal, including the ways in which the selected AI platforms are used. A recommended approach is to provide a graphical representation that shows how the MVP utilizes the technology.
In this section, detail the data sources leveraged within the MVP as well as the data sources necessary for the continued development and successful implementation of the solution, beyond the MVP. If available, please detail the location and availability of the data sources and/or the plan for collecting the necessary additional data.
MVP documentation should include a section describing the detailed capabilities of the team to implement the proposed solution. At a minimum, teams should include individuals with technical expertise necessary for development of the tool and content experts with knowledge of the domain being addressed.