Strategic Vision and Institutional Alignment
As of April 11, 2026 update, Project TRAIL (Transformative, Responsible AI Learning).
Thank you to our VU Grants Team: Angela Dougherty, and Stephanie Stemle for their help with the grant process.
Thank you to the TRAIL core team and to the TRAIL Taskforce and many others along the way who have helped this proposal take shape.
The overarching mission of Project TRAIL is: "Leveraging AI to enhance learning, modernize teaching, and align operations to deliver measurable student success."
Project TRAIL isn’t a pivot to a new concept, but rather the natural evolution of an AI strategy already in motion at Vincennes University. This initiative builds upon our established foundation, significantly advancing our timeline and expanding our technical capabilities. Project TRAIL scales our existing vision into a definitive model for AI-driven higher education, delivering a future where academic achievement is seamlessly linked to professional success in a global, AI-enabled economy.
Four Pillars: Student Success, Academic Innovation, Operational Excellence, Responsible AI
1. Pillar: Student Success
Student Success is the primary mandate of Project TRAIL, to directly address the Lilly Endowment’s focus on AI-enabled student supports. Our strategy moves beyond traditional advising to create a personalized, data-rich ecosystem that supports our students in their educational journey.
Central to this effort is the Ellucian Ecosystem, a suite of agentic tools designed to automate the student journey:
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Smart Plan: Functioning as the "GPS for graduation," this guided pathways tool empowers students and advisors to map precise academic trajectories.
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Smart Award: An automated degree auditor that provides real-time verification of graduation requirements, eliminating manual audit friction.
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Virtual Advisor: A 24/7 support hub that utilizes the full Ellucian data ecosystem to provide student-specific, authenticated answers rather than generic responses.
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Scholarship Universe: A sophisticated matching engine connecting students to millions in internal and external financial aid.
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Student Forms: A cloud-based workflow engine that digitizes manual paper processes, accelerating financial aid and registration tasks.
Complementing this is the Blackboard Integration suite, which prioritizes academic equity through advanced analytics. Tools like AVA provide real-time, course-aware support, while Video Student optimizes instructional materials via engagement analytics. Most critically, Illuminate aggregates multifaceted learning signals—analyzing both behavioral patterns and academic performance—to provide the actionable insights necessary for risk detection and personalized intervention.
2. Pillar: Academic Innovation
The AI/ML Innovation Lab serves as the university’s multidisciplinary R&D engine. This lab is not a mere computer center; it is a "Living Laboratory" where students engage in full-stack development.
In this environment, students are not just consumers of AI—they are builders. They design and deploy embedded IoT sensors, data pipelines, and sophisticated predictive models built entirely in-house. These solutions are applied across critical regional sectors, including:
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Campus infrastructure and Public Safety.
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Healthcare research and Agriculture.
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Urban planning, Business Analytics, and Community Services.
To ensure curriculum remains relevant, we are working to deploy a Business and Industry Portal, providing partners with real-time technological benchmarks. This is guided by the AI Curriculum Scorecard, which utilizes an AI Curriculum Maturity Model. This model serves as the foundational toolkit for faculty, guiding them from surface-level adoption to the deep systemic integration required to produce graduates with the high-tier AI fluencies demanded by the modern workforce.
3. Pillar: Operational Efficiency Excellence
We are transitioning from fragmented legacy systems to a Scalable Operations Framework that optimizes institutional productivity.
The core of this framework is the Straia AI-native operating system, which unifies disparate data streams to allow for real-time, agentic decision-making. This is bolstered by Slate AI tools to automate admissions and foundation workflows, and the deployment of Google Workspace Education Plus to provide administrative staff with premium, AI-enhanced productivity licenses.
A critical innovation in this pillar is the transformation of Faculty and Adjunct Credentialing. By deploying a Credential LLM talent acquisition portal, we have moved from manual, high-friction audits to a deterministic AI scoring framework. This rule-based automated flow provides objective, real-time validation of instructional qualifications, transforming hiring from a reactive necessity into a proactive, data-informed strategy.
Furthermore, we have engineered an Assessment Accreditation Support system. This AI-driven evidence ecosystem automates the classification and tagging of academic artifacts, providing real-time gap detection to ensure that instructional outcomes remain perpetually aligned with accreditation benchmarks. Supported by robust Identity and Access Management, this secure operational infrastructure provides the foundation for deep technical R&D.
4. Pillar: Responsible AI
Ethical adoption is the prerequisite for institutional trust. This Pillar focuses on building student, faculty, and staff capability and ensuring that our AI integration is both transparent and rigorous.
We have established a comprehensive Institutional Literacy Framework:
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Student AI Certification Pathway: A formal credentialing process ensuring all students master foundational AI knowledge and applied skills using Google AI tools.
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Faculty AI Literacy Course: A structured progression with stackable badges that moves faculty from foundational literacy to departmental leadership and AI mentorship.
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AI-Enhanced Curriculum "Labs": Collaborative design sprints focused on increasing the "AI-resistance" of assignments, compelling students to engage in higher-order critical thinking.
To translate broad policy into practice, we are developing "Field-Specific Ethical AI Use" Guides. These peer-developed frameworks provide discipline-specific best practices, ensuring that a nursing student and a cybersecurity student understand the unique ethical nuances of AI within their respective professions. This holistic ethical framework ensures the project's sustainability and integrity as we finalize our resource commitments.