Advancing Ethical, Human-Centered AI
for Learners and Industry
About the Institute
The AI Excellence Institute is a strategic 3-year initiative to position Sinclair as a national leader in AI-integrated education, pedagogy, and workforce engagement. The Institute will drive transformation in:
Faculty-led curriculum innovation
Community-driven reciprocal learning ecosystem
Sustainable AI integration in eLearning and institutional systems
Sinclair’s AI Excellence Institute is not just about AI adoption—it is about ensuring students and faculty use AI responsibly, critically, and effectively. By embedding meta-AI skills, modernizing pedagogy, and forging industry partnerships, Sinclair will lead the charge in equitable, future-focused AI-empowered education and workforce-readiness.
Objectives
+ Redefine AI as a transformative opportunity
AI should not be seen as a cheating risk but as a cognitive partner that enhances learning. Traditional AI detection and prohibition models aren’t effective and widen equity gaps—students with more access to AI develop greater technological fluency, while others fall behind. The Institute will focus on policy creation/modification, direct faculty support, and ethical use guidelines to underpin the transformative work of AI at Sinclair.
Measurable Goals
Shift policy from restriction to empowerment by embedding AI literacy into all programs.
Support faculty in AI-integrated pedagogy rather than relying on ineffective detection tools.
Develop clear ethical AI use guidelines for students and faculty, emphasizing AI responsibility rather than prohibition.
+ Embed Meta-AI skills into curriculum and learning outcomes
AI disrupts traditional skill progressions in education, requiring a fundamental revision of learning outcomes. Sinclair will prioritize meta-AI skills among core fundamentals of learning in every program.
Measurable Goals
Prioritize meta-AI skills as core competencies, including:
Strategic Agency: Identifying when and how to use AI effectively.
Problem Recognition: Understanding what AI can and cannot do.
Task Decomposition: Dividing cognitive work between humans and AI.
Verification Skills: Developing productive skepticism towards AI-generated content.
AI Output Pattern Recognition: Detecting biases, strengths, and weaknesses in AI-generated responses.
Distributed Responsibility: Teaching students to take full accountability for AI-generated work.
Revise general education (Gen Ed) requirements to include AI-assisted problem-solving, AI-enhanced writing and communication, and AI-integrated quantitative reasoning.
Develop AI-infused discipline-specific courses, such as:
AI in Business: From data analysis to strategic decision-making.
AI in Engineering: From computational design to ethical system oversight.
AI in Health Sciences: From AI-assisted diagnostics to human-centered patient care.
+ Transform pedagogy to integrate AI as a learning partner
Traditional scaffolding models assume students must eventually work independently of instructional support. However, AI is a permanent companion in students' professional lives, requiring a shift in teaching strategies. The Institute will guide Sinclair in the exploration of temporary and permanent AI scaffolding, and redesign assignments and assessment to support this shift.
Measurable Goals
Differentiate between temporary and permanent scaffolding:
Temporary scaffolding: Supports students in learning meta-AI skills such as prompt engineering and AI verification.
Permanent scaffolding: Recognizes that AI remains a lifelong learning assistant, shifting emphasis to strategic AI collaboration.
Redesign assessments to measure AI-augmented critical thinking rather than procedural skills.
Develop new AI-integrated assignments, distinguishing when students should use AI and when they should work independently.
+ AI-powered personalized learning and student support
AI offers unprecedented opportunities to personalize learning. The Institute will explore, test, and make formal recommendations as to tools and approaches to be implemented at-scale.
Measurable Goals
Explore RAG-based (Retrieval-Augmented Generation) AI tutoring systems that provide course-specific support while preventing misinformation.
Pilot AI-powered adaptive learning tools to adjust instruction based on student progress.
Issue final recommendations on tools/approaches Sinclair should consider implementing collegewide.
+ Faculty and staff AI Competency development
For AI integration to succeed, faculty and staff development is critical. The Institute will advance this work in a meaningful, non-transactional way. Training is not enough: AI faculty fellows and Institute staff will lead work to guide their peers through latticed development in AI in pedagogy, assessment design, student engagement, and peer-supported communities of learning.
Measurable Goals
Launch an AI Faculty Fellowship Program to train faculty in AI-enhanced pedagogy and curriculum redesign.
Develop structured AI micro-credentials for faculty and staff, covering:
AI in teaching and learning.
AI-enhanced assessment design.
AI-driven student engagement techniques.
Create an AI community and resource repository, for best practices, case studies, and AI-integrated lesson plans.
+ Industry and community engagement for workforce readiness
AI-driven industries require workers skilled in AI collaboration. The Institute will position Sinclair as a leader in the regional conversation with employers and community organizations to understand AI literacy in the workforce, evolving workforce competency standards, and opportunities for students to learn in collaboration with employers and partners.
Measurable Goals
Establish an AI Advisory Council with regional employers and industry leaders to align educational outcomes with workforce needs.
Develop AI literacy workshops for businesses and community members, positioning Sinclair as a leader in regional AI education.
Integrate real-world AI capstone projects where students solve practical AI challenges in collaboration with employers.