AI Confidence Series

Build Confidence & Develop a Pedagogical Foundation

Designed for faculty who are new to AI or want a stronger foundation for making informed decisions about its role in their teaching.

Explore AI capabilities and limitations, responsible and ethical use, accessibility and equity, academic integrity, and the relationship between AI and meaningful student learning.

Best fit if you're thinking:

"I want to better understand AI and determine how it fits—or doesn't fit—within my teaching."

Translate Ideas into Teaching Practice

Ready to move from understanding AI to making intentional instructional decisions? This track focuses on applying AI concepts to course design, assignments, assessment, feedback, and other teaching practices.

Explore ways AI can support learning while maintaining transparency, assessment integrity, and meaningful evidence of student learning.

Best fit if you're thinking:

"I understand the basics. Now, how do I actually apply this to my courses?"

Lead & Advance AI-Informed Teaching

Designed for faculty ready to move beyond their own classroom and contribute to broader conversations about AI-informed teaching.

Explore emerging developments in AI, share effective practices, collaborate with and mentor colleagues, and contribute to instructional innovation across Jax State.

Best fit if you're thinking:

"I'm ready to share what I've learned and help advance quality teaching with AI at Jax State."

Take Your Learning Further (Next section after the above dropdown boxes)

Foundations – AI Readiness Reflection & Alignment Statement 

Participants will submit a brief reflective statement describing their current understanding of AI, how it aligns with their teaching philosophy, and how they anticipate approaching AI within their own courses. 

Deliverable components may include: 

• Reflection on current AI knowledge and confidence
• Personal philosophy regarding AI use in teaching
• Opportunities and challenges within their courses
• Individual goals for future implementation

Applied Practice – AI Teaching Action Plan 

Participants will develop a practical implementation plan for incorporating AI into one of their courses. The emphasis is on intentional instructional planning rather than producing a finalized instructional resource. 

Deliverable components may include: 

• A prototype AI-aware assignment or learning activity
• AI transparency statement or course policy
• Assignment-level AI expectations
• Student verification or reflection strategy
• Brief pedagogical rationale supporting instructional decisions

AI Pedagogy Leader – AI Teaching Leadership Portfolio 

Participants will document how they have implemented AI-informed teaching practices while reflecting on instructional growth and leadership. 

Deliverable components may include: 

• Reflection on implementation and instructional impact
• Documentation of instructional changes
• Evidence of mentoring, collaboration, or sharing with colleagues
• Personal leadership goals for continued AI-informed teaching

Faculty who want to take their participation a step further can earn a digital microcredential for each learning track completed. 

To earn a track's microcredential: 

• Attend all three sessions in the learning track + Complete the track-specific deliverable
• Each deliverable gives you an opportunity to apply what you've learned to your own teaching without creating unnecessary additional work.

Foundations 

AI Readiness Reflection & Alignment Statement: 

Reflect on your current understanding and confidence with AI, how AI aligns with your teaching philosophy, opportunities and challenges within your courses, and your goals for moving forward. 

Applied Practice 

AI Teaching Action Plan: 

Develop a practical plan for incorporating AI-aware teaching into one of your courses. Your plan might address an assignment or learning activity, AI expectations, transparency, student verification or reflection, and the pedagogical reasoning behind your decisions. 

AI Pedagogy Leader 

AI Teaching Leadership Portfolio 

Document your implementation of AI-informed teaching practices, reflect on their instructional impact, and provide evidence of how you have shared, collaborated, or mentored colleagues around AI-informed teaching. 

AI Teaching Innovation Microgrants 

Have an idea for an AI-aware assignment or learning activity that you're ready to develop further? 

Faculty may apply for a limited number of AI Teaching Innovation Microgrants to support the development or redesign of instructional materials for classroom implementation. 

Microgrants are optional and separate from the microcredential requirements. You do not need to receive a microgrant to earn an AI Confidence Series microcredential.  

Selected faculty will receive implementation support and contribute their finalized instructional materials to the Jax State AI Teaching Repository. 

Each learning track includes a track-specific deliverable designed to demonstrate meaningful application of the concepts explored throughout the professional development sessions. These deliverables serve as the culminating artifact required to earn a digital microcredential and emphasize reflection, planning, implementation, or instructional leadership depending on the track. 

The AI Teaching Innovation Microgrants are separate from the microcredential pathway. Rather than serving as another required deliverable, they provide faculty with an optional opportunity to further develop and refine AI-aware instructional materials for classroom implementation and inclusion in the Jax State AI Teaching Repository. This distinction allows the AI Confidence Series to remain focused on professional learning while encouraging interested faculty to contribute lasting instructional resources that benefit the broader university community. 

Eligibility 

Applicants should: 

• Participate in one or more AI Confidence Series learning tracks (preferred but not required, if you decide to open it more broadly).
• Develop or substantially redesign an AI-aware assignment or learning activity.
• Demonstrate that instructional decisions are grounded in pedagogy and aligned with student learning outcomes.

Application Materials 

Applicants will submit: 

• Final assignment or learning activity
• Assignment instructions
• Learning outcomes
• AI transparency statement or student expectations
• Assessment criteria or rubric
• Verification and/or reflection component
• Brief pedagogical rationale explaining instructional decisions

Selected faculty will receive implementation support and will contribute their finalized instructional materials to the Jax State AI Teaching Repository.