Institution AI Posture Baseline
Establish a shared understanding of how students, faculty, and institutional teams are currently using AI and where the greatest opportunities and risks exist.
Higher Education Workshop
Build an AI-Augmented Institution Without Sacrificing Learning
A hands-on workshop for higher education leaders, faculty, and learning teams developing practical AI practices that strengthen learning while protecting academic integrity, judgment, and institutional trust.
Participants move beyond debating whether students and faculty should use AI and begin designing how AI can be used deliberately, transparently, and measurably across learning, teaching, and academic operations.
Practical Outcomes
What Your Institution Will Leave With
The workshop is designed to produce concrete decisions, learning practices, governance mechanisms, and next steps — not just discussion about AI.
Student Posture Map
Identify where learners are underdeveloped, under-leveraged, appearing fluent without understanding, or genuinely becoming AI-augmented.
AI-Augmented Learning Practices
Design learning activities that build subject-matter expertise, AI fluency, core reasoning skills, and the meta-skills needed to judge AI-generated work.
Verifiable Assessment Model
Move beyond product-only assessment by incorporating evidence of reasoning, sources, validation, ownership, reflection, and oral defense.
Governed AI Use Plan
Define where AI is encouraged, constrained, validated, escalated, or prohibited based on learning goals and consequence.
30-Day Adoption Roadmap
Leave with a prioritized set of actions that can move faculty, academic leaders, and students from policy discussion to practical adoption.
AI Has Already Changed the Learning Environment
Students and faculty are already using generative AI. The institutional question is no longer whether AI will be present in education.
The challenge is determining how to preserve learning, expertise, judgment, academic integrity, and trust when AI can participate in almost every stage of academic work.
Institutions are wrestling with questions such as:
- How do we distinguish AI-assisted learning from AI substitution?
- What should students still know and be able to do independently?
- How should faculty redesign assignments and assessment?
- How can students demonstrate ownership of AI-assisted work?
- Where should AI use be encouraged, controlled, or restricted?
- How do we know whether AI is actually improving learning?
Workshop Journey
01 ------ Diagnose
02 ------ Activate
03 ------ Controls
04 ------ Execute
05 ------ Measure
06 ------ Scale
Move From AI Policy Debate to an Institutional Operating Model
The workshop follows the six stages of the AI-Augmented Operating System, providing a repeatable method for designing, governing, executing, and measuring AI-supported education.
Create a Shared AI Vision
Examine current AI use across students, faculty, programs, learning practices, policies, and institutional priorities.
Build Balanced Capability
Strengthen the subject-matter, AI, core reasoning, and meta capabilities required for students and faculty to use AI effectively.
Protect Learning and Integrity
Define expectations for sources, validation, transparency, ownership, escalation, evidence, and acceptable AI use.
Redesign Real Learning Experiences
Apply AI to authentic courses, assignments, advising, research, and academic workflows with clear human responsibility.
Prove What Is Improving
Evaluate learning quality, student understanding, correction burden, faculty effort, integrity, and other meaningful outcomes.
Expand With Institutional Confidence
Turn successful pilots into repeatable practices, faculty enablement, governance patterns, and institution-wide standards.
AI-Augmented Learning
Develop More Than AI Skills
AI fluency alone is not enough. Students need a balanced set of capabilities that allows them to use AI without becoming dependent on it.
Subject-Matter Expertise
Students still need enough domain expertise to frame good questions, recognize weak answers, challenge assumptions, and judge quality.
AI Capability
Learners need practical fluency in prompting, model interaction, grounding, comparison, iteration, and appropriate AI tool selection.
Core Skills
Communication, critical thinking, analysis, problem solving, collaboration, and other durable human capabilities become even more important when AI accelerates production.
Meta Skills
Students must learn to reflect, verify, challenge, adapt, understand limitations, and recognize when their own expertise is insufficient.
Sample Half-Day Experience
What Happens During the Workshop
Participants work together on real institutional questions and leave with concrete learning practices, governance mechanisms, and next actions.
0:00 → 0:30 ------ Diagnose the Learning Shift
Establish a shared view of current AI adoption, student behavior, faculty concerns, policies, opportunities, and risk.
Takeaway: Institutional and student posture baseline.
0:30 → 1:15 ------ Define the Capabilities That Still Matter
Examine how subject expertise, AI capability, core skills, and meta-skills must work together in an AI-rich learning environment.
Takeaway: Capability priorities for students and faculty.
1:15 → 2:00 ------ Redesign Learning Activities
Explore AI-SME sparring, model comparison, reasoning trails, source verification, failure analysis, and other practices that require students to actively engage with the learning.
Takeaway: Revised learning activities and instructional patterns.
2:00 → 2:45 ------ Make Learning Verifiable
Design assessment approaches that capture process evidence, reasoning, sources, validation, reflection, and student ownership.
Takeaway: Individual Integrity Packet and assessment pattern.
2:45 → 3:30 ------ Govern AI Without Freezing Innovation
Identify where institutional guidance, faculty discretion, transparency, validation, and escalation are required.
Takeaway: Governed-learning model and priority controls.
3:30 → 4:00 ------ Build the First 30 Days
Prioritize initial pilots, faculty enablement, learning-design changes, governance actions, and evidence to collect.
Takeaway: 30-day adoption roadmap.
Full-day workshops add deeper curriculum redesign, faculty working sessions, institutional use-case development, governance exercises, and implementation planning.
Who Should Attend
Designed for Cross-Institutional Participation
AI adoption touches teaching, learning, governance, technology, and institutional strategy. The strongest sessions bring multiple perspectives into the room.
Academic Leadership
Provosts, deans, department chairs, program leaders, and others responsible for academic quality and institutional direction.
Faculty & Learning Teams
Faculty, instructional designers, teaching centers, librarians, assessment teams, and learning-technology professionals.
Institutional & Technology Leaders
CIOs, AI leaders, policy teams, student-success leaders, research administrators, and others shaping institution-wide adoption.
This Is Not an AI Tools Workshop
The workshop is not centered on demonstrating the newest AI product or teaching faculty a list of prompt tricks.
Tools will continue to change. Institutions need a durable model for determining how AI affects learning, capability, assessment, governance, and institutional decision-making.
The focus is on questions that survive tool changes:
- What must the learner still understand?
- What work can AI appropriately accelerate?
- How is student reasoning made visible?
- How is AI-assisted work validated?
- Who remains accountable for the result?
- What evidence tells us the approach is improving learning?
Built Around Your Institution
Use Your Courses, Policies, Challenges, and Priorities
Before the workshop, we identify institutional goals, faculty concerns, student populations, existing AI guidance, priority programs, and desired outcomes.
We then adapt the examples and working exercises to your environment while preserving the core workshop structure. Participants work with institutional scenarios that reflect the decisions they are actually facing.
Engagement Options
Choose the Right Starting Point
Half-Day Leadership Workshop
Align leaders around the learning shift, institutional posture, capability model, governance priorities, and immediate actions.
Full-Day Working Session
Add faculty exercises, learning redesign, assessment patterns, use-case development, and deeper implementation planning.
Workshop Series / Follow-Up Engagement
Extend the workshop into faculty enablement, pilot design, governance development, measurement, and ongoing adoption support.
Sessions can be delivered onsite or virtually and can involve a single academic unit, leadership team, faculty cohort, or cross-institutional working group.
Bring AI-Augmented Education to Your Institution
Move From AI Reaction to Deliberate Institutional Adoption
Tell us what your institution is trying to protect, improve, and accomplish with AI. We'll help determine the right workshop format and starting point.