Reduce ambiguity
Give students and employees dependable language for what is expected, where, and why.
A proposed engagement for Central Texas College
Turn AI uncertainty into shared direction.
A practical, faculty-centered path to responsible AI, grounded in CTC’s mission, shaped by its people, and designed for real classrooms and services.
StrategyGovernanceTeaching & learningResponsible adoption
1 / Why now
The case for a CTC framework
AI is already part of the learning environment. Clarity must catch up.
Students, faculty, and staff are encountering generative AI now, often with uneven expectations, rapidly changing tools, and legitimate questions about learning, integrity, access, privacy, and workload.
A college-wide framework can create common ground without flattening the differences among disciplines. The goal is not a blanket permission or ban. It is a consistent process for making thoughtful choices, communicating them clearly, and supporting people well.
Protect trust
Pair innovation with human verification, academic integrity, privacy, and responsible-use guardrails.
Build capacity
Equip faculty and staff to make sound, context-specific decisions instead of chasing every new tool.
2 / Proposed engagement
A discovery-led approach
Build with CTC, not for CTC.
Holly Tech Solutions would serve as facilitator, translator, and implementation partner, bringing structure, benchmark context, and practical tools while CTC stakeholders shape the decisions.
1Listen & map
Stakeholder discovery
Leadership, faculty, students, IT, instructional design, compliance, and student services shape the starting point.
2Assess & align
AI readiness assessment
Inventory current practices, approved tools, risks, capabilities, and policy gaps, then agree on priorities.
3Design & pilot
Framework + practical pilots
Co-create guidance, course-level language, disclosure practices, training, and a small set of supported pilots.
4Enable & sustain
Launch, learn, improve
Deliver resources, ongoing professional development, governance rhythms, success measures, and an improvement cycle.
Value to CTCFaster alignmentLower implementation riskFaculty-ready resourcesA sustainable governance rhythm
3 / AI readiness assessment
A practical diagnostic
Determine readiness with evidence, not assumptions.
The assessment would establish a shared baseline before CTC chooses priorities or pilots. It is not a grade and it is not an evaluation of individual employees. It is a structured way to locate strengths, friction points, risks, and the support people need.
Holly Tech Solutions would combine document review, stakeholder listening, a short campus survey, workflow and tool inventories, and representative course examples. Findings would be validated with CTC before recommendations are finalized.
Leadership & governance
Goals, ownership, decision rights, policy alignment, review cadence, communication, and executive sponsorship.
Teaching & assessment
Course expectations, assignment design, disclosure, authentic assessment, faculty choice, and current classroom practice.
People & capacity
AI literacy, confidence, professional-development needs, instructional support, student understanding, and change readiness.
Technology, data & risk
Approved tools, licensing, privacy, accessibility, cybersecurity, procurement, records, integrations, and verification practices.
Access & student experience
Device and account access, digital fluency, disability and language needs, support pathways, and consistency across courses.
GatherListen and inventory
Interviews, focus groups, survey, document review, course samples, and tool mapping.
ScoreUse a four-stage rubric
Emerging, developing, established, or leading, with evidence attached to every rating.
ValidateSense-check with CTC
Review findings with stakeholders, surface differences by role or department, and correct gaps.
PrioritizeTurn findings into action
Produce a readiness heatmap, risk register, quick wins, pilot recommendations, and phased roadmap.
4 / Guiding principles
A durable decision lens
Principles that travel across tools, roles, and disciplines.
The framework should remain useful even as products change. These proposed principles give committees, departments, and individuals a shared way to evaluate new uses.
Human firstAI may support the work. People provide the purpose, care, judgment, creativity, and accountability.
Mission first
Use AI where it improves learning, access, service, or stewardship, not simply because it is available.
Faculty agency
Faculty retain meaningful choice to allow, limit, or prohibit AI by course and assignment, with clear rationale.
Equity by design
Account for access, disability, language, digital literacy, and the uneven consequences of automated systems.
Human connection
AI should strengthen, never replace, relationships among students, faculty, staff, and the communities CTC serves.
Privacy & security
Protect institutional, student, employee, clinical, and other sensitive information from unapproved tools.
Learn in the open
Document use, evaluate outcomes, share what works, and refine guidance as technology and evidence change.
5 / Teaching & learning framework
Clear choices. Clear communication.
One shared language for course-level AI expectations.
Faculty could select and adapt an expectation for each course or assignment, then explain the learning reason behind it.
Protect the practice
AI-free
Independent performance is essential to the learning outcome. Students complete the work without generative AI.
Guide the use
AI-limited
Specific uses are allowed, such as brainstorming or feedback, while core thinking and production remain the student’s own.
Teach the capability
AI-integrated
AI is intentionally part of the learning design. Students use it critically, document their process, and verify outputs.
Student disclosure
If AI is used, make the work visible.
- PermissionWas AI allowed for this task?
- ToolWhat system or feature was used?
- PurposeWhat did it help the student do?
- ExtentWhich parts of the process or product were affected?
- VerificationHow were facts, citations, calculations, and bias checked?
6 / Ongoing professional development
From policy to practice
Professional development must be ongoing and responsive.
AI will keep changing. CTC’s support cannot be a one-time workshop. Faculty, staff, and students need recurring opportunities to learn, experiment, share evidence, and revisit responsible practices together. Sessions would be customized to the needs, experience levels, roles, and schedules of each audience while preserving the human relationships at the center of learning and service.
Ongoing learning cycle
- 1AI foundations, limitations, bias, and verification
- 2Course and assignment expectation design
- 3Authentic assessment redesign studios
- 4Department communities of practice
- 5Regular refreshers as tools and evidence change
Design for evidence of learning
Process + product + conversation
Drafts & decisions+Final performance+Brief defense
Use local context, checkpoints, reflection, live demonstration, oral explanation, and verified sources to make learning more visible and resilient.Hands-on sessions built for conversation
Interactive workshops, department sessions, assignment-redesign studios, leadership briefings, and student learning experiences delivered on campus. In-person formats support practice, questions, peer exchange, and work with CTC-specific scenarios.
Flexible access wherever people work and learn
Live online workshops, shorter topic sessions, office hours, demonstrations, and recorded resources can extend participation across locations and schedules. Virtual delivery also makes timely refreshers easier as tools and guidance change.
The right experience for each audience
Faculty may focus on teaching, assessment, and course expectations. Staff may focus on service, workflow, privacy, and verification. Students may focus on AI literacy, disclosure, critical evaluation, career readiness, and responsible use.
7 / Suggested tools & resources
A curated starting set
Use tools to improve judgment, not bypass it.
These resources can support faculty exploration, assessment redesign, and responsible experimentation. Inclusion here is a suggestion for review, not institutional approval or endorsement.
Before adoption, CTC should evaluate privacy, accessibility, security, cost, account requirements, evidence quality, and fit with course outcomes.
Educator hub
ChatGPT for college educators
Official higher-education training and examples for teaching, learning, staff work, and campus-wide integration with ChatGPT.
Explore OpenAI education resources ↗Feedback designDr. Sarah Pax’s PAF Grading
Explore the Process, Agency, and Feedback approach and its frequently asked questions for learning-centered assessment.
Visit PAF Grading ↗Assignment reviewMichael Hernandez’s Cheat Vulnerability Index
Use the index as a conversation starter for identifying assignment vulnerabilities and redesigning for more visible, authentic learning.
Open the index ↗Co-intelligenceEthan Mollick’s One Useful Thing
Follow practical experiments and research on working with AI while preserving human agency, judgment, responsibility, and choice.
Read One Useful Thing ↗Microsoft 365 educationCopilot Study and Learn
Microsoft’s learning-focused agent uses guided questions, productive struggle, practice, and feedback to help students reason instead of simply receiving an answer.
Explore Study and Learn ↗Higher education researchMIT AI and Education Report
Review MIT’s Ad Hoc Committee report on generative AI use in teaching, learning, and research training, including institutional challenges, opportunities, and recommendations.
Read the MIT report ↗Pilot idea
Turn Copilot into an evidence-checking studio.
In a Copilot Notebook grounded only in instructor-approved readings, ask the tool to draft a plausible but imperfect explanation. Students identify errors or omissions, annotate corrections with course sources, and explain which human judgments improved the result. The activity makes verification visible while keeping the learning goal in charge.
8 / Department-specific design partnership
Designed with faculty
Turn departmental goals into useful AI learning experiences.
Holly Tech Solutions can work directly with faculty to design artifacts, assignments, and AI use cases specific to their disciplines, learning outcomes, students, and professional contexts.
The focus is practical: use AI for appropriate productivity gains while creating deeper, more authentic learning for students.
Collaborative design process
Faculty expertise sets the direction.
Together, we identify the learning goal, decide the appropriate role of AI, build the activity or artifact, test it against course expectations, and refine it from student evidence.
Develop
AI-Generated Faculty Activity
Create instructor-facing materials, examples, prompts, scenarios, rubrics, or demonstrations that save time and support teaching.
Analyze
In-Class AI Artifact
Use an AI-produced text, image, answer, data display, or recommendation as an object students can critique, verify, revise, and discuss.
Demonstrate
Authentic Performance Assessment
Design a performance, presentation, defense, simulation, portfolio, or live demonstration that makes student thinking and skill visible.
Challenge
AI-Integrated Student Activity
Build a rigorous activity in which students use AI transparently, evaluate its output, apply disciplinary judgment, and produce deeper original learning.
Adaptable by departmentBusiness, Office Tech & Legal StudiesHealth Sciences & Emergency ServicesSTEM & Technical TradesLiberal & Fine ArtsProtective ServicesKinesiology & Agriculture
Two examples in practice
The same four-part model, shaped by each discipline.
These are starting points for co-design with CTC faculty. Course outcomes, accreditation expectations, student level, and instructor judgment would determine the final activity.
Example 1
Business, Office Tech & Legal Studies
A workplace communication and decision-making sequence.
Develop
Customer-response scenario kit
Faculty use AI to draft three versions of a fictional customer complaint, supporting documents, and a scoring guide, then verify and adapt every detail before class.
Analyze
Audit an AI-written business memo
Students identify unsupported claims, weak tone, missing evidence, confidentiality concerns, and legal or ethical risks in an AI-generated memo.
Demonstrate
Deliver and defend a recommendation
Students present a revised response or business recommendation, explain their evidence, and answer live questions about the decisions they made.
Challenge
Build a human-verified client proposal
Students may use approved AI for brainstorming or revision, but submit their prompts, verification log, source checks, final proposal, and reflection on what they accepted or rejected.
Example 2
Health Sciences & Emergency Services
A safety-centered clinical reasoning sequence using fictional, de-identified information.
Develop
Create a simulation starter
Faculty use AI to draft a fictional patient or emergency scenario with symptoms, timeline, and distractors, then clinically validate it and remove unsafe or unrealistic content.
Analyze
Find the risks in AI guidance
Students critique AI-generated discharge instructions or an incident summary for inaccuracies, omissions, bias, unclear language, and unsafe recommendations.
Demonstrate
Perform a handoff and clinical defense
Students complete a live handoff, triage explanation, or response simulation and defend their priorities using course protocols and verified evidence.
Challenge
Compare, correct, and teach
Students compare an AI-generated plan with approved clinical references, correct it, document verification, and create a plain-language teaching artifact for a patient or community audience.
9 / Responsible operations
Staff & administrative use
Move routine work forward without outsourcing accountability.
Approved AI can support drafting, summarization, ideation, and analysis. Employees remain responsible for accuracy, records requirements, accessibility, confidentiality, fairness, and the final decision.
Student records, protected health information, credentials, confidential personnel data, or restricted institutional information into unapproved tools.
Facts, calculations, sources, legal or policy claims, accessibility, bias, and whether the output fits CTC’s context and voice.
Do not use AI as the sole basis for high-impact decisions involving admission, grading, employment, discipline, aid, or student support.
10 / Benchmark context
Regional insight, local design
CTC is building from progress, not starting from zero.
CTC has already made meaningful inroads through faculty guidance, classroom integration ideas, and early conversations about responsible AI. That work gives the college a strong starting point.
The next opportunity is to make those efforts easier to find, more consistent across the student experience, and supported by ongoing learning experiences for faculty and students.
The leadership opportunity
CTC can move beyond isolated guidance by connecting policy, faculty development, student AI literacy, department-specific design, and responsible tool adoption into one visible framework. That combination would position CTC to lead the Central Texas region, not simply keep pace with it.
InstitutionPublicly visible AI activityHow CTC can lead
Faculty AI levels of usage, transparency expectations, classroom examples, approved-tool guidance, and early responsible-use work.
Build next: Connect current efforts through a shared framework, recurring development, student AI literacy, and department-specific learning experiences.
Public course and department materials show assignment-level expectations, disclosure, citation, verification, privacy, and instructor-defined AI use.
CTC leadership move: Make expectations consistent and easy to find, then pair them with student practice and faculty design support.
The Center for Excellence in Learning and Teaching has offered sessions on AI policy, scholarship, and student perspectives.
CTC leadership move: Establish ongoing, multimodal development with department studios, student learning sessions, and measurable follow-through.
Visible activity includes responsible-integration messaging, governance and data priorities, plus undergraduate and graduate AI and machine-learning programs.
CTC leadership move: Connect responsible AI literacy to technical, workforce, transfer, and general-education pathways.
Its Center for Teaching and Learning provides an AI reference within instructional design and professional-development support.
CTC leadership move: Offer a complete pathway from awareness to applied, discipline-specific learning.
Public records show college-wide academic discussion of AI and department-level consideration of student use.
CTC leadership move: Convert discussion into a visible framework, pilot activities, support tools, and a regular review cycle.
District programming includes ethical AI-use learning, faculty workshops, accessibility applications, and applied student-support pilots.
CTC leadership move: Combine ethical-use guidance with equitable access, authentic assessment, and local workforce relevance.
Faculty-led work includes a multidisciplinary GenAI productivity guide, professional learning, disclosure practices, and responsible human-AI collaboration.
CTC leadership move: Build a CTC-owned collection of tested activities and artifacts for every department.
Recent activity includes responsible-use policy work and a funded AI-enabled teaching and learning initiative focused on faculty development, curriculum redesign, and workforce skills.
CTC leadership move: Move quickly with a right-sized roadmap connecting governance, instruction, and workforce readiness.
Both publish extensive faculty guidance, professional learning, responsible-use principles, and teaching resources. UT Austin also supports purpose-built AI tutoring.
CTC leadership move: Adapt their strongest patterns to a practical, personal, faculty-centered community-college model.
CTCA&M–Central TexasUMHBTarletonMcLennanBlinnAlamo CollegesCollinDallas CollegeUT AustinUT San AntonioNational research
This directional snapshot is based on the proposal research and publicly visible institutional materials reviewed in August 2026. Public visibility does not capture every internal initiative. Discovery should validate current practices before recommendations are finalized.
11 / Concrete deliverables
A usable foundation
Leave CTC with decisions, tools, and momentum.
The final scope and sequence would be confirmed after discovery. A proposed engagement could include:
AI readiness findings
Current-state map, stakeholder themes, risk-and-opportunity scan, and priority recommendations.
Governance blueprint
Roles, decision rights, review cadence, tool-evaluation pathway, and continuous-improvement process.
Responsible-use framework
Guiding principles, privacy guardrails, staff guidance, and human-accountability standards.
Faculty & student toolkit
Course language, assignment-level options, disclosure template, verification checklist, and FAQs.
Ongoing professional development
Leadership briefings, recurring faculty workshops, department studios, communities of practice, and refreshers.
Pilot & measurement plan
Selected use cases, implementation support, evidence measures, feedback loops, and scale criteria.
12 / Sources
Evidence behind the comparison
Sources and reference points.
The proposal draws first from CTC’s supplied consulting brief, governance presentations, faculty guide, student disclosure practices, community-college research, and regional benchmarking research. These direct institutional sources support the public-facing comparison in Section 8.
Central Texas CollegeAI Levels of UsageCentral Texas CollegeFaculty AI resources and approved-tool guidanceTexas A&M University–Central TexasDepartment AI policy and disclosure expectationsUniversity of Mary Hardin-BaylorCenter for Excellence in Learning and TeachingTarleton State University2025 IT Impact ReportTarleton State UniversityAI and Machine Learning degree programMcLennan Community CollegeCenter for Teaching and Learning AI referenceBlinn CollegeCollege-wide AI discussionAlamo Colleges DistrictEthical AI use and faculty learningCollin CollegeGenerative AI productivity guideDallas CollegeAI-Enabled Teaching and Learning InitiativeUT AustinResponsible Adoption of AI frameworkUT San AntonioFaculty generative AI guideMassachusetts Institute of TechnologyAI and Education: Report of MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training
Research note: institutional practices change quickly. Links should be checked again immediately before a formal presentation or final proposal submission.
13 / Evaluating effectiveness
Measure what matters
Define success before the first pilot begins.
CTC and Holly Tech Solutions would establish a small, practical measurement plan during discovery. The goal is not to reward AI use for its own sake. It is to determine whether the initiative improves clarity, confidence, learning, access, responsible practice, and the college’s ability to respond as technology changes.
Measures should combine numbers with human evidence and be reviewed by role, department, location, and learning modality where appropriate.
Confidence & capability
Use brief baseline and follow-up surveys, workshop reflections, and practical demonstrations to track whether faculty, staff, and students can make informed AI decisions.
Teaching & assessment
Review course language, redesigned assignments, disclosure practices, and faculty-created artifacts for clearer expectations and stronger evidence of learning.
Student learning & integrity
Examine AI literacy, verification habits, disclosure quality, student work samples, and academic-integrity patterns without treating detection scores as proof.
Equity & experience
Check access, accessibility, consistency across locations and modalities, student support needs, and differences in experience among programs and populations.
Governance & responsible use
Track decision clarity, approved-tool reviews, privacy and security concerns, support response times, policy questions, and the speed at which guidance stays current.
BaselineBefore implementation
Document current confidence, practices, gaps, risks, and student experience.
Pilot checkAfter 60–90 days
Review participation, early artifacts, barriers, support requests, and quick wins.
Semester reviewEvidence and outcomes
Compare surveys, work samples, course practices, integrity patterns, and stakeholder feedback.
Annual refreshImprove and scale
Retire weak practices, strengthen effective ones, update guidance, and choose the next priorities.
A useful evaluation question
Are people making clearer, safer, more learning-centered decisions because of this initiative, and what should CTC adjust next?
14 / Guiding Questions
Leadership conversation
Questions for a collaborative discovery conversation.
What are CTC’s highest-priority opportunities or concerns related to AI right now?
What outcomes would make AI support worthwhile for the college: faculty confidence, student AI literacy, academic integrity, workforce readiness, operational efficiency, or something else?
What guidance, policies, or practices already exist for faculty and students, and where do you see the biggest gaps?
Which groups need to be at the table for this work to succeed: academic affairs, faculty, IT, distance education, student services, library, workforce programs, or others?
How ready are faculty to use AI in teaching, assessment, and feedback, and what concerns are you hearing most often?
How could CTC support faculty across the main campus, satellite locations, online programs, and adjunct populations in a consistent but practical way?
Would leadership prefer to start with a short readiness assessment, a focused faculty-development pilot, policy and guidance work, or a broader strategic plan?
What would a successful first semester of AI consulting support look like to you?
A helpful closing question
Who else should be included in a discovery conversation before I recommend a scope, timeline, or deliverables?
A practical next step
Begin with a working session.
Bring the right CTC voices together to clarify goals, surface current practice, and define the most useful first phase.
Visit hollytechsolutions.com
Holly Landez, PMP, CETLHolly Tech Solutions LLCAI strategy • governance • implementation
Proposal concept for discussion with Central Texas College. Recommendations shown are not adopted CTC policy.