The University of Nebraska at Omaha’s Artificial Intelligence Learning Lab has moved from pilot experiments to scalable campus programs, producing measurable gains in productivity, pedagogy, and research readiness. Early evidence from year one shows significant time savings and adoption among faculty, staff, and students, while year two priorities emphasize controlled access to advanced tools, ethical frameworks, and targeted professional development. This technical overview synthesizes metrics, program design, operational considerations, and strategic vendor integrations that can guide higher-education practitioners and technology managers seeking to implement or scale AI on campus.
AI Learning Lab Year One Outcomes and Metrics
Year one at the AI Learning Lab delivered robust empirical signals about adoption and impact. An end-of-semester survey involving over 200 respondents documented substantial time savings and improved productivity from institutional access to generative tools such as ChatGPT, tied to the Lab’s Open AI Challenge. The shift from exploratory pilots to evidence-driven deployment illustrates how campus-level programs can accelerate responsible use while maintaining governance constraints.
Quantitative outcomes and interpretation
Key indicators from the first academic cycle include reported weekly time savings and frequency of use. Survey results indicated that 95% of faculty and staff reported weekly time savings using ChatGPT, with 20% claiming they saved five or more hours per week. Productivity improvement was almost universal: 96% reported some improvement, and 80% described moderate or significant gains. Frequency metrics showed 55% using AI daily and 86% using it several times per week or more.
- Time savings supported reallocation of effort to high-priority academic tasks.
- Instructors reported lower workload stress and improved content quality.
- Administrative staff used AI to streamline repetitive processes and draft communications.
These outcomes align with broader industry patterns documented in vendor and academic reports: cloud providers such as Microsoft Azure AI et AWS Machine Learning enable scalable deployments, while model vendors like OpenAI and research suites from Google AI provide the core generative capabilities used in teaching and administration.
Table: Year One Summary Metrics
| Métrique | Valeur | Practical Effect |
|---|---|---|
| Survey respondents | 200+ | Representative cross-campus sample |
| Faculty/staff reporting weekly time savings | 95% | More time for curriculum design |
| Respondents using AI daily | 55% | High operational integration |
| AI grants completed | 36 | Cross-college curricular pilots |
Beyond the numbers, the Lab’s initiatives—such as AI-powered teaching grants and the AI Summit—generated qualitative returns: enhanced pedagogy, new research questions in machine learning, and increased institutional confidence to integrate third-party platforms such as NVIDIA AI toolsets for compute-intensive tasks and IBM Watson for domain-specific NLP pipelines.
- Large-scale training completions: over 1,300 learners completed the Generative AI Cybersecurity Training.
- Credentialing: 124 faculty and staff earned an AI Advantage Badge; 189 earned an AI Jumpstart Badge.
- Community engagement: 36 presentations at the AI Summit with >200 attendees.
These metrics establish a baseline for planning the 2025–2026 academic year where the Lab will prioritize controlled ChatGPT EDU access, student pilot programs, and the publication of institutional resources such as guiding principles and sample syllabus statements. The core insight: robust measurement and targeted credentialing accelerate safe adoption while building institutional capacity for AI-enabled workflows.
Integrating AI into Teaching: Grants, PD, and Syllabus Design
Systematic curricular integration requires a combination of instructional grants, scaffolded professional development, and clear course-level policy artifacts. The AI Learning Lab’s tiered grant structure and modular PD offerings provide a replicable model for other institutions aiming to operationalize AI in the classroom without compromising academic integrity.
Designing scalable professional development
Two main PD tracks are available: a one-week micro-course (AI Jumpstart) and a comprehensive six-week program (AI Advantage). Both are structured to ensure faculty and staff progress from conceptual understanding to practical implementation. The six-week AI Advantage course includes a stipend incentive, aligning professional learning with compensation frameworks to increase completion rates.
- AI Jumpstart: one-week module, accessible rolling start, quick exposure to generative tools.
- AI Advantage: six-week program, practical assignments, $300 stipend for completion.
- Mandatory cybersecurity micro-training ensures baseline data hygiene before tool access.
Professional development emphasizes vendor-agnostic pedagogy while illustrating tool-specific workflows with platforms like OpenAI, Google AI, and enterprise-grade environments facilitated through Microsoft Azure AI ou AWS Machine Learning. Course modules cover prompt engineering, rubric redesign for AI-aware assessments, and project-based uses where AI is explicit and scaffolded.
Grant tiers and course implementation
The Lab’s grant program operates on three tiers to support incremental adoption:
- Tier 1: Single assignment integration, minimal design overhead.
- Tier 2: Module-level adoption—3–4 AI-enabled activities across a unit of instruction.
- Tier 3: Course-wide redesign with integrated AI across assessments and feedback loops.
Working with instructional designers, grantees align AI use with learning outcomes and academic policies. This design partnership mitigates risks such as improper data exposure or overreliance on generative outputs, which are common concerns documented in operational reviews and vendor advisories (see materials on AI security and cybersecurity risk et privacy implications of generative tools).
| Program Element | Durée typique | Intended Outcome |
|---|---|---|
| AI Jumpstart | 1 week (1 module) | Rapid orientation to generative tools |
| AI Advantage | 6 weeks (6 modules) | Implementable AI course components and pedagogy |
| AI Teaching Grants (Tier 1–3) | 1–2 semesters | Course redesign at scale |
Additional practical artifacts available from the Lab include an AI Prompt Book for Faculty/Staff, sample syllabus statements, and a Canvas Commons package for an AI-for-students course page. These resources make adoption repeatable and auditable, integrating with academic technology operations and learning analytics suites such as those discussed in reports on AI-enhanced analytics et productivity dashboards.
- Faculty can request up to 50 ChatGPT EDU student licenses per class for semester-long pilots.
- All users must complete a generative AI cybersecurity awareness module before receiving tool access.
- Monthly check-ins and a requirement to report on AI usage or present at the spring summit are enforced for pilot participants.
Effective pedagogical integration balances innovation with governance: well-designed PD and grant-supported redesigns produce measurable improvements in learning outcomes while reducing faculty workload. The practical takeaway: invest in scaffolded faculty support, create reusable templates, and require cybersecurity training before tool deployment—this yields both adoption and academic integrity.
Operational and Research Opportunities with AI Tools at UNO
Beyond classroom use, the AI Learning Lab has identified operational and research pathways to leverage AI for administrative efficiency, campus services, and sponsored research. Vendors and platforms provide complementary strengths: enterprise models from OpenAI for natural language workflows, C3 AI et DataRobot for enterprise ML lifecycle management, and NVIDIA AI for compute-optimized model training.
Administrative automation and campus services
Administrative teams implemented AI to automate routine communications, summarize meeting notes, and accelerate grant writing. Use cases include automated help-desk triage, intelligent scheduling assistants, and initial drafts for HR and procurement documents. These implementations rely on a combination of cloud platforms—Microsoft Azure AI for secure enterprise deployment and AWS Machine Learning services for scale—and domain-specific tooling from partners like Cognizant AI et Salesforce Einstein for CRM and student engagement.
- Automated triage reduces response times and frees staff for high-complexity cases.
- AI-driven customer insights inform retention strategies when integrated with CRM systems.
- Workflow automation minimizes repetitive administrative overhead and improves accuracy.
Research infrastructure benefited from targeted compute allocations and vendor credits. High-performance clusters powered by NVIDIA GPUs accelerated model experimentation, and partnerships with enterprise vendors enabled reproducible experiments in applied machine learning. Research areas include bioinformatics, environmental informatics, and educational AI—each leveraging different toolchains and governance approaches.
Strategic vendor integrations and ecosystem mapping
Mapping vendor capabilities to campus needs helps prioritize investments. For example:
- OpenAI : generative capabilities, conversational agents, and custom GPTs for classroom support.
- Google AI: research-oriented models and multimodal capabilities for experimental labs.
- IBM Watson : domain-specific NLP and enterprise integration in healthcare and legal informatics.
- DataRobot and C3 AI: MLOps and model governance for operationalizing predictive systems.
Case studies from the Lab illustrate how cross-functional teams combine vendor tools for composite solutions. One example involved a collaboration between the Bioinformatics and Machine Learning (BML) lab and the Light Game Lab: researchers used a combination of NVIDIA AI GPU clusters, AWS Machine Learning data pipelines, and fine-tuning approaches with OpenAI APIs to accelerate model development for multimodal datasets. Operational lessons included budgetary planning for GPU hours and stringent data handling protocols aligned with the Lab’s forthcoming Guiding Principles of AI use.
Relevant external resources and analyses support these operational approaches: materials on enterprise intelligence integration, AI deployment pitfalls, and recommended cybersecurity practices such as those described in Tactiques de sécurité de l'IA et les perspectives en matière de cybersécurité et d'IA.
- Operationalization requires MLOps frameworks and reproducible data pipelines.
- Vendor selection should weigh data residency, model training policies, and compliance.
- Interdisciplinary governance reduces technical debt and increases adoption velocity.
Planned campus deliverables for the 2025–2026 year include published sample syllabi statements, a codified set of guiding principles, and expanded access to ChatGPT EDU for faculty and staff. Strategic insight: aligning research infrastructure with operational governance and vendor ecosystems unlocks both innovation and institutional trust.
Student-Centered AI Initiatives: ChatGPT EDU and the ChatGPT x Students Pilot
Student engagement with AI is being treated as a research and pedagogical experiment, not merely a tool rollout. The Lab’s ChatGPT EDU program and the upcoming ChatGPT x Students Pilot Program aim to build student competencies while collecting structured feedback to inform evidence-based policy.
Eligibility, commitments, and student onboarding
Students—both undergraduate and graduate—can apply to a pilot that requires a faculty or staff recommendation and a commitment to weekly exploration. Approved students receive ChatGPT EDU access through the spring semester and must complete the generative AI cybersecurity training. The pilot emphasizes responsible use: students agree to follow course-level AI policies, engage in monthly usage check-ins, and may present findings at the spring AI Summit.
- Student access is contingent on completion of cybersecurity and data-hygiene training.
- Faculty sponsorship serves as a gating mechanism to ensure alignment with pedagogy.
- Failure to meet participation requirements results in removal from the EDU environment.
The student pilot is designed to investigate practical questions: Can AI improve comprehension of complex concepts? Does access to custom GPTs change study habits? What safeguards are necessary to preserve assessment integrity? Early operational parameters include the ability for students to create personalized GPTs, utilize project features, and integrate AI into study workflows.
Examples of student use-cases and research hypotheses
Illustrative use-cases planned for study include:
- Concept decomposition: students use AI to break down difficult concepts into stepwise explanations.
- Study scaffolding: generating practice questions and iterative feedback loops for self-assessment.
- Project acceleration: leveraging AI to draft literature reviews and generate data cleaning scripts under oversight.
Each use-case is paired with research hypotheses and evaluation metrics. For example, a study on concept decomposition will measure pre/post understanding using validated rubrics and compare AI-assisted study against control groups. These structured experiments draw on learning analytics and may integrate vendor telemetry with privacy-preserving aggregation.
Additional student-facing resources include an AI Prompt Book for Students and a customizable Canvas Commons page to help faculty define course expectations. Cross-references to external material provide broader context for instructors and students, including practical guides on AI in education and cybersecurity resources such as L'IA dans l'éducation and training pathways described at privacy guidance for generative tools.
- Student pilots will prioritize iterative feedback and transparent reporting.
- Outcomes will inform institutional policy and criteria for scale.
- Findings will be presented at the UNO AI Summit, supporting knowledge transfer across campus.
Collecting structured student feedback while imposing accountability mechanisms (recommendations, training, monthly check-ins) enables safe exploration and produces high-quality evidence about AI’s pedagogical value. Final insight: student pilots that couple tool access with research-grade evaluation yield actionable policy recommendations.
Security, Ethics, and Infrastructure: Safeguards for Campus AI Adoption
Robust AI adoption requires layered safeguards across infrastructure, training, and policy. The AI Learning Lab’s approach emphasizes mandatory cybersecurity training, restricted enterprise access, and an AI Core Consortium to develop institutional resources such as guiding principles and sample syllabus language to standardize ethical practices.
Security posture and mandatory controls
Access to enterprise environments like ChatGPT EDU is contingent on completing a short generative AI cybersecurity training module. This training clarifies what data is appropriate to input into free and paid consumer tools versus enterprise deployments. Such procedural controls are essential because misconfiguration or inappropriate data input can lead to information exposure and compliance risks.
- Baseline cybersecurity training is mandatory before obtaining enterprise tool access.
- Enterprise licenses (ChatGPT EDU) are configured to prevent model training on institutional data.
- Access controls and audit logs are central to compliance and forensics.
Technical infrastructure planning considers vendor attributes and risk tradeoffs. For compute-intensive experiments, NVIDIA AI GPU clusters with controlled network access are preferred. For enterprise-grade language models, partnerships with providers such as OpenAI and managed cloud offerings from Microsoft Azure AI ou AWS Machine Learning support secure deployments. Additional vendor tools—IBM Watson, DataRobot, C3 AI, Cognizant AI, et Salesforce Einstein—fill niche roles in domain-specific analytics or CRM-driven student interventions.
Ethics, governance, and the AI Core Consortium
Governance mechanisms launched by the AI Core Consortium include proposed guiding principles and model syllabus statements that will roll out in the 2025–2026 academic year. These governance artifacts serve several functions: they standardize student-facing expectations, provide faculty with language for course policies, and create a forum for cross-disciplinary feedback about acceptable AI uses.
- Guiding principles establish values and operational boundaries for AI deployment.
- Sample syllabus statements provide consistent messaging across courses.
- Consortium feedback loops ensure policy evolves with practice and evidence.
Operational controls must be accompanied by ethical literacy. The Lab’s training and PD courses embed modules on bias mitigation, citation practices for AI-generated content, and responsible prompt engineering. Resources and external readings—such as reports on AI hallucinations and adversarial risks—are integrated into the curriculum and operational playbooks (see resources like analysis of AI hallucinations et adversarial testing in cybersecurity).
Technical teams must also plan for observability and incident response. Recommended practices include model and data lineage tracking, version-controlled prompts and templates, and logging of model outputs used in consequential decisions. Tools for model monitoring and MLOps—such as those discussed in research on LLM risk management et Architecture d'observabilité de l'IA—are pivotal to maintaining trust and enabling continuous improvement.
- Enforce role-based access control for AI tool provisioning.
- Require documented data handling procedures and regular audits.
- Implement incident response playbooks tailored to AI-specific risks.
Secure, ethical adoption of AI on campus depends on a triad: technical controls, curricular literacy, and participatory governance. The decisive insight: embedding these safeguards into onboarding and operational processes protects institutional data while enabling scalable innovation.