AI Literacy Practice & Evidence Workspace

Schools and colleges often need a practical bridge between publishing an AI policy and helping people apply it. This custom workspace would support induction programs, classroom literacy units, library-led workshops, and educator development. Each program could use its own scenarios while sharing a consistent process for practice, reflection, and instructor review.

A student might investigate unsupported claims in a generated explanation, while an educator might review a draft activity for privacy risks or exclusionary assumptions. Program leaders could inspect completed evidence and unresolved topics across cohorts. Participation reports would describe activity, not certify competence or claim that the platform improves attainment.

Activities would ask learners to explain their choices rather than submit only a polished answer. A scenario could include an initial prompt, a generated response, a verification task, and a revised attempt. Learners would attach supporting material, identify remaining uncertainty, and explain whether using AI was appropriate for that task.

Instructors would review these artifacts against locally approved criteria, request revisions, and record feedback before approving portfolio entries. AI-generated feedback would remain a draft and be visibly identified. The design would avoid authorship accusations, hidden behavioral scoring, and automatic high-stakes decisions based on prompt history or superficial engagement signals.

Learning management connections could be proposed for Moodle or Canvas through available APIs or LTI-based workflows. Depending on institutional permissions, implementation might cover course launch, roster import, assignment context, or approved completion records. These are development options rather than existing connectors, and each data flow would require explicit scope and testing.

Identity options could include Microsoft Entra ID or Google Workspace, while selected model APIs could supply supervised generation. Document repositories could contribute approved reference materials where access permits. Provider availability, contractual restrictions, age requirements, data handling terms, and administrative approvals would be checked before any integration is selected for implementation.

The application can be scoped for hosted SaaS with separated institutional workspaces or a client-server installation within a client-controlled environment. Discovery would establish infrastructure ownership, hosting region, network exposure, backup responsibilities, and administrator access. Deployment documentation would describe operational dependencies rather than imply that every hosting environment is equally suitable.

Institutions restricting external inference could evaluate locally hosted models or use curated response examples for selected exercises. Hardware benchmarks and licensing checks would guide that decision. Browser clients would still depend on the chosen server architecture, and offline capability would require separate design, storage controls, synchronization rules, and acceptance testing.

Approved policies, learning objectives, rubrics, and scenario notes would form a versioned knowledge collection. Retrieval could supply relevant passages to feedback workflows while preserving document permissions and showing references to reviewers. When supporting material is absent or conflicting, the system would be designed to surface uncertainty rather than invent institutional guidance.

Adaptation would begin with configurable instructions, structured output templates, and representative evaluation cases rather than assuming model fine-tuning is necessary. Language and reading-level variants would require educator validation. Any later fine-tuning would need a separate scope, rights-cleared datasets, privacy review, and evidence that it improves the agreed classroom tasks.

Security design would define what learners, instructors, program managers, and technical administrators may see or change. Controls would include tenant boundaries, encryption, protected secrets, and auditable approval actions. Data minimization would limit personal information in prompts, while institution-specific rules would govern transcript retention, portfolio visibility, consent records, and deletion requests.

Uploaded documents and generated text would be treated as untrusted content, with no automatic authority to alter permissions or execute actions. Learners would see clear AI labeling and instructions not to enter sensitive information. Concerning content could be routed to designated staff under agreed procedures, without promising reliable crisis detection or emergency response.

DOD IT Solutions would deliver the agreed custom application with full source code, database migrations, configuration templates, deployment assets, and technical documentation. The handover would include the implemented scenario and portfolio workflows, administrator guidance, and integration specifications. Third-party software, model weights, and licensed learning resources would remain governed by their own terms.

Acceptance would use agreed test cases covering permission boundaries, policy versioning, evidence review, export accuracy, model failures, and representative classroom load. A supervised pilot would inform adjustments before wider adoption. Training and operational handover would identify responsible owners, known limitations, backup procedures, and change-management steps without promising fixed outcomes or delivery guarantees.