DoWEA Education Platform RFI_4.22.26.pdf

Sources Sought/Request For Information (RFI)

Education Platform Requirements

1. Purpose and Scope

This Sources Sought/ Request for Information is issued solely for market research and acquisition planning purposes. This notice does not constitute a Request for Quote (RFQ), Invitation for Bid (IFB), or Request for Proposal (RFP) and does not obligate the Government to issue a solicitation or make an award.

Responses are voluntary. No reimbursement will be provided for costs associated with responding to this notice.

The Department of War Education Activity (DoWEA) seeks information to better understand current industry capabilities, architectural approaches, data strategies, and maturity related to a single, cohesive enterprise education platform that supports DoWEA’s global Pre-K – 12 mission.

2. Background and Objective

DoWEA operates a globally distributed education environment supporting instructional delivery, student services, and enterprise operations. As part of long-term modernization and consolidation planning, DoWEA is conducting market research to assess platforms that can:

• Unify instructional and enterprise systems on a shared or tightly integrated data foundation.

• Reduce system fragmentation and manual data movement.

• Enable secure, near-real-time reporting, analytics, and AI-enabled insights at global scale.

• Support interoperability with external analytics platforms and DoWEA-managed data environments.

3. Teaching, Learning & Student Systems

3.1 Integrated Platform Approach

DoWEA is interested in platforms that support tightly integrated, natively developed or fully supported modules operating on a shared identity and data framework. Vendors may describe point to point or loosely coupled integrations; however, such approaches should be clearly identified and explained.

3.2 Student Information System (SIS)

DoWEA seeks information on Student Information Systems (SIS) that can serve as the primary system of record for managing student data across the full student lifecycle. Areas of interest include, but are not limited to:

• Enrollment, registration, scheduling, attendance, grading, gradebook management, transcripts, discipline/behavior tracking, and program participation.

• Secure student records management with rolebased access control, audit logging, alignment with the Privacy Act of 1974 and applicable DoW privacy regulations, and configurable data retention rules.

• Support for DoWEA specific calendars, grading policies, credits, and reporting requirements.

• Standards based, documented APIs that support data import and export for students, enrollment, roster, attendance, grades, transcripts, behavior, and program status data, including near real time exchange where available.

3.3 Learning Management System (LMS) – Optional Module with Mandatory Integration

DoWEA seeks information on education platforms that either include a native LMS module or provide strong, production ready integration with commonly used third party LMS solutions (for example, Schoology and Google Classroom) but not limited to those solutions. Areas of interest include the platform’s ability to support:

• Seamlessly integrate with external LMS platforms using standards-based mechanisms (e.g., LTI 1.3/Advantage, OneRoster, and secure APIs).

• Support automated provisioning and synchronization of:

o Courses, sections, teachers, students, and roles.

o Assignments and grading workflows.

o Enrollment changes, schedule updates, and staff reassignments.

• Enable bidirectional grade pass back so that instructional grading performed in the LMS flows reliably to the SIS gradebook and transcript processes.

• Maintain consistent user identity, authentication (SSO), and role-based access across the SIS and integrated LMS environments.

• Provide visibility for instructional activity and learning signals (e.g., assignment completion, engagement indicators) for reporting and analytics, regardless of whether instruction occurs in a native or third-party LMS.

• Ensure mobile support and parity of experience where supported by the external LMS.

If a native LMS module is offered, vendors may describe capabilities related to:

• Course and content management, assignments, rubrics, discussions, collaboration tools, and mobile applications.

• Standards-based grading aligned with SIS grade calculation and transcript workflows.

• Learning analytics focused on engagement, progress, pacing, mastery, and course completion.

3.4 Assessment

DoWEA seeks information on assessment capabilities that support formative, benchmark, and summative use cases. Areas of interest include:

• Centralized, reusable item banks with tagging, version control, and alignment to standards.

• Accommodation management aligned to IEP/504 requirements and multilingual learner needs.

• Secure test administration with support for offline or low-bandwidth operation in global environments.

• Proctoring and academic integrity safeguards, as applicable.

• Import/export APIs supporting assessment results, accommodations, and item metadata.

• Integration with LMS platforms (native or third-party) for assignment-based and classroom-embedded assessments.

3.5 Special Education and Student Support Programs

DoWEA seeks information on platforms that support special education and student services workflows. Areas of interest include support for:

• IEP/504, Multilingual Learner Programs (MLP), Advanced Academic Programs (AAP), and MTSS/RTI frameworks.

• Intervention planning, service delivery logging, progress monitoring, and compliance reporting.

• Strict role-based privacy controls to safeguard sensitive student data.

• Standards-based APIs supporting exchange of accommodations, service records, progress data, and compliance indicators.

4. Reporting, Analytics, and Data Interoperability

DoWEA is interested in understanding how platforms support reporting and analytics across instructional and enterprise domains. Areas of interest include:

• Real-time or near real-time reporting across SIS, Assessment, Special Programs, and LMS platforms (native or third-party).

• Role-appropriate dashboards for teachers, school leaders, district/regional leadership, and headquarters personnel.

• Reporting shall not be degraded or functionally limited using an external LMS.

Vendors should describe how reporting data is structured and made available for analytical and AI use cases.

5. Enterprise Operations & Business Services

DoWEA seeks information on enterprise education platforms that provide or integrate with operational and business services to support a global education environment. The following areas represent capabilities of interest for market research purposes.

5.1 Finance / ERP

Areas of interest include:

• General Ledger, Accounts Payable/Receivable, multi-fund accounting, grants management, budgeting and forecasting.

• Procurement and eProcurement workflows.

• Inventory management and school activity funds.

• Auditability and reporting are suitable for federal oversight.

5.2 Human Resources /Human Capital Management (HCM)

Areas of interest include:

• Manpower management, including position control, Staff Deployment (by school, program, or funding source), Trend and Forecasting (enrollment driven staffing models), and Scenario Planning and Forecasting.

• Recruiting, onboarding, licensure and credential tracking, staff evaluations, and substitute management.

• Alignment of staff roles and credentials with instructional and system access.

• Integration with enterprise identity and access management systems.

5.3 Facilities /Computerized Maintenance Management System (CMMS)

Areas of interest include:

• Work order management, preventive maintenance scheduling, asset lifecycle tracking, and space utilization.

• Safety, security, and facilities related compliance workflows.

5.4 Transportation

Areas of interest include:

• Route planning, ridership management, and parent notifications.

• Fleet management and vehicle maintenance tracking.

5.5 IT Service Management (ITSM) & End-User Computing

Areas of interest include:

• Device, software, and asset management.

• IT ticketing and service workflows.

• Integration with Unified Endpoint Management (UEM/MDM) platforms.

• Classroom device management and instructional visibility.

6. Unified Data, Analytics & Interoperability

6.1 Unified Data Architecture

DoWEA seeks information on platform support unified or federated data approaches across instructional and enterprise domains. Areas of interest include:

• A shared or tightly federated data model across SIS, LMS, ERP, HR, facilities, transportation, and IT operations.

• Near real-time data flows with minimal manual reconciliation.

6.2 APIs & Interoperability Standards

Vendors may describe support for open APIs and industry standards, including:

• Open REST APIs with pagination, filtering, and webhook/event support.

• Support for current and future standards, including but not limited to:

o IMS OneRoster 1.1+ o Ed-Fi Data Standard 4.0+ (native or certified adapter) o LTI 1.3/Advantage o QTI/APIP (as applicable) o SCORM (legacy) and xAPI o Identity standards: SAML 2.0, OIDC/OAuth 2.0, SCIM 2.0

6.3 AI-Enabled Analytics & Business Intelligence

DoWEA is interested in understanding how platforms support analytics and AI enabled insights across instructional and operational domains. Areas of interest include:

• Executive, instructional, and operational dashboards.

• Early warning indicators for attendance, grades, behavior, and course completion with configurable thresholds.

• Machine learning explainability, including feature importance and counterfactual explanations.

• Intervention tracking linked to analytic triggers.

• Self-service analytics, including natural language queries where appropriate.

• Export or integration with DoWEA managed data warehouse or lakehouse infrastructure.

• Availability of Metadata catalogs with data lineage visualization.

7. Data Quality & Master Data Management

Areas of interest include:

• Automated validation for completeness, accuracy, timeliness, and consistency.

• Pre-configured quality checks for critical data (student demographics, attendance, grades, staff credentials).

• Data quality dashboards, alerts, and trend analysis.

• Support for DoWEA defined custom validation rules.

• Master Data Management for students, staff, schools, and courses.

• Data classification, PII tagging. and retention/minimization controls.

8. Reporting Integration with External AI-Enabled Analytics Platforms (e.g., MS Fabric)

DoWEA seeks information on how platform reporting and analytics capabilities interoperate seamlessly with external, AI enabled enterprise analytics platforms, including but not limited to MS Fabric, Power BI, and DoWEA managed data warehouse or lakehouse environments. The reporting architecture must support both native platform reporting and advanced external analytics without data duplication, vendor lock-in, or loss of data fidelity. Vendors shall describe how their solutions enable secure, scalable, and transparent integration across instructional, operational, and enterprise analytics environments.

9. Data Access & Export

The platform shall provide secure, documented, and supported mechanisms to expose reporting data to external AI-enabled analytics platforms, including:

o REST-based APIs.

o Event- or change-based data feeds.

o Bulk and incremental data export capabilities for historical and snapshot data.

• Data access methods shall support near real-time and scheduled delivery patterns suitable for ingestion by AI-enabled analytics platforms.

• Exported data shall be available in open, analytics-optimized formats, including but not limited to:

o Parquet o Delta o CSV o JSON

• The solution shall not require proprietary or vendor-locked connectors to integrate with MS Fabric or comparable AI-enabled analytic platforms.

10. Compatibility with AI-Enabled Analytics Architectures

The reporting capability shall be compatible with modern analytics and AI architecture, including:

• Lakehouse and warehouse ingestion models, enabling reporting data to land directly in DoWEA-managed analytic environments.

• Semantic and analytical models that support downstream BI, AI, and machine learning workloads without reengineering.

• Data pipelines, notebooks, and AI workflows, enabling DoWEA to apply advanced analytics, machine learning, and cross-domain insights beyond the platform’s native capabilities.

Vendors shall clearly describe:

• How reporting data is structured for analytical and AI consumption.

• How data supports instructional, operational, and enterprise analytics use cases.

• How platform data can be joined with non-platform data sources (e.g., finance, HR, logistics, cybersecurity, or operations data) in external analytics environments.

11. Data Model Transparency & Metadata

The vendor must provide full transparency of the reporting data model, including:

o Tables, fields, relationships, and keys.

o Business definitions for metrics, indicators, and calculated values.

• A metadata catalog shall be available natively or exportable, supporting:

o Schema discovery.

o Data lineage from source systems through reporting outputs.

o Field-level descriptions suitable for ingestion into enterprise metadata catalogs.

• Any native semantic layers must be exportable, reproducible, or documentable for use in external AI-enabled analytic platforms.

12. Security, Governance & Access Control

Reporting interfaces shall comply with DoWEA security and data governance requirements, including:

o Role-based access controls aligned to SIS, HR, and enterprise roles.

o Row-level and column-level security enforcement that is preserved or enforceable in external platforms.

The solution shall support enterprise identity integration to enable:

o Consistent authentication and authorization across the platform and external analytics environments.

o Centralized logging and auditability.

Data classification, PII tagging, retention, and minimization controls shall be preserved when reporting data is exposed to or consumed by AI-enabled analytic platforms.

13. Incremental, Event-Driven & Near Real-Time Reporting

The platform shall support incremental data extraction (e.g., change data capture or equivalent) to reduce latency and processing overhead. Event driven updates via webhooks or event streams consumable by AI enabled platforms.

• Examples include but are not limited to:

o Enrollment changes, o Attendance updates, o Grade postings, o Intervention changes, o IEP status updates.

Vendors shall identify which reporting domains support incremental delivery versus full refresh models.

14. AI & Advanced Analytics Enablement

Reporting outputs must be suitable for downstream AI, machine learning, and advanced analytics in external platforms.

The platform shall:

• Not restrict DoWEA from applying its own AI/ML models or analytic logic.

• Allow feature engineering and custom modeling on exported data.

• Native AI-generated metrics or indicators provided by the platform must be:

o Explainable.

o Traceable to source data.

o Reproducible or auditable in external analytics environments

14.1 Performance & Scalability

Reporting capabilities shall be scaled to support:

o Enterprise-wide analytics.

o Multi-year historical analysis.

o Concurrent use by dashboards, BI tools, AI pipelines, and extract processes.

Vendors shall describe performance controls that prevent reporting workloads from degrading operational system performance.

15. Global PaaS Operations & Resilience

The solution shall provide:

• Cloud-native PaaS architecture with at least 99.9% monthly availability.

• Published maintenance windows and a public system status page.

• Multi-region performance, time-zone and calendar localization, multilingual family communications, and CDN/edge optimization.

• Offline-first options for assessments and mission-critical workflows.

• Immutable backups, documented RPO/RTO by service tier, and periodic disaster recovery exercises with evidence.

• All data must reside in the United States, and system access must be limited to U.S.

citizens.

16. Artificial Intelligence (AI) & AI-Enabled Analysis (Education & Business)

The solution shall provide responsibly governed AI capabilities that enhance instruction, student support, operational efficiency, and decision-making, fully aligned with federal and DoW AI principles.

16.1 Academic Integrity & Instructional AI

Capabilities may include:

• real-time activity visibility,

• picture-in-picture or screen capture,

• logs of visited sites, activity screenshots,

• revision history and authorship analysis,

• secure/lockdown browsers.

16.2 Operational AI

Capabilities may include:

• Predictive analytics for enrollment forecasting, at-risk student identification, and staffing optimization.

• Automated scheduling and resource optimization.

• Anomaly detection for financial and operational data.

• Intelligent document classification and processing.

16.3 Communication AI

Capabilities may include:

• Multilingual translation for inbound and outbound communications.

• Intelligent chatbots for staff and family inquiries.

• Sentiment analysis with escalation triggers.

16.4 AI Governance

Vendors shall confirm:

• Prohibition on training AI models using DoWEA data without explicit written authorization.

• No use of DoWEA data to train models used by other customers.

• Model documentation (“model cards”) for all AI/ML features.

• Human-in-the-loop requirements for high-stakes or consequential decisions.

• Agency-controlled opt-out mechanisms for AI features.

17. Communication & Engagement Capabilities

17.1 Multi-Channel Outreach

• Email, SMS, voice calls, push notifications, in-app messaging, web banners, print/mail-merge, and social cross-posting.

17.2 Two-Way Messaging

• Unified inbox with routing, service levels, escalation, canned responses, sentiment flags, and translation.

17.3 Audience Management

• Roster-aware targeting by school, grade, class, program, route, or role.

• Dynamic segments, contact verification, and preference management.

17.4 Personalization & Templates

• Merge fields, conditional content blocks, brand controls, and preview by role, language, and channel.

17.5 Translation & Accessibility

• Automated translation across dozens of languages with human escalation paths.

• WCAG 2.2 AA and Section 508-compliant user interfaces and content validation.

17.6 Web & Mobile Experience

• CMS templates, news and event publishing, emergency banners.

• Mobile applications with personalized feeds and deep links into SIS and LMS workflows.

17.7 Instructional & Operational Tie-Ins

• LMS announcements, attendance alerts, transportation updates, digital forms, and e-signature workflows.

17.8 Governance & Workflows

• Role-based access across district, school, and classroom levels.

• Configurable approval workflows.

• Full audit trails.

18. Reporting & Analytics Capability

DoWEA seeks to understand how vendor solutions support reporting, analytics, and integration with external data and AI ecosystems.

18.1 Native Reporting Capabilities

Vendors are requested to describe:

• Built-in dashboards, reports, and self-service analytics.

• Supported user roles (teachers, principals, HQ leadership, analysts).

• Real-time vs. batch reporting capabilities.

18.2 Integration with MS Fabric or Other AI-Enabled Analytic Platforms

Vendors shall describe how reporting data interfaces with external AI-enabled analytic environments, including but not limited to:

o MS Fabric o Other modern lakehouse, data warehouse, or AI analytics platforms

At a minimum, vendors shall address:

• Data access methods (APIs, CDC, events, exports).

• Supported data formats (e.g., Parquet, Delta, CSV, JSON).

• Incremental vs. full-refresh data delivery.

• Compatibility with external:

o Data pipelines o Semantic models o BI tools o AI/ML workflows

• How data supports cross-domain analysis (instructional + HR + finance + operations).

Vendors shall state clearly:

• Whether any proprietary connectors, licenses, or tools are required.

• Any limitations on frequency, volume, or scope of data extraction.

18.1 Data Model & Metadata Transparency

Describe:

• Reporting data model (tables, keys, relationships).

• Metadata availability (schemas, definitions, lineage).

• Whether semantic layers are exportable or reproducible in external analytics platforms.

• Approach to data quality, validation, and master data management.

18.2 AI & Advanced Analytics Enablement

For both native and externally enabled analytics, describe:

• AI-enabled analytics currently available.

• How platform data supports downstream AI/ML in external platforms.

• Explainability of AI outputs (features, thresholds, indicators).

• How customers may apply their own AI models to exported data.

18.3 Security, Governance & Compliance

Describe:

• Role-based access control, row-/column-level security.

• Identity integration approach (e.g., enterprise SSO).

• Data classification and PII handling across reporting exports.

• Controls to ensure AI governance and data sovereignty.

• Any known constraints or exceptions.

18.4 LMS Integration (If Applicable)

If applicable, describe:

• A native LMS describe capabilities.

• Integration with third-party LMSs (e.g., Schoology, Google Classroom), describe depth of:

o Roster sync o Grade passback o Analytics visibility o Reporting consistency

Vendor Response Instructions

Vendors are encourage to:

• Respond only to the sections requested in this RFI and relevant to their capabilities.

• Clearly distinguish between:

o Currently available mature (production) capabilities o In-development or roadmap items (include estimated timelines if available)

• Provide concise and structured response using plain language.

• Use diagrams or tables where helpful.

• Identify any key dependencies, assumptions, third-party components, or licensing considerations.

Note: Marketing materials alone are not sufficient but may be included as appendices.

Vendors may include the following:

1. Company Overview

a. Relevant experience with large, complex, or global K-12 or federal education environments.

2. Solution Overview

a. High level description of the proposed solution and value proposition

b. Key differentiators related to data integration, analytics, AI enablement, and interoperability.

3. Architecture Overview

a. Description of overall solution architecture, including:

i. Core platform components (SIS, assessment, special programs, operations, reporting).

ii. Identification of components as:

1. Native

2. Integrated/OEM

3. Third-party integration

b. Logical and physical architecture diagrams.

c. Deployment model (SaaS, PaaS) and cloud service dependencies.

4. Implementation Considerations

a. Provide high-level information on:

i. Typical implementation approach.

ii. Dependencies on customer-managed analytics platforms.

iii. Required skills on the customer side for analytics integration.

5. Licensing, Constraints & Assumptions

a. Disclose, as applicable:

i. Licensing impacts for reporting, APIs, exports, or analytics.

ii. Feature limitations or tiering that affects reporting or integrations.

iii. Known scalability or performance constraints.

Submission Guidelines:

• Business name and address

• Name of company representative and business title

• CAGE Code

• Maximum length: 10 pages (excluding appendices)

• Clearly label sections to align with this RFI

• Diagrams and tables are encouraged.

• Responses should be suitable for public-sector review.

• Pricing Overview (High level only) – Detailed pricing is not required for this RFI.

o Pricing should describe per user/seat based o Enterprise licensing o Tiered packages o Implementation or onboarding costs (if applicable)

Optional Attachments:

Vendors may include:

• Sample data schemas or dictionaries.

• Reference architecture diagrams for AI-enabled analytics integration.

• Case studies of similar deployments.

Use of Responses:

Responses will be reviewed to better understand:

• Architectural approaches and integration strategies

• Openness and interoperability

• Data, analytics. and AI capabilities

• Implementation considerations and risks

• Transparency regarding limitations

Proprietary information and trade secrets, if any, must be clearly marked on all materials.

All information received that is marked Proprietary will be handled accordingly. Please be advised that all submissions become Government property and will not be returned. All Government reviewing submitted responses will have signed non-disclosure agreements and understand their responsibility for proper use and protection from unauthorized disclosure of proprietary information as described in 41 USC 423. The Government shall not be held liable for any damages incurred if proprietary information is not properly identified.

19. Industry Day

The Government is considering hosting an Industry Day in support of this market research effort.

The purpose of the Industry Day is to:

• Provide additional context regarding DoWEA’s enterprise education platform objectives

• Clarify technical, architectural, and integration expectations

• Offer vendors an opportunity to ask questions and provide feedback

• Promote open dialogue with industry to inform acquisition strategy

At this time, no date or format has been finalized. The event may be conducted virtually, in-person, or in a hybrid format.

The Government is particularly interested in demonstrations or architectural deep dives during a potential Industry Day.

Vendor Feedback Requested:

Interested parties are requested to indicate the following within their RFI response:

• Interest in attending an Industry Day (Yes/No)

• Preferred format (Virtual, In-Person, Hybrid)

• Recommended topics, areas of clarification, or questions for Government discussion

• Interest in one-on-one sessions (if offered)

Participation in any future Industry Day is voluntary and not required for participation in any future solicitation.

The Government may issue a separate notice with finalized Industry Day details at a later date.

The Government may consider extending the response period based on industry feedback.