AI Integrated Career Pathways Platform

The vendor required to provide AI integrated career pathways platform solution will guide students from middle and high school through postsecondary education and into the workforce, providing personalized insights into career pathways, programs of study, and labor market opportunities in state.
- Objectives
•    Design and deploy an AI-integrated platform that maps student interests and skills with education and training to potential career pathways in state. 
•    Develop an intuitive, age-appropriate web interface for middle school through postsecondary learners, parents, and educators; develop a separate web interface for administrators to access dashboards and reports. 
•    Integrate data across HIDOE, uh, and labor market information systems. 
•    Ensure data privacy and security in compliance with FERPA and state and federal cybersecurity policies
•    Provide implementation, training, documentation, and ongoing support.
- Functional requirements
1. AI model
•    Provide AI-integrated career exploration and pathway recommendations based on user profiles, interests, skills, available education and training programs, and state workforce opportunities. 
•    Incorporate institutionally approved datasets to generate recommendations, ensuring factual accuracy and preventing “hallucinations.” 
•    Include explainable AI (XAI) capabilities that allow users to understand why recommendations were made. 
•    Include configurable parameters for data source updates, weighting, and filtering to maintain current and relevant guidance. 
•    If de-identified record level data is attainable, utilize machine learning on prior academic history data to inform users based on patterns and trends.
2. Reporting and analytics
•    Provide dashboards and allow on-demand reports for policymakers and educators to monitor utilization, pathway engagement, usage trends, and outcomes.
3. System architecture and scalability
•    Provide a modular and scalable solution, allowing new data sources, user types, and AI features to be added over time to enhance the user experience.
•    Provide APIs for integration with future systems 
•    Include data caching and load balancing to maintain high availability and performance during peak usage periods.
4. Administration and maintenance
•    Include an administrative dashboard for managing user roles, permissions, and content updates. 
•    Allow for administrative review of routing rules, modes, and system prompt that determine AI-generated content
•    Provide data update workflows and version tracking to maintain synchronization with source systems. 
•    Support continuous monitoring, logging, and error reporting to ensure high system uptime and performance.
- Technical requirements
A. System architecture and design
1. Hosting infrastructure
•    The hosting platform must prioritize scalability and performance to accommodate high traffic volumes, deliver fast results, and scale efficiently over time. 
•    Offerors shall propose options for deployment on-premise, cloud or hybrid environments, compliant with applicable agency, state and institutional technology and security standards.
2. Containerized deployment 
•    The infrastructure solution shall utilize containerization technologies to encapsulate application components and dependencies ensuring consistency and portability across development, testing, and production environments.
3. Scalability and load balancing 
•    The architecture must support horizontal and vertical scalability and include load balancing mechanisms to ensure high availability and performance stability.
•    The platform must accommodate statewide usage, including students from middle school, high school, and post secondary, as well as educators, advisors, and administrators.
4. Optimization for speed and performance 
•    The system shall implement caching mechanisms at both the application and database layers to reduce latency and provide responsive user experiences, especially for frequently accessed datasets. 
•    The database design must incorporate indexing, query optimization and efficient data modeling to support rapid read and write operations and minimize response times for complex queries.
B. AI model design requirements
1. Model type and approach 
•    The AI and ML models and components to be used. 
•    The proposed design must demonstrate how the system will deliver personalized, scalable, explainable, and cost-efficient recommendations. 
2. Model optimization and LLM usage
•    Payload management details, including average token usage per query. 
•    Estimated monthly model calls and associated cost. 
•    Latency and throughput benchmarks for user interactions.
3. Explainable AI
•    The model must adhere to explainable AI principles to ensure that users can understand the rationale behind recommendations. 
•    Explanations should be interpretable, user-friendly, and context-aware.

- Contract Period/Term: 5 years
- Questions/Inquires Deadline: February 3, 2026