AI Integration for Ellucian Banner Degree Audit

AI integration targets Banner's core degree audit objects and surfaces to augment, not replace, existing logic. The primary touchpoints are the Degree Works (or equivalent) audit engine via its API or database views (STVMAJR, SHRDGMR, SHRTCKN, SHRTCKG), the Student Self-Service portal for interactive scenario planning, and the Advising Module (SGASTDN, advising notes) where alerts and recommendations are surfaced. AI agents act as a contextual layer, consuming audit results, course history, and catalog rules to generate 'what-if' simulations, plain-language explanations of complex requirements, and predictive alerts about potential roadblocks like course sequencing or prerequisite chains.

Implementation typically involves a middleware agent that subscribes to Banner events (e.g., grade posting, registration changes via SFBETRM) and calls the audit engine. For a student changing majors, the AI can instantly simulate the new audit, highlight credit applicability, and draft a revised academic plan, pushing a summary to the advisor's queue in Banner. For proactive alerting, the system analyzes audit data alongside historical success rates (SSBSECT enrollment) to flag courses with high failure rates that are critical for a student's path, triggering a SHRCOMM communication or advising note. This shifts the workflow from 'run audit, interpret results' to 'receive prioritized insights and actionable next steps'.

Rollout requires careful governance, starting with read-only integrations for advisor copilots in a sandbox BANNER schema. A human-in-the-loop design is critical: AI generates the scenario analysis and draft communication, but the advisor approves and sends from within Banner, maintaining an audit trail. The final architecture often includes a vector store of catalog policies and course descriptions for RAG-based explanation, coupled with a rules engine that respects Banner's institutional logic, ensuring recommendations are always grounded in the official STVTERM and SCBCRSE rules. This approach reduces manual audit interpretation by advisors, helps students avoid costly missteps, and keeps the system of record authoritative.

The integration connects to Banner's core student data tables (SGASTDN, SGBSTDN for general student info, SFRSTCR, SHRTCKG for course history) via secure, read-only API calls or direct ODS queries. The AI layer acts as a middleware service that ingests a student's academic record, current program requirements (from SOACOMM, STVAPRO), and audit results to power three core functions: scenario planning ("what-if" analyses for major changes), requirement explanation (natural-language breakdowns of complex rules), and proactive alerting (notifications for upcoming deadlines or at-risk status). This service typically sits outside the Banner application server, calling its SOAP or REST APIs, to avoid performance impact on the production SIS.

For a production deployment, the architecture includes a vector database (like Pinecone or Weaviate) that stores embedded versions of the institution's catalog, program guides, and policy documents. When an advisor or student queries the system, a retrieval-augmented generation (RAG) pipeline fetches the most relevant, grounded context before an LLM (like GPT-4 or Claude) formulates a response. Key workflows are triggered via Banner's SELFSERVICE portal events or scheduled batch jobs that identify students with pending requirements. All AI-generated recommendations and explanations are logged to a separate audit table with a human review flag, ensuring advisors maintain final approval before any official notes (SGAADVR) are updated.

Rollout follows a phased approach: start with a read-only copilot for advisors that explains audit results, then add student-facing "what-if" scenarios in the self-service portal, and finally implement proactive alerts that push to Banner's communication management system. Governance is critical—establish a cross-functional team (Registrar, IT, Academic Affairs) to validate AI interpretations of degree rules, and implement strict data access controls aligned with FERPA. The goal isn't to replace the DAPRAUDT process but to augment it, turning complex audit output into actionable, plain-language guidance that reduces advisor manual lookup time and helps students stay on track.

Governance starts with a clear data access policy. AI agents should operate with a least-privilege service account within Banner, accessing only the specific tables and views required for degree audit functions—primarily the student record (SGASTDN), course history (SHRTCKG), program/requirement rules (SORCMFR), and audit results (SHRDAUD). All queries and updates should be logged to a separate audit table, creating a traceable lineage for every AI-generated scenario or explanation. This ensures compliance with FERPA and institutional data policies while providing a rollback path if needed.

For security, the integration architecture should never allow direct, unmediated LLM access to the Banner database. Instead, implement a secure middleware layer that validates user sessions, enforces role-based access control (RBAC) from Banner, and uses parameterized API calls to fetch and update data. Student Personally Identifiable Information (PII) should be tokenized or masked before being sent to any external AI model for processing. All communication between systems should be encrypted in transit, and API keys for services like OpenAI must be securely managed via a vault, never hard-coded.

A phased rollout is critical for adoption. Start with a pilot group of 5-10 trusted advisors and a single, high-impact use case, such as AI-powered "what-if" scenario generation for students considering a major change. This controlled environment allows for prompt tuning, workflow refinement, and gathering qualitative feedback. Phase two expands functionality to include automated, plain-language explanations of complex requirement blocks (e.g., "Why is my transfer credit not fulfilling the Humanities Core?"). The final phase introduces proactive, alert-based workflows, where the AI monitors audit exceptions and flags students who are at risk of falling off track, triggering a task in the advisor's workflow queue. Each phase should include training, clear communication of the AI's role as an assistant (not a decision-maker), and a formal feedback loop to continuously improve accuracy and utility.