Monitor, evaluate, and push degree audit results from a Google Sheet to a ready-to-review AI Degree Summary.
The AI agent reads the Senior_data sheet in Google Sheets and gathers StudentID, Name, Program, Year, and CompletedCourses. It compares CompletedCourses against built-in program requirements and identifies missing core courses, Gen Eds, electives, and upper-division credits. It writes back an AI Degree Summary to the sheet and surfaces gaps for registrar review.
Executes a precise, end-to-end degree audit for each senior student.
Read seniors and their completed courses from Google Sheets.
Compare completed courses to per-program requirements.
Compute missing items and generate a concise summary.
Write the AI Degree Summary back to the same Google Sheet.
Optionally map missing items to a dedicated column or separate sheet.
Distribute results via email or export PDFs for advising folders.
Before: manual, inconsistent degree checks slow decisions and introduce errors. After: automated, auditable audits produce consistent results and faster decision workflows.
A simple 3-step flow that non-technical users can follow.
Read the Senior_data sheet from Google Sheets to collect StudentID, Name, Program, Year, and CompletedCourses.
Compare the CompletedCourses to the per-program requirements and build a Missing list and a short Summary.
Append or update the row with the AI Degree Summary and optionally map Missing to a dedicated column for dashboards.
One realistic scenario.
Scenario: A Computer Science BS senior has completed CS 101, CS 201, GEN-101, GEN-103, and several core CS courses. The agent processes this row in under a minute and returns a Missing list detailing 6 Gen Ed credits, 6 CS major electives (200+ level), 18 upper-division credits, and 54 remaining elective credits. Summary: All core CS courses are complete; the system highlights exact remaining categories and recommended electives for completion by graduation. Time: ~45–60 seconds on a standard setup. Outcome: The sheet is updated with the AI Degree Summary and a structured Missing list for advising.
One supporting sentence.
Ensures consistent checks across students and campuses with auditable results.
Speeds up advising meetings with up-to-date degree gaps.
Supports dashboards and prototype analytics for graduation readiness.
Facilitates integration with SIS pipelines and dashboards.
Verifies program progression and compliance across cohorts.
Manages credentials and protects data access.
One supporting sentence with short explanation.
Reads Senior_data and writes AI Degree Summary to the sheet (or mapped columns).
Runs GPT-5 to compare CompletedCourses against requirements and generate Missing + Summary.
Emails summaries to advisors or students.
One supporting sentence with short explanation.
One supporting sentence with short explanation.
The input sheet must include StudentID, Name, Program, Year, and CompletedCourses. CompletedCourses uses the pipe '|' to separate course IDs (e.g., GEN-101|GEN-103|CS-101). The agent reads the Senior_data tab to run the audit and writes the AI Degree Summary back to the same row or mapped column. Access is controlled via credentials and Google Sheets permissions; do not share private data beyond authorized users.
Yes. You can adjust the built-in rules to align with institutional policies by editing the agent’s system prompt and rule set. This supports changes for new programs or policy shifts. Changes apply to future audits and can be rolled out across cohorts. Maintain a versioned prompt to track updates over terms.
The Missing items are derived from the configured requirements and the CompletedCourses data. The Summary is a concise interpretation of those gaps meant for registrar review. Because requirements can vary by college or term, human validation is recommended for edge cases. The tool should be viewed as an audit aid rather than a final authority.
Yes. The prompt and configuration allow updates to Gen Ed rules, core requirements, electives, and upper-division thresholds. You can extend or modify the program set and credit rules as institutions change. After updating, run a test audit to verify outputs before production use. Keep a changelog of changes to ensure traceability.
Results are written to the Google Sheet in the AI Degree Summary column or a mapped Missing column, depending on configuration. Access is governed by Google account permissions and sheet sharing settings. The data remains in your Google Drive unless you export it. Use credentials and role-based access to limit exposure.
Triggering is manual via the described action, such as clicking 'Execute workflow'. You can also automate the trigger with a Google Apps Script or a scheduler if permitted by your environment. Ensure credentials are valid and that the input sheet is accessible. Consider adding an audit log to track when audits run and by whom.
The audit relies on hard-coded program rules; changes must be maintained term-by-term. It’s not an official registrar decision and may require human validation for complex rules like program-specific capstones or minor/cognate constraints. Data quality in the source sheet directly affects outputs. Performance depends on sheet size and API rate limits; plan for occasional retries or batching for large cohorts.
Monitor, evaluate, and push degree audit results from a Google Sheet to a ready-to-review AI Degree Summary.
