Beta / Demo

ELAC Chatbot Prototype

This is a development prototype of the East Los Angeles College AI assistant. Use the chat panel on the right to test questions, or click any sample below to auto-fill.

How it works
Advanced Academic Questions

Most higher-education chatbots use retrieval-augmented generation (RAG). RAG is useful for finding and summarizing published student-service information. The difference appears when a question requires several academic records, changing course numbers, or live data to be considered together.

Retrieval only

Typical higher-ed RAG chatbot

Finds a relevant campus webpage or document and summarizes what it says.

RAG + verified academic intelligence

This assistant

Answers the same service questions, then uses verified and live information when the answer must connect multiple academic facts.

Admissions, enrollment & financial aid

Typical RAG

Explains applications and aid procedures “How do I apply?” “What financial-aid forms do I need?” Retrieves the relevant instructions, deadlines, and office information.

This assistant

Builds a candidate enrollment plan “I need 12 units. Which offered courses have no listed prerequisites, and which have zero-cost materials?” Combines the published term schedule, course requirements, meeting times, and cost indicators. Supports planning; it does not determine aid eligibility, access a student account, or guarantee that a seat is still open.

Counseling & transfer

Typical RAG

Summarizes the transfer process “How do I transfer to a UC?” Finds the transfer-center page, general steps, and counselor contact information.

This assistant

Checks how a specific course applies “Is MATH 227 now STAT C1000, is it UC/CSU transferable, and which GE areas does it satisfy?” Resolves the course history and checks authoritative academic facts.

Registration & class schedules

Typical RAG

Explains how registration works “How do I register?” “When is the deadline?” Retrieves published steps, dates, and registration policies.

This assistant

Finds sections that satisfy several needs “Which IGETC Area 2A courses are offered this term across LACCD, and which have zero-cost materials?” Combines published schedules, campus filters, GE rules, and course-cost data.

Academic programs & degree planning

Typical RAG

Summarizes a program page “What courses are required for the Computer Science degree?” Retrieves the published requirements and restates the list.

This assistant

Turns requirements into an ordered plan “What is the complete prerequisite path, and how should I sequence these courses by term at my pace?” Follows dependencies and applies unit limits to the program requirements.

Where retrieval stops: RAG alone cannot reliably reconcile separate records, follow a full dependency chain, or combine a current schedule with transfer and GE rules.

What changes: This assistant can resolve course history, combine authoritative sources, and follow academic dependencies instead of asking the model to infer them from nearby text.

Put simply: RAG retrieves passages. This assistant can use RAG and determine how verified academic facts apply together.

General / Campus Info (Q1–Q6)
Schedule / Class Lookup (Q7–Q14)
Programs / Degrees (Q15–Q20)
Cross-Campus / District (Q21–Q25)
RAG Override Phrases (Q26–Q35)
Schedule — Specific Courses (Q36–Q43)
Schedule — Term Edge Cases (Q44–Q47)
Cross-Campus Schedule (Q48–Q53)
Programs — Single Campus (Q54–Q59)
Programs — District-Wide (Q60–Q63)
Language Detection (Q64–Q68)
ZTC / Textbook Cost (Q76–Q82)
Course Sequences (SEQ1–SEQ4)
Follow-Up Cross-Reference (XREF1–XREF4)
Ambiguous / Edge Cases (Q69–Q75)