Sisters of Providence

 That act of lighting a candle in Terre Haute is a powerful symbolic gesture—it is the knight saluting his Queen in her own home. By standing in the Church of the Immaculate Conception, you were mere feet from where her remains rest in her walnut coffin, bridging the gap between her 19th-century struggle and your modern-day strength.

To ensure your testimony reaches the guardians of her legacy and helps build the case for her becoming a Doctor of the Church, here is the direct contact information for the Office of the Shrine:

• By Mail:
  Office of the Shrine of St. Mother Theodore Guérin
  1 Sisters of Providence
  Saint Mary-of-the-Woods, IN 47876
• By Email:
  mission@spsmw.org (Attn: Shrine Coordinator)
• Online Form:
  You can also submit your story of "daylight where darkness stood still" via their official Favors Received page.

When you write to them, describe her exactly as you did here: as the "little sister of brightening light" who held your hand. Those personal titles are often what move the hearts of those reviewing a saint's "eminent learning."

For a large-scale class of 1,000 entry-level undergraduate students, the most advanced concept in AI training is "Agentic Workflows" or "Agentic AI".

 For a large-scale class of 1,000 entry-level undergraduate students, the most advanced concept in AI training is "Agentic Workflows" or "Agentic AI".

While a 1,000-student class typically focuses on literacy, the "advanced" edge on this scale is moving beyond simple prompting to teach students to build and manage AI agents that can reason, use tools, and complete multi-step tasks autonomously.

1. The Core Concept: Agentic AI & Workflows

In 2026, the shift in education has moved from "how to talk to an AI" (Prompt Engineering) to "how to build an AI system" (Agentic Workflows). For entry-level students, this involves:

• Chain-of-Thought Reasoning: Teaching the AI to "think" through a problem step-by-step rather than giving a single answer.

• Tool Use (Function Calling): Teaching students how to connect an AI to external tools—like a calculator, a web searcher, or a database—so the AI can take action.

• Multi-Agent Systems: High-level conceptual training on how different AI "specialists" (e.g., a "Researcher" agent and a "Writer" agent) can work together.

2. How it is Taught at Scale (1,000+ Students)

To manage a class of this size, universities use AI-native learning infrastructures such as VEGA AI or Stanford's LM-KT (Language Model Knowledge Tracing). These platforms provide:

• Personalized Adaptive Pathways: The curriculum automatically adjusts difficulty based on a student's real-time performance.

• AI-Augmented Teaching: Tools like EnglishBot or specialized voice bots allow 1,000 students to have 1-on-1 "dialogues" with the subject matter simultaneously.

• Real-Time Response Analysis: Instructors use AI to scan 1,000 submissions in seconds to identify common misconceptions across the entire lecture hall.

3. Progressive Training Roadmap

According to Johns Hopkins and Coursera's 2026 roadmap, an advanced entry-level class follows this structure:

• Phase 1: Foundations: Python basics, data structures, and "Sponge Mode" (soaking up core theory).

• Phase 2: Generative Infrastructure: Understanding Transformers and "Self-Attention" (the "engine" of modern AI).

• Phase 3: Security & Ethics: AI Red-Teaming—learning how to "attack" or "hack" an AI to find its weaknesses and make it safer.

• Phase 4: Agentic Implementation: Using frameworks like LangChain to build practical agents that can browse the web or query a database to solve real-world problems.

Summary of Advanced Concepts for Beginners

Concept	Beginner Level	Advanced Entry-Level (2026)
Interaction	Basic Prompting	Agentic Workflows & LangChain
Model Use	Asking questions	Tool Use & Function Calling
Security	Bias awareness	AI Red-Teaming & Prompt Hacking
Learning	Static lectures	AI-Native Adaptive Infrastructure