MENTOR

A multimodal AI teaching system for reflective architectural learning.

MENTOR is an AI-powered educational system designed to support deeper cognitive engagement in architectural education. Rather than functioning as a question-answering assistant, MENTOR guides students through structured reasoning, dialogue, and visual interpretation to encourage reflection, discovery, and independent thinking.


Developed and evaluated over nine weeks, the system integrates large language models, vision-language models, and agent-based orchestration to scaffold learning across conceptual, spatial, and technical dimensions. MENTOR was benchmarked against a GPT-only baseline, with results demonstrating improved engagement, reduced cognitive offloading, and increased depth of design reasoning.

The Story

2

5

5

MENTOR is built as a coordinated multi-agent system, where each agent supports a distinct pedagogical role.

System Architecture

Core Agents

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Orchestration

2

  • Context Reasoning Agent
    Interprets user input and situational context, acting as the entry point for all interactions.

  • Knowledge Synthesis Agent
    Connects ideas across domains, synthesizing theory, precedent, and technical knowledge.

  • Socratic Dialogue Agent
    Drives questioning, reflection, and conceptual exploration through guided prompts.

  • Cognitive Enhancement Agent
    Regulates cognitive load, pacing, and depth of engagement.

Knowledge Infrastructure

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The workflows are implemented in LangGraph.

The Context Reasoning Agent routes interactions through single-agent or multi-agent coordination paths, including sequential, parallel, and adaptive flows. A synthesizer stage merges agent outputs into a coherent educational response.


Models

  • Microsoft Magma for vision-language reasoning

  • GPT-4 Turbo and Claude 3.5 Sonnet for layered interpretation and dialogue

MENTOR’s Domain Expert relies on a hybrid Retrieval-Augmented Generation pipeline.


RAG Pipeline


  • Persistent vector store using ChromaDB

  • Hybrid search combining semantic, keyword, and query expansion strategies

  • Smart chunking and metadata enrichment

  • Sentence-transformer embeddings


Educational Safeguards

  • Pedagogical filtering reformats retrieved content for learning contexts

  • Progressive disclosure reduces extraneous cognitive load

  • Citation tracking returns sources in APA, MLA, or Chicago style

  • Results are deduplicated and scored based on authority, relevance, and pedagogical suitability

Cognitive Enhancement

Analysis

Agent

Context

Agent

Domain

Expert

Socratic

Tutor

Knowledge

Transfer

Cognitive

Development

Performance

Optimization

Capability

Building

The Oracle

The Catalyst

The Core Transaction

The Outcome Design

The Interaction Dynamic

PDF Repository

local

Semantic Search

vector similarity

Context Analysis

user intent

Text Extraction

PyMuPDF

Keyword Matching

text matching

Response

multi-source

Text Cleaning

Preprocessing

Query Expansion

enhanced terms

Citation

tracking

Smart Chunking

Overlaps

Hybrid Search

merged result

Quality

format text

DOCUMENT INGESTION

SEARCH

STRATEGIES

CHROMA DB

KNOWLEDGE

SYNTHESIS

document

chunks

extraction

context

data

search

result

citation


enhanced

response

cleaned txt

vector query

hybrid

results

sentence

transformers

+

embeddings

semantic

search

keyword

match

centralized

state

knowledge

search

processor

knowledge

base

context

analysis

processor

knowledge

synthesis

processor

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Learning Framework

4

Interactivity

5

The system is structured around three learning phases, each with distinct objectives and agent roles.


Phase 1: Ideation

Focuses on creativity, conceptual exploration, and questioning.
- Led by the Socratic Dialogue Agent, with minimal technical constraints.

Phase 2: Visualization

Bridges ideas with spatial representation.
- Balances creative inquiry and technical input using multimodal interaction and analysis support.

Phase 3: Materialization

Emphasizes technical knowledge, system integration, and professional practice.
- Guided primarily by the Domain Expert Agent and real-world constraints.


A weighted completion formula values questioning, conceptual clarity, and response quality, encouraging balanced progression across phases.

Task generation


MENTOR generates adaptive challenges aligned with each learning phase:

  • Open-ended exploration in ideation

  • Spatial reasoning and representation in visualization

  • Real-world technical constraints in materialization


Image Generation


Visual outputs translate abstract reasoning into diagrams, spatial layouts, and performance visualizations, reinforcing connections between concept and form.


Gamification


Progress metrics, phase completion formulas, and challenge-based learning maintain engagement. Learners advance through levels tied to ideation, visualization, and materialization, reinforcing both creativity and rigor.

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Evaluation and Benchmarking

Cognitive Analysis


Student interactions are logged and transformed into interaction graphs.

Metrics include:

  • Cognitive offloading prevention

  • Depth of thinking

  • Design move connectivity


Automated fuzzy linkography identifies critical moves and knowledge gaps.


Machine Learning Integration


  • Graph Neural Networks detect higher-order cognitive patterns

  • Ensemble classifiers assess proficiency levels

  • Statistical validation using mixed-effects models, ANOVAs, and Bayesian hierarchical analysis


Dashboards visualize learning progress, agent performance, and cognitive metrics in real time.

Results

MENTOR is designed to help students think through design decisions, not around them.

MENTOR demonstrated measurable improvements over a GPT-only baseline:


  • 84.7% cognitive offloading prevention rate

  • 73.9% deeper thinking engagement rate


Students spent more time reasoning through decisions, articulating intent, and reflecting on alternatives rather than accepting generated answers.

MENTOR shows how multimodal, agent-based AI systems can function as cognitive scaffolds rather than content generators. By structuring dialogue, vision, and knowledge retrieval around pedagogical goals, the system supports reflective, discovery-based learning in architectural education.


Project developed in collaboration with Seda Soylu and Biel Pitman. You can view the original article here, and the published paper here.