AI FOUNDATIONS
for industry-ready research
students
WELCOME friends!
Feb 5, 2026
Agenda
🌱 0. Welcome
Applied AI Institute
Introductions
Land & actions
What we’re doing today
👾 1. Introduction to AI
Brief History
Definitions
Core branches
Industry use cases
⚖ 3. Ethics & Responsible AI
Bias and transparency
Data privacy and ethical use
Responsible frameworks and
tools
󰲐 4. AI in the Workplace
Popular tools and use cases
How Companies Use GenAI
Practicing prompts
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🌱 Welcome
Introductions
Land & actions
Tell me about yourselves
What we’re doing today
Confidential
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Applied AI Institute
The Applied AI Institute is a research-based institute working to bridge
and build opportunities between academic researchers and professional
industry networks.
Our interdisciplinary team designs and develops AI-based tools and
delivers training and consulting to support digital literacy, upskilling,
and strategic adoption of new technological solutions for public and
private partners.
We believe incorporating critical perspectives and responsible
practices empower our partners to make more informed decisions, and
have more sustainable impacts and outcomes.
We are excited to be partnering with Mitacs!
Sign up for our newsletter
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☺ Happy to be here with you!
My name is Paloma (she/they).
I’m a researcher, educator, and polymath motivated
by the challenges of change.
I am committed to:
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Creative approaches to problem-solving
Advocating for/with those most impacted by
design
Growing our collective skills in critical media
analysis and tech literacy
Co-building engineering practices, systems, and
technology that center relationships and trust
building.
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I’ve helped clients and organizations to:
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Understand the customer journey, identify market trends, and opportunities for Audi.
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Create more accessible health and coaching services for students and families with
ADHD for Springboard Clinic.
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Develop a luxury travel experience platform for Four Seasons.
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Develop a technical proof-of-concept to improve tracking of surgical implants in
American operating rooms for a healthtech startup.
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Deliver audience insights on media consumption trends and behaviours as well as
recommendations to build more meaningful listener engagement for CBC.
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Land & learning
It’s important we continue to recognize the ways
emerging technologies resist or enable extraction
from land, water, ecosystems, and people.
Challenge colonial narratives native-land.com
Land Back course and Treaty Map
– Yellowhead Institute
Dewemaagannag/My Relations: Indigenous
Engagement Guide
– Office of Community Engagement at Concordia
Illustration from Land Back: A Yellowhead Institute Red Paper,
by Cree-Métis artist, illustrator and author, Julie Flett.
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Actions & prompts
Designing with consent
The Spectrum of Consent – Red Paper Report
Building Consentful Tech – Allied Media Projects
Wealth /resource redistribution
- Mohawk Mothers
- Mutual Aid Canada
- Toronto Indigenous Harm Reduction
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How can I move from acknowledgements to
actions?
Who will support and hold me accountable to
my principled commitments?
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🥳 Tell me about you!
● Name
● Where your calling from
● Field of study
● Favorite snack
● Share an awesome skill or talent that AI could not do
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👾
Introduction
to Artificial
Intelligence
(AI)
Confidential
Brief History
Definitions
Core branches
Industry use cases
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💫 ACTIVITY #1
What is “Artificial Intelligence”?
When you hear the words “AI,” what are the first
3-4 things that come to mind?
Take 5 minutes to name /describe AI
Share and discuss in the chat or out loud
What is AI?
Artificial Intelligence is a nebulous term with
no clear specific definition!
AI as a scientific discipline/ field of study
examining how machines are programmed to
mimic and simulate kinds of intelligence.
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Confidential
Generative AI
Machine Learning
Neural Networks
Deep Learning
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We need to be clear and specific with language and
cautious with how we use broad generalist words.
💡Marvin Minsky, a leading cognitive scientist and
innovator in the field of AI, coined the term “suitcase
words” to refer to a single “catch-all” term that includes a
variety of meanings.
💼 These suitcase terms risk losing specificity and
context, creating essentialist categories and
misinterpretations.
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🤯 The multiplicity of
intelligences, learning
and knowing
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What is Generative AI?
GenAI is a multipurpose tool,
also known as a generator, used
to produce content based on
instructions and criteria
provided in natural language
(no code required).
Models are designed according to
tasks:
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Text generation (OpenAI, Gemini)
Image generation or modification
(Midjourney, Nano Banana Pro)
Code generation (Claude Sonnet
4.5)
Video or music (Runway, Suno)
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Types of GenAI models
Task-focused models
Designed to do complete a
specific task.
General models
Manage multiple tasks based
on defined boundaries/criteria
Example:
Determine a fare with Lyft, or a
Netflix recommendation engine
Example:
Drafting a report with Claude
in French
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What’s under the hood?
🧱 Data are the building blocks
that models use to analyze and
make decisions based on a
given task.
Data can be numbers, words,
images, videos, sounds, etc.
󰲐 A Model is a computer
program trained on data.
Models are shaped and
informed by the data inputs.
They generate outputs for a
task based on pattern
recognition.
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Machine
Learning
(ML)
Machine Learning (ML) is a branch of AI that enables programs
to learn from data and make predictions or decisions without
being explicitly programmed for each task. These algorithms
identify patterns in data and improve their performance over
time through experience.
Generally there are two core approaches:
1.
2.
supervised learning
unsupervised learning
Use cases: Email spam detection, sales forecasting models,
personalized recommendation engines (like Netflix suggesting
shows based on viewing history), and fraud detection in financial
transactions.
Confidential
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Machine
Learning
algorithms/
types
Supervised learning: Supervised learning involves training a
model using labeled data, where input data is paired with
correct output labels to generate accurate classifications and
predictions.
Unsupervised learning: Unsupervised learning involves training
models on unlabeled data. The model identifies patterns,
structures, or relationships within the data without predefined
output labels. The goal is to uncover hidden patterns or
groupings using techniques like clustering and dimensionality
reduction. Data visualization is one application of unsupervised
learning.
Reinforcement learning (RL): A machine learning technique
where an AI agent learns to make decisions by receiving rewards
and penalties based on its actions in a dynamic environment.
Reinforcement learning is used in autonomous driving, game
design, and robotics.
Confidential
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(Artificial)
Neural
Networks
Artificial Neural Networks (ANN) are computational models
inspired by the human brain's network of neurons. Neural
networks include interconnected layers of nodes (neurons) that
process large amounts of data and learn from complex patterns
to recognize patterns and make decisions. A neural network
includes:
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Input layer: raw data
Multiple hidden layers: equations that transform inputs
into representations, which are multiplied by their
associated weights and biases to move through multiple
layers. This process between inputs and outputs is known
as linear transformation.
Output layer: Following the linear transformation, a
nonlinear activation function (tanh, sigmoid, ReLU ) is
added to generate a final prediction (such as a number
for regression, or a probability distribution for
classification).
Use Cases
Artificial Neural Networks are used to detect email spam and are
foundational to computer vision, speech recognition,
forecasting, and natural language processing (NLP).
Confidential
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Deep
Learning
Deep Learning is a subset of machine learning inspired by how
our biological brains are structured. Deep learning uses at least
4 layers layers of deep neural networks (DNN) to process more
complex tasks.
Models are typically trained via supervised learning, where human
experts label data for tasks like classification (computer vision) and
regression (predicting housing prices). Increasingly, unsupervised
learning methods allow models to learn from unlabeled data
without human annotation.
Confidential
Use Cases
Autonomous driving and robotics where “agents” are designed to
detect traffic signs, lane markings, and pedestrians in dynamic
environments based on feedback.
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Critical questions to ask about data:
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What data was used to build the model?
What additional data was used to improve it?
Where the data came from?
What’s the quality of the data?
Who’s data is this? Who owns it?
What data was included/ excluded? And why?
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⚖
Ethics and
Responsible
AI
Confidential
Bias and transparency
Data privacy and ethical use
Responsible frameworks and
tools
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Critical Systems Thinking
Systems Thinking
A holistic approach to understanding the complexity of
the world by examining it in terms of wholes and
relationships, rather than by dissecting it into isolated
parts.
Critical thinking is a key part of research, data
collection, and analysis as well as a vital skill to
understand how problems and opportunities are framed.
Critical examination allows us to better identify and
mitigate existing and potential risks, and to support
people and teams in more informed decision-making.
https://anatomyof.ai/
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What is a PESTLE framework?
The PESTLE analysis studies the key
external and macro factors (Political,
Economic, Sociological, Technological, Legal
and Environmental) that impact an
organisation or industry.
This analytical tool is used in a range of
different scenarios to help professionals
and senior managers understand the
broader contexts, forces and influences
that impact a particular industry or project.
This framework:
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Provides contextual information to
identify risks, brand positioning,
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Helps with scoping and scaling
projects.
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Empower decisions makers to make
more informed and strategic
decisions
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Iceberg-model for systems analysis
The Iceberg model is a tool to identify the less
obvious underlying conditions that shape what we
see and experience in everyday events. This
framework is used to:
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Uncover root causes and conditions of why
these events happen
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Understand the underlying historical
conditions, mental models, structural
influences, and behavioural patterns that
underpin and shape everyday observable
events.
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Support teams in critically assessing complex
problems and identify opportunities for
learning and meaningful change.
The Iceberg model serves to
understand complex problems
including:
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What I am seeing or noticing now?
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What are the conditions that
make this possible?
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Who and what has created these
conditions over time and why?
Example: Questioning human-centered design and
decentering mental models of human ownership
… rethinking laws, responsibilities, and ‘rights of nature.’
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Hick’s Law
The time it takes to make a decision increases
with the number and complexity of choices.
Takeaways
● Minimize choices when response times are
critical to decrease decision time.
● Increased cognitive load impacts ability to
take action
● Avoid overwhelming users by highlighting
recommended options.
● Be careful not to simplify to the point of
abstraction.
Design Strategy
● Break information into small manageable
steps to support usability and
decision-making.
● Use progressive loading and onboarding
Miller’s Law
The average person can only keep 7 (plus or minus
2) items in their working memory.
Takeaways
● Don’t use the “magical number seven” to
justify unnecessary design limitations.
● Organize content into smaller chunks to help
users process, understand, and memorize
easily.
● Remember that short-term memory capacity
will vary per individual, based on their prior
knowledge and situational context.
Strategy
Chunking content to manage cognitive
load and to sustain engagement
Laws of UX (User experience)
Laws of UX is a collection of best
practices that designers can consult
to better understand the user
interface design.
This is useful to:
- Understand cognitive biases
- Identify deceptive designs
- Learn and build better/best
design practices
www.lawsofux.com
Deceptive Patterns
Deceptive patterns are strategies used in design, websites and apps to conceal the
underlying purpose or function.
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Science is constructed and conducted by humans who are
inherently flawed, messy creatures, full of cognitive biases.
As researchers, scientists and engineers, we can proactively:
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Consider the needs, the people, and environments most
impacted, as well as existing systemic inequalities that risk
being reproduced in these systems.
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Identify biases, positionality, and contexts.
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Evaluate the strengths and limits of our methodologies
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Invite a learner’s mindset to practice being more open,
experiential, and challenged in our thinking and work.
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Develop an awareness of our worldviews, stories, feelings
to reduce bias, mitigate human error, and build cognitive
flexibility.
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Build accountability strategies to course correct, address
mistakes, make impactful changes, and build or repair trust.
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🙌 Consentful Tech
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ACTIVITY #2
⭐ Identify Guiding Values
10 mins
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Name 1-2 core values or guiding principles that are
important to you.
Share the value/s with the group (in-person or in the chat)
Discuss how these values might guide your practice
Design Justice Principles
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Responsible AI
Impact, all tech is
human (2025)
Human Contexts &
Ethics Toolkit, Berkeley
(2020)
Recommendations on
the Ethics of AI
AI Competency
framework for teachers
– UNESCO (2024)
Montréal Declaration for
Responsible
Development of AI
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📚 Readings and learnings
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🤓 Evaluating Sources with ESCAPE
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󰲐
AI in the
Workplace
Confidential
Popular tools and use cases
How Companies Use GenAI
Practicing prompts
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💫 What are the most used AI
tools?
Large Language Models (LLMs)
are the most utilized generative
AI tool in the workplace
What do you use
LLMs for?
They are trained on a large
volume of texts to generate
human-like language outputs.
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🛠 Popular GenAI products
Claude
Perplexity AI
Google NotebookLM
ChatGPT
Gemini AI
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What tools can I explore right now?
What field or industry am I interested
in exploring?
How are different LLMs or AI tools
being used in my field of study or
industry?
One way to learn is to experiment and
play with the tools.
🧐 Be curious, critical, and check your
sources!
Based on your guiding principle(s), how
adn why would use/not use these
products/tools?
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Refining outputs
⭐ LLMs can be enhanced with the
use of tools to:
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Address more complex
workflow requests
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Create a more engaging outputs
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Expand responsiveness
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Improve accuracy
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󰢚 What can I do with LLMs?
Employees and teams use LLMs to:
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Generate ideas and brainstorm
Conduct research
Summarize information
Prioritize tasks
Plan and manage projects
Coding and debugging
Questions and Answers
Support with problem solving
Guidance around making decisions
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🧐 How do I choose an LLM?
Need
Technical requirements Criteria
Examples
Quick answers or writing
support
Fast fluent model
ChatGPT4o, Claude Sonnet
4, Gemini 2.5 Flash
Multi-step tasks and decision
making
Model designed for reasoning that can explain break down a
problem show the logic and steps
GPT-4o, Claude Opus 4,
Gemini 2.5 Pro
Accuracy is critical
A model known for careful analysis or labeled as a "deep reasoning
model"
Claude 3 Opus, GPT-4o,
Mistral Large
Current or real-time
information is needed
A model that can search the internet or connect to real-time data
ChatGPT with search,
Perplexity, Claude with tools
Tasks involving tools
(e.g., spreadsheets, code)
A model that can interact with tools,
not just generate text
GPT-4o with tools, Claude 4
with subscription, Gemini Pro
*Note: Choosing an LLM will depend on the problem to be solved / need to be addressed, your guiding principles, and how the technology changes over time.
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🧰 Open-source tools
https://openalternative.co/
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💬 Practicing with prompts
As a [role / job ]
I want to [action]
So that I can
[accomplish / benefit]
Create share [tips/strategies/a plan] on how I can do this.
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💫 ACTIVITY #3:
Prompt people & programs (15 mins)
As a [role / job ]
1.
2.
I want to [action]
So that I can
[accomplish / benefit]
Find a few humans to consult and get guidance on how to work toward your goal
Select an LLM ask the prompt about [tips/strategies/a plan] to meet your goal
Reflect on your experience. What are the strengths, limitations and learnings of
asking people and prompting programs.
Thank you!
You can find me
here
Paloma Holmes
Educational Programs Lead
Applied AI Institute, Concordia University
paloma.holmes@concordia.ca
https://www.linkedin.com/in/palomaholmes/