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AI Tools For Educators

This 24-hour introductory course is designed to empower educators with practical knowledge and hands-on experience in leveraging artificial intelligence tools to enhance teaching, learning, and classroom management. The course focuses on foundational AI tools such as ChatGPT, Canva AI, EdPuzzle, and Google’s AI-based educational platforms, equipping participants to personalize instruction, streamline assessments, and foster student engagement. Through interactive sessions, educators will explore AI-driven strategies that support inclusive, innovative, and effective learning environments.

Course Series

Looking to future-proof your teaching? Discover how AI is transforming the classroom with this hands-on course designed specifically for educators. You’ll explore the latest AI-powered tools for lesson planning, interactive content creation, student feedback, and inclusive teaching. Learn how to save time, engage students, and bring innovation to your teaching practice — no coding required!

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INTRODUCTORY COURSES

4 courses

  • Why take this course?

    This course introduces learners to Agentic AI - systems that go beyond passive response generation and instead act autonomously toward goals. In contrast to traditional AI tools, agentic systems can plan, make decisions, use external tools, and even reflect on their actions. Designed for learners who have already explored Generative AI, this course bridges the gap between using AI and designing AI-driven workflows that simulate autonomous behavior.
    Through hands-on projects and real-world examples, students will gain foundational knowledge in multi-step reasoning, task decomposition, tool integration, and memory - the building blocks of agentic systems. This course sets the stage for future industry-specific tracks in business, education, finance, and more.

    AI is evolving — and it's learning to take initiative. This course explores the next frontier: Agentic AI. Unlike basic chatbots or assistants, agentic systems can plan, act, and adapt to achieve goals. You’ll learn how these intelligent agents work, how to design them, and how they use tools, memory, and multi-step reasoning to function independently. Perfect for professionals, technologists, or innovators who want to go beyond using AI and start building with it. This course lays the foundation for advanced, industry-specific agentic AI training.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: What is Agentic AI?

      • Definitions: Agents vs Assistants vs Tools

      • Agent architecture: Perception → Reasoning → Action

      • Task completion vs goal pursuit

      • Key frameworks: Auto-GPT, BabyAGI, LangChain agents, CrewAI

      • Demos: How agents differ from GenAI tools

      Session 2: Planning and Reasoning in Agents

      • Multi-step reasoning: from LLMs to planning agents

      • Task decomposition and sub-goal generation

      • Prompt-chaining vs autonomous reasoning

      • Case study: An AI that plans a marketing campaign or writes code

    • Session 3: Tool Use and Integration

      • Toolformer and beyond: how agents call APIs, search, use calculators

      • The agent “toolbelt”: defining and assigning tools

      • LLM + plugin architectures (LangChain, OpenAI functions, HuggingFace agents)

      • Hands-on: Design a simple agent to perform web search + summarize

      Session 4: Memory and Context

      • Short-term vs long-term memory in agents

      • Retrieval-augmented generation (RAG) and vector databases

      • Embeddings 101 and memory stores (Pinecone, FAISS)

      • Use case: AI assistant with persistent memory of a client or task

    • Session 5: Reflection and Self-Correction

      • Agentic loops and thinking steps (e.g., ReAct framework)

      • Iterative reasoning, retry logic, and error handling

      • Critic and planner roles in multi-agent systems

      • Use case: Agents that debug or revise their own outputs

      Session 6: Multi-Agent Collaboration

      • Autonomous teams: CrewAI, AutoGen, LangGraph

      • Roles and personas in agentic systems

      • Inter-agent communication

      • Use case: Agent team executes a content pipeline or workflow

    • Session 7: Limitations, Ethics, and Failure Modes

      • Over-reliance and hallucination in agentic behavior

      • Security, misuse, and safety concerns

      • Prompt injections, tool misuse, data privacy

      • Design principles for safe, bounded autonomy

      • What current agentic systems can’t do (yet)

      Session 8: Capstone Workshop — Design Your Own Agent

      • Choose a goal or workflow (e.g., research assistant, business analyst, personal concierge)

      • Define tools, memory, goals, roles

      • Create a prompt-based or low-code agent prototype

      • Demo and feedback: peer review + instructor insights

  • Why take this course?

    This hands-on, beginner-friendly course introduces learners to the fast-evolving field of prompt engineering - the art and science of communicating effectively with Generative AI. In just 24 hours, participants will learn how to craft prompts that produce better, more accurate, and more useful outputs from tools like ChatGPT, Claude, DALL-E, and others. Through real-world examples, interactive exercises, and guided experimentation, learners will gain practical skills to unlock the full potential of AI in creative, professional, and technical contexts.


    No technical or programming background is required - just curiosity and a willingness to experiment!

    Generative AI is powerful—if you know how to ask the right questions. This course teaches you how to speak the language of AI through effective prompt engineering. Over four weeks, you'll learn how to design, refine, and experiment with prompts to unlock smarter, more accurate, and more creative responses from GenAI tools like ChatGPT, Claude, and Midjourney. From writing and coding to design and customer service, prompt engineering is the skill that puts AI to work for you. Perfect for professionals, creators, educators, and anyone curious about working better with AI.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: What is Generative AI? A Beginner’s Map

      • What is GenAI? Definitions and context

      • Evolution: From early AI to Generative AI

      • Examples in action: ChatGPT, DALL·E, Midjourney, GitHub Copilot, etc.

      • Real-world applications (business, education, art, software, etc.)

      • What GenAI can do today

      Session 2: How Generative AI Works — The Basics (No Math!)

      • What is a model? What is "training"?

      • Overview of LLMs, diffusion models, GANs, VAEs

      • Why GenAI seems smart (but isn’t conscious)

      • Tokens, prompts, hallucinations explained

      • Limitations of current models and where they break

    • Session 3: Exploring Text-Based GenAI (LLMs like ChatGPT, Claude, Gemini)

      • How LLMs generate language

      • Prompting basics: getting what you want

      • Use cases: summarization, writing, planning, coding

      • Prompt engineering basics and best practices

      • Limitations and risks of text-based GenAI

      Session 4: GenAI for Images, Audio, and Video

      • Overview of image generation tools (DALL·E, Midjourney, Stable Diffusion)

      • Basics of how image GenAI works (diffusion explained simply)

      • Intro to audio and music GenAI (e.g., Suno, ElevenLabs)

      • Video GenAI (e.g., Runway, Sora—emerging tools)

      • Demos and ethical considerations (deepfakes, misuse, bias)

    • Session 5: Real-World Use Cases and Tools

      • Business use: marketing, productivity, customer service

      • Education use: tutoring, writing support, personalized learning

      • Design, art, and creative content

      • GenAI in software development and automation

      • Demo of multi-modal GenAI tools (text+image, text+code)

      Session 6: What GenAI Can’t Do (Yet)

      • Understanding hallucination and misinformation

      • Reasoning, logic, and factual gaps

      • Biases and fairness in GenAI

      • Creativity vs replication: is GenAI truly creative?

      • The future: AGI vs narrow GenAI

    • Session 7: Ethics, Risks, and Responsible Use

      • Copyright and IP issues

      • Deepfakes, misinformation, and manipulation

      • AI safety and alignment challenges

      • Open vs closed-source models

      • Regulatory trends (Canada, EU, US, etc.)

      Session 8: Hands-On Workshop: Build Your Own GenAI Workflow

      • Choose a use case (content creation, customer response, design, etc.)

      • Hands-on with GenAI tools (text, image, audio)

      • Crafting effective prompts and refining results

      • Wrapping up: GenAI literacy for everyday use

      • Final Q&A and resources for continued learning

  • Why take this course?

    This 24-hour introductory course is designed for absolute beginners who want to understand Generative AI (GenAI) and how it is transforming industries. Through 8 engaging sessions, participants will explore how GenAI works, what it's good at (and what it isn't), and how to begin using GenAI tools in real-world scenarios. You’ll gain foundational knowledge of the technologies behind GenAI, including large language models (LLMs), image generation, and audio synthesis. We'll also discuss the risks,
    ethical concerns, and current limitations to using GenAI responsibly. By the end of the course, learners will be confident in using popular GenAI tools, understanding how they work under the hood, and recognizing their potential and boundaries in various applications.


    No prior technical knowledge is required.

    Unlock the power of Generative AI in just four weeks. This hands-on, beginner-friendly course will take you from zero to confident user by exploring how GenAI tools like ChatGPT, DALL·E, and others work—and how they’re being used across industries. Learn what GenAI can (and can’t) do, experiment with leading tools, and understand the ethical, legal, and societal implications of this rapidly evolving technology. Whether you're a business professional, educator, artist, or curious learner, this course offers practical skills and critical insights to help you navigate and apply GenAI effectively.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: What is Generative AI? A Beginner’s Map

      • What is GenAI? Definitions and context

      • Evolution: From early AI to Generative AI

      • Examples in action: ChatGPT, DALL·E, Midjourney, GitHub Copilot, etc.

      • Real-world applications (business, education, art, software, etc.)

      • What GenAI can do today

      Session 2: How Generative AI Works — The Basics (No Math!)

      • What is a model? What is "training"?

      • Overview of LLMs, diffusion models, GANs, VAEs

      • Why GenAI seems smart (but isn’t conscious)

      • Tokens, prompts, hallucinations explained

      • Limitations of current models and where they break

    • Session 3: Exploring Text-Based GenAI (LLMs like ChatGPT, Claude, Gemini)

      • How LLMs generate language

      • Prompting basics: getting what you want

      • Use cases: summarization, writing, planning, coding

      • Prompt engineering basics and best practices

      • Limitations and risks of text-based GenAI

      Session 4: GenAI for Images, Audio, and Video

      • Overview of image generation tools (DALL·E, Midjourney, Stable Diffusion)

      • Basics of how image GenAI works (diffusion explained simply)

      • Intro to audio and music GenAI (e.g., Suno, ElevenLabs)

      • Video GenAI (e.g., Runway, Sora—emerging tools)

      • Demos and ethical considerations (deepfakes, misuse, bias)

    • Session 5: Real-World Use Cases and Tools

      • Business use: marketing, productivity, customer service

      • Education use: tutoring, writing support, personalized learning

      • Design, art, and creative content

      • GenAI in software development and automation

      • Demo of multi-modal GenAI tools (text+image, text+code)

      Session 6: What GenAI Can’t Do (Yet)

      • Understanding hallucination and misinformation

      • Reasoning, logic, and factual gaps

      • Biases and fairness in GenAI

      • Creativity vs replication: is GenAI truly creative?

      • The future: AGI vs narrow GenAI

    • Session 7: Ethics, Risks, and Responsible Use

      • Copyright and IP issues

      • Deepfakes, misinformation, and manipulation

      • AI safety and alignment challenges

      • Open vs closed-source models

      • Regulatory trends (Canada, EU, US, etc.)

      Session 8: Hands-On Workshop: Build Your Own GenAI Workflow

      • Choose a use case (content creation, customer response, design, etc.)

      • Hands-on with GenAI tools (text, image, audio)

      • Crafting effective prompts and refining results

      • Wrapping up: GenAI literacy for everyday use

      • Final Q&A and resources for continued learning

  • Why take this course?

    This 24-hour introductory course offers a hands-on, non-technical introduction to the most impactful AI tools available today. Designed for curious beginners from any background, the course showcases real-world applications of AI in writing, image creation, productivity, communication, data analysis, and more. Through guided demos and hands-on practice, learners will discover how to use AI tools to boost creativity, improve efficiency, and solve everyday problems - no programming required. By the end of the course, participants will understand the capabilities, limitations, and best uses of today’s most promising AI tools, and feel empowered to start using them in their personal and professional lives.

    AI is no longer just for coders and data scientists—it’s for everyone. This beginner-friendly course introduces you to today’s most powerful and accessible AI tools, from ChatGPT to Canva AI, and shows you how to use them to write faster, design smarter, automate tasks, create content, and more. Learn by doing through real-world demos and hands-on challenges as you build your own custom AI toolkit. Whether you're a professional, student, entrepreneur, or just curious, this course will show you what’s possible when AI works with you.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: Welcome to the AI Tool Ecosystem

      • What are AI tools and why are they everywhere now?

      • Categories of AI tools: text, images, audio, video, productivity, etc.

      • Core concepts: Generative AI, automation, personalization

      • Intro to foundational tools: ChatGPT, DALL·E, Grammarly, Notion AI

      • Hands-on: Try your first AI-assisted task

      Session 2: Text-Based Tools and Assistants

      • ChatGPT, Claude, Gemini: what they can do and how they differ

      • Writing support: GrammarlyGO, Jasper, Notion AI

      • Email, reports, summarization, translation, and rewriting

      • Hands-on: Crafting prompts and workflows for writing tasks 

    • Session 3: Visual AI: From Images to Design

      • Image generators: DALL·E, Canva AI, Adobe Firefly

      • Design help: Layout suggestions, branding ideas, social media content

      • Practical uses: marketing, personal branding, storytelling

      • Hands-on: Create custom visuals with AI

      Session 4: Audio, Video, and Voice AI Tools

      • Voice AI: ElevenLabs, Descript

      • Music generation: Suno AI, Soundraw

      • Video tools: Synthesia, Runway, Pictory

      • Use cases: explainer videos, training, podcasts, voiceovers

      • Hands-on: Generate a short video or podcast clip

    • Session 5: AI Tools for Productivity & Automation

      • Notion AI, Microsoft Copilot, Google Duet, Superhuman

      • Meeting transcription: Otter.ai, Fireflies

      • Scheduling, brainstorming, note-taking, content planning

      • Hands-on: Build a personal productivity stack with AI

      Session 6: AI for Data and Research

      • Tools: ChatGPT (with data plugins), Perplexity AI, Browse AI

      • AI search and research assistants

      • Visualizing data with AI: ChatGPT + charts, Power BI integrations

      • Hands-on: Use AI to analyze or summarize a dataset or research topic

    • Session 7: Responsible Use and Tool Comparison

      • AI hallucination and fact-checking

      • Privacy, data ownership, copyright

      • Free vs paid tools: what’s worth it?

      • Tool longevity and choosing sustainable platforms

      • Hands-on: Compare tool results for the same task

      Session 8: Build Your Own AI Toolkit (Capstone Workshop)

      • Pick your domain: business, education, content creation, etc.

      • Identify useful tools for your goals

      • Build a repeatable AI-powered workflow

      • Final showcase: present your personalized AI toolkit

APPLIED
COURSES

8 courses

  • Why take this course?

    This 24-hour introductory course is designed to empower educators with practical knowledge and hands-on experience in leveraging artificial intelligence tools to enhance teaching, learning, and classroom management. The course focuses on foundational AI tools such as ChatGPT, Canva AI, EdPuzzle, and Google’s AI-based educational platforms, equipping participants to personalize instruction, streamline assessments, and foster student engagement. Through interactive sessions, educators will explore AI-driven strategies that support inclusive, innovative, and effective learning environments.

    Looking to future-proof your teaching? Discover how AI is transforming the classroom with this hands-on course designed specifically for educators. You’ll explore the latest AI-powered tools for lesson planning, interactive content creation, student feedback, and inclusive teaching. Learn how to save time, engage students, and bring innovation to your teaching practice — no coding required!

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: Introduction to AI in Education

      • Overview of AI and its impact on education

      • Applications: content generation, personalization, automation

      • Tools: ChatGPT, Canva AI, and Google AI tools

      • Hands-on: Generate a sample lesson plan using AI

      Session 2: Enhancing Lesson Delivery with AI

      • Using AI to create visuals, slides, and instructional materials

      • Tools: Canva AI, SlidesAI, PowerPoint Designer

      • Hands-on: Design a complete AI-supported lesson resource

    • Session 3: AI for Student Engagement and Feedback

      • Using AI chatbots and tutoring tools

      • Formative and summative feedback using EdTech AI platforms

      • Tools: Quizizz, EdPuzzle, Formative, Curipod

      • Hands-on: Create an interactive activity and quiz

      Session 4: Personalization and Inclusion through AI

      • Differentiating instruction with AI-generated materials

      • Text-to-speech, translation, and accessibility tools

      • Tools: Microsoft Immersive Reader, Speechify, DeepL

      • Hands-on: Adapt a lesson for diverse learner needs

    • Session 5: Assessment and Grading Automation

      • AI tools for formative and summative assessment

      • Auto-grading tools and feedback generators

      • Tools: Gradescope, ChatGPT rubrics, Edform

      • Hands-on: Build an assessment and generate feedback

      Session 6: Content Curation and Resource Discovery

      • Using AI to find and curate educational resources

      • Summarization, translation, and scaffolded content

      • Tools: ChatGPT, ScholarAI, Diffit, MagicSchool AI

      • Hands-on: Curate a multi-level resource pack

    • Session 7: Ethical and Responsible AI in Classrooms

      • Understanding limitations, bias, and data concerns

      • AI and academic integrity: plagiarism, detection, misuse

      • Establishing classroom policies and best practices

      Session 8: Capstone Project – Designing an AI-Enhanced Lesson

      • Understanding limitations, bias, and data concerns

      • AI and academic integrity: plagiarism, detection, misuse

      • Establishing classroom policies and best practices

  • Why take this course?

    This 24-hour course explores how personalized and adaptive learning systems use artificial intelligence to tailor instruction to individual student needs. Educators will examine how diagnostics, progress monitoring, and learning analytics can generate customized learning pathways, targeted practice, and timely interventions. Through hands-on activities and tool exploration, participants will learn to evaluate adaptive platforms, design AI-supported differentiation strategies, and implement intelligent tutoring approaches in ways that improve engagement, mastery, and equity across diverse learning contexts.

    One-size-fits-all teaching is fading fast. In this hands-on course, learn how adaptive learning tools and intelligent tutoring systems personalize instruction in real time—helping every student progress at the right pace. Explore diagnostic assessments, mastery tracking, AI-powered practice, and learning analytics so you can design smarter learning experiences, boost outcomes, and support diverse learners—without needing to be a data scientist.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: What is Generative AI? A Beginner’s Map

      • What is GenAI? Definitions and context

      • Evolution: From early AI to Generative AI

      • Examples in action: ChatGPT, DALL·E, Midjourney, GitHub Copilot, etc.

      • Real-world applications (business, education, art, software, etc.)

      • What GenAI can do today

      Session 2: How Generative AI Works — The Basics (No Math!)

      • What is a model? What is "training"?

      • Overview of LLMs, diffusion models, GANs, VAEs

      • Why GenAI seems smart (but isn’t conscious)

      • Tokens, prompts, hallucinations explained

      • Limitations of current models and where they break

    • Session 3: Exploring Text-Based GenAI (LLMs like ChatGPT, Claude, Gemini)

      • How LLMs generate language

      • Prompting basics: getting what you want

      • Use cases: summarization, writing, planning, coding

      • Prompt engineering basics and best practices

      • Limitations and risks of text-based GenAI

      Session 4: GenAI for Images, Audio, and Video

      • Overview of image generation tools (DALL·E, Midjourney, Stable Diffusion)

      • Basics of how image GenAI works (diffusion explained simply)

      • Intro to audio and music GenAI (e.g., Suno, ElevenLabs)

      • Video GenAI (e.g., Runway, Sora—emerging tools)

      • Demos and ethical considerations (deepfakes, misuse, bias)

    • Session 5: Real-World Use Cases and Tools

      • Business use: marketing, productivity, customer service

      • Education use: tutoring, writing support, personalized learning

      • Design, art, and creative content

      • GenAI in software development and automation

      • Demo of multi-modal GenAI tools (text+image, text+code)

      Session 6: What GenAI Can’t Do (Yet)

      • Understanding hallucination and misinformation

      • Reasoning, logic, and factual gaps

      • Biases and fairness in GenAI

      • Creativity vs replication: is GenAI truly creative?

      • The future: AGI vs narrow GenAI

    • Session 7: Ethics, Risks, and Responsible Use

      • Copyright and IP issues

      • Deepfakes, misinformation, and manipulation

      • AI safety and alignment challenges

      • Open vs closed-source models

      • Regulatory trends (Canada, EU, US, etc.)

      Session 8: Hands-On Workshop: Build Your Own GenAI Workflow

      • Choose a use case (content creation, customer response, design, etc.)

      • Hands-on with GenAI tools (text, image, audio)

      • Crafting effective prompts and refining results

      • Wrapping up: GenAI literacy for everyday use

      • Final Q&A and resources for continued learning

  • Why take this course?

    This 24-hour course explores how artificial intelligence can be used to design, administer, grade, and provide feedback on assessments at scale while maintaining fairness, transparency, and academic integrity. Educators will examine AI-supported formative and summative assessment tools, automated grading systems, rubric generation, and feedback workflows. Emphasis is placed on human-in-the-loop assessment design, ethical boundaries, and practical classroom implementation.

    Assessment doesn’t have to be time-consuming to be effective. In this hands-on course, learn how AI tools can help you design better assessments, grade more efficiently, and deliver meaningful, personalized feedback—without sacrificing fairness or academic integrity. Discover how to balance automation with professional judgment and reclaim valuable time for teaching and learning.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: Rethinking Assessment in the Age of AI

      • The purpose of assessment: formative vs. summative vs. diagnostic

      • Challenges of traditional grading at scale

      • Where AI adds value—and where it should not be used

      • Human-in-the-loop assessment models

      • Examples of AI-supported assessment workflows

      Session 2: Designing Assessments with AI Support

      • AI-assisted question generation (MCQs, short answers, scenarios)

      • Aligning AI-generated questions with learning outcomes

      • Bloom’s taxonomy and cognitive complexity checks

      • Bias risks in AI-generated assessments

      • Hands-on: generate and refine an assessment using AI

    • Session 3: Automated and Semi-Automated Grading Systems

      • How automated grading systems work

      • Rule-based vs. ML-based grading approaches

      • Use cases: quizzes, coding, math, short answers

      • Tools and platforms: Gradescope-style workflows

      • Hands-on: configure an auto-grading rubric

      Session 4: Rubrics and Consistency with AI

      • Designing clear, criteria-based rubrics

      • Using AI to draft and refine rubrics

      • Ensuring consistency and transparency in grading

      • Moderation and calibration using AI-assisted samples

      • Activity: build a rubric and test it on sample responses

    • Session 5: Feedback Generation and Learning-Oriented Assessment

      • What makes feedback effective for learning

      • AI-generated feedback vs. canned comments

      • Personalized feedback at scale

      • Tone, clarity, and growth-oriented language

      • Hands-on: generate feedback for multiple learner profiles

      Session 6: Academic Integrity, Detection, and Misuse

      • AI-related academic integrity challenges

      • Plagiarism vs. acceptable AI assistance

      • Detection tools and their limitations

      • Designing assessments that reduce misuse

      • Classroom policies and communication strategies

    • Session 7: Ethics, Bias, and Transparency in AI-Assisted Assessment

      • Algorithmic bias in grading and feedback

      • Transparency and explainability requirements

      • Student trust and perception of fairness

      • Data privacy and retention considerations

      • Developing ethical assessment guidelines

      Session 8: Capstone – Building an AI-Supported Assessment Plan

      • Select a course or unit for implementation

      • Design assessment types and grading workflows

      • Define human checkpoints and override rules

      • Create student-facing guidance on AI use

      • Peer review and refinement of assessment plans

  • Why take this course?

    This 24-hour course explores how artificial intelligence tools are reshaping the teaching and learning of arts and design disciplines. Educators will examine AI-powered platforms for visual creation, generative design, animation, multimedia storytelling, and creative exploration. The course emphasizes how AI can support ideation, skill development, critique, and iterative design processes while preserving artistic intent, authorship, and ethical practice. Participants will gain hands-on experience designing AI-enhanced creative learning activities aligned with curriculum goals in art, design, media, and creative technologies.

    Creativity is evolving—and so is how it’s taught. This hands-on course shows educators how AI tools can spark imagination, accelerate ideation, and transform arts and design education. Explore generative visuals, interactive media, and creative AI platforms that empower students to experiment, iterate, and express ideas in new ways—while keeping creativity, ethics, and human judgment at the centre.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: AI and Creativity in Arts and Design Education

      • How AI is influencing contemporary art and design practices

      • Generative vs. assistive AI in creative workflows

      • Opportunities and limitations of AI in arts education

      • Examples from visual arts, graphic design, animation, and media

      • Discussion: redefining creativity, authorship, and originality

      Session 2: AI Tools for Visual Ideation and Concept Development

      • Using AI for brainstorming, mood boards, and concept sketches

      • Text-to-image and image-to-image generation workflows

      • Prompting techniques for creative exploration

      • Tools: image generators, style transfer, concept refinement

      • Hands-on: generate and refine visual concepts using AI

    • Session 3: Generative Design and Iterative Creative Processes

      • AI-assisted design iteration and variation generation

      • Exploring constraints, parameters, and creative control

      • Applying AI in typography, layout, and product concepts

      • Balancing automation with designer intent

      • Workshop: iterate a design concept using AI variations

      Session 4: Teaching Visual Communication with AI

      • Using AI to support composition, color theory, and visual hierarchy

      • Critiquing AI-generated visuals for clarity and effectiveness

      • AI as a critique and feedback assistant

      • Integrating AI into studio-based learning environments

      • Activity: analyze and refine AI-generated visuals

    • Session 5:  AI for Multimedia, Animation, and Interactive Media

      • AI tools for animation, video, and motion design

      • Text-to-video, avatar-based presentation, and audio generation

      • Storyboarding and narrative design with AI support

      • Accessibility considerations in multimedia creation

      • Hands-on: create a short AI-assisted multimedia artifact

      Session 6: Supporting Skill Development and Creative Practice

      • Using AI as a learning companion for technique development

      • Scaffolded practice and guided experimentation

      • Feedback loops for creative growth

      • Avoiding over-reliance and homogenization of style

      • Designing practice activities with AI support

    • Session 7: Ethics, Authorship, and Responsible AI in Creative Education

      • Copyright, attribution, and training data concerns

      • Student ownership of AI-assisted creative work

      • Bias, representation, and cultural sensitivity in generated content

      • Developing classroom guidelines for responsible AI use

      • Case studies and ethical decision-making scenarios

      Session 8: Capstone – Designing an AI-Enhanced Arts or Design Learning Experience

      • Select an arts or design learning objective

      • Design an AI-supported creative assignment or studio activity

      • Define assessment criteria and originality expectations

      • Plan student guidance and reflective components

      • Peer review and presentation of course designs

  • Why take this course?

    This course explores how AI tools can enhance the teaching of STEM disciplines through simulation, modeling, intelligent tutoring, and data-driven feedback while preserving rigorous scientific reasoning.

    Bring complex STEM concepts to life using AI-powered simulations, tutors, and visualizations—without sacrificing critical thinking or hands-on learning.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1:  AI in Humanities and Social Sciences

      • Opportunities and risks of AI in interpretive disciplines

      • AI as assistant vs authority

      • Supporting inquiry and discussion

      • Discipline-specific considerations

      • Examples across humanities fields

      Session 2: AI for Reading and Writing

      • Summarization and comprehension support

      • Drafting and revision tools

      • Teaching critical reading with AI

      • Avoiding over-reliance

      • Analyzing AI-generated text

    • Session 3: History and Geography with AI

      • Timelines and spatial analysis

      • Contextualizing historical narratives

      • Bias in generated content

      • Source evaluation

      • AI-supported historical inquiry

      Session 4: Social Science Data Analysis

      • Qualitative and quantitative analysis

      • Visualizing social data

      • Research question formulation

      • Ethical data use

      • Designing social analysis tasks

    • Session 5: Argumentation and Debate

      • Exploring multiple perspectives

      • Supporting debate preparation

      • Evaluating claims and evidence

      • Reasoning vs generation

      • Structured debate design

      Session 6: Literature and Cultural Studies

      • Literary analysis with AI

      • Thematic exploration

      • Cultural representation issues

      • Responsible creative writing support

      • AI-supported close reading

    • Session 7: Ethics and Academic Integrity

      • Integrity in writing-heavy disciplines

      • Assessment design for authenticity

      • Transparency with students

      • Policy development

      • Ethical case discussions

      Session 8: Capstone – AI-Enhanced Humanities Activity

      • Select learning objective

      • Design AI-supported inquiry

      • Define evaluation criteria

      • Create student guidance

      • Peer review and reflection

  • Why take this course?

    This 24-hour course examines emerging technologies and trends shaping the next generation of education, with a focus on AI-enabled learning systems, immersive environments, and metaverse-style educational experiences. Participants will explore how augmented reality (AR), virtual reality (VR), extended reality (XR), simulation platforms, and intelligent digital agents can support experiential learning, skill development, and personalized instruction across K–12, higher education, and workforce training. The course emphasizes instructional design for immersive learning, practical adoption strategies, and the critical evaluation of impact, accessibility, privacy, student well-being, and equity. Educators will leave with a concrete implementation plan for a future-ready learning experience that balances innovation with safety, ethics, and clear learning outcomes.

    The classroom is expanding beyond screens—and the future is arriving fast. In this hands-on course, educators explore how AI, immersive technologies, and metaverse-style learning environments can transform teaching and training through simulations, interactive scenarios, virtual labs, and intelligent learning companions. Learn how to design engaging experiential learning experiences, support collaboration in virtual spaces, and evaluate the real-world value of AR/VR tools—all while addressing accessibility, privacy, safety, and student well-being. If you’re ready to prepare learners for a rapidly evolving digital world, this course gives you the frameworks and design strategies to lead that transformation with confidence.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: AI in Humanities and Social Sciences

      • Opportunities and risks of AI in interpretive disciplines

      • AI as assistant vs authority

      • Supporting inquiry and discussion

      • Discipline-specific considerations

      • Examples across humanities fields

      Session 2: AI for Reading and Writing

      • Summarization and comprehension support

      • Drafting and revision tools

      • Teaching critical reading with AI

      • Avoiding over-reliance

      • Analyzing AI-generated text

    • Session 3: History and Geography with AI

      • Timelines and spatial analysis

      • Contextualizing historical narratives

      • Bias in generated content

      • Source evaluation

      • AI-supported historical inquiry

      Session 4: Social Science Data Analysis

      • Qualitative and quantitative analysis

      • Visualizing social data

      • Research question formulation

      • Ethical data use

      • Designing social analysis tasks

    • Session 5: Argumentation and Debate

      • Exploring multiple perspectives

      • Supporting debate preparation

      • Evaluating claims and evidence

      • Reasoning vs generation

      • Structured debate design

      Session 6: Literature and Cultural Studies

      • Literary analysis with AI

      • Thematic exploration

      • Cultural representation issues

      • Responsible creative writing support

      • AI-supported close reading

    • Session 7: Ethics and Academic Integrity

      • Integrity in writing-heavy disciplines

      • Assessment design for authenticity

      • Transparency with students

      • Policy development

      • Ethical case discussions

      Session 8: Capstone – AI-Enhanced Humanities Activity

      • Select learning objective

      • Design AI-supported inquiry

      • Define evaluation criteria

      • Create student guidance

      • Peer review and reflection

  • Why take this course?

    This 24-hour course equips students with the knowledge and tools to use AI in the fashion design process, from concept to digital garment prototyping. It focuses on tools such as CLO3D, Runway ML, Fashwell, and Heuritech to automate pattern generation, predict style trends, and simulate garments in 3D environments. Students will gain hands-on experience developing AI-enhanced fashion concepts ready for virtual display or physical realization.

    From virtual models to predictive styles, fashion is going digital. Learn how AI is transforming garment design, fitting, and forecasting with the power of CLO3D, Runway ML, and Heuritech. Design, simulate, and showcase your fashion ideas in 3D with trend-aware intelligence.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: The Future Landscape of Education and Learning Innovation

      • Key forces reshaping education: automation, personalization, and new credential models

      • AI-enabled learning ecosystems: from platforms to intelligent assistants

      • Shifts in educator roles: facilitator, designer, coach, and evaluator

      • Future-ready competencies: digital literacy, adaptability, problem-solving, collaboration

      • Examples of innovation in K–12, postsecondary, and corporate training

      Session 2: Foundations of Immersive Learning (AR, VR, XR)

      • Understanding AR vs. VR vs. XR and what each is best for

      • Learning theory: experiential learning, situated cognition, and simulation-based mastery

      • Immersive design principles: presence, interaction, feedback, and pacing

      • Hardware and platform overview (headsets, mobile AR, web-based XR)

      • Opportunities and constraints: cost, space, usability, and motion comfort

    • Session 3: AI + Metaverse Learning Environments

      • What “metaverse learning” means in practical education terms

      • AI-driven avatars, tutors, and coaching agents in virtual spaces

      • Persistent environments: digital campuses, labs, and skills studios

      • Collaboration and social learning: group tasks, role-play, and peer feedback

      • Examples of use cases: orientation, lab learning, communication skills, and safety training

      Session 4: Designing Immersive Learning Experiences

      • Instructional design for immersive spaces: objectives, tasks, and assessment alignment

      • Storytelling and scenario-based learning design for engagement

      • Designing for feedback: AI-enabled coaching and performance cues

      • Accessibility and inclusion in immersive learning (captions, alternatives, scaffolds)

      • Workshop: draft an immersive lesson flow and learner journey map

    • Session 5: Simulation-Based Learning and Virtual Labs

      • Why simulations work: risk-free practice and repeatable mastery learning

      • Virtual labs for STEM and clinical practice (procedures, safety, experimentation)

      • Role-play and decision simulations for humanities, business, and public sector training

      • Embedding AI feedback: adaptive hints, performance scoring, and reflective prompts

      • Hands-on: outline a simulation with checkpoints and feedback loops

      Session 6: Blended Models – Connecting Physical and Immersive Learning

      • Hybrid learning approaches that combine classroom + immersive sessions

      • Using AI analytics to monitor engagement and learning progress across modalities

      • Facilitation strategies: guiding learners inside and outside immersive environments

      • Classroom integration: time, scheduling, setup, and learner onboarding

      • Evaluation strategies for blended experiences (learning outcomes + practical skills)

    • Session 7: Ethics, Safety, Privacy, and Well-being in Immersive Education

      • Privacy and identity in virtual spaces: voice, movement, biometric signals, and behavior data

      • Student well-being: motion comfort, cognitive load, and screen/immersion fatigue

      • Safety and moderation: harassment, content boundaries, and classroom management

      • Equity of access: cost, device availability, and alternatives for learners

      • Policy considerations: consent, recording, data retention, and acceptable use guidelines

      Session 8: Capstone – Designing a Future-Ready Immersive Learning Plan

      • Select a learning context and define measurable learning outcomes

      • Choose an immersive approach (AR/VR/XR/simulation) and supporting AI components

      • Design learner activities, assessment strategy, and feedback mechanisms

      • Develop onboarding, accessibility supports, and risk mitigations

      • Peer review and refinement: present the plan and receive structured feedback

  • Why take this course?

    This course examines the ethical, legal, and inclusive dimensions of using AI in education. Educators will explore bias, fairness, accessibility, privacy, academic integrity, and governance to ensure responsible and equitable adoption of AI tools in learning environments.

    Adopt AI in education responsibly. This course equips educators with the knowledge and frameworks needed to ensure fairness, inclusion, transparency, and trust when integrating AI tools into teaching and learning.

    What will you receive upon completion?

    Certificate of Completion, and Badge to use across Professional Platforms

    What will you learn each week?

    • Session 1: Ethics and Responsibility in Educational AI

      • Why ethics matter in AI-supported education

      • Human judgment vs automated decision-making

      • Examples of ethical failures in educational technology

      • Power, agency, and accountability in AI use

      • Educator responsibilities and oversight

      Session 2: Bias, Fairness, and Equity

      • Sources of bias in educational AI systems

      • Impact on assessment, placement, and support

      • Equity considerations for marginalized learners

      • Auditing AI outputs for fairness

      • Inclusive design strategies

    • Session 3: Accessibility and Universal Design for Learning

      • AI tools for accessibility and accommodation

      • Supporting neurodiverse learners

      • Language, translation, and assistive technologies

      • Universal Design for Learning principles

      • Inclusive classroom implementation

      Session 4: Student Data, Privacy, and Consent

      • Student data collection and usage

      • Privacy laws and institutional responsibilities

      • Vendor risk and data governance

      • Consent, transparency, and communication

      • Developing privacy-aware practices

    • Session 5: Academic Integrity and Responsible Use

      • Defining acceptable and unacceptable AI use

      • Preventing misuse and over-reliance

      • Assessment redesign for authenticity

      • Communicating expectations to students

      • Policy development and enforcement

      Session 6: Institutional Policies and Governance

      • Developing AI governance frameworks

      • Aligning AI use with institutional values

      • Professional standards and codes of conduct

      • Staff training and capacity building

      • Cross-department collaboration

      • Developing AI governance frameworks

      • Aligning AI use with institutional values

      • Professional standards and codes of conduct

      • Staff training and capacity building

      • Cross-department collaboration

    • Session 7: Evaluating AI Tools and Vendors

      • Criteria for selecting educational AI tools

      • Evidence of effectiveness and impact

      • Risk assessment and mitigation

      • Transparency and explainability requirements

      • Decision-making frameworks

      Session 8: Capstone – Ethical AI Implementation Plan

      • Select an educational context

      • Identify ethical and inclusion risks

      • Design safeguards and guidelines

      • Create student-facing policies

      • Peer review and refinement

Course Registration

Ready to take the next step? Browse our available and upcoming courses below and secure your spot today. Don’t miss out—enroll now to unlock new opportunities!

COURSE NAME
COURSE DURATION
COURSE FEE
REGISTRATION

Foundations of Agentic AI: Building Intelligent, Goal-Directed Systems

Month Year - Month Year

Session / Time

$xxxx

Registration Opens Month Year

Prompt Engineering for Beginners: Mastering the Language of AI

Month Year - Month Year

Session / Time

$xxxx

Registration Opens Month Year

Introduction to Generative AI

Month Year - Month Year

Session / Time

$xxxx

Registration Opens Month Year

Introduction to AI Tools: Exploring What’s Possible

Month Year - Month Year

Session / Time

$xxxx

Registration Opens Month Year

Experential Learning

​By completing a Conifer 2C course, students gain exclusive access to upcoming charrettes, research projects, and pilot placement opportunities - bridging learning with real-world innovation.

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Charrette

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Research Project

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Pilot Placement

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