Curriculum

Introduction to Experience Design and AI

Jan 21 · 1 min read

This introductory class will go over the course syllabus, course expectations, and introduce to students key concepts in designing AI systems. We will cover case studies of AI in learning systems, such as, the use of social agents to scaffold learning, and chatbots for scaffolding problem-solving. We will discuss students’ early understandings of applications and implications of using AI systems in learning environments. Through seminal works, students will get introduced to basic concepts in AI for learning, such as, personalization, social interaction, adaptive systems, emotion recognition, contextual feedback, and data analytics.

In-class Activity

Speculate AI Futures: Consider your current learning environment. How do you gain new knowledge? How do you pick new skills? How do you interact with your peers and technology while learning? Now imagine a futuristic learning environment. Consider using emerging technologies to design an ideal learning environment. Create a scenario, in the form of a short story, of a learner living in this speculative world.

Now you take the role of a given stakeholder in this future world. What would your role as this stakeholder be, in this future learning environment?

Check out the pre-class setup instructions here

No readings prior to the introductory class.

Slides

Designing Human-centered AI - I

Jan 28 · 1 min read

We often hear of the human and AI dichotomy, but can they flourish in tandem? Students are introduced to key concepts in human-centered AI (HCAI), and we go over the two-dimensional HCAI framework (Shneiderman, 2022). We discuss the HCAI concepts: user-centered design, transparency, explainability, inclusivity, equity, safety, and regulation. We especially dive deeper into Explainable AI (XAI), and explainability implications for AI systems in learning.

Week 2 Readings

Supplemental Reading (optional)

Reading Reflection: https://forms.gle/ERvUbYe4GREuaSE39 (due on 01/27, 5:00 PM EST)

Assignment 1

Code activity (Jupyter Notebook) - Introduction to using AI APIs. Through a guided code notebook, students will be able to use AI APIs to design a learning chatbot (e.g. a history bot that guesses a historical figure or event). This activity will also get students up to speed with Python programming.

Assignment Instructions

Assignment Submission (due on 01/27, 5:00 PM EST)

Designing Human-centered AI - II

Feb 4 · 1 min read

Students will examine current day AI systems (such as text-generation, vision tools, and image generation tools) from the lens of the HAI, Explainability, and Design Justice frameworks. How do contemporary tools such as ChatGPT hold up? What are they urgently missing?

Guest Lecture: Dr. Sooyeon Jeong, Professor of Computer Science, Purdue University

Week 3 Readings

Supplemental Reading (optional)

Ali, S. (2024). Guidelines for Inclusive Creative Human-AI Interaction

Reading Reflection: https://forms.gle/KbzNnbGc4bGUfCYK8 (due on 02/03, 5:00 PM EST)

Assignment 2

Ideate on final project ideas, and prepare two minute pitches for your final projects. Remember that these are preliminary ideas of what you’re interested in, and you don’t have to commit to them for your final projects.

Make a copy of the slide template in the assignment folder, and fill in the details of your project ideas. Be sure to rename the slides to your name.
Slide 1: Discuss your target learner(s), interaction modality, and learning objectives.
Slide 2: Write a research question that you are interested in studying with this learning experience.
You will be presenting your 2-minute pitches on 02/11. This will be followed by group formations based on interest synergy.

Assignment 2: https://drive.google.com/drive/folders/1lnL8kJfupv4-aQXAtY42xghxrmac5VQ7?usp=sharing (due on 02/03, 5:00 PM EST)

Class Slides Intro to AI Slides

Project pitches; Introduction to AI in Learning

Feb 11 · 1 min read

Students will present their 2-minute project pitches (individually) that you submitted in week 3. You can modify your slides if needed. Use individual pitches to form project teams. We will then continue the AI overview lecture from week 2.

Week 4 Readings

Write or draw a brief scenario explaining how you imagine AI will transform teaching and learning. Scenarios may be inspired from the readings, or your own speculations.

Supplemental Reading (optional):

1.Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education.

Reading Reflection: https://forms.gle/4Ts1yqvBxSLtdj1Z8 (due on 02/10, 5:00 PM EST)

Class Slides

Project Pitches

Revise your project pitches for next week’s class. This is to gauge early interests. We will use these project pitchs to form groups for your final projects. Make sure your slides are in the group drive.

AI in Learning - II

Feb 25 · 0 min read

No class due to Monday schedules

Reading Reflection: (due on 02/24, 5:00 PM EST)

AI in Learning

Feb 25 · 2 min read

In a flipped classroom style, we discuss historic case studies of instructional technology, and students’ lived experiences of their long-term impact. What changed with calculators in the classroom? With computers? With AI? We will discuss scaffolding using AI chatbots, and early findings from research pieces discussing personalized scaffolding. Students will explore key concepts in personalized ML algorithms, AI for assistive learning, adaptive systems, AI tutors, social AI companions, educational data mining and learning analytics. We will dive deeper into AI for fostering learning behaviors, such as, curiosity, growth mindset and creativity.

In-class Activity

Students will interact with a co-writing tool scaffolding different writing styles and vocabulary development in storytelling. Students will reflect on ownership in a co-created human-AI artifact.

Week 5 Readings

Self-study: Technology has transformed educational spaces and pedagogies in the past. Choose a technology (i.e., radio, television, computers, or the internet) and research (use any sources: internet, magazines, movies, etc.) its reported impact on classrooms and teacher pedagogy.
A History of Instructional Design and Technology: Part I: A History of Instructional Media, Robert A, Reiser, 2001
Technology and School Reform: A View from Both Sides of the Tracks, Mark Warschauer, 2000

Supplemental Reading (optional):

Reading Reflection: https://forms.gle/i9m6SCPz3JDezm6o8 (due on 02/24, 5:00 PM EST)

Project Update 1

Meet with your teams to decide on your problem statement. Teams will write the first draft of their problem statement, including:

Outline their target learning audience
An identified learning need (with references, or personal experiences)
A rationale for how you imagine AI may assist this learning need
Footnote: Contribution of each group member (this footnote need to be a short line after each group submission)

Project Update Submission [GROUP]: Project Update 1 (due on 02/24, 5:00 PM EST) –>

Class Slides Creative AI Slides

AI prototyping [REMOTE]

Mar 4 · 0 min read

Project Update 3

Students will use the needfinding techniques discussed in class to investigate user/learner needs in their chosen topic. Update your github pages to add findings from your needfinding activities.

Students will write their design plan for their final project. This will include rapid paper prototypes, sketches, blueprints, or design plans.

Nothing is due this week - see next week for submission info.

Building Blocks of AI - Neural Networks

Mar 11 · 1 min read

How does the AI work? Students learn about what different terms such as big data, machine learning, deeplearning, reinforcement and artificial intelligence mean, and how they relate with one another. We then explore datasets, such as the imagenet and LAION-5B to gain a sense of datasets used for training AI. Through an interactive simulation, students learn about how the feedforward and backpropagation processes in Neural Networks work.We dive deeper into Recurrent Neural Networks and their applications in Natural Language Processing.

In class activity

Students play the contours to classification game, where students act as nodes in a neural network, and participate in a feedforward and backpropagation process to simulate an Artificial Neural Network. Students also use transfer learning in Teachable Machines to train their own classification algorithm. Students are then asked to reflect how they could use a tool such as Teachable Machines in their teaching/learning process.

Week 8 Readings

Reading Reflection: https://forms.gle/vnL2HF3TD1q65jTG7 (due on 03/10, 5:00 PM EST)

Project Update 3

Students will work on developing early working prototypes of their projects. Submit the designs of your early prototypes. Project Update Submission: Rapid Prototypes and Needfinding reports (due on 03/10, 5:00 PM EST)

Create with AI Slides Class Slides

Creative AI & Project Check-in 1

Mar 18 · 0 min read

Can AI help humans create? Will AI impact human creativity? How do we design AI to scaffold creative expression? This class will focus on tools and algorithms designed to foster creative expression in humans and human-AI co-creation.

Students will present the current status of their project, along with evaluation plans. Instructor and peer feedback will enable students to strengthen their projects.

No pre-class readings

Spring Break

Mar 25 · 0 min read

No class for Spring Break.

How Large Language Models (LLMs) work & Evaluation

Apr 1 · 1 min read

Using simulation activities, students learn about Attention Models, Transformers, Large Language models, how ChatGPT works (generative pre-training, supervised fine-tuning, reinforcement learning from human feedback). They participate in a game simulating the experience of building a dataset of a character, and training themselves as a model, and using the model to make predictions. Using visualizations and metaphors, students learn about Convolutional Neural Networks, Diffusion models and CLIP. They participate in a game simulating generating drawings based on training data from hypothetical doodle datasets.

Learning evaluations Usability evaluations

Week 11 Readings / explorations

Task 1 (Option 1): Watch this video to explore how Stable Diffusion models work: https://www.youtube.com/watch?v=hb-KT66rCT8 OR
Task 1 (Option 2): Read this blog to explore the inner workings of Diffusion Models: https://poloclub.github.io/diffusion-explainer/
Task 2: Explpre this simulation of the Stable Diffusion process to understand how text-to-image generators work: https://colab.research.google.com/github/touretzkyds/DiffusionDemo/blob/master/demo.ipynb

Assignment 3

Nothing to submit! But come to class having explored these resources.

Building with AI - Makeathon!

Apr 8 · 1 min read

Students will map features for their projects and share their prototyping progress with peers. They will continue developing their prototypes from last week, refining their designs and functionality. By the end of the session, students will build client-ready apps that include an AI demo component.

Feature Mapping Sheet

Assignment 4

Update your course websites with your prototype progress and your feature maps. View a peer team’s project that is assigned to you, and think of yourself as potential users of the product to provide some early feedback. Provide them with the following feedback:

Are the user/learner/teacher needs well communicated?
Are the design features relating well to the needs that are described? Explain.
What do you find the most valuable about this design product?
Are there ethical implications that you are concerned about?

Peer Feedback Form

No pre-class readings

Societal and Ethical Implications of AI - I

Apr 15 · 1 min read

Students will dive deeper into current fairness, accountability and transparency concerns in AI. Students will learn about the dangers of large language models, particularly in learning. Students will dive deeper into bias, data privacy and security, equity, learner autonomy, plagiarism, emotional well-being, AI hype, accountability, implications on the future of work, misinformation, environmental impact, and power dynamics in AI ecosystems. We will discuss the uncanny valley, and digital presence and persuasive powers of generative AI personas. We will also consider when not to use AI.

In-class Activity

Students will interact with the Bias Explorer to understand bias in visual AI systems, and how societal stereotypes are represented in AI-generated media.

Week 13 Readings

Reading Reflection: (due on 04/14, 5:00 PM EST)

Societal and Ethical Implications of AI - II + Evaluations

Apr 22 · 2 min read

Instructor will share some evaluation good practices and examples.

Students will present the current state of their project prototype, and evaluation plan or early data, and receive peer and instructor feedback.

Students will participate in a debate-style AI Audit game, where they will set up hypothetical AI learning businesses, and challenge their competitors with the lens of societal and ethical implications. They will utilize the Ethical implications handout to form an informed critique of, and design mitigation steps for, AI applications in learning. Through this debate game, students will research, and reflect on the ethical and societal implications of everyday AI systems.

Week 14 Readings

Reading Reflection: https://forms.gle/Z1yKjZFQ5zqdyhgt9 (due on 04/21, 5:00 PM EST)

Project Update 6

Update your project websites to include an ethical implications, and a societal implications section (1-2 paragraphs). Discuss how your approach in your project may mitigate AI harms. Make sure to add this section on your project site.
Add slides in your project update with (1) Your prototype demo. (2) Your evaluation plan, or evaluation results if you got a chance to test it early. Think of what you will want to measure to evaluate the efficacy of your tool. You will present these in the next class. 5 minutes presentation time. Project Update Submission: https://drive.google.com/drive/folders/1qNgxq8qCvgFw741hppSi9uI6yf7zq6Pl?usp=drive_link (due on 04/21, 5:00 PM EST)

Class Slides

Project check-in

Apr 29 · 0 min read

Instructor will discuss some advances in Creative AI applications and algorithms.

Students will use the rest of their time working on their projects, and working with the instructor to remove any roadblocks.

No pre-class readings.

List of Resources Discussed in Class:

Tools:

Toolkits:

Notebooks:

Cool Datasets:

Article:

Final Project Presentations

May 6 · 2 min read

Students will share their final projects.

Project Presentations:

Add your presentation slides in the final project folder.
Time: 8 minutes + 2 minutes for questions
1. Team introductions
2. Hook: Interesting one liner about your project
3. Target audience
4. Problem statement / user need
5. Prototype demo
6. Design process / lessons
7. Evaluation plan
8. Findings (if any)
9. Next steps

Project Report reuqirements:

Add your project reports in the final project folder. Two options:

You may create a PDF document with a paper style formating, OR
you may choose to use your github page website. If you use your website, add a googe doc in the submission folder with your project website link, and please format your website well (fix the bold formating, add bullets, link the appropriate links, etc.). Here are some formating tips for markdown pages.

Final project report required sections:

Abstract: A one paragraph summary of your project.
Introduction: A 1 pager describing the target audience, the need you identified, the research questions or problem statement, the prorotype design, and findings in brief.
System design: Add your design features (from the feature mapping), your early prototypes, and your design process here.
Prototype: Add a description of your final project prorotype here. Call out all the features and describe what works.
Evaluation: A 1 pager about what you wish to evaluate, what data needs to be collected to evaluate this, and how you will conduct this evaluation.
Findings: If you conducted any evaluation.
Ethical implications: What you foresee as ethical and societal implications of your work, and how you plan to mitigate them.
Future work or next steps

Project reports and project presentations are due on 05/05. No extensions.

Name your file as LastNames_FinalProject (e.g. Lastname1_Lastname2_FinalProject)

Final Project Folders