Curriculum
Introduction to Experience Design and AI
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.
Designing Human-centered AI - I
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
- Vaithilingam, P., Arawjo, I., & Glassman, E. L. (2024, July). Imagining a future of designing with ai: Dynamic grounding, constructive negotiation, and sustainable motivation. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (pp. 289-300).
- Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495-504.
Supplemental Reading (optional)
- Riedl, M. O. (2019). Human‐centered artificial intelligence and machine learning. Human behavior and emerging technologies, 1(1), 33-36.
- Ehsan, U., Liao, Q. V., Muller, M., Riedl, M. O., & Weisz, J. D. (2021, May). Expanding explainability: Towards social transparency in ai systems. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-19).
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 Submission (due on 01/27, 5:00 PM EST)
Designing Human-centered AI - II
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
- Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., … & Horvitz, E. (2019, May). Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on human factors in computing systems
- Costanza-Chock, S. (2018). Design justice, AI, and escape from the matrix of domination. Journal of Design and Science
Supplemental Reading (optional)
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)
Project pitches; Introduction to AI in Learning
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.
- ChatGPT is going to change education, not destroy it: https://www.technologyreview.com/2023/04/06/1071059/chatgpt-change-not-destroy-education-openai/
- Chen, C. (2023). AI will transform teaching and learning. Let’s get it right. Stanford HAI.
- Gordon, G., Spaulding, S., Westlund, J. K., Lee, J. J., Plummer, L., Martinez, M., … & Breazeal, C. (2016, March). Affective personalization of a social robot tutor for children’s second language skills. In Proceedings of the AAAI conference on artificial intelligence
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)
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
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):
- Vazhayil, A., Shetty, R., Bhavani, R. R., & Akshay, N. (2019, December). Focusing on teacher education to introduce AI in schools: Perspectives and illustrative findings. In 2019 IEEE tenth international conference on Technology for Education (T4E) (pp. 71-77). IEEE.
- Ali, S., Devasia, N., Park, H. W., & Breazeal, C. (2021). Social robots as creativity eliciting agents. Frontiers in Robotics and AI, 8, 673730.
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) –>
AI prototyping [REMOTE]
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
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
- Lee, I., & Ali, S. (2021, May). The contour to classification game. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 15583-15590).
- Heikkilä, M., Arnett, S. (2024, Dec) This is where the data to build AI comes from. MIT Tech review.
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)
Creative AI & Project Check-in 1
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
How Large Language Models (LLMs) work & Evaluation
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!
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.
Assignment 4
Students interact with 2 other peers’ mini interactive learning experience, and comment with their learning experience and feedback. Students review feedback on their own interactive learning experiences.
No pre-class readings
Societal and Ethical Implications of AI - I
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
- Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?🦜. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623).
- Turkle, S. (2024). Who Do We Become When We Talk to Machines?.
Societal and Ethical Implications of AI - II
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.
In-class Activity
Ethical Matrix - students will develop an ethical matrix for their own final projects outlining different direct and indirect stakeholders that will be impacted by their systems, their values, and how the system design aligns (or could align) with their values.
Week 14 Readings
- Ehsan, U., Singh, R., Metcalf, J., & Riedl, M. (2022, June). The algorithmic imprint. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1305-1317).
- Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., … & Gebru, T. (2023, August). AI Art and its Impact on Artists. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (pp. 363-374).
Reading Reflection: (due on 04/21, 5:00 PM EST)
Project Update 6
Students will write an ethical implications, and a societal implications section in their report, discussing how their approach may mitigate AI harms. Project Update Submission: (due on 04/21, 5:00 PM EST)
Project check-in
Students will present the current status of their project, along with evaluation plans. Instructor and peer feedback will enable students to strengthen their projects. Students will utilize class time to work on their projects, and meet with the instructor to present progress and address roadblocks.
No pre-class readings.