KAIST Fall 2023

CS492G: Human-AI Interaction

Humans and AI are more closely interacting than ever before, in all areas of our work, education, and life. As more intelligent machines are entering our lives, their accuracy and performance are not the only important factor that matters. As designers of such technology, we have to carefully consider the user experience of AI in order for AI to be of practical value. In this course, we will explore various dimensions of human-AI interaction, including ethics, explainability, design process involving AI, visualization, human-AI collaboration, recommender systems, and a few notable application areas.

A side goal of this course is to encourage all of us to bridge the gap between the two fields of HCI and AI. As a step toward this vision, we want to encourage students with HCI and AI background to mingle, interact, discuss, and collaborate through this course. We expect most students taking this course to have background knowledge in either HCI or AI through at least intro-level coursework. If you’re unsure if you meet this criterion, please contact the course staff immediately. Having background in both is great, although not required.

This is a highly interactive class: You’ll be expected to actively participate in activities, projects, assignments, and discussions. There will be no lectures or exams. Major course activities include:

  • Assignments: You'll design, implement, and analyze a few human-AI interaction scenarios.
  • Design Project: In a semester-long team project, you'll investigate a topic in human-AI interaction of your interest. If you have an ongoing research project that might benefit from having a human-AI interaction component, connecting to your research is encouraged.
  • In-class Activities: Each class will feature activities that will help you experience and practice the core concepts introduced in the course.

Course Staff

Instructor: Prof. Juho Kim
    Office Hours: by appointment

Tae Soo Kim
Bekzat Tilekbay

    Office Hours: by appointment

Staff Mailing List: human-ai@kixlab.org

Time & Location

When: 2:30-3:45pm Mon/Wed
Where: Zoom live sessions (As active participation in in-class activity, discussion, and presentation is expected, attending live sessions is required.)


Course Website: https://hai.kixlab.org/
Submission & Grading: KLMS
Discussion and Q&A: Classum



Week Date Topic Reading Due
1 8/28 Introduction & Course Overview
1 8/30 A Quick Tour of Human-AI Interaction (1) Licklider, Joseph CR. "Man-computer symbiosis." IRE transactions on human factors in electronics 1 (1960): 4-11.
(2) Shyam Sankar. The Rise of Human Computer Cooperation. TED Talk Video, 2012 (12 mins).
2 9/4 Primer on AI (Part 1): Application Domains & AI/ML 101 (1) Lubars, Brian, and Chenhao Tan. "Ask not what AI can do, but what AI should do: Towards a framework of task delegability." In Advances in Neural Information Processing Systems, pp. 57-67. 2019.
(2) Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). pp. 6000–6010. 2017.
2 9/6 Primer on AI (Part 2): Deep Learning, Generative AI, & Large Language Models (LLMs) (1) Xu, Anbang, Zhe Liu, Yufan Guo, Vibha Sinha, and Rama Akkiraju. "A new chatbot for customer service on social media." In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 3506-3510. 2017.
(2) Nityesh Agarwal. "Getting started with reading Deep Learning Research papers: The Why and the How", a blog post at Towards Data Science (2018).
3 9/11 Primer on HCI (Part 1): HCI 101, Usability Testing, & Heuristic Evaluation (1) Amershi, Saleema, et al. "Guidelines for human-AI interaction." Proceedings of the 2019 chi conference on human factors in computing systems. 2019.
(2) Google PAIR. People + AI Guidebook. Published May 8, 2019.
3 9/13 Primer on HCI (Part 2): Prompting as an Interface & Human-AI Interaction Design (1) Shneiderman, B., "Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy." International Journal of Human-Computer Interaction 36, 6, 495-504. 2020.
(2) Henriette Cramer and Juho Kim. "Confronting the tensions where UX meets AI." interactions 26.6 (2019): 69-71.
4 9/18 Ethics and FAccT of AI (Part 1) (1) Davidson, Thomas, Debasmita Bhattacharya, and Ingmar Weber. "Racial bias in hate speech and abusive language detection datasets." arXiv preprint arXiv:1905.12516 (2019).
(2) Bolukbasi, Tolga, et al. "Man is to computer programmer as woman is to homemaker? debiasing word embeddings." Advances in Neural Information Processing Systems. 2016.
4 9/20 Tutorial: LLM & Prompting (Basics, APIs, & Libraries)
5 9/25 Ethics and FAccT of AI (Part 2) (1) Timnit Gebru. "Computer vision in practice: who is benefiting and who is being harmed?" (video, 51 mins) Slides
(2) Kate Crawford and Trevor Paglen, “Excavating AI: The Politics of Training Sets for Machine Learning" (September 19, 2019)
5 9/27 Historical Perspectives on Human-AI Interaction (1) Horvitz, Eric. "Principles of mixed-initiative user interfaces." In Proceedings of the SIGCHI conference on Human Factors in Computing Systems, pp. 159-166. 1999.
(2) Ben Schneiderman and Pattie Maes. "Direct Manipulation vs. Interface Agents". Interactions 1997.
Assignment #1
Project 0: Team Formation
6 10/2 No class (Chuseok Holiday)
6 10/4 Project proposal feedback
7 10/9 No class (Hangul Proclamation Day)
7 10/11 Project Pitches Project 1: Pitches
8 10/16 No class (Midterms week)
8 10/18 No class (Midterms week)
9 10/23 Metrics to Measure Human-AI Performance (1) Gagan Bansal, Besmira Nushi, Ece Kamar, et al. "Beyond accuracy: The role of mental models in human-AI team performance." In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 2019.
(2) Matthew Kay, Shwetak N. Patel, and Julie A. Kientz. "How good is 85%? A survey tool to connect classifier evaluation to acceptability of accuracy." In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015.
9 10/25 Interpretable and Explainable AI (1) Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. ""Why should I trust you?" Explaining the predictions of any classifier." In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016.
(2) Zachary C. Lipton. "The mythos of model interpretability." 2018.
(3) Daniel S. Weld, and Gagan Bansal. "The challenge of crafting intelligible intelligence." Communications of the ACM. 2019.
(4) Alison Smith-Renner, Ron Fan, Melissa Birchfield, et al. "No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML." In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020.
10 10/30 AI and Crowds (1) Jennifer Wortman Vaughan. 2018. Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research. Journal of Machine Learning Research 18, 193: 1–46.
*** Instructor note: the sections 3 and 5 could be skimmed.
(2) Aniket Kittur, Jeffrey V. Nickerson, Michael Bernstein, et al. The Future of Crowd Work. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work (CSCW '13), 1301–1318. 2018.
10 11/1 Tutorial: Web Programming Assignment #2
11 11/6 AI Application Design Process (1) Mitchell, Margaret, et al. "Model cards for model reporting." Proceedings of the conference on fairness, accountability, and transparency. 2019.
(2) Sculley, David, et al. "Hidden technical debt in machine learning systems." Advances in neural information processing systems. 2015.
11 11/8 Recommender Systems (1) Olteanu, Alexandra, Fernando Diaz, and Gabriella Kazai. "When Are Search Completion Suggestions Problematic?." Proceedings of the ACM on Human-Computer Interaction 4.CSCW2 (2020): 1-25.
(2) Gomez-Uribe, Carlos A., and Neil Hunt. "The netflix recommender system: Algorithms, business value, and innovation." ACM Transactions on Management Information Systems (TMIS) 6.4 (2015): 1-19.
12 11/13 Prototyping AI Experiences (1) Lam, Michelle S., et al. "Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design." Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 2023.
(2) Park, Joon Sung, et al. "Generative Agents: Interactive Simulacra of Human Behavior." In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST '23).
12 11/15 Human-AI Collaboration (1) Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S. Weld, Walter S. Lasecki, and Eric Horvitz. 2019. "Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff." Proceedings of the AAAI Conference on Artificial Intelligence 33, 01: 2429–2437.
(2) Zhou, Sharon, Melissa Valentine, and Michael S. Bernstein. "In search of the dream team: temporally constrained multi-armed bandits for identifying effective team structures." Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 2018.
Assignment #3
13 11/20 Application Areas (Part 1): Education (1) Subramonyam, Hariharan, Colleen Seifert, and Eytan Adar. "ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces." In 26th International Conference on Intelligent User Interfaces, pp. 48-58. 2021.
(2) Amershi, Saleema, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. "Software engineering for machine learning: A case study." In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 291-300. IEEE, 2019.
13 11/22 Project Feedback Meetings
14 11/27 Guest Lecture: AI+Creativity by John Joon Young Chung, Midjourney
14 11/29 No class (Undergraduate Admission Interviews Day) Project 2: Prototype
15 12/4 Application Areas (Part 2) (1) Hara, Kotaro, Jin Sun, Robert Moore, David Jacobs, and Jon Froehlich. "Tohme: detecting curb ramps in google street view using crowdsourcing, computer vision, and machine learning." In Proceedings of the 27th annual ACM symposium on User interface software and technology, pp. 189-204. 2014.
(2) Stangl, Abigale, Meredith Ringel Morris, and Danna Gurari. ""Person, Shoes, Tree. Is the Person Naked?" What People with Vision Impairments Want in Image Descriptions." In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-13. 2020.
15 12/6 Final Presentations & Course Wrap-up (in-person)
16 12/11 No class (Finals week)
16 12/13 No class (Finals week)


Major topics include: Ethics and FAccT in Machine Learning, Metrics to Measure HAI Performance, AI Design Process, Interpretable and Explainable AI, Recommender Systems, and Human-AI Collaboration


  • Design project: 40%
  • Assignments: 50%
  • Class participation: 10%
Late policy: For assignments and project milestones, you'll lose 10% for each late day. Submissions will be accepted until three days after the deadline.


You need to have at least introduction course-level knowledge in either HCI (e.g., CS374, CS473) or AI (e.g., CS470, CS376). If you're unsure whether you quality, please contact course staff.