Human-AI Interaction

Course manual 2025/2026

Course content

This course on human-AI interaction teaches a human-centered approach to interaction design of systems that feature artificial intelligence (AI). Starting with basic concepts, types, and history of AI, the course then explores design guidelines and principles that become of key importance when a system includes AI, with special focus on topics like transparency and explainability, societal impact, evaluation methods, bias, fairness and embedded values, as well as contestability (designing for failure).

In the seminar that runs in parallel, students apply these concepts to a concrete example and produce a design portfolio that showcases a professional HAII design case study and connects design considerations and rationale to scientific evidence.

Study materials

Literature

• Links provided

Objectives

• The student recognizes and can explain key theoretical concepts and challenges in the design and research of human-AI interaction.
• The student applies the relevant concepts from literature to a specific system with human-AI interaction, thereby designing coherent, useful, and ethical human-AI interaction experiences.
• The student recognizes relevant concepts from the literature in systems with human-AI interaction, thereby using them to understand and improve user experience with AI.
• The student reflects critically on state-of-the-art human-AI interaction research and development, as well as the history of the field.
• The student demonstrates professional responsibility by incorporating timeliness and iterative design into their working habits while developing a design portfolio across structured deadlines that mirror professional design workflows - showcasing growth, critical reflection, and integration of feedback across multiple stages of the course.

Teaching methods

• Seminar
• Lecture
• Working independently on e.g. a project or thesis
• Self-study

Learning activities

Attendance

In TER part B of this programme no requirements regarding attendance are mentioned.

Additional requirements for this course:

Attendance in 80% of the seminars is mandatory (see learning objectives), i.e., students can be absent at one seminar with no questions asked. Missing of further seminars must be announced with a sufficient reason to the seminar instructor prior to the seminar for the student to be counted as “absent with excuse”, otherwise this impacts the attendance requirement for passing the course.

Attendance in lectures is highly recommended but not mandatory.

Assessment

To pass the course, students must:

• pass each exam with ≥ 5.5
• achieve ≥ 5.5 on the final portfolio submission (*)
• achieve a pass on seminar attendance

(*) Note on grading:

The in-seminar activities prepare portfolio deliverables during the course (building up to the final portfolio) that are processed as pass/fail. Pass = submitted and meets requirements. Late and missing portfolio deliverables directly impact the final portfolio grade - see late policy.


Resit policy:

If either exam is failed, then the student must attend the resit exam. The resit exam covers both comprehensive and essay exam portions and then overwrites the combined exam grade.

If the portfolio submission is failed (not submitted or results in a failing grade), then the student must resubmit at a later date. The resubmission date is decided on a case-by-case basis. A resubmitted portfolio is subject to a grade cap at 7 to account for the greater time available to work on the portfolio content. 

Inspection of assessed work

Inspection of exam grades is announced via the digital exam platform. A separate message via Canvas will announce how and by what date questions about the grading must be submitted. Design portfolio grades are presented with feedback. 

Assignments

All assignments are individual. Feedback is given by the seminar instructor and through peer feedback in the seminars. Portfolio deliverables are not graded, but late or missing portfolio deliverables impact the final portfolio grade, and part of the final portfolio grading rubric reflects how well the portfolio deliverables were expanded upon for the final portfolio submission.      

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Additional information

Late Policy:

For the portfolio deliverables:

The portfolio deliverables are not graded, but late submission and/or missing deliverables impact the final portfolio grade.

Late submissions of the portfolio deliverables are accepted for a 3-day window after the deliverable deadline, but cause a 0.1 penalty on the final portfolio grade per day of delay (i.e., what would have been a 7.5 becomes a 7.4 when one deliverable was submitted one day late). I.e., if all six portfolio deliverables are submitted late by one day, this results in a 0.6 penalty. If all six deliverables are submitted three days late, this results in a 6*0.3=1.8 penalty, i.e., a 7.5 would then turn into a 5.7.

Three days after each deadline, portfolio deliverables are no longer accepted and count as missing. Missing portfolio deliverables carry a 0.5 penalty on the final portfolio grade. So a 7.5 would become 7 if one deliverable is missing. Missing all deliverables would thus be a 3-point penalty on the portfolio grade. Students would need to get an 8.5 on the final portfolio submission to still pass this grade component without submitting any of the deliverables (as 8.5-3=5.5 i.e., passing).

For the final portfolio:

For the final portfolio submission, each late day is counted as a 5% (0.05) grade penalty, so a 7.5 would become a 7 (one day late), then a 6.5 (2 days late), then a 6 (3 days late). After 3 days, submissions are no longer accepted.

AI Policy

The use of generative AI is not encouraged.

It is permitted for the final portfolio submission in very constrained ways:

• It must only be used for purposes that do not constitute "thinking"-steps or purposes that do not immediately contribute to the learning objectives that are covered in the course.
• Students should weigh their usage of generative AI against the environmental consequences of that generative AI tool's development, training, deployment.
• Students must provide a short declaration on their use of generative AI in their design portfolio using the provided template.
• Based on the CEUR taxonomy for AI contributions, the following use of AI is permitted without reservation:
  • generate images
  • grammar & spelling check
  • citation management (i.e., formatting!)
  • peer review simulation
• Based on the CEUR taxonomy for AI contributions, the following use of AI is permitted only with strong constraints - specifically, use on single word(s) and/or sentence(s) constituting up to 30% of the design portfolio length as a whole, and with comprehensive pre-/post-prompt documentation:
  • paraphrase and reword
  • improve writing style
  • text translation
• Any use of generative AI must be in accordance with the above contributions (permitted and constrained-use), but also must be documented comprehensively in the design portfolio appendix, listing:
  • the contribution role based on the CEUR taxonomy,
  • the AI tool that was used, and
  • a detailed account of the full prompt. In case of the constrained-use AI contributions (listed above), this must include the pre-prompt word or sentence and the post-prompt word or sentence.

Students are responsible for any submissions they make and must be able to explain it if prompted.

Any hallucinated / non-existing references or other indications that the work was not completed by the student (beyond what is outlined earlier in this section) are treated as evidence of fraud and submitted to the examination board for further investigation.

The point of writing tasks or design activities is not to create a portfolio or more prototypes or more student essays and texts. The point is to make the students go through the process of figuring out what they think about a topic, creating ideas and reflecting on them, and finally, putting those words together in a way that communicates their thought process to others.

“This process cannot be short-circuited by outsourcing it to Al; to do so removes the entire fucking point." - Dr. Andrew Perfors

Contact information

Coordinator

• dr. K.S. Rogers