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IS41870

Academic Year 2026/2027

Explainable AI (XAI) and Culture (IS41870)

Subject:
Information Studies
College:
Social Sciences & Law
School:
Information & Comms Studies
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Sun Park
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module explores the principles and applications of Explainable AI (XAI) within cultural and computational contexts, drawing on perspectives from AI studies, Information and Communication Studies, the arts, heritage studies, and the cultural and creative industries. Students will gain a foundational understanding of AI transparency and interpretability, exploring how explainability shapes the responsibilities of AI developers, users, and cultural practitioners operating within socio-cultural contexts.

Through both theoretical and practical applications, the module examines how transparency in AI must be understood not only as a technical challenge but as a cultural one, requiring consideration of multi-dimensional layers of inter/transdisciplinary AI stakeholders. By the end of the module, students will be able to critically evaluate and apply XAI methodologies and approaches to real-world challenges at the intersection of AI and culture, and to engage in the professional issues in the AI industry.

About this Module

Learning Outcomes:

On successful completion of the module, students should be able to:

- Demonstrate an understanding of Explainable AI (XAI) and its applications across cultural and computational contexts.
- Apply XAI methods and frameworks to practical tasks and real-world projects in the arts, heritage, and information sectors.
- Equip with scholarly competencies and management skills in planning and implementing inter and transdisciplinary AI-culture projects

Indicative Module Content:

- A critical understanding of the concept of explainable AI
- The need for explainability in cultural research
- Practical exercises on inter/transdisciplinary explainability of AI

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Autonomous Student Learning

101
Total

125

Approaches to Teaching and Learning:
The module uses a combination of teaching and learning methods to support students in developing both theoretical knowledge and practical skills, including:

Lectures, active/task-based learning, case-based learning, peer and group work, and reflective learning.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) Offered
Group Work Assignment: Group proposal and presentation on planning an XAI project in the cultural field Week 10 Graded No
30
 Yes
Reflective Assignment: A report that includes the group’s reflection and each member’s individual reflection on their experience planning their XAI projects Week 15 Graded No
70
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
Yes
 

Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Hoffman RR, Mueller ST, Klein G and Litman J (2023). Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance. Front. Comput. Sci. 5:1096257. doi: 10.3389/fcomp.2023.1096257.

Kopecka, H., & Such, J. (2020). Explainable AI for Cultural Minds. Paper presented at Workshop on Dialogue,
Explanation and Argumentation for Human-Agent Interaction. https://kclpure.kcl.ac.uk/portal/en/publications/explainable-ai-for-cultural-minds/

Park, S. (2025). "Revisiting Transparency in AI-based Creation: Unpacking ‘Cultural Black Boxes’ towards ‘Cultural Explainability’," 2025 IEEE International Symposium on Technology and Society (ISTAS), Santa Clara, CA, USA, 2025, pp. 1-7, doi: 10.1109/ISTAS65609.2025.11269653.

Name Role
Dr Courtney Ford Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 15:00 - 16:50
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