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DRPS : Course Catalogue : Edinburgh Futures Institute : Edinburgh Futures Institute

Postgraduate Course: Artificial Intelligence and Storytelling (fusion online) (EFIE11142)

Course Outline
SchoolEdinburgh Futures Institute CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe course invites students to play with Artificial Intelligence (AI) in order to create and tell stories, using image and text. The AI technology is seen as a tool that can both generate new ideas and complement those of a person, while also acting as a potential bridge for collaboration between people. Starting from no technical background, the student will learn how AI, and especially Machine Learning, functions along with its strengths and limitations. With a combination of hands-on experience, workshops on creative storytelling, and invited lectures from experts in the diverse areas on AI and its application for creating stories, we will prepare students for a creative career with AI, participating in AI projects, or starting on a more technical path.
Course description In the pre-intensive days, the opening lecture will introduce you to Artificial Intelligence (AI) and the various options it provides for Storytelling. This is followed by a creativity workshop, which aims to ease you into the overall theme of the course. The idea is that you will take the skills and ideas developed there with you into the two days of intensive day workshops and the course projects. These projects primarily involve writing a story with the use of AI (no previous programming experience required).

Most self study time, as well as the intensive day workshops, will have you working with a high level AI tool-set that has been custom made for the course (and which you can 'take home' when the course is over).

In the intensive days, we will focus a bit more on some technical aspects of AI, helping you better understand how the tools you have been using work. At the same time, you will be invited to build AI solutions 'by hand', replicating the process Machine Learning algorithms go through. These days will culminate in collaborative activities using the tools you have been practising with in weeks prior, and testing the limits of their creative potential, while also exposing you to the dangers of their uncritical use.

Evaluation is done via 2 pieces of coursework, at 20% and 80%, both of which follow a similar structure, with 85% of the mark attached to story generation exercises. Using the provided AI tool-set, you will be tasked with directing the AI into producing a story that accomplishes certain feats. The rest 15% of each coursework involves technical tasks related to AI engineering, such as working with code repositories or introductory level programming, and critique of provided examples.

Following the intensive days, you will continue to self-study and work on the projects. A short series of invited lectures will run during this period, providing you with guidance on further possibilities for storytelling with AI, each focussing on a different AI area or application.

Edinburgh Futures Institute (EFI) - Online Fusion Course Delivery Information:

The Edinburgh Futures Institute will teach this course in a way that enables online and on-campus students to study together. This approach (our 'fusion' teaching model) offers students flexible and inclusive ways to study, and the ability to choose whether to be on-campus or online at the level of the individual course. It also opens up ways for diverse groups of students to study together regardless of geographical location. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities. Students should note that their interactions may be recorded and live-streamed. There will, however, be options to control whether or not your video and audio are enabled.

As part of your course, you will need access to a personal computing device. Unless otherwise stated activities will be web browser based and as a minimum we recommend a device with a physical keyboard and screen that can access the internet.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  6
Course Start Semester 2
Course Start Date 15/01/2024
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 5, Seminar/Tutorial Hours 4, Supervised Practical/Workshop/Studio Hours 5, Online Activities 6, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 78 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Assessment 1 (20%):
- 85% of mark from Creative Story Writing project with AI. Built around creating a story with 3 acts. Focusses on having the student use all techniques demonstrated in the course, with most questions explicitly identifying which techniques to use.
- 15% of mark from execution of technical tasks. For this coursework, these will have very specific steps, with significant instructor support.
Released: Week 1 of Semester 2.
Submission Deadline: The end of the week after the week of Intensive Days.
Feedback: Within 1 week of submission deadline.

Assessment 2 (80%):
- 85% of mark from Creative Story Writing project with AI. Built around creating a story with 5 acts. Allows for more flexibility in what techniques to use for each question, while also involving the need to creatively use combinations of techniques.
- 15% of mark from execution of technical tasks. For this coursework, these will mostly have very specific steps, with significant instructor support, but also provide some more open ended challenges.
Released: 1 week before week of Intensive Days (ignoring Flexible Learning Week).
Submission: The end of the week 3 weeks after the week of Intensive Days (equivalently, 2 weeks after deadline for receiving feedback for Coursework 1).
Feedback: Within 2 weeks of submission deadline.

Both pieces of coursework are primarily evaluated on the successful achievement of specified types of output from the AI (e.g. 'make the AI make an absurd statement' or 'investigate the AI's bias in relation to the attributes of certain items or people'). There will be a limit to the maximum size of each project, determined by maximum number of AI-calls, and a limit on the number of human-authored words. This accounts for 85% of each coursework, and primarily tests learning outcomes 1 and 2.

15% of each coursework will consist of exercises primarily testing learning outcomes 3 and 4. These will provide some alternative tasks for the student to pick, each of whom however will present an equivalent level of difficulty and attainment. These tasks focus on the development of further transferable skills by the students, such as working with online code repositories (e.g. GitHub), running experiments on the Cloud, starting with AI programming, or critiquing existing work.

Students will be given guidelines on the assessment and will have the opportunity to ask questions about them online. They will work on the coursework also along their instructor during the intensive day workshops, and will be able to ask questions about the relevant material online throughout the course.
Feedback Students will be given formative feedback online, during and both prior and after the intensive days, which will feed forward into their final assessment.

For each of the 2 pieces of coursework, while that coursework is released, and especially during the 2 weeks before the submission deadline for the respective coursework, the students will be able to share their solutions so far with the teaching staff (Lecturer and TA) and receive an evaluation with constructive feedback and advise. Where this can be done without allowing for plagiarism, the communication will be open to the class on the course's forum. Specifically for the majority of questions that are evaluated on the generated text, students will be able to share the generated text but not the steps followed to produce it (commands, setup, or self-authored text).

Students will have access to technical support during the same timeline, helping them set up their environments and with executing commands for the coursework.

In the intensive, students will also receive feedback from the teaching team and from their peers as they work through the activities. Students who are unable to attend the intensive will be able to ask questions in an online forum which the course TA and Lecturer will monitor and respond to.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. understand how AI, and in particular ML, functions along with its use cases and limitations, with a focus on its use for storytelling;
  2. work with AI tools to produce a story using text and/or image;
  3. actively participate in and critique AI projects, having sufficient background to understand and communicate with AI engineers;
  4. start developing more technical skills in AI.
Reading List
[Essential Engagement]:

Creativity and AI
re:publica 2019 - Joanna Zylinska: AI Art: Machine Visions and Warped Dreams. []

Machine Learning Concepts
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. Chapters 1 & 5 (without the Maths). []
Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. " O'Reilly Media, Inc.", 2019. Chapters 1 to 4 & 10 to 11 (Without the Maths and Code). []

Story Generation
Alabdulkarim, Amal, Siyan Li, and Xiangyu Peng. "Automatic story generation: challenges and attempts." arXiv preprint arXiv:2102.12634 (2021). []

Style Transfer
Jing, Yongcheng, et al. "Neural style transfer: A review." IEEE transactions on visualization and computer graphics 26.11 (2019): 3365-3385. []

Sentiment Analysis & Emotion Recognition
Zhang, Lei, Shuai Wang, and Bing Liu. "Deep learning for sentiment analysis: A survey." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8.4 (2018): e1253. []
Alswaidan, Nourah, and Mohamed El Bachir Menai. "A survey of state-of-the-art approaches for emotion recognition in text." Knowledge and Information Systems 62.8 (2020): 2937-2987. []

[Further Engagement]:

Creativity (and AI)
Barry, Lynda. Making comics. Drawn & Quarterly, 2020. []
ual:creative computing institute, institute of coding. Apply Creative Machine Learning, Accessed: April 2022. []
Zylinska, Joanna. AI art: machine visions and warped dreams. Open Humanities Press, 2020. []

Interactive Fiction
Lapan, Maxim. Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. Packt Publishing Ltd, 2018. Chapter 1. []
Hausknecht, Matthew, et al. "Interactive fiction games: A colossal adventure." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 05. 2020. []
Ammanabrolu, Prithviraj, et al. "Bringing stories alive: Generating interactive fiction worlds." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. Vol. 16. No. 1. 2020. []
Ziegfeld, Richard. "Interactive fiction: A new literary genre?." New Literary History 20.2 (1989): 341-372. []

Doing Research
Leedy, Paul D., and Jeanne Ellis Ormrod. Practical research: Planning and design. Pearson. One Lake Street, Upper Saddle River, New Jersey 07458, 2019. [,_Jeanne_Ellis_Ormrod%5D_Practical_Res(]

AI Ethics
Jobin, Anna, Marcello Ienca, and Effy Vayena. "The global landscape of AI ethics guidelines." Nature Machine Intelligence 1.9 (2019): 389-399. []

Fairness in AI
Andreadis, Pavlos, Ceppi, Sofia, Rovatsos, Michael, and Subramanian Ramamoorthy. "Diversity-Aware Recommendation for Human Collectives." Procs DIVERSITY (2016). []
Richardson, Brianna, and Juan E. Gilbert. "A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions." arXiv preprint arXiv:2112.05700 (2021). []
Baleis, Janine, et al. "Cognitive and Emotional Response to Fairness in AI - A Systematic Review." (2019). []
Additional Information
Graduate Attributes and Skills A. Research and Enquiry (Graduates of the University will be able to create new knowledge and opportunities for learning through the process of research and enquiry): developed through readings, class discussions (both in person and online) and group activities in class.

B. Personal and Intellectual Autonomy (Graduates of the University will be able to work independently and sustainably, in a way that is informed by openness, curiosity and a desire to meet new challenges): developed through individual work on assessments using the technologies the course has introduced students to.

C. Communication (Graduates of the University will recognise and value communication as the tool for negotiating and creating new understanding, collaborating with others, and furthering their own learning): developed through contributions to in-person and online discussions.

D. Personal Effectiveness (Graduates of the University will be able to effect change and be responsive to the situations and environments in which they operate): developed through the entire suite of learning activities (critical readings, participation in discussion, participation in class activities and production of assessment) which engage the student in critically applying AI technology in the production of stories (through both text and image) individually and collaboratively.
KeywordsStorytelling,Creative Writing,Artificial Intelligence (AI),Machine Learning (ML)
Course organiserDr Pavlos Andreadis
Tel: (0131 6)50 8281
Course secretaryMiss Abby Gleave
Tel: (0131 6)51 1337
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