My name is Jasper. This is an outline of my ARP project.
I’m an Associate Lecturer at UAL Creative Computing Institute (CCI), I teach the Mathematics and Statistics for Data Science unit on BSc (Hons) Data Science and AI, and the Exploring to Machine Intelligence unit on MSc Creative Computing. I’m pursuing a PhD degree in AI and music technology.
I hope to use this PgCert as a point of reflection, reflecting on my identity as a researcher (my PhD works)/ teacher (my teaching at CCI)/ student (me being a research student)/ artist (me being a music composer and producer), to situate myself into this state of disequilibrium.
In my Action Research Project, I look into students’ use of Generative AI (GenAI) coding tools for learning and assignments in Creative Computing. In particular, the creation of practical guidelines on the use of AI coding tools for BSc Data Science and AI students at CCI, to reduce the barrier to AI literacy in a technical programming context?
I was inspired by the use of technology probes in action research (Madden et al., 2014), that is, creating a digital intervention and then evaluating users’ responses to it in their own environment. In this case, the “Rubber Duck” and the GenAI checklist are the probes to be introduced to the learners, seen as probes in a field-testing setting, taken to the learners’ workspace, to investigate their responses.
Participants
Students in the BSc Year 1 Mathematics and Statistics for Data Science (Math&Stats) unit who gave consent to join the research project.
Intervention
The GenAI checklist and the Rubber Duck chat mode (described in the GenAI Checklist and the Rubber Duck Chat Mode blog post) will be added to the Math&Stats class repository at the beginning of the unit. The GenAI checklist will be a compulsory submission element for all students, the chat mode will not be compulsory, but an encouraged tool to use for coding. Data will only be collected from the participants.
GenAI Workshop
A GenAI workshop (~60mins) will take place during the class time of Math&Stats, using the last 60mins of the class, which is usually a flexible time for Q&As, debugging, and self-study. Workshop schedule:
0 – 15mins: I’ll give a brief on the use of AI and AI coding tools
15 – 30mins: I’ll walk through the principles and guidelines of using GenAI coding tools in class, the adapted GenAI checklist, and examples of how to keep track of the use of AI for coding.
30 – 40mins: Practical notes on AI coding tools, including how they work, how to set up, applying for educational benefits (free access for students), and limitations of AI coding tools.
40 – 60mins: Several programming tasks will be prepared. In this activity, students will get into groups of two, choose a task, use the Rubber Duck chat mode and prompt the AI to do the task, keep a chat log of their use according to the GenAI checklist, and put their results in Miro and share with the class.
Group discussion on participants’ perceptions toward AI in academia, including anonymised answers to:
What comes to mind when you hear the term AI? (Words/Feelings, what excites you about using AI? What worries you about using AI)
What did you use AI for? (What was the model? What questions/prompts did you ask? How did it go?)
I’ll collect sample chat logs submitted by participants in their final assignment.
Data Analysis
I’ll deliver the Miro discussion in the form of a word cloud.
I’ll reflect on the sample chat logs to discuss whether the GenAI checklist has been well-received by participants, I’ll discuss on the following points:
The motivation of the research question is threefold:
The need to tailor and adapt guidelines to specific creative disciplines
The need to communicate clear expectations to students for the use of AI in advance of assessments.
The need for technical programming skills creates barriers for learners to be AI-literate.
Research question
How can we create practical guidelines on the use of AI coding tools for BSc Data Science and AI students at the Creative Computing Institute, to reduce the barrier to AI literacy in a technical programming context?
Actions Plan
A GenAI Checklist tailored from the UAL Student Guide to Generative AI. It aims to provide guidelines on the responsible use of AI coding assistants, including more detailed instructions on:
How to use AI coding tools in an academic context,
How to keep track of code generation/editing done by AI,
How to add the “Generative AI Disclosure” in coding.
A “Rubber Duck” Chat Mode will be implemented in GitHub Copilot (the AI coding tool integrated into the software used by CCI students) and provided to students in the unit. Chat Mode is a set of “pre-task instructions” for Copilot, to tailor its behaviours by setting the overall goal. I hope this can better support learners, and potentially help students identify what knowledge/skills are needed.
Project Plan
The BSc Mathematics and Statistics for Data Science (Math&Stats) unit runs from Sep 2025 to Jan 2026.
GenAI Checklist: To be finalised by 10-Oct
The “Rubber Duck” Chat Mode: To be finalised by 10-Oct
Data collection plan: Mid-Oct
Prepare the Miro board, workshop activities: Mid-Oct
GenAI workshop with students: Week 5 (31-Oct) Week 7 (14-Nov)
I began my ARP by reflecting on my teaching in the Spring and Summer terms of 2025 (because early 2025 is when AI coding tools such as “vibe coding” have risen). I wrote down a list of issues I have noticed:
The UAL Student Guide to Generative AI is not tailored to the context of CCI. Students at CCI use Generative AI not just for writing, but also for a range of programming practices (e.g., coding, debugging, explaining codebase, setting up workspaces, writing code repository description).
The guidelines we gave to students on AI disclosure in coding are superficial. For instance, we require students to keep a log of the use of AI, but how do they do this? When to do this? What to include in the log? This can lead to unintended academic misconduct.
The perception of AI (the consequences, risks, and broader impacts) varies across students.
Commercial AI coding assistants offer subscription services, which give access to higher-quality AI models: this is an inequal access to technical resources.
I looked at the relevant guidelines and principles that have been proposed. Below, I’ll list some of the documents in higher education in response to the use of GenAI.
The Russell Group principles on the use of generative AI tools in education(Russell Group, 2023) make a stance of moving toward the ethical and responsible use of GenAI, including: “2. Staff should be equipped to support students to use generative AI tools effectively and appropriately in their learning experience. 4. Universities will ensure academic rigour and integrity is upheld.”
In response, the UAL Position Statement on AI (UAL, 2025a) calls for the need to tailor and adapt these guidelines to specific creative disciplines: “Appropriate use of AI is likely to differ between the creative disciplines. We will encourage staff to consider how these tools might be applied appropriately for different student groups or those with specific learning needs.”
The AI and Teaching Q&As (UAL, 2025b), as a staff-facing document, highlights: “Create a clear expectation of AI in advance of students commencing assessed work (define acceptable use).” “Incorporate discussion about our AI guidance.” “Be aware of the context and the learning outcome“.
The Student Guide to Generative AI (UAL, 2025c), as a student-facing document, explains academic misconduct with GenAI and highlights the importance of keeping track of the use of GenAI.
The AI Guidance Summary provides a list of sources and a summary of the use of AI (LCC, 2023):
In addition, the proceedings of the Resistance AI Workshop include several articles that have mentioned the risk of how colonialism, militarism, and imperialism can lead to the resistance of AI tools in an academic context (Agnew et al., 2020).
The rapidly changing landscape of GenAI brings new challenges and opportunities at an exponential speed. The explosive appearance of AI tools and materials in higher education is transforming the ways of teaching, learning, and assessing (Attewell, 2025).
I look at the field of Creative Computing, where students use GenAI not just for writing, but to engage in a range of computer engineering practices – for instance, writing code, debugging, explaining the codebase, setting up workspaces, writing a code repository description, and more. In this respect, AI literacy is shifted to a set of technical and practical skills closely related to computer science. Dincă et al. (2023) highlight that the benefits of AI coding tools in computer science rely on the human programmers’ safeguard:
“The use of specialized AI tools in software development has the potential to increase productivity when utilized by experienced users, particularly for repetitive coding tasks. The implementations, however, must be subjected to meticulous scrutiny.”
This highlights that the requirement for technical skills can create barriers for students to be AI-literate. The ability to review, curate, and safeguard AI outputs assumes one to have a programming or computer science background. For instance, being able to understand and correct programming concepts, and to accurately articulate tasks and goals when prompting the AI.
Therefore, the problem of the use of AI coding tools in the pedagogy in creative computing is twofold: (a) the ability to use AI coding tools can be desired in future workspaces in the industry, we would therefore like to encourage students’ use and integration of these tools in their programming workflow, and (b) the misuse of AI coding tools (e.g., shortcut learning, misconduct) need to be avoid, and ensuring that those learning outcomes regarding technical programming skills can be accurately assessed.
My positionality
I’m an associate lecturer in the BSc Data Science and AI and MSc Creative Computing courses at CCI. During the ARP cycle, I’ll be teaching BSc year 1 students at the Mathematics and Statistics for Data Science unit. Apart from being a lecturer at the Creative Computing Institute, I am also a researcher and software engineer in a techno-scientific field of AI and music technology. I started using AI coding tools about a year ago, and now I use them extensively in my technical practice for productivity.
Summary and signposting
Given these challenges above…
Reference list at: https://jaspersz.myblog.arts.ac.uk/2025/11/20/arp-references/
Agnew, W., Birhane, A., Caroll, M., Creager, E., Foryciarz, A., Kalluri, P. R., Kapoor, S., Kite, S., Gontijo Lopes, R., Sabin, M., Png, M.-T., Skoularidou, M., Tesfaldet, M., Vasileva, M., Vilarino, R., Wang, R. E. (eds) (2020). Proceedings of the Resistance AI Workshop during the NeurIPS 2020 Conference. Online, Dec. 11, 2020. Available at: https://sites.google.com/view/resistance-ai-neurips-20/accepted-papers-and-media (Accessed: 22 Nov 2025).
Dincă, A.M., Axinte, S.D., Tod-Raileanu, G. and Bacivarov, I.C., (2024) ‘AI Tools introduced in Software Development. Analysis of Code quality, Security and Productivity Implications.’ In 2024 IEEE 30th International Symposium for Design and Technology in Electronic Packaging (SIITME) (pp. 32-39). IEEE. Available at: https://doi.org/10.1080/15710882.2014.881884 (Accessed: 20 Nov 2025).
Madden, D., Cadet-James, Y., Atkinson, I. and Watkin Lui, F., (2014) ‘Probes and prototypes: a participatory action research approach to codesign.’ CoDesign, 10(1), pp.31-45.
I’m a lecturer at UAL Creative Computing Institute (CCI). I teach BSc Data Science and AI and MSc Creative Computing. Both courses combine art and computer science, with a strong focus on lecture and tutorial delivery.
My positionality is informed by my background as: a man who grew up in China, an international student (2019-2022), and an EAL (English as Additional Language) learner. It is also informed by my role at UAL as both a PhD student and an early career lecturer. As a researcher in the field of engineering and computer science, I often value interventions that are operational, finding solutions to problems, or providing concrete recommendations for future work.
This report is about my reflection on planning a living manifesto on hearing impairment accessibility.
2. Context and Motivation
In the Digital System unit during the Spring term, one of the learning outcomes is to understand how sound/audio is encoded in digital systems. To achieve this, during the lecture, I ran a listening activity in which I displayed audio waveforms produced by different signals, and had students listen to sounds produced by these signals. However, this activity has been inaccessible for one of the students in my class, who has a diminished hearing channel.
The experience of working with this student, as well as the PgCert journey, has prompted me to think about accessibility for deaf and hard of hearing (DHH) students and, more broadly, how to accommodate different hearing preferences in the pedagogy of audio programming.
A common learning outcome in teaching digital audio/music has involved communicating experiences about sound, audio, and music. However, traditional audio pedagogy often centres around listening as a primary mode of engagement, assuming auditory access as a baseline. This assumption reinforces marginalisation of DHH learners.
3. Rationale
The intervention will be a living manifesto on hearing impairment accessibility, a living document initiated by me, and co-edited with colleagues and students, as a call for changes in the pedagogy of audio, sound, and music. It aims to invite colleagues to take a counterfactual approach and re-evaluate how we should communicate our experiences around sound, audio, and music.
I hope this can help us move toward an inclusive learning environment that takes account of the intersection of disability, different hearing preferences, and cultural/linguistic nuances in ways of expressing sound.
The following sections elaborate on the literature and theories that will inform (has informed) this manifesto.
3.1. Students’ hearing preferences are differently situated.
Informed by the literature on hearing impairment, I learned that hearing conditions vary among individuals with DHH. For instance, in the study by Looi et al. (2008), their results suggested that a large amount of variation was observed in individuals’ ability to differentiate between different aspects of sound, including pitch, rhythm, and timbre. Their study also shows that conditions can vary within these categories (ibid). For instance, certain types of DHH conditions can diminish the ability to perceive female voices, whereas some other conditions diminish the ability to perceive male voices.
Why is it important at UAL CCI?
In music schools specifically focusing on music and audio education, Cheng and Mcgregor (2024) described a viable approach of offering personalised audio workstations to accommodate individuals’ needs, creating an inclusive learning environment for all.
However, in the context of UAL CCI, music and audio only make up a small portion of the learning outcome. The course design often focuses on more general aspects of creative computing to support students’ diverse interests. This places constraints on time, effort, and funding for the pedagogy of music and audio. In addition, not all teaching staff are domain experts in music and audio. UAL Dashboard (2025) also shows that only 1.1% of students at CCI have declared “sensory, medical or physical impairment”. It is not likely that all teaching staff have experience working with DHH students in this environment, and new lecturers (such as me) may have limited knowledge of accommodating different hearing preferences.
Figure 1. Caption
Therefore, I hope this manifesto can be helpful for colleagues who need an introduction to adapting to the mindset of acknowledging students’ differently situated hearing preferences, and using available assets at CCI to accommodate these preferences.
3.2. Ways of understanding sounds intersect with race, language, and culture.
In the review by Nichols and Stahl (2019), grounded in the literature on intersectionality (Crenshaw, 1991), they highlight how elements of intersectionality shape students’ social and personal experiences in higher education. In the context of the pedagogy of music and audio, the intersections between race, language, culture, and disability can shape how sound is described, understood, and valued. Studies in Human Cognitive Processing (HCP) have shown that the way we communicate embodied experience (such as sound) is a cross-cultural matter (Yu, 1995).
Why is it important at UAL CCI
Looking at UAL data on students’ home ethnicity, this profile of multicultural backgrounds suggests that students’ ways of internalising sound are diverse. This cultural nuance can be a key to introducing the element of intersectionality in the pedagogy. For instance, as a Chinese EAL learner and teacher myself, describing the bodily metaphor of a sound is a common strategy I use. Yu (1995) speculated that the explanation for this references the theories of yin-yang and the five elements of Chinese medicine. In a more recent study, Reed, Strohmeier and McPherson (2023) show that the use of metaphors between teachers and students can reach some mutual understanding across backgrounds but still vary in nuanced details.
Figure 2. Caption
I also considered the research on linguistic diversity in academia. For instance, Sharma et al. (2025) took an ethnographic approach to examine how socio-culturally nuanced expressions can lose their meaning when translating from other languages to English. The paradigm of “speaking the same kind of English” in academics already amplifies a monocultural proficiency in English. Therefore, by promoting students’ own ways of describing experiences and feelings, I hope the manifesto can call for an inclusive language of communicating sound.
3.3. Embodied listening is a multisensory approach.
Traditional pedagogy of sound assumes that hearing is a singular and primary channel for sonic engagement. However, embodied cognition, especially embodied music cognition (Godøy, 2009), argues that embodied listening is a multisensory (synaesthesia) approach that extends beyond the ear. The oscillation of sound can be perceived as visual, tactile, or vibrational cues. An example of this multisensory experience of sound is the Chladni patterns (shown in the Figure 3), which opens the use of visual and haptic to engage with sonic phenomena.
Figure 3. Chladni patterns in vibrating plates. Source: Max Planck Institute. License: CC-BY-SA.
Why is it important at UAL CCI?
CCI students often come from interdisciplinary backgrounds, with interests spanning code, music performance, visual art, choreography, and more. In my own teaching, I noticed how synaesthesia had become a repeating theme in students’ work to connect multisensory experiences. This resonates with the reflection from Murgia (2015), that synaesthesia is a “gateway” to teaching creativity in higher education because it “forming a new way of thinking and experiencing the world”. In doing so, sonic pedagogy through a sensory-inclusive lens is not just an assistive act, but also an inclusive pedagogical tool in creative computing.
4. Action
To develop this living manifesto, I will initiate a draft as the initial prompt to start the conversation, and share it with colleagues using the bulletin board at CCI High Holborn campus. Meanwhile, to bring students’ voices into it, I aim to plan a workshop for students around the theme of communicating sound experiences, and focus on guiding students to reflect on ways of communicating sound using their language/cultural elements.
5. Reflection & Evaluation
The idea of considering DHH started from the IP unit’s first workshop, in which the Social Model (UAL, 2025) motivated me to think about disabilities as barriers in the learning environment that have prevented individuals from full engagement. It was particularly the talk from Sadiq (2023) that has prompted me to think not assuming what kinds of support are needed, instead, to understand that every individual is different and go beyond “box ticking”. In addition, the discussion about intersectionality during workshops, as well as the first tutorial with Tim, have inspired me to open up to the race, culture, and language aspects of hearing, and to be mindful and consider the variety of hearing preferences.
This has also sparked my reflection on my own positionality, I realised that I needed to shift my perspective from being a “developer” (who builds tools and solutions) to a “facilitator” (who encourages reflection and fosters enquiries).
During the peer-to-peer presentation, we posted feedback to each other on a Miro board (https://miro.com/app/board/uXjVIjgKNKI=/?share_link_id=217453660255), where we discussed how to evaluate this intervention. Claire mentioned the way to measure the success of this is to consider whether it raises staff awareness of different hearing experiences. It is indeed true that the aim of this manifesto is not to offer a straightforward solution to problems, nor concrete recommendations that can be immediately implemented in one’s teaching. Instead, it seeks a reflexive way in which we are encouraged to examine our assumptions, think about “what if”, and provide directions for future work. I believe Oulasvirta and Hornbæk (2022)’s way of describing this counterfactual way of thinking could be the best way to approach the evaluation of this: to “redefine problems and sensitise [us] to the problems”.
Reference
Cheng, L. and Mcgregor, I. (2024) ‘Pedagogical Approaches in Music and Audio Education for Deaf and Hard-of-Hearing Students’, Organised Sound, 29(2), pp. 188–196. Available at: https://doi.org/10.1017/S1355771824000074. [Accessed: 09-Jul-2025]
Crenshaw, K. (1991) ‘Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color’, Stanford Law Review, 43(6), pp. 1241–1299. [Accessed: 09-Jul-2025]
Godøy, R.I. (2009) ‘Gestural Affordances of Musical Sound’, in Musical Gestures. Routledge.
Looi, V., McDermott, H., McKay, C., & Hickson, L. (2008). Music perception of cochlear implant users compared with that of hearing aid users. Ear and hearing, 29(3), 421–434. https://doi.org/10.1097/AUD.0b013e31816a0d0b [Accessed: 09-Jul-2025]
Murgia, M.D. (2015) ‘Teaching Synaesthesia as a Gateway to Creativity’, Exchanges: The Interdisciplinary Research Journal, 2(2), pp. 305–313. Available at: https://doi.org/10.31273/eirj.v2i2.118. [Accessed: 09-Jul-2025]
Nichols, S. and Stahl, G. (2019) ‘Intersectionality in higher education research: a systematic literature review’, Higher Education Research & Development, 38(6), pp. 1255–1268. Available at: https://doi.org/10.1080/07294360.2019.1638348. [Accessed: 09-Jul-2025]
Oulasvirta, A. and Hornbæk, K. (2022) ‘Counterfactual Thinking: What Theories Do in Design’, International Journal of Human–Computer Interaction, 38(1), pp. 78–92. Available at: https://doi.org/10.1080/10447318.2021.1925436. [Accessed: 09-Jul-2025]
Reed, C.N., Strohmeier, P. and McPherson, A.P. (2023) ‘Negotiating Experience and Communicating Information Through Abstract Metaphor’, in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery (CHI ’23), pp. 1–16. Available at: https://doi.org/10.1145/3544548.3580700. [Accessed: 09-Jul-2025]
Sadiq, A. (2023) Diversity, Equity & Inclusion. Learning how to get it right. TEDx [Online]. Youtube. 2 March. Available at: https://www.youtube.com/watch?v=HR4wz1b54hw [Accessed: 09-Jul-2025].
Sharma, S. et al. (2025) ‘Lost in Translation: Researchers’ Reflections on Writing in English for CHI’, in Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery (CHI EA ’25). Available at: https://doi.org/10.1145/3706599.3716231. [Accessed: 09-Jul-2025]
UAL. (2025) ‘The Social Model of Disability at UAL’, YouTube. Available at: https://youtu.be/mNdnjmcrzgw [Accessed: 09-Jul-2025]
Yu, N. (1995) ‘Metaphorical Expressions of Anger and Happiness in English and Chinese’, Metaphor and Symbol, 10, pp. 59–92. Available at: https://doi.org/10.1207/s15327868ms1002_1. [Accessed: 09-Jul-2025]
Sadiq (2023)‘s talk on teaching Diversity, Equity & Inclusion deeply resonates with my teaching experience and has sparked reflection on inclusive considerations that I had in my current teaching practice, in particular, on linguistic diversity and inclusion.
A recent intersection between considerations learnt from this social model and my own teaching context is linguistic diversity and inclusion. In an MSc unit that I’m teaching, one of the observations is that a number of bilingual students have difficulties in joining class conversations, reading, or articulating ideas due to language barriers. I thought about adding considerations on language in my teaching, for instance, I relate myself as an EAL (English as Additional Language) learner to figure out how I can adapt my teaching materials with less jargon and more accessible language, or encourage students to join language support sessions offered by the language centre. However, I realised that I have only considered the needs of EAL learners from my own singular perspective, and I certainly can’t speak for all students who all have unique ways of using language and learning with language.
In relation to this, my takeaway from Sadiq’s talk is that assuming what kinds of support are needed from underrepresented groups is not a path to inclusive learning environments. And inclusive support should go beyond “boxes ticking”, to understand that every individual is different, each and every individual is part of the change. In particular, when Asif mentioned his experience of being asked about the view of Muslims while he felt that he certainly doesn’t speak for a group of 2 billion people, or being assumed that “this is the experience of people who look like me”.
Another source that I found on the topic is the paper from Sharma et al. (2025), which highlights how the use of language in academic environments also has a strong connection with race, culture, and identity, and how the in-built monolingual/monocultural context has made it difficult for non-native English-speaking students/researchers to participate in the academic world (Bradbury, 2020). This is especially true in the context of arts and design, where cultural and emotional nuances from other languages are difficult to convey in English. As suggested by Schiffer (2020) (i.e., shifting from a needs-based to an asset-based intervention), it is the learning environment we provide that should remove barriers for non-native English speakers.
For Sadiq’s talk, I found practical challenges in implementing an experiential learning approach in many teaching contexts. It’s not the burden of underrepresented groups to educate others about their needs and preferences, therefore, we do need to learn about needs and appropriate support through a journey of experience with students. However, a long-term engagement with students isn’t always available in a 12-month or 15-month course. My opinion on addressing this challenge is that we as educators need to actively talk to individuals, understand their stories and journeys, and get feedback, in order to provide a timely response to difficulties.
References
Bradbury, A., 2020. A critical race theory framework for education policy analysis: The case of bilingual learners and assessment policy in England. Race Ethnicity and Education, 23(2), pp.241-260, (Accessed: 12 June 2025).
Sadiq, A. (2023) Diversity, Equity & Inclusion. Learning how to get it right. TEDx [Online]. Youtube. 2 March. Available at: https://www.youtube.com/watch?v=HR4wz1b54hw (Accessed: 12 June 2025).
Schiffer, A. (2020) ‘Issues of Power and Representation: Adapting Positionality and Reflexivity in Community-Based Design’, International Journal of Art & Design Education, 39(2), pp. 418–429. Available at: https://doi.org/10.1111/jade.12291.
Sharma, S. et al. (2025) ‘Lost in Translation: Researchers’ Reflections on Writing in English for CHI’, in Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery (CHI EA ’25). Available at: https://doi.org/10.1145/3706599.3716231 (Accessed: 12 June 2025).
