In this extra post, Andrei Ghira, Eduardt Nica, and Aurora Constantin, from the School of Informatics, and Gennaro Imperatore, from Computer and Information Sciences (University of Strathclyde), discuss the learning outcomes for students and educators from two MSc projects aimed at embedding accessibility into the curriculum. This is post is the second of two, which look at how accessibility can be embedded in Computing Education during the MSc dissertation process. Part 2 builds on the foundations outlined in Part 1, and focuses on what students and educators have learned when accessibility became the starting point for real-world projects.
What students learned from their MSc dissertation projects
In Part 1, the authors describe two MSc Artificial Intelligence dissertation projects, NaviWheel and AURA, which required students to design accessible digital systems. NaviWheel is an Android mobile application designed to help wheelchair users navigate urban environments safely and independently. AURA (Accessible User-centric Responsive Application) was a prototype designed to make museum environments more inclusive for visitors with disabilities. By engaging directly with users, real-world contexts, and complex data, the students developed a more holistic understanding of how design decisions shape not only system functionality, but also inclusivity, fairness, and the overall user experience. Below, we share the key learning outcomes that the students engaed with during the design process, as well as lessons for educators in Computing and beyond.
Key student learning outcomes:
- Accessibility as a system-wide principle: Recognising that accessibility must be embedded across the entire system, from data and architecture to interfaces, rather than treated as an add-on.
Example: In NaviWheel, accessibility was integrated into mapping, reviews, and safety features (e.g. emergency button), while in AURA, it shaped onboarding, navigation, and personalised content delivery. - Designing with, not for, users: Engaging directly with people who experience accessibility barriers helped ground decisions in lived experience and challenged initial assumptions.
Example: Feedback from wheelchair users informed features such as simplified navigation in NaviWheel and adjustable content complexity in AURA. - Accessibility as a lived, contextual experience: Understanding that accessibility is shaped by environmental, social, and infrastructural factors, such as inconsistent spaces or incomplete information.
Example: Users highlighted how missing or outdated accessibility information in urban spaces (NaviWheel) or museums (AURA) affects independence and experience. - Working with community-driven data: Learning to design systems where users contribute data, while addressing challenges of data quality, validation, bias, and incompleteness.
Example: NaviWheel relies on user-submitted locations and reviews, while AURA incorporates user preferences to personalise content and interactions. - Representation and participation: Recognising that not all users contribute equally, and that datasets may reflect imbalances that need to be considered in design.
Example: In NaviWheel, some areas may have richer accessibility data than others; in AURA, personalisation depends on the diversity and completeness of user input. - Ethical and responsible design: Reflecting on trust, safety, privacy, and moderation when working with user-generated content and real communities.
Example: NaviWheel required consideration of how to validate user-generated reviews, while AURA raised questions about handling personal profile data and preferences. - Navigating real-world complexity: Adapting to messy, evolving conditions rather than idealised scenarios, requiring iterative and flexible design approaches.
Example: NaviWheel deals with constantly changing urban infrastructure, while AURA had to simulate indoor navigation and adapt to varied museum layouts. - Interdisciplinary thinking: Integrating perspectives from AI, Human–Computer Interaction, accessibility, and social contexts to address complex problems.
Example: Both projects integrated AI (summaries, personalisation), HCI (interface design), and accessibility principles to create cohesive user experiences. - Holistic evaluation: Assessing systems not only for technical performance, but also for usability, accessibility, and overall user experience in context.
Example: Projects considered safety, autonomy, confidence (NaviWheel), but also sustained attention, retention of and reflection on the information received.
Lessons for Educators
While these projects highlight what students can learn, they also offer insights into how accessibility can be meaningfully embedded through teaching practice, supervision, and curriculum design.
The supervision model supported students through a team with complementary expertise, including two supervisors (the second being from another university). This provided students access to different perspectives across technical development, research, and accessibility. Students were also connected to a wider network, including alumnae, PhD students and experts in HCI, UX, and industry accessibility (e.g. a Head of Accessibility, Accessibility Analyst). Regular meetings provided opportunities to reflect on progress, design decisions, and challenges, particularly around user feedback and data.
- Frame accessibility as a starting point: Positioning accessibility at the core of projects encourages students to treat it as a driver of design and innovation rather than a constraint.
Example: Students, encouraged by supervisors, defined their systems around real accessibility challenges, shaping decisions across functionality, interaction, and architecture. - Enable authentic engagement with users and expert communities: Direct interaction with diverse users and practitioners deepens understanding and grounds design decisions.
Example: Supervisors facilitated initial connections with users with disabilities, accessibility experts, industry practitioners (e.g., accessibility leads), and former students working on similar projects, enabling students to engage with diverse perspectives throughout their work. - Support learning through diverse guidance and reflection: Exposure to complementary expertise and regular reflection helps students navigate complex design challenges.
Example: Students benefited from a multi-layered support structure (including cross-institutional and PhD-level input) and used regular meetings to reflect on design decisions, user feedback, and data-related challenges. - Emphasise process alongside outcomes: Learning is strengthened when students are assessed on how they engage with complexity, iterate, and respond to feedback, not just the final artefact.
Example: Supervisors encouraged students to iteratively refine their systems by incorporating user feedback, documenting how their designs evolved, and reflecting on the challenges encountered and trade-offs made throughout the process. - Expose students to real-world complexity and ethical challenges: Working with user-generated data and real contexts introduces issues of data quality, bias, trust, and representation.
Example: Supervisors supported students in dealing with user-generated data (e.g., reviews, preferences), prompting discussions on validation, reliability, and bias. - Promote system-level and interdisciplinary thinking: Accessibility requires integrating multiple components and perspectives.
Example: Projects combined AI, user interfaces, and data systems, while drawing on knowledge from HCI, UX, and accessibility practice. - Highlight transferability across disciplines: Accessibility-focused approaches can inform project-based learning beyond computing.
Example: Engaging with diverse users and real-world challenges is equally relevant in fields such as design, education, and healthcare.
Beyond Computing: Embedding accessibility in Higher Education
These projects show how a lightweight, experience-led approach can help students develop both disciplinary expertise and a deeper understanding of responsible, inclusive practice. By working on real-world challenges, engaging directly with users, and embedding accessibility from the outset, students created solutions grounded in genuine needs while building interdisciplinary skills.
Importantly, this approach can extend beyond Computing. Across disciplines, from engineering to healthcare to education, accessibility can be approached not as an added requirement, but as a meaningful way to engage with diverse users and real-world contexts. In doing so, it supports both students and educators in seeing accessibility as an integral part of learning and practice, rather than an additional burden.
Read Part 1 here: Embedding accessibility in Computing Education: Examples from MSc dissertation projects (Part 1)
Andrei Ghira
Andrei Ghira is a recent MSc graduate from the School of Informatics, University of Edinburgh. They are currently working as a Software Engineer and are interested in building technology to solve accessibility problems.
Eduardt Nica
Eduardt Nica is a recent MSc graduate from the School of Informatics, University of Edinburgh. They are currently working as a Software Engineer and are interested in building technology to solve accessibility problems.
Aurora Constantin
Aurora Constantin is a Lecturer at the University of Edinburgh’s School of Informatics and a Senior Fellow of the Higher Education Academy. She leads the School’s teaching support staff training programme and co-organises the Computer Science Education Group and the Accessibility and Inclusivity Working Group. Her research spans Human-Computer Interaction (including Child-Computer Interaction), applied AI, Educational Technology, assistive technologies and Participatory Design.
Gennaro Imperatore
Gennaro Imperatore is a Teaching Associate at the University of Strathclyde, with a particular interest in accessibility. He is an HCI and mobile development expert. His work focuses on developing inclusive and accessible solutions with real-world impact to assist those with disabilities.
