The doctoral dissertation in the field of Computer Science will be examined at the Faculty of Science, Forestry and Technology, Joensuu campus.
What is the topic of your doctoral research? Why is it important to study the topic?
My dissertation focuses on enhancing accessibility education with Artificial Intelligence. This is relevant because digital accessibility is a fundamental requirement of social equity and inclusion (a human right) and a prerequisite for developing a digitally inclusive environment for all in the 21st century.
Approximately 1.3 billion individuals (16% of the global population) experience some form of disability; improperly designed systems create significant barriers that hinder their full participation in daily life, including education and commerce. The development of inaccessible technology is a direct result of a lack of emphasis on accessibility in computing education, leaving graduates unprepared to meet the needs of diverse users.
Therefore, this research is vital to address this pedagogical oversight and reduce the digital divide by equipping future technology developers with the necessary inclusive design skills.
What are the key findings or observations of your doctoral research?
Key findings show Multi-modal Large Language Models (MLLMs) enhance software students' accessibility awareness and skills. Structured engagement with an MLLM led to a 51.7% improvement in UI design heuristic scores, showing tangible application of accessibility concepts.
In this study, AI served as a collaborative partner, perceived by students as valuable for iterative feedback, design reminders, and retrieving WCAG standards. AI feedback was similar to human feedback in immediate performance, but human feedback proved better for lasting support and critical thinking. This research is valuable as it provides empirical evidence on AI vs. instructor feedback for inclusive design, especially in resource-constrained contexts like Ethiopian higher education. It introduces hybrid evaluation metrics to assess AI's pedagogical impact. For the public and practitioners, the key finding is the promotion of a hybrid feedback model.
How can the results of your doctoral research be utilised in practice?
These findings can be practically used by stakeholders to bridge the accessibility gap in computing education.
First, educators and academic institutions can integrate AI into HCI and software engineering curricula. This equips students with practical skills and bridges accessibility knowledge gaps. Educators can use the study's hybrid feedback model: MLLMs provide initial, scalable, standards-aligned feedback (like WCAG checks), allowing instructors to focus on deeper, critical-thinking-focused mentorship.
Second, software developers and designers can leverage MLLMs as collaborative design partners and instant information retrieval tools in their workflows, ensuring User Interface designs comply with accessibility guidelines.
Third, policy-makers can use this work to promote policy reforms that prioritize accessibility as a core competency in computing education. This helps reduce the digital divide by equipping future developers with inclusive design skills.
What are the key research methods and materials used in your doctoral research?
The dissertation followed a mixed-sequential design across four papers. Paper I, a systematic review, assessed accessibility in digital educational technologies (Virtual Labs) to identify design and assessment gaps. Paper II used qualitative methods to explore MLLMs' effectiveness in teaching UI design accessibility and to gather student perceptions. Paper III employed a 12-week quasi-experimental design with computer science undergraduates, using pre-post tests and UI artifact collection to measure MLLMs' impact on accessible UI design comprehension and implementation. Paper IV used a quasi-experimental design to compare AI-generated vs. instructor-delivered feedback on accessibility learning in a web development. Participants were primarily undergraduate computer science students, notably in a resource-constrained context (Ethiopia). Evaluation involved heuristic walk-throughs of UI designs, accessibility audit scores, and SDT to assess pedagogical impact.
The doctoral dissertation of Fitsum Deriba, MSc, entitled Enhancing the Education of Accessibility with Artificial Intelligence will be examined at the Faculty of Science, Forestry and Technology, Joensuu campus. The opponent will be Professor Maha Bali, American University of Cairo (AUC), and the custos will be Associate Professor Mohammed Saqr, University of Eastern Finland. Language of the public defence is English.
For more information, please contact:
Fitsum Deriba, [email protected]
