Research Interests
Research Interests
Fitsum Deriba’s research focuses on the intersection of Human-Centered AI (HCAI) and Smart Learning Environments. His work is dedicated to the design, development, and rigorous evaluation of accessible, ethical, and intelligent digital learning technologies to enhance Inclusion, Equity, and Diversity in EdTech.
Area related to the responsible and effective integration of AI in learning, ensuring human needs and values are paramount.
Human-Centered AI & Ethics: Expertise in key HCAI principles including Explainability (XAI), Algorithmic Bias, Data Privacy, Human Oversight, Transparency, Trust, and Accountability. Focuses on Human-AI Interaction, ensuring AI feedback and decision-making systems enhance human autonomy and critical thinking (Human-in-the-Loop, Co-Pilot).
AI Education & Smart Learning: Developing frameworks and curricula for Responsible AI in education. Explores the ethical and practical integration of AI to create Personalized Learning pathways through Intelligent Tutoring Systems, Conversational Agents, and the use of Virtual/Augmented Reality for enhanced learning experiences.
Computational Thinking & Programming Education: Expertise in CT pedagogy (K-12/Higher Ed), curriculum development, and the assessment of thinking skills using various tools (e.g., visual programming, robotics).
Creating digital learning resources that are universally usable and accessible to all learners.
Human-Computer Interaction & UX: knowledge of User-Centered Design, Iterative Design, Usability Engineering, User Interface /User Experience Design, and Usability Testing, particularly for educational contexts (e.g., programming apps, Virtual Laboratories).
Web Accessibility & Inclusive Design: Expertise in Auditing and Evaluating digital accessibility (Web, Mobile, Software) against standards like WCAG Conformance Levels (A, AA, AAA). Promotes Universal Design for Learning (UDL) and leverages Assistive Technologies (AT).
Mitigating Algorithmic Bias and addressing Harm Prevention to advance Inclusion, Equity, and Diversity in educational technology deployment.
Applying analytical and methodological techniques to understand learner and evaluate technological impact.
Learning Analytics & Educational Data Mining: Focus on Predictive Modeling (Classification, Regression Analysis), Behavioral Pattern Analysis (Sequential Pattern Mining), Early Alert Systems, and developing LA Dashboards.
Learning science Methods: Proficiency in a wide range of research methods including Quantitative/Qualitative/Mixed Methods Research, Experimental Design, Design-Based Research, Statistical Analysis, Systematic Review, Bibiometric analysis, Content/Thematic Analysis.
Research Analysis Toolkit: Utilizing techniques like Text Mining for Sentiment Analysis, Social Network Analysis, and Bibliometrics (Citation Analysis, Co-authorship) for scientific literature mapping and research trend analysis.
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Research IDs
Google Scholar: https://scholar.google.com/citations?user=vCP674EAAAAJ
Web of Science : NKQ-4070-2025
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<<Open for Grant collaboration, MSc supervision, and Teaching opportunities >>
Possible supervision areas/idea but not limited to:
HCAI & Ethics: Ethics, Trust, Explainability (XAI), Accountability, Algorithmic Bias, Data Privacy, Human-AI Interaction.
EdTech & Learning Technology: E-Learning, Blended Learning, Personalized Learning, Virtual Reality, Augmented Reality, Computational Thinking.
Learning Analytics: Predictive Modeling, Sequential Pattern Mining, Student Trajectories, Grade Prediction, Early Alert Systems, Dashboards, Behavioral Pattern Analysis.