Taha Hassan | Virginia Tech

Grants and Awards

1. Research Grant AutoMR: AI-assisted automation of DIT2 moral reasoning inventory, $20k. (PI)
Source: UA School of Education, July 2025.
Co-PI: Shenglin Li.

2. Travel Grant Human-centered AI for accelerating productivity and outreach in non-profit work.
Source: Communities in Schools of Greater Tarrant County (CISGTC), September 2025.

Research Directions

I. StoryStudio: AI-mediated data storytelling in computing and data science education

CHI'26 SIGCSE'26 ITiCSE'25

Storytelling with data is a crucial communication skill for STEM students. It allows them to communicate evidence-based narratives to a variety of audiences, and enables pathways to careers in journalism, corporate leadership, and higher education. The emergence of LLMs creates opportunities for students to learn and practice their data storytelling skills, including creating insights from raw data, discovering storylines, and understanding audience expectations. To advance this space, we create StoryStudio: an AI-assisted workspace for data storytelling instruction. More Info

StoryStudio's interactive workspace allows users to connect Jupyter notebooks to import visuals. They can then annotate these images, group them into insights, craft narratives, and bring these insights and narratives together into a coherent and compelling evidence-driven story. They can also access instructor and AI feedback during this proccess, and collaborate with colleagues. Our works at CHI 2026 and SIGCSE 2026 describe the design of our AI-mediated feedback process and our pipeline for data story curation and annotation, in that order.

Keywords: Data storytelling, AI-mediated learning, practice-based learning, computing education

II. Data-driven storytelling for institutional support in higher education

CSCW'24 LDT'22 ITiCSE'20

Understanding the adoption and impact of learning management system (LMS) services is central to faculty, university administrators, and instructional designers in better designing and evaluating course content. However, it is challenging and expensive to (a) petition, store, combine, and analyze LMS data to identify meaningful use-contexts, and to (b) evaluate learning, design and support outcomes across departments. More Info

These, in turn, challenge IT leadership’s ability to consolidate evidence on outcomes and best practices. These also threaten faculty’s trust in new LMS tools, course redesign efforts, and professional development initiatives.

In my work at CSCW 2024 and ITiCSE 2020, we address these challenges by developing “Depth-of-Use” (DOU), a first-principles measurement process for assessing user engagement with LMS tools. At Virginia Tech IT, DOU has been successfully applied towards (a) adoption analyses of Canvas LMS services, (b) course redesigns, and (c) pandemic-era allocation of institutional support.

Keywords: Learning management system, adoption, institutional support, learner intervention, instructional design, emergency remote teaching

III. AI trust and explainability in higher education

ITiCSE'25 UMAP'21

Editorial processes are a critical guardrail for a trustworthy user experience of novel information systems. An editorial process represents a consensus, formal or informal, of domain stakeholders’ beliefs about ability, authority, utility, safety, and responsibility. In domains of higher education and religio-spiritual storytelling, these editorial processes are often protected or informally negotiated. More Info

In my work at UMAP 2021, for instance, we discover that faculty vary widely in their allocation of editorial tasks for educational recommendation. Some favor a role-preserving or conservative model, where students can view (and rate or comment on, in a subset of cases) suggested readings, but not create or remove them. Others lean towards a collaborative authoring or egalitarian model where students are actively involved in all or most authoring and feedback tasks. This distinction of editorial task allocations is linked to the trust faculty and teaching assistants place in students and recommendation agents. Role-preservers often believe in instructor prerogatives, burdens of algorithm supervision, and overall student disengagement. Collaborative authors often cite the needs for student feedback and content moderation.

Keywords: Explainable AI, editorial processes, trustworthiness in recommendation, transparency affordances

IV. Storytelling for organizational work in remote and rural settings

GROUP'27 CSCW'25 CHI'24

I examine how modern tools for AI-enabled data analysis, visualization, and storytelling can support the work of organizations operating with resource constraints, such as rural non-profits and trail conservancies. More Info

Keywords: Rural computing, data storytelling, organizational work

V. Trust in digital communities

CHIPlay'22 WWW'19 WebSci'19 ASONAM'19

I examine social media signatures of how perceptions of competence, benevolence, and integrity affect technology use in communities like higher education (e.g., faculty-student trust signals on RateMyProfessors) and the outdoors (e.g., social mistrust in location-based gaming and hiking communities on Reddit). These beliefs can provide a nuanced look at how initial social relationships in online communities are preserved and disrupted by new information systems. More Info

Keywords: Social computing, online communities, higher education, location-based gaming, long-distance hiking