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Alaa H. A Abusafia

dblp:433/4875 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0000-2051-9510ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 44% Human-robot interaction · 44% Design research and methods · 13%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
automated vehicles
1.012026
Exploring Women's Perspectives on Learning and Trust in Automated Vehicles: A Socio-Ecological Lens · CHI 2026
Human-AI interaction › automation
trust in automation
1.012026
Exploring Women's Perspectives on Learning and Trust in Automated Vehicles: A Socio-Ecological Lens · CHI 2026
Design research and methods
feminist HCI
0.312026
Exploring Women's Perspectives on Learning and Trust in Automated Vehicles: A Socio-Ecological Lens · CHI 2026

Methods — techniques the papers use, named apart from their topics

thematic analysis · 2.0socio-ecological framework · 2.0semi-structured interviews · 2.0
YearPublicationVenuePosition
2026 Exploring Women's Perspectives on Learning and Trust in Automated Vehicles: A Socio-Ecological Lens
abstract
As automated vehicles (AVs) move toward mainstream adoption, understanding how users learn about and build trust in them is critical. Prior research shows that women hold safety concerns and report low trust and familiarity with AVs. While limited exposure is often cited as a cause, growing evidence indicates that women’s needs, preferences, and safety priorities remain insufficiently addressed in AV design and governance. We conducted ten dyadic and five individual semi-structured interviews with fifteen women drivers, guided by feminist HCI principles. We then analysed the findings for themes and through a socio-ecological framework to explore trust and learning across individual, interpersonal, institutional, community, and policy levels.
Alaa H. A Abusafia, Ronald Schroeter, Alessandro Soro
CHI1