About the project
Objective
Online proctoring systems (OPS) in higher education settings are evolving fast. Their use is encouraged by the need to preserve the academic integrity of online assessment, particularly during the COVID-19 and post-pandemic. The acceptance of and trust in these tools are hindered by several ethical challenges, where students’ privacy is at the top. This postdoc research project aims to identify the main privacy issues around the use of OPS in higher education and how they can be addressed. This project will offer higher education institutions the privacy protection framework to be considered in educational and design practices to address the identified challenges.
About the Digital Futures Postdoc Fellow
Chantal Mutimukwe is a postdoc researcher at the Department of Computer Systems and Sciences (DSV), Stockholm University. Before joining Stockholm University, she worked at the KTH Royal Institute of Technology. Chantal got her PhD in informatics from Örebro University in 2019.
Her PhD research concerned the protection of information privacy in an e-government context. The main research goal was to provide an understanding of how the practices of information collection and dissemination by government organizations can match with the protection of citizens’ privacy. Her primary research interest is data privacy and security protection in an online service context.
Main supervisor
Teresa Cerratto-Pargman, Professor, Human-Computer Interaction (HCI), Stockholm University.
Co-supervisor
Olga Viberg, Associate Professor, Division of Media Technology and Interaction Design, KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
This research project aims to design and develop an AI prototype that strengthens the collaborative work in the sighted guiding partnership. In sighted guiding, the guide bends and offers its arm to the person being guided. Their physical connection allows companions to accomplish navigation collaboratively.
This project will advance perspectives that stress how access and independence are achieved through interdependence, opening up new opportunities to design AI-AT that supports cooperation between people through/with AI, better responds to people’s capacities, and therefore empowers people with VI in social life. Results will be technically innovative because of the increasing adaptability of AI-based AT to contextual, situational and personal factors and the capabilities of people with VI. This differs from previous approaches focused on object recognition and discrete tasks where the end-to-end scenario is easily defined.
Background
Over 30 million people live with Visual Impairments (VI) in Europe. Often this medical condition interferes with the individual’s abilities to perform activities of everyday life since in a world with a predominance of visual content, information access can be hard, tiring and frustrating. Nowadays, people with VI still suffer from exclusion, such as marginalisation and powerlessness, in an increasingly digitalised society. People with VI are early adopters of Assistive Technology (AT), and AI-based AT (e.g., smartphone applications) plays an increasing part in their daily lives. In Human-Computer Interaction (HCI) research, increased attention has been given to independent navigation. Here, AI technologies aim to solve a functional task, where the user follows turn-by-turn instructions to successfully reach a destination and receive physical spatial information, such as the identification and proximity of obstacles and landmarks. A promising alternative approach builds on the interdependence framework that sees AT as a way to extend the relations between one another, focusing on how actors are made more or less able, relationally, through other actors and through AT.
About the Digital Futures Postdoc Fellow
Beatrice Vincenzi, University of London, is a postdoc in the Interaction Design research group at KTH Royal Institute of Technology. She is interested in inclusivity and designing AI assistive technology for/with people with disabilities. She is passionate about exploring design methods which make space for accessibility, interdependence, and AI.
Main supervisor
Marianela Ciolfi Felice, KTH.
Co-supervisor
Sanna Kuoppamäki, KTH.
About the project
About the Digital Futures Postdoc Fellow
Mareike Glöss main research interest is understanding digital transformations and their diffusion into everyday life. She looks at how novel computing technologies are appropriated and how this impacts everyday life. An important part of her work is translating results to inform the design and development of new technologies. Mareike is especially interested in public spaces and how people move through them. She has studied auto-mobility aspects – on the road with Swedish commuters or as a passenger in Californian cabs and Uber rides.
But there is a big chance that auto-mobility will only be a small part of future mobility (at most). Thus, more recently, she started to think about new approaches to mobility away from single modes of transportation. According to Mareike Glöss, we must start thinking much more about personal journeys and intermodal forms of transport. Closely related to this is her interest in Smart Cities. Those are still treated very much like a future vision, but cities are already very smart. Just in a much more chaotic form than we had imagined. And this chaos is something she would like to untangle.
Main supervisor
Rob Comber, Associate Professor, Division of Media Technology and Interaction Designs, KTH.
Co-supervisor
Jonathan Metzger, Professor at the School of Architecture and the Built Environment, Urban Planning and Environment, KTH.
About the project
Objective
This project has two parts – the feminist design part is about reflecting on how feminist ideas can influence social robot design, firstly ensuring that robots do not propagate harmful norms, e.g. about women, but then actually going beyond that to see if we can actively challenge such norms with the design decisions we make. The part on working with children is about exploring how children can be invited to co-design robots designed for their use, and particularly whether we can use human-in-the-loop automation approaches to have them ‘teach’ robots how to support them best.
Background
Involvement in robotics research and development is typically limited to a privileged few who generally lack diversity with regards to, e.g. gender, cultural background, etc., yet are expected to develop systems that work for all. These projects are both fundamentally concerned with trying to bring different perspectives into robotics, motivated by a desire to avoid some of the harms we already see in technology development (biased systems, inappropriate use of gender cues) but also build better systems from the beginning – who better craft robot behaviours than those who are ultimately supposed to benefit from that robot’s deployment?
About the Digital Futures Postdoc Fellow
Katie Winkle, after originally studying to be a mechanical engineer, undertook a PhD in social robotics at the Bristol Robotics Laboratory in the UK. She also undertook work on responsible robotics with partners from the University of Oxford. She defended her thesis ‘Expert-Informed Design and Automation of Persuasive, Socially Assistive Robots’ in Summer 2020. Now, Katie is a Digital Futures Postdoctoral Research Fellow at KTH, based in the Social Robotics group at the Division of Robotics, Perception and Learning. Her research is hugely interdisciplinary, drawing on psychology and the social sciences and the latest in robotics and AI to engineer effective, meaningful, impactful human-robot interactions.
Main supervisor
Iolanda Leite, Associate professor, Department of Robotics, Perception and Learning, KTH.
Co-supervisor
Donald McMillan, Associate professor, Department of Computer and Systems Sciences, Stockholm University.
About the project
Objective
Artificial Intelligence (AI)-based applications in higher education, particularly in Science, Technology, Engineering, and Mathematics (STEM), have grown rapidly in recent years. Educators are on the front lines of this process. They are tasked with acquiring a sufficient understanding of AI to become proficient users and educators. Therefore, it is crucial to ensure they can utilize AI tools responsibly. This project addresses this topic by focusing on fairness in STEM education. We ask: how do STEM lecturers interpret algorithmic-generated recommendations, and how can they ensure they are trustworthy? More specifically, we examine under what conditions STEM lecturers are willing to use AI tools and whether cultural norms regarding algorithmic fairness shape their decisions.
Background
The rapid evolution of AI tools, especially with the emergence of generative AI, is revolutionizing education technology. While this transformation has the potential to enhance educational practices significantly, it also raises concerns about AI fairness and the risk of algorithmic bias that could harm disadvantaged students. Fairness in AI contexts means algorithmic decisions should not create discriminatory or unjust consequences. What is considered fair might also differ between contexts. For example, what is deemed fair in one culture may not be considered fair in another.
Within a common culture, focal points emerge, emphasizing values such as individualism, equality, and uncertainty avoidance. Therefore, perceptions of fairness can vary depending on cultural norms. Thus, it is crucial to consider these cultural nuances when developing AI systems, particularly those involving decision-making processes. There is a need to create AI tools that are not only innovative but also equitable, bridging rather than widening educational gaps.
About the Digital Futures Postdoc Fellow
Before joining KTH and Digital Future, Yael was a PhD student and a post-doctorate fellow in the Department of Science Teaching at the Weizmann Institute of Science. During that time, she also taught at the Open University of Israel. In her dissertation, conducted in the Chemistry Group, she examined the learning behaviours of students and teacher-learners in online, information-rich environments. In her post-doctorate, as part of the “Computational Approaches in Science Education (CASEd)” group, she studied integrating AI technologies in science education, mainly focusing on trust and explainability.
Yael Feldman-Maggor has expertise in advanced quantitative and qualitative methods. Her main research interests are: 1. Education technology 2. Self-regulated learning 3. Integrating artificial intelligence in science education, and 4. Learning analytics and their application to chemistry education. Before starting her academic career, Yael worked in the health sector, developing blended learning strategies for medical professionals. Yael is an editorial board member of the International Journal of Science Education.
Main supervisor
Olga Viberg, Associate Professor, EECS – School of Electrical Engineering and Computer Science, Media Technology & Interaction Design, KTH.
Co-supervisor
Teresa Cerratto Pargman, Professor in HCI, Department of Computer and Systems Sciences (DSV), Stockholm University.
About the project
Objective
My research aims to examine how digitalising urban water, sanitation, and hygiene (WASH) infrastructures can enhance public wellbeing – acknowledging that secure and equitable access to critical WASH infrastructures and facilities is a core human right.
This project draws on the transdisciplinary intersection of architecture with human-computer interaction (HCI) and the shift to human-building interaction (HBI) in querying the ethical implications of spatiotemporally immersive urban spaces.
Background
The digitalisation of cities is a well-established concept; however, the COVID-19 crisis has highlighted the need to optimise existing, Sustainable Smart City urban infrastructures to protect public health and well-being. As society negotiates the long-term threat of disease transmission, digital technologies offer the opportunity to transform human nature, human-built, and human-human relationships.
About the Digital Futures Postdoc Fellow
Stacy Vallis completed her doctoral studies in architecture at the University of Auckland, New Zealand. Her doctoral research responded to the risks to public urban safety posed by natural hazards. It explored the applications of geospatial and drone technologies for rapid assessment of contemporary and historic urban centres to inform the selection of retrofit solutions that generate safer streetscapes.
Stacy’s postdoctoral research will also be driven by overarching themes of public well-being, disaster response, and the integration of emerging technologies in urban centres. She will examine human-centred approaches for optimizing the development of digital technologies in post-pandemic Sustainable Smart Cities. Her work will provide insights into how digitalization is used to enhance public health and well-being by retrofitting the built environment.
Stacy is also passionate about using cultural heritage and intergenerational dialogue as tools for addressing many societal challenges.
Main supervisor
Andrew Karvonen, Researcher, Urban and Regional Studies, KTH.
Co-supervisor
Elina Eriksson, Associate Professor, Division of Media Technology and Interaction Designs, KTH.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
This project aims to develop efficient Deep Neural Network (DNN)-based approaches for solving inverse problems in Structural Health Monitoring (SHM) by integrating physical principles into data-driven models. By embedding physics into the otherwise black-box neural network framework, the approach seeks to enhance both the accuracy and credibility of predictions while improving interpretability.
The study will focus on two key strategies: utilizing high-fidelity Finite Element (FE) simulations to establish correlations between realistic damage parameters and observable, damage-sensitive structural responses, and incorporating system physics to ensure practical and physically consistent outputs. By leveraging these techniques, the project aims to formulate a robust framework for near real-time monitoring and management of large-scale bridge networks, enabling proactive maintenance and improved infrastructure resilience.
Background
Aging and heavily loaded civil infrastructure, particularly bridges, demand proactive maintenance strategies to ensure structural safety and longevity. Catastrophic failures, such as the Genoa bridge collapse in 2018, emphasize the urgency of effective monitoring systems. These challenges are further pronounced by corrosive pollutants and climate change-induced extreme weather conditions, accelerating structural deterioration.
The American Society of Civil Engineers (ASCE) Report Card reveals that the average age of bridges in the U.S. is 57 years, with 7.5% classified as structurally deficient. Similarly, the Trans-EU Transport Highway Network (TEN-T) faces mounting concerns, as numerous highway bridges in countries like France, the UK, and Germany exhibit significant structural vulnerabilities. The financial burden of maintenance, repair, and potential reconstruction, coupled with the risk of cascading failures, makes systematic bridge monitoring an urgent necessity .
Despite decades of research and substantial investments—such as the EU’s funding of FP7 and H2020 projects—industrial adoption of SHM remains limited. To effectively mitigate risks and optimize infrastructure management, the transition from periodic inspections to continuous, data-driven monitoring is essential.
About the Digital Futures Postdoc Fellow
Dr. Sharma is a dedicated researcher specializing in AI and ML/DL-based solutions for SHM bridges, buildings, and offshore structures. Her work focuses on addressing key challenges like data scarcity, rapid damage detection, and real-time frameworks for bridge infrastructure. Dr. Sharma has worked as a Postdoctoral Fellow at the Basque Centre for Applied Mathematics in Bilbao, Spain, where she contributed to assessing the condition of mooring lines in offshore wind turbines using deep learning techniques.
She was involved in the IA4TES (Artificial Intelligence for Sustainable Energy Transition) project, which was part of a government-funded renewable energy initiative. Her expertise includes condition assessment and modeling using tools like CSi-Bridge, Ansys, SAP, and OpenFAST, along with developing AI/ML/DL algorithms in Matlab and Python. Dr. Sharma’s research achievements include many international publications and several well-received conference presentations.
Main supervisor
Raid Karoumi, Professor in Structural Engineering and Bridges, KTH.
Co-supervisor
John Leander, Professor in Structural Engineering and Bridges, KTH.