Rising unemployment has led to many discouraged job seekers, making it crucial to understand their motivation and performance during this period. Our research aims to support job seekers throughout their unemployment spell by designing and testing technological interventions in three key areas: job search, interview training, and self-presentation. We are designing and building tools using advanced techniques from the field of artificial intelligence with theoretical underpinnings from self-determination theory. The goal of these interventions is to enhance job seekers’ motivation and performance, helping them overcome periods of unemployment.
Project Leader: Pooja Rao
UNIL collaborators: Rafael Lalive, Hélène Benghalem.
External collaborators: Michele Pellizzari (UNIGE), Matthias Kliegel (UNIGE), Tawanna Dillahunt (U. Michigan), Dinesh Babu Jayagopi (IIIT Bangalore)
Publications:
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Pooja S.B. Rao, Laetitia Renier, Marc-Olivier Boldi, Marianne Schmid Mast, Dinesh Babu Jayagopi, and Mauro Cherubini. On the potential of supporting autonomy in online video interview training platforms. International Journal of Human-Computer Studies 191 (2024): 103326. https://doi.org/10.1016/j.ijhcs.2024.103326
- Tawanna R Dillahunt, Lucas Siqueira Rodrigues, Joey Chiao-Yin Hsiao, Mauro Cherubini, Self-regulation and Autonomy in the Job Search: Key Factors to Support Job Search Among Swiss Job Seekers, Interacting with Computers, 2022;, iwac008, DOI: 10.1093/iwc/iwac008
- Mauro Cherubini, Alex Jiahong Lu, Joey Chiao-Yin Hsiao, Muhan Zhao, Anandita Aggarwal, and Tawanna R. Dillahunt. 2021. Elucidating Skills for Job Seekers: Insights and Critical Concerns from a Field Deployment in Switzerland. In Designing Interactive Systems Conference 2021 (DIS’21), June 28-July 2, 2021, Virtual Event, USA. ACM, New York, NY, USA, 25 pages. DOI: 10.1145/3461778.3462049, [presentation video] [teaser video]
