Behavior-Change Technology to Support Job Seekers

This research aims at supporting job seekers through the unemployment spell. We are designing and testing technological interventions in the following areas: a. job search; b. interview training; c. self-presentation.  We are building tools using advanced techniques from the fields of Artificial Intelligence, Natural Language Processing and Computer Vision to help job seekers enhance their interview related skills. The goal of these interventions is to increase the sense of self-efficacy of the job-seekers. The interventions build on Self-Determination Theory and Theory of Planned Behaviour.

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)


  • 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]