Workforce Development Applied Research Fund (WDARF) Grant

This project proposes to create an AI teaching assistant using a Chatbot, to assist tutors in providing individualized learning experiences and pathways for their adult learners. The Chatbot will have access to learning analytics information of learners to provide highly customizable content for learners with various needs. For example, for the academically slow progressing CET learners, the Chatbot identifies the domains that require more help and directs them to more relevant and specific resources. The instantaneous and efficient communication allows the learner to be proactive in asking questions, seeking clarifications and assistance on the fly, without the fear of being judged for the quantity or content of their inquiries. The Chatbot can also contain assessment or assignment information to help learners to prioritize their time.

The proposed project seeks to investigate and possibly develop a new evaluation measure known as the “capability-task quotient” (C-TQ) using neurophysiological characteristics of the operator over a time period when handling various task complexities. The aim is to measure and analyse both short and sustained level of neurophysiological activities and characteristics of the operator over a period of time when handling various task complexities in a certain work environment setting. The results would have potential to reshape how humans are hired, allocated or assessed with regards to a given task or job scope. If market size is an indicator, this would translate into billions of dollars of savings with a more efficient, effective fulfilled workforce with greater latitude to minimise human errors. The type of task learning and training required by the operator can also be enhanced.

The research seeks to understand how the learning of new work skills can be promoted amongst lower-income workers. It will also test novel, cost-effective, and scalable interventions that can help lower-income workers learn new work skills. The designed interventions will be implemented in local organisations through a randomised controlled trial to reveal which approaches produce the most benefit for low-income workers and their organisations.

This project aims to understand the impact of psychosocial and institutional factors on the pursuit of lifelong learning among low-wage workers. Furthermore, our aim is to investigate the challenges low-wage workers face in developing and applying transferrable skills in both work and life. The results can be used to refine and develop policy, scheme, or pedagogy that caters to the needs of the low-wage working population and helps them engage better in learning, particularly the transferable skills.

This research aims to address the psychological barriers that could hinder workers’ motivation to engage in lifelong learning. The study introduces two theoretically novel mindsets as potential solutions to reduce these psychological barriers (i.e. do not believe that they can learn new, different kind of skills, fear of failure). The first is an “expansive growth mindset” (the belief that one can take up and master new, different kinds of skills) and the second, an “experimental mindset” (an orientation towards approaching any problem through iterative experimenting—including generating various possible solutions, trials-and-error, gaining feedback, revising methods based on the feedback and trying again). The team will conduct psychological research on Singapore working adults to measure and test the global efficacy of these mindsets. In Aim 1 (i.e. understanding two important psychological barriers to upskilling and reskilling), the team will design and validate measures of these theoretically novel mindsets, and examine their relation with people’s learning attitudes and motivations. In Aim 2 (i.e. designing and testing mindset interventions to address the psychological barriers), the team will design and test scalable mindset interventions to motivate greater engagement in upskilling and reskilling (e.g. greater uptake of SkillsFuture credits). The findings will contribute to possible solutions to enhance lifelong learning in Singapore.