A mid-career worker shares their experience of pursuing a master's degree in AI to address anxieties about job disruption and navigate the changing landscape of technology.
Mid-career workers are facing real anxiety about AI. Tackling that by upskilling has been a painful but rewarding process, says Liang Kaixin.New: You can now listen to articles. Add CNA as a trusted source to help Google better understand and surface our content in search results.
SINGAPORE: In 2024, I took up a master’s degree in artificial intelligence and innovation, while holding a full-time job. It was a decision I made on a whim – hoping to better understand a technology that was threatening major disruptions, though part of me also wondered if it could just be a passing fad. Developments since then have eliminated these doubts. Worldwide spending on AI is set to top US$2 trillion in 2026, up from US$1.5 trillion last year, according to IT research firm Gartner. Governments are shaping policies around it. Businesses are recalibrating strategies, while the average man on the street is increasingly engaging with AI tools. Amid all these, the anxiety about job disruption is very real, especially for mid-career workers like me. Many of those who took up the same programme as I did are worried that AI will transform our industries altogether, taking over our jobs. Addressing this anxiety by upskilling, however, has been quite a painful process. As I’ve learned, balancing multiple night classes a week with quizzes, projects, assignments and final exams over two years – all while juggling work and family commitments – requires immense discipline. Fortunately, there are accommodating professors who stream their lectures over Zoom or allow students to catch up via recorded lectures. One even went the extra mile to record his audio separately to give us a clean transcript, while allowing the use of AI tools such as NotebookLM to generate learning aids. Many of my course mates and I are not STEM graduates. Picking up Python and coding from scratch, understanding the mathematical basis of machine learning and even finding a scientific calculator for an exam have been no small feat. In particular, the return to high-school mathematics – from dy/dx and logarithmic equations – after 20 years of working life meant reawakening a part of our brains that had been dormant for a long time. But we soldiered on as grasping them is invaluable to understanding how machine learning works.Commentary: Helping SMEs sustain, not just adopt, AI will be key for Singapore Beyond the theoretical, we also learn practical applications, such as structured prompting of AI large learning models , the use of guardrails to prevent hallucinations and workshopping business models. Take for example, a module on the management of technological innovation with AI. Many of us may feel that LLMs are prone to errors and hallucinations, and resist consulting it for work. Some may veer to the opposite end by relying too much on it without verification. This module teaches us to structure a business idea with some help from AI, but also prompt it effectively with an “8-block system” to ensure we get an output that is closest to what we have in mind. We are also taught to fact check against sources and refine the output iteratively, rather than accepting it blindly. This has been very useful for my work in public relations. My team now uses AI with this approach to brainstorm ideas, gather information and create mock-ups of brand guidelines and website landing page – while ensuring that the work is fully checked by humans in the end.Reflecting on this journey, some of us find it more worthwhile to pursue a master’s degree, which bears the brand name of an autonomous university, instead of short SkillsFuture courses. For the latter, we often do not know the qualifications of the trainers and whether the certificates will be recognised by prospective employers. I was lucky enough to get a sponsorship from my employer but tuition fees remain one area of concern for some of my course mates. Thefor mid-career workers announced in Budget 2024 came in handy to cover almost one semester’s worth of school fees. Hence, some were dismayed that no additional top-ups were announced in this year’s Budget.for those aged 40 and above taking up training courses, this excludes master’s and postgraduate programmes. When this issue was discussed in parliament last year, Senior Minister of State for Education Janil Puthucheary had said this was done to avoid creating “ However, it is far more likely to pivot into a new career armed with a master’s degree, than a short course designed for specific tasks. For mid-career managers who face the unfortunate plight of being laid off, pursuing a master’s degree is also another way for them to bounce back to a similar role in a growth industry such as AI. For those of us who are not trained computer engineers, a master’s degree doesn’t mean that we are becoming “AI engineers”. But the course has taught us advanced ways to harness the power of AI, such as integrating AI workflows into our current jobs, and hopefully, this can help to future-proof us from the next retrenchment or recession. Some may even be inspired to set up new businesses with AI-enabled innovation. Anyone can use ChatGPT to get answers. But to truly upskill with AI capabilities, structured support is vital for those who are determined to stay ahead of the curve. It was a whim that I started this journey, but it is resilience that will help me finish it, as I am reminded by the machine learning quiz I have to get back to studying after this. Liang Kaixin is Senior Associate Director at the Institute of Policy Studies, National University of Singapore. She is currently pursuing a two-year part-time Master’s degree in AI and Innovation. We know it's a hassle to switch browsers but we want your experience with CNA to be fast, secure and the best it can possibly be.
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