Academic programs are increasingly recognizing the importance of personal and professional skills and their critical role alongside technical expertise in preparing students for future success in diverse career paths. With this growing demand comes the need for scalable systems to measure, evaluate, and develop these skills. Situational Judgment Tests (SJTs) offer one potential avenue for measuring these skills in a standardized and reliable way, but open-response SJTs have traditionally relied on trained human raters for evaluation, presenting operational challenges to delivering SJTs at scale. Past attempts at developing NLP-based scoring systems for SJTs have fallen short due to issues with construct validity of these systems. In this article, we explore a novel approach to extracting construct-relevant features from SJT responses using large language models (LLMs). We use the Casper SJT to demonstrate the efficacy of this approach. This study sets the foundation for future developments in automated scoring for personal and professional skills.

CHINA has unveiled its latest technological exploit - an AI news anchor who claims to have the professional skills of a "thousand presenters". The virtual robot journalist, named Ren Xiaorong, now exists to deliver the news and answer questions 24 hours a day, yet there is a catch. The creepily human-like avatar, however clever, can only respond from a pre-written script and also peddles the Chinese Communist Party's official line. Ren has joined China's state-controlled newspaper, People's Daily, as their newest employee and claims to have the skills of "thousands of news anchors". She was unveiled this week as the host of People's Daily app, where she can answer questions relating to the "Two Sessions" government conference.

Remember, programming has its own jargon. For example, you will have to deal with such a concept as cycles no matter what language you use. Home programming education is now more affordable thanks to educational technology offering a wide range of courses and programs. During the pandemic, when people have more free time, these resources will help both novice and experienced programmers. The former will get acquainted with the basics of coding, and the latter will be able to hone their professional skills.

Over 120 million workers will have to be retrained within the next three years due to mass disruption caused by AI and automation. That's according to a new study by the IBM Institute for Business Value, based on input from 5,670 global executives in 48 countries. The issue is not just one of jobs being lost, but the lack of a strong skills base to underpin the emerging technologies, with the institute warning that the time it takes to close a skills gap through training is growing. Interestingly, the report finds that executives see technical core STEM capabilities – along with basic computer and software skills – as significantly less important than they did three years ago. According to the global research, the time it takes to close a skills gap through training has increased ten-fold in just four years. In 2014, it took three days on average to close a capability gap through training in the enterprise; in 2018, it took 36 days.

What should be done to prepare for a job displacement across many industries due to AI? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. There has been much speculation about the number of jobs that could be displaced by automation, but, in my view, this might not occur at the dramatic pace some are predicting. That said, there are still some important principles that should guide us as we prepare for the broader advent of AI. It will be beneficial for individuals to focus on developing professional skills for jobs that demand quintessentially human skills - jobs that require creativity and innovation, human interaction, and jobs that are technological in nature, such as software programming. Retraining programs for displaced workers will undoubtedly be critical.

In this paper, we conduct a Markov analysis of learners’ professional skill development based on their conversations in virtual internships, an emerging category of learning systems characterized by the epistemic frame theory. This theory claims that professionals develop epistemic frames, or the network of skills, knowledge, identity, values, and epistemology (SKIVE) that are unique to that profession. Our goal here is to model individual students’ development of epistemic frames as Markov processes and infer the stationary distribution of this process, i.e. of the SKIVE elements. Our analysis of a dataset from the engineering virtual internship Nephrotex showed that domain specific SKIVE elements have higher probability. Furthermore, while comparing the SKIVE stationary distributions of pairs of individual students and display the results as heat maps, we can identify students that play leadership or coordinator roles.

Robots are coming for all our jobs, but we've still got the edge in a few key areas. McKinsey's new report on the future of automation notes that humans are better than robots at: spotting new patterns, logical reasoning, creativity, coordination between multiple agents, natural language understanding, identifying social and emotional states, responding to social and emotional states, displaying social and emotional states, and moving around diverse environments. The report named four skills where robots matched us and five where they beat us. McKinsey finds that almost half of work activities globally could be automated using current technology. Of course, the robots are getting better.

Broadly speaking educational activities can be split into two categories – "Life skills" and "Professional Skills". The Life skills that we all need to learn and the way we learn them have remained relatively consistent across the ages – how we all learn to communicate, socialise and survive. But you can argue that today's education system is skewed towards the second category, the teaching of Professional Skills and it's this category that will face the greatest opportunities and challenges over the next fifty years. While educators prepare their students for a life of learning it's more true to say their role is to prepare students for life long careers. But while that was a relatively simple task in the past it's now much more difficult.

Broadly speaking educational activities can be split into two categories – "Life skills" and "Professional Skills". The Life skills that we all need to learn and the way we learn them have remained relatively consistent across the ages – how we all learn to communicate, socialise and survive. But you can argue that today's education system is skewed towards the second category, the teaching of Professional Skills and it's this category that will face the greatest opportunities and challenges over the next fifty years. While educators prepare their students for a life of learning, it's more true to say their role is to prepare students for life-long careers. But while that was a relatively simple task in the past, it's now much more difficult.