This study examines the impact of lifelong learning on the professional lives of employed and unemployed individuals. Lifelong learning is a crucial factor in securing employment or enhancing one's existing career prospects. To achieve this objective, this study proposes the implementation of a multi-criteria decision support system for the evaluation of training courses in accordance with their capacity to enhance the employability of the students. The methodology is delineated in four stages. Firstly, a `working life curve' was defined to provide a quantitative description of an individual's working life. Secondly, an analysis based on K-medoids clustering defined a control group for each individual for comparison. Thirdly, the performance of a course according to each of the four predefined criteria was calculated using a t-test to determine the mean performance value of those who took the course. Ultimately, the unweighted TOPSIS method was used to evaluate the efficacy of the various training courses in relation to the four criteria. This approach effectively addresses the challenge of using extensive datasets within a system while facilitating the application of a multi-criteria unweighted TOPSIS method. The results of the multi-criteria TOPSIS method indicated that training courses related to the professional fields of administration and management, hostel and tourism and community and sociocultural services have positive impact on employability and improving the working conditions of citizens. However, courses that demonstrate the greatest effectiveness in ranking are the least demanded by citizens. The results will help policymakers evaluate the effectiveness of each training course offered by the regional government.

The 2008 financial crisis that hit the world's economies has had a particularly acute impact in Spain (Guardiola and Guillen-Royo, 2015). It is only since 2014 that Spain seemed to begin its recovery (Martí and Pérez, 2015). However, this recuperation is still far to be acceptable with regard to the labor landscape (Casares and Vázquez, 2018). One of the main Spanish weaknesses that the crisis exposed was the so-called duality of the labor market. Thus, Spain is characterized by the existence of two very different types of workers. On one hand, long term workers on indefinite contracts, having both a very high job security and a very high cost for companies (especially in terms of dismissals) and usually with university studies even for jobs that do not require them.

Educational Data Mining has become extremely popular among researchers in last decade. Prior effort in this area was only directed towards prediction of academic performance of a student. Very less number of researches are directed towards predicting employability of a student i.e. prediction of students performance in campus placements at an early stage of enrollment. Furthermore, existing researches on students employability prediction are not universal in approach and is either based upon only one type of course or University/Institute. Henceforth, is not scalable from one context to another. With the necessity of unification, data of professional technical courses namely Bachelor in Engineering/Technology and Masters in Computer Applications students have been collected from 17 states of India. To deal with such a data, a unified predictive model has been developed and applied on 17 states datasets. The research done in this paper proves that model has universal application and can be applied to various states and institutes pan India with different cultural background and course structure. This paper also explores and proves statistically that there is no significant difference in Indian Education System with respect to states as far as prediction of employability of students is concerned. Model provides a generalized solution for student employability prediction in Indian Scenario.

Educational Data Mining (EDM) is a promising field, where data mining is widely used for predicting students performance. One of the most prevalent and recent challenge that higher education faces today is making students skillfully employable. Institutions possess large volume of data; still they are unable to reveal knowledge and guide their students. Data in education is generally very large, multidimensional and unbalanced in nature. Process of extracting knowledge from such data has its own set of problems and is a very complicated task. In this paper, Engineering and MCA (Masters in Computer Applications) students data is collected from various universities and institutes pan India. The dataset is large, unbalanced and multidimensional in nature. A cluster based model is presented in this paper, which, when applied at preprocessing stage helps in parsimonious selection of variables and improves the performance of predictive algorithms. Hence, facilitate in better prediction of Students Employability.

In eXplainable Artificial Intelligence (XAI), counterfactual explanations are known to give simple, short, and comprehensible justifications for complex model decisions. However, we are yet to see more applied studies in which they are applied in real-world cases. To fill this gap, this study focuses on showing how counterfactuals are applied to employability-related problems which involve complex machine learning algorithms. For these use cases, we use real data obtained from a public Belgian employment institution (VDAB). The use cases presented go beyond the mere application of counterfactuals as explanations, showing how they can enhance decision support, comply with legal requirements, guide controlled changes, and analyze novel insights.

A great deal of ink has been spilt recently following the launch of ChatGPT and the advent of AI that can generate text and answers of a sufficient standard to be used by students in their assignments. Responses, in my opinion, have thus far been rather predictably, if not troublingly, conformist. Writers here in THE and elsewhere have variously suggested that if we can't beat it, we ought to join it and that hybrid or asynchronous communication ought to be embraced and integrated as part of the brave new world of human-machine creativity. More informal discussions with colleagues have suggested that resistance seems futile and that we ought to embrace AI to equip students to operate in a hybrid world of the artificial and the real for the purposes of employability. Some slightly more ambitious voices have suggested that AI-generated assignments make the case for authentic assessment or a more "human" form of assessment even more urgent, but how this might transpire in ways other than falling back on the adage of assessment for learning, constructive feedback and alignment with skills seems less clear.

The field of artificial intelligence (AI) is progressing rapidly and, as a data-driven technology, AI-powered tools lend themselves to a wide range of applications. In this blog we will look at potential opportunities for AI integration into teaching and learning, current case studies of successful use, and the explicit limitations and vulnerabilities of using this technology-driven approach. For assessment, AI-assisted marking and feedback software goes beyond what has been achieved through multiple-choice quizzes. In STEM subjects, AI-powered tools can help significantly in the semi-automation of marking. Rather than grading hundreds of calculus submissions, an AI tool is very fit for purpose, using a technology called'replay grading' ensuring that similar submissions are not marked twice, and grouping similar answers so they can be given a consistent grade at the same time.

Nearly 42% of companies increased their upskilling efforts after the pandemic stalled meaningful economic progress. While 68% of them did it to help meet changes inside the companies, 65% of them did it to enhance their employee's tech skills. More organizations realized the importance of leveraging the best technology-driven solutions in the post-COVID era. As layoffs and furloughs kicked in, even newly independent people understood that if they need to survive in the changing world, they have to upgrade their tech skills. It is now widely acknowledged that digitization helped companies survive the hard times of the pandemic.

Artificial intelligence is influencing the future of almost every business and every person on the planet. AI Future Reality for employ-ability has functioned as the driving force behind new technologies such as big data, robots, and the Internet of Things, and it will continue to do so for the foreseeable future.AI Future Reality for employ-ability The shoe-box-sized device uses machine learning and computer vision to identify and classify numerous "safety events." It doesn't see everything, but it sees a lot. Such as which way the driver is looking while he drives, how quickly he's going, where he's driving, where the people around him are, and how other forklift operators are moving their trucks. "Because of smartphones, camera and image sensors have gotten extremely affordable, yet we collect a lot of information," "When you think about a camera, it truly is the richest sensor accessible to us today at a very intriguing price point."

It takes a typical U.S. employer six weeks to fill a role, which costs roughly $4,000. So the desire to reduce hiring costs and speed up the recruitment process has understandably piqued people's curiosity about AI. In turn, AI vendors promise to help find the right person for the job and screen out unfit candidates more quickly and affordably. For instance, AI-driven candidate assessments analyze people's facial movements, word choice, and tone of voice in an attempt to determine their employability. But what type of ethical, legal, and privacy implications does all this introduce into the hiring process?