LABOUR MARKET CHALLENGES: PRIORITY TASKS IN SPECIALIST TRAINING
LABOUR MARKET CHALLENGES: PRIORITY TASKS IN SPECIALIST TRAINING
Dr. Saodat Nasirova, Rector of Tashkent State University of Oriental Studies
The strategic goals and objectives for reforming the national education system set forth by the President of the Republic of Uzbekistan, Shavkat Mirziyoyev, are opening up entirely new horizons for the development of higher education, including through the fundamental transformation of universities.
Tashkent State University of Oriental Studies is a unique institution with a distinguished history. Its uniqueness lies in the fact that, at every stage of economic development and scientific and technological progress, the University has reformed its education and management systems in strict accordance with the demands of the times. As a result of this strategy, the University has become a training ground for generations of outstanding professionals, including 12 academicians, more than 500 Doctors and Candidates of Sciences, over 70 heads of ministries and government agencies, and representatives of Uzbekistan’s diplomatic and trade and economic missions abroad. More than 80 professors and academic staff members of the University have received prestigious state awards.
At present, Tashkent State University of Oriental Studies (hereinafter referred to as the University) has more than 8,000 students aged between 18 and 25. Over the past ten years, 5,800 students have completed undergraduate programmes and 2,300 have graduated from master’s programmes across 38 fields of study. This raises two legitimate questions: What proportion of our graduates have found employment in their fields of specialisation? Has the higher education they received been sufficient to ensure successful professional careers?
When admitting up to 900 students annually, we must assess the actual prospects of our graduates finding employment in an increasingly dynamic labour market. It is well known that some students are primarily motivated by the opportunity to obtain higher education funded by the state budget. At this stage, however, they may not have a clear understanding of whether their qualifications will be in demand once they complete their studies.
Meanwhile, artificial intelligence (AI), data centres and cloud technologies are having an increasingly profound impact on the economy, business, education and science. According to the International Monetary Fund (IMF), AI is approaching the global labour market like a “slow-moving tsunami”, with recent university graduates potentially among those most vulnerable to its effects.
The IMF estimates that approximately 60% of jobs in advanced economies are exposed to the impact of AI, compared with 23% in low-income countries. In the near future, around 40% of jobs worldwide could be transformed through the adoption of AI.
According to estimates by McKinsey & Company, approximately 400 million people could lose their jobs entirely or partially as a result of AI adoption in the foreseeable future. In some countries, a university degree can no longer be regarded as a guarantee of employment.
According to IBM, an increasing share of employment opportunities will favour people who know how to work effectively with AI. Of course, concerns about declining employment have arisen at every stage of scientific and technological progress. Industrial revolutions—including the transition from manual to machine labour, the introduction of electricity, the development of computers and nuclear energy, and, more recently, widespread digitalisation, advances in biotechnology and the development of AI—have reduced employment in the most labour-intensive areas. At the same time, however, these processes have created entirely new professions.
AI is compelling us to rethink the very concept of higher education. In response to these challenges, leading universities around the world are fundamentally transforming their education and management systems.
For example, the University of Oxford is integrating AI into its tutorial system and providing students, faculty and staff with access to ChatGPT Edu. An AI Study Assistant has been deployed across leading educational platforms, including Law Trove, Business, Politics and Science Trove. These tools help students analyse textbooks and engage more deeply with course material.
Harvard University actively employs adaptive learning technologies and has its own version of ChatGPT. Participants are provided with access to AI Sandbox, as well as Claude Code and OpenAI Codex. Some courses have introduced interactive avatars for language learning, simulating conversations with native speakers, alongside AI tutors available around the clock. Harvard’s CS50 course is also used to better understand patterns in students’ use of AI tools and to integrate these technologies into learning and classroom activities.
The Massachusetts Institute of Technology (MIT) is increasing its emphasis on collaborative problem-solving. Traditional essays and written tests are increasingly being supplemented by oral examinations, project demonstrations and the defence of portfolios compiled throughout the semester. The University of Washington, the University of California and the University of Michigan use Microsoft Azure to develop integrated tools designed to protect data privacy while ensuring cost-effectiveness.
It must be acknowledged that the transition to AI and new technologies at Tashkent State University of Oriental Studies is progressing unevenly. Some members of the academic staff and some students continue to rely on approaches rooted in the past. In this regard, the University faces the task of transforming itself to improve the quality of education and develop advanced digital competencies.
To achieve these objectives, we plan to pursue the following priorities:
First, adapt higher education to the requirements of AI technologies.
Second, integrate AI-related courses and practical applications across all fields of study, regardless of specialisation, and establish AI competencies as a fundamental requirement for both students and academic staff.
Third, move towards interdisciplinary education to address complex challenges associated with AI.
Fourth, introduce AI tools to support feedback on students’ essays, coursework and graduation theses, as well as master’s dissertations, doctoral research and the scholarly work of academic staff.
Fifth, develop skills-based learning and research-based education.
Sixth, provide students with real-world case studies from employers. Establish a digital database of employers’ quality standards and competency requirements for in-demand profes
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