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Numerous education providers are running risks to become less relevant in an evolving global/regional environment. Some experts suggest two most optimal strategies to elevate the threats and made decisive transformations – modular learning and AI-driven personalization. With the right technological background, these two strategies can facilitate education adaptation, improve learning outcomes, enhance “operational efficiencies”, as well as create more flexible and future-oriented education systems.
Background
Since the beginning of the present century, several main challenges have been guiding the states’ political economy, which required transformations in such spheres as sustainability and digital transition, circular economy and climate change adaptation, green growth and renewables, etc. These and some other challenges dramatically affected socio-economic fabric, corporate management and states’ governance. National decision-makers have been –often desperately – trying to tackle the challenges by modernising old-fashioned growth patterns.
New facets of socio-economic development inspired by these challenges required adaptive work-forces, new skills and/or re-skilling to be used in a modernized labor market; hence, governance structures have been turning to a “silver bullet”, i.e. transformations in education and training systems aimed at supplying necessary labor forces adaptable to the most vital aspects in progressive growth.
Contemporary governance in most states has correctly realized that only feasible way includes modernizing educational systems and turning them towards re-settled national socio-political priorities. Never before national education, science and research systems faced such a fundamental transformation; no doubt it is being presently popularly called an “education revolution”.
However, in some venues education is not taking seriously and often without a due adaptation of changing workforce’s need and re-skilling due to contemporary global challenges. Some fresh approaches to “modernized education provision” have appeared recently, which might be of interest to our readers.
First, modular education
While traditional degree programs continue to provide a robust foundation for life and work, modular learning unlocks greater accessibility by enabling students to pursue education in smaller, stackable modules and pathways, often called micro-credentials.
This model allows learners to customize their education to match their individual goals, progressing at their own pace, whether they are recent graduates, adult learners, career changers or working professionals.
So-called micro-credentials enable universities to be more responsive to employer needs: thus, education providers can collaborate with industry and corporate facilities to co-design courses that ensure that graduates are fit for new employment facilities, while addressing skills gaps in high-demand fields, such as digital technology, data science, healthcare, renewable energy, etc.
Such an approach is enabling universities to respond swiftly to local and global market demands.
Second, AI-driven personalization
As modular learning gains attraction, there is a need to a “personal approach” to graduates: i.e. in this approach an AI-driven solution is most appropriate, as AI-models have already reshaped the work of education providers by integrating adaptive learning algorithms with the modular transition.
The use of digital technologies in education process has numerous advantages: e.g. to provide a personalized info-support for individual student’s progress. With AI-powered tools, as some experts correctly assume, universities can assist students “to train” existing information (aka, the notion of GPT as a pre-trained facility), to provide adjustments and speed to learning content while ensuring each student’s specific and altering needs.
By incorporating advanced digital analytics and modern AI tools, universities can create a more inclusive, responsive educational process based on both resolving modern challenges and providing adequate knowledge and experience for each graduate.
Conclusion
Modern universities have to be future-oriented; it means they have to optimize existing (mostly outdated) education provisions methods to answer the pressing needs of combining relevant knowledge and practical skills for contemporary labor market. Only this approach can “lead to losing relevance for learners and employers needed presently flexible, skills-focused and operational educational models”, argues Martin Bean*).
It is true that modern national labour markets increasingly need “new practical skills” coped with academic credentials; both provide graduates with micro-credentials (with adequate knowledge of digital solutions) and a personalized approach to the “individual success”.
Besides, the process of “universities-partnering with corporate sectors” provides an optimal background on which the expected employers would “co-develop” micro-credentials with the corporate needs; the latter would serve as “a strategic opportunity for a nation-wide socio-economic progress”, notes M. Bean and adds that “by leveraging technology and reimagining educational delivery, higher education can meet the needs of today’s learners and help drive a dynamic, skills-orientated economy fostering innovation, employability and long-term economic growth”.
*) Source and citations from Professor Martin Bean’s article in: https://www.universityworldnews.com/post.php?story=20250527093324372&utm_source=newsletter&utm_medium=email&utm_campaign=COMMNL8037