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The “educational provision” is presently shared with digital progress: the effects are dealing with the modern challenges and rooted in the need to train newly appeared workforce to “cover” the emerging employment requirements; but not only! At least two aspects heave to be dealt with: quickly providing new skills for evolving industries and finding proper “assistance tools” from the digital technologies in education and training.
Instead of introduction…
The digital inheritance of “quasi-intellectual power” embedded in various AI models and algorithms is politically and educational neutral: they are historical artefacts (i.e. transformed and/or trained data) from different and often “privileged” languages, national histories and international markets.
It is well-known that the total “stock of knowledge” is constantly increasing; good news is that much of it is already “digitally stored” in the Internet and “clouds”. The process is varied and complicated, as the global “digital libraries” and networks are greatly surpassing any individual’s capacity to acquire and possess these volumes.
Actually, the data storage and processing have been the main tasks behind the present extraordinary AIs deployment in numerous science’s spheres: i.e. in law and engineering, in medicine and management, to name a few. In these “data-training” the Gen AIs appeared to be the only feasible and excellent means that could hold knowledge that most humans are just physically unable to command (and never will) in our so-called “technological societies”.
The general observation is that since the digital inception about four decades ago, the GenAI “managed” to gain the “intellectual capacity” in education and research (besides, or next to), human input, understanding and control. Most people and even scientists are actively using AIs in saving time in organising a lot of otherwise time-consuming efforts to process enormous piles of information; and many have discovered these AIs as quite useful, helpful and often even highly productive “assistants”.
The human thought has presently evolved into a mental system “resembling computerised information processing”: the dominating evidence is that the AIs are presently becoming an “instrumental rationality” at the time when “sanctioned knowledge” is disseminated and “managed” by scholarly digital gurus, noted Dr. Steven P Dandaneau, associate professor of sociology at Colorado State University, US.
General references from: https://www.universityworldnews.com/post.php?story=2025102908070722
Some practical digital outcomes in education and training are attributable to the inherent assimilation process of human life to any new technologies. The general assumption is that the new digital (mostly AI-tools) have “assembled and consumed” all available knowledge in every language and in all spheres of knowledge and science, even movies and poetry on the computer’s codes and algorithms through complex mathematical manipulations.
Present human life’s digitalisation process leaves individuals, as experts suggests, with only one practically prudent option: assimilating this “digitalisation” into regular use, i.e. both learning to use AI applications in mental capacities to think and “enjoy AI-generated content”) in the hope that numerous new digital technologies would help people along human progress.
Just like the not-so-distant calculators, and word processors, etc. provided humanity with additional support to people’s abilities in the “pursuit for happiness”.
First, towards the skills and micro-credentials
With a growing speed, more higher education institutions, employers and professional organisations are offering micro-learning with the certified credentials of acquire skills. The newly established education-training systems have suggested a more formally recognised “micro-credentials” as a prudently emerging facility alongside already existing “credit frameworks” in educational policies at the national, regional and global levels.
The provision of micro-credentials around the world has grown phenomenally, spurred on by employer and learner demand, massive online learning platforms and a pandemic that accelerated digital and short course learning. Credit recognition is the new frontier, as universities and countries work to make micro-credentials matter.
At the end of November 2025, at a webinar titled “Recognising micro-credentials: Lessons from the world’s best”, participants revealed the evolving global micro-credential landscape, emerging tools and frameworks for credit recognition and the implications for academic work, as well as curricula, the work of higher education institutions and that of the learners.
Without formal recognition, short courses remain peripheral training, but anyway – they are becoming the “legitimate components” of higher education. Recognised micro-credentials enable learners to earn ‘stackable’ credits towards degrees, easily re-enter formal learning throughout their lives and move professionally across institutions and countries.
Recognised micro-credentials from respected providers is giving the employers confidence that credentials meet academic and industry standards, while signaling quality and rigor in a crowded educational marketplace. As evolving technologies reshape the work of education providers, recognised micro-credentials can provide people with flexible and trusted pathways for skills acquisition through the lifelong learning.
However, the questions still remain, such as: what do ‘micro-credentials’ and ‘recognition’ actually mean; how do recognition systems turn micro-credentials into degree credits; which national and international frameworks are leading the recognition of micro-credentials; how might effective recognition boost the value, quality and equity of micro-credentials…?
Source for possible answers in: “Recognising micro-credentials: Lessons from the world’s best”. (2025), in: https://www.universityworldnews.com/post.php?story=20251017111744884
Second, the digital education…
AIs are no longer futuristic concepts: they are becoming a classroom companion: i.e. from personalized tutoring systems that adapt to each student’s learning pace to automated grading tools that free up teachers’ time, AI is quietly reshaping the educational landscape. The digital solutions are presently helping educators identify learning gaps, design more inclusive curricula, and even predict student success.
Artificial intelligence models have entered with astonishing speed the universities and high schools, their accreditation and grading systems, lectures, curricular and research. The AIs assist students to draft essays and articles, making readings summarises readings and generate studies’ plans in just seconds. However, within the digital “fluency” lies some troubling questions: whose “intelligence” and in what languages, what part of the world the model represents, and what “interests” the AI serve, generally?
And as opportunities grow, the questions multiply: how to balance efficiency with empathy, what would happen with the teacher’s competence/role when “teaching robots” appear? Analysing these questions would help in contemplating such issues as: e.g. how AI is changing education, and what kind of learning modern society wants for the next generation.
Those countries and regions in the world with access to premium digital platforms, high-speed internet, and digital mentors can easily “translate” machine outputs into learning advantages; but those countries and regions without such facilities are being left outside the digital progress.
However, there is a positive sign: the newly appearing – in some universities – the so-called CPUs – Central Processing Units – have been aimed at advancing AIs to a point where they might displace the digital providers as the universities’ principal locus of education and “knowledge production”.
As AI enters social strata, it multiplies inequality, as experts confirm, e.g. in Africa and across the Global South, access to reliable internet, computing power and digital literacy is profoundly uneven. The danger is that AI will not only mirror these inequalities, but will magnify them. If we are not vigilant, the algorithm will become the new frontier of exclusion.
AI tools promise efficiency, but they often amplify structural divides. Students in well-resourced institutions gain instant access to personalised tutoring and data-driven feedback. At the same time, those in underfunded schools and universities are left with broken connectivity and outdated devices. What appears as a “technological revolution” for some becomes a new layer of deprivation for others. The data that feeds generative AI is mainly drawn from the Global North: i.e. English dominates in most language models and the western-type logics now defines the “core aspects” of intelligence, knowledge and success.
Thus, AI extends the epistemic hierarchies of colonialism, making Western ways of acquiring education and knowledge to become universal while marginalising local and indigenous traditions.
Education providers are facilitating new learning models as e.g. a dialogue (or a conversation) between human insight and machine inputs, helping students build critical thinking and creativity. The two “sides” are not competing with AI: the task is more complicated – to teach students to “think alongside AI”, understand how algorithms influence their perceptions and, finally regain control over those digital systems.
Hence, the “pedagogical process” in teaching is altering: thus, drafts, reflections and feedback (to name a few) are placing emphasis on a student’s intellectual development: e.g. assessment moves beyond just having answers to cultivating genuine understanding. It appreciates curiosity, persistence and ethical integrity – qualities that algorithms cannot replicate. In this context, assessment becomes an integral part of learning design and the cultivation of critical epistemic skills.
All kind of education providers have to learn/teach students the ethics of a new “post-authorship”, the reality of humans writing with generative AI. In this new education pattern of co-creation, the authorship’s issue becomes a shared and negotiated act. Students must learn to acknowledge assistance, as well as question algorithmic outputs and exercise moral judgment in deciding when and how the AIs were used. This “accommodation legacy” has transformed scientific writing into an “ethical issue” to practice the notion that using AI tools was a “c0-partnership” in research activity (rather than a substitute), which strengthened the innovation process rather than weaking its integrity.
Source: Fataar A. “Decolonising the algorithm: Teaching for justice in age of AI” (2025), in https://www.universityworldnews.com/post.php?story=20251029072506658
In the conclusion…
Finally, something about the so-called modern “shared knowledge” concept; generally, “knowledge sharing” is the process through which individuals, teams and/or organisations exchange information, skills, insights and experiences to support learning, collaboration, innovation and inclusive decision-making.
Examples of shared knowledge include academic disciplines like chemistry, company-wide documentation (like user manuals and project plans) and cultural elements such (as language and customs). Other examples include technical and procedural knowledge, such as software codes or recipes, and knowledge shared through communication, like presentations, articles and conversations.
Presently, both the general public and governance bodies have been accustomed to the idea that the “consequential knowledge” (in contrast to “private knowledge” possessed by a single person) belongs to society as an information resource upon which any “public guidance” can be drawn.
Recently, huge volumes of vital practical knowledge have been embedded in the digital and AI apps/systems; the “shared knowledge” has become almost an exclusive province of digital experts, specialists and even institutions/ organisations to manage data beyond people’s control: that’s a worried sign of creeping democracy!