Institutional Change in Higher Education AI Era: Micro-Credentials & Digital Tools

www.hjournal.ru
Journal of Institutional Studies, 2026, 18(1): 63-80
DOI: 10.17835/2076-6297.2026.18.1.063-080
INSTITUTIONAL CHANGE AND SUSTAINABLE
TRANSFORMATION OF HIGHER EDUCATION IN THE AI ERA:
MICRO-CREDENTIALS, DIGITAL TOOLS, AND INNOVATIONS
WADIM STRIELKOWSKI,
Ph.D., Professor,
Cambridge Institute for Advanced Studies, United Kingdom,
e-mail: strielkowski@cantab.net,
Prague Business School,
Prague, Czech Republic,
e-mail: strielkowski@pbs-education.cz;
IGOR MOLODTSOV,
Ph.D., Associate Professor,
Financial University under the Government of the Russian Federation,
Moscow, Russia,
e-mail: inmolodtsov@fa.ru;
ELENA KORNEEVA,
Ph.D., Associate Professor,
Financial University under the Government of the Russian Federation,
Moscow, Russia Togliatti State University,
Togliatti, Russia,
e-mail: enkorneeva@fa.ru;
RAISA KRAYNEVA,
Ph.D., Associate Professor,
Financial University under the Government of the Russian Federation,
Moscow, Russia,
e-mail: rkkrayneva@fa.ru
Citation: Strielkowski W., Molodtsov I., Korneeva E., Krayneva R. (2026). Institutional change
and sustainable transformation of higher education in the AI era: micro-credentials, digital tools, and
innovations. Journal of Institutional Studies 18(1): 63–80. DOI: 10.17835/2076-6297.2026.18.1.063-080
Higher education institutions (HEIs) worldwide are experiencing unprecedented institutional change
facing both digitalization and the rise of artificial intelligence (AI). This paper adopts the institutional
economics perspective in order to analyse how the rise of large language models (LLMs) and the rising
popularity of employer-driven micro-credentials are transforming the institutional landscape of higher
education. We show that these innovations were accelerated by the COVID-19 pandemic’s disruption and
now act as drivers for both formal and informal institutional change. Formal rules (such as accreditation
standards, government policies on digital learning, and certification frameworks) are being reshaped
alongside informal norms (such as societal perceptions of educational credentials and the role of
universities). The traditional dominance of university degrees is increasingly challenged by digital badges
and micro-credentials thus raising questions about market signalling, governance, curriculum design, as
© Стриелковски В., Молодцов И., Kорнеева Е., Крайнева Р., 2026
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В. Стриелковски и др. / Journal of Institutional Studies, 18(1), 63-80
well as the overall legitimacy of HEIs. Our analysis includes global perspectives but focuses on post-COVID
Russia as a main case study. In the Russian Federation, governmental initiatives (e.g. the “University
2035” digital platform) and corporate actors (e.g. Sberbank’s GigaChat) are actively reconfiguring higher
education, illustrating how governments, universities, and private providers form new alliances. We
find that sustainable transformation in higher education in Russia and beyond would require adaptive
institutional frameworks that balance innovation with quality assurance and equity. Our results that
employ institutional theory reveal how HEIs stakeholders and policymakers can navigate these shifts to
ensure that smooth transition toward micro-credentials and AI-supported learning enhances the long-term
credibility and inclusivity of higher education systems.
Keywords: institutional change; higher education; Russia; institutional economics; micro-credentials;
AI; digital transformation
JEL: B25, I21, I23, L86
ИНСТИТУЦИОНАЛЬНЫЕ ИЗМЕНЕНИЯ И УСТОЙЧИВАЯ
ТРАНСФОРМАЦИЯ ВЫСШЕГО ОБРАЗОВАНИЯ В ЭПОХУ ИИ:
МИКРОСЕРТИФИКАТЫ, ЦИФРОВЫЕ
ИНСТРУМЕНТЫ И ИННОВАЦИИ
ВАДИМ СТРИЕЛКОВСКИ,
Кембриджский институт современных исследований, Великобритания,
e-mail: strielkowski@cantab.net,
Пражская бизнес-школа,
Прага, Чешская Республика,
e-mail: strielkowski@pbs-education.cz;
ИГОРЬ МОЛОДЦОВ,
Финансовый университет при Правительстве Российской Федерации,
Москва, Российская Федерация,
e-mail: inmolodtsov@fa.ru;
ЕЛЕНА КОРНЕЕВА,
Финансовый университет при Правительстве Российской Федерации,
Москва, Россия,
Тольяттинский государственный университет,
Тольятти, Россия,
e-mail: enkorneeva@fa.ru;
РАИСА КРАЙНЕВА,
Финансовый университет при Правительстве Российской Федерации,
Москва, Россия,
e-mail: rkkrayneva@fa.ru
Цитирование: Strielkowski W., Molodtsov I., Korneeva E., Krayneva R. (2026). Institutional change
and sustainable transformation of higher education in the AI era: micro-credentials, digital tools, and
innovations. Journal of Institutional Studies 18(1): 63–80. DOI: 10.17835/2076-6297.2026.18.1.063-080
Высшие учебные заведения по всему миру переживают беспрецедентные институциональные
изменения, связанные как с цифровизацией, так и с развитием искусственного интеллекта
W. Strielkowski et al. / Journal of Institutional Studies, 18(1), 63-80
65
(ИИ). В данной работе с позиции институциональной экономики анализируется то, как развитие крупных языковых моделей (LLM) и растущая популярность микроcертификатов на основании пожеланий работодателей трансформируют институциональный ландшафт высшего
образования. Мы показываем, что эти инновации были ускорены дестабилизацией, вызванной
пандемией COVID-19, и теперь выступают движущей силой как формальных, так и неформальных институциональных изменений. Формальные правила (такие как стандарты аккредитации, государственная политика в области цифрового обучения и системы сертификации)
перестраиваются наряду с неформальными нормами (такими как общественное восприятие
документов об образовании и роль университетов). Традиционное доминирование университетских степеней все чаще подвергается сомнению со стороны цифровых дипломов и микросертификатов, что ставит под сомнение рыночные сигналы, управление, разработку учебных
программ, а также общую легитимность ВУЗов. Наш анализ учитывает глобальные перспективы, но в качестве основного примера мы рассматриваем Россию после пандемии COVID-19. В
Российской Федерации государственные инициативы (например, цифровая платформа «Университет 2035») и корпоративные игроки (например GigaChat Сбербанка) активно перестраивают систему высшего образования, демонстрируя как правительства, университеты и частные
поставщики образовательных услуг формируют новые альянсы. Мы считаем, что устойчивая
трансформация высшего образования в России и за рубежом потребует адаптивных институциональных структур, обеспечивающих баланс между инновациями, контролем качества и
равенством. Наши результаты, основанные на институциональной теории, показывают, как
заинтересованные стороны вузов и политики могут управлять этими изменениями, обеспечивая плавный переход к микросертификатам и обучению с поддержкой ИИ, что повышает долгосрочную надежность и инклюзивность систем высшего образования.
Ключевые слова: институциональные изменения; высшее образование; Россия; институциональная экономика; микросертификаты; ИИ; цифровая трансформация
Introduction
In the last few years, higher education institutions (HEIs) around the world are undergoing a
profound transformation caused by the rapid technological advances and changing economic and
social needs (Akour and Alenezi, 2022). The emergence of powerful artificial intelligence (AI) tools
represented by the large language models (LLMs) together with the growing popularity of microcredentials and digital badges, is challenging long-established institutional norms in academia as
we know it (Idris et al., 2024; Strielkowski et al., 2025). These developments coincide with, and have
been fostered by, the destructive change brought about by the COVID-19 pandemic, which forced
universities to rapidly adopt online tuition and exposed both the vulnerabilities and opportunities in
traditional higher education models (Purcell and Lumbreras, 2021; Kurbatova et al., 2021a; Crowford,
2023). As a result, what was once viewed as a relatively stable sector now needs to adapt promptly to
the environment of high uncertainty and continuous change.
From an institutional economics perspective, these developments can be perceived as the changes
in the “rules of the game” that govern higher education (Meramveliotakis, 2021). According to North
(2005), institutions consist of the formal and informal constraints that structure human interaction.
In the context of universities and colleges, formal institutions include accreditation standards, degree
frameworks, and government regulations, whereas informal institutions encompass academic norms,
cultural expectations about the value of degrees, and unwritten codes of academic conduct (Hodgson,
2025; Campos-Medina et al., 2025). Innovations such as micro-credentials (short, employment-focused
qualifications often represented by digital certificates or badges) are introducing new rules and norms
around how learning is certified and valued. At the same time, AI language models represent not just
new tools for education but also agents of change that challenge informal norms about pedagogy (for
instance, by altering notions of authorship, assessment, and the role of instructors).
All these changes described above raise some critical questions regarding how digitalization and AI
are redefining the institutional frameworks of higher education or in what ways the micro-credentials
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can alter the balance between traditional and alternative certification mechanisms. However, what is
more important are the implications for governance, market signaling, and the legitimacy of higher
education institutions. This paper seeks to address these questions by developing a theoretical
analysis grounded in institutional economic theory. We draw on classic works by Douglass North,
Elinor Ostrom, Oliver Williamson, as well as Ronald Coase, among others. These perspectives offer
extensive grounds for examining exogenous shocks (such as the COVID-19 pandemic, geopolitical
tensions or economic sanctions) and technological innovations (such as AI deployment) can induce
shifts in both formal rules and informal norms within a sector as complex as higher education.
Our analysis includes a broad outlook but places special emphasis on the post-COVID Russia
as a main case study. There are several reasons for this. To start with, Russia’s higher education
system, with its mix of strong state involvement and recent drives for modernization, provides a
vivid example of institutional change in action. The Russian Federation has, in the aftermath of
COVID-19, launched significant digital education initiatives indicating a formal commitment to a
modern, safe digital environment in education. Moreover, Russia has developed its own AI platforms
such as Sberbank’s GigaChat, which are being actively piloted in educational contexts (for example,
GigaChat was tested on the national Unified State Exam and demonstrated a human-level grasp of
social science knowledge) (HSE, 2023). In addition, Russian experience reflects global trends while also
highlighting unique institutional responses, such as the creation of University 2035, a state-affiliated
digital platform granting industry-aligned micro-credentials to thousands of learners (Zavalina
and Lyubimova, 2020). By examining the case study of Russia, we can observe how a large higher
education system navigates the tension between traditional institutional logics (e.g. the prestige of
diplomas) and new pressures (e.g. demands for continuous learning throughout the whole lifespan via
micro-courses) under conditions of geopolitical and economic hardships.
At the same time, global perspectives, including developments in the United States U.S.),
European Union (EU), and China, can also be incorporated to illustrate common patterns and
strategies. For instance, U.S. higher education, driven by market competition, has seen an explosion
of micro-credential offerings through Massive Open Online Course (MOOC) providers and corporate
programs, raising debates about quality control and credit recognition. The EU has moved toward
formally recognizing micro-credentials, exemplified by a 2022 European Commission recommendation
to standardize their definition and quality criteria. China, on the other hand, has invested heavily
in AI education and online learning platforms at a national scale, though its approach to credentials
remains largely state-driven with an emphasis on vocational certificates.
This paper aims to contribute to a theoretical understanding of how digital innovations and shocks
catalyze institutional change in higher education, and what this means for the sustainable evolution
of universities and learning ecosystems. The main insights stemming from this research are intended
to help both scholars and practitioners make sense of the current rapid changes and innovations as
well as to design strategies that employ new tools like AI and micro-credentials to strengthen, rather
than undermine, the main aims and goals of higher education.
Institutions, institutional changes, and higher education
Institutional economics studies how rules, norms, and organizations evolve in response to changes
in the environment (Hodgson, 2006; Volchik, 2017; Vargo et al., 2023). For example, Douglass
North described institutions as the humanly devised constraints that structure human interaction,
including both formal constraints (such as laws, regulations, constitutions) and informal constraints
(such as social norms, conventions, and codes of conduct) (North, 1990). In his view, institutions
create the incentive structure of society and, together with the prevailing technology, determine
transactions and production costs (Telles, 2024). An important implication of this definition is that
when transaction costs are significant (as they invariably are in the real world), institutions matter
greatly in economic and social outcomes. Therefore, any institutional change represents a process
driven by individuals and organizations who seek better outcomes within a given competitive market
environment. However, this change can sometimes be induced by exogenous shocks represented by
W. Strielkowski et al. / Journal of Institutional Studies, 18(1), 63-80
67
unexpected events or technological innovations that can change the existing arrangements (North,
2016). The COVID-19 pandemic can be seen as one such shock to higher education, abruptly shifting
the cost-benefit calculations of digital vs. face-to-face instruction and forcing a renegotiation of
institutional rules (such as attendance policies, assessment methods, and credit recognition for online
learning) (Tilak and Kumar, 2022).
North’s framework also emphasizes the interaction between institutions and organizations: if
institutions are the rules, organizations (such as universities, ministries of education, accreditation
bodies, ed-tech companies) are the players of the game. Organizations arise to take advantage of
opportunities that the institutional context provides. For example, within the context of higher
education, if the traditional institutional framework rewards accredited degree programs as the sole
pathway to skilled employment, then universities (as organizations) dominate the market. But if the
framework shifts to reward specific competencies or lifelong learning, new organizational players
such as online learning platforms or corporate training academies will attempt to meet the demand.
North’s insight is that organizations would alter contracts or lobby to change rules when they perceive
they could do better under a different arrangement is particularly relevant (North, 2005). One can
see this today as universities adjust their strategies to incorporate micro-credentials (sometimes in
partnership with industry) and as tech firms seek more influence in credentialing. These actions
represent attempts to restructure exchanges in the education market considering new opportunities
(e.g. technological efficiencies or learner and employer needs).
Moreover, Williamson (2000) complemented North by offering a multi-level schema of institutions
and stressing the differential pace at which they change describing four levels: i) embeddedness (informal
institutions like culture, norms, religion) which change only very slowly (on the order of centuries); ii)
institutional environment (formal rules like laws and property rights) which change on a decadal scale;
iii) governance structures (contracts and organizational forms) which can change in years; and iv) day-today resource allocation and prices, which adjust continuously (Hodgson, 2015). When applied to higher
education, level 1 includes deep-seated cultural values such as the prestige associated with a university
diploma or the social norm that equates formal education with success. Level 2 includes education laws,
government degree frameworks, and funding models. Level 3 includes the management and alliances of
institutions (for example, partnerships between universities and ed-tech firms, or new governance models
for online program delivery). Level 4 corresponds to operational decisions like course offerings and tuition
pricing. Williamson’s hierarchy helps clarify why certain aspects of higher education are slower to change
than others. For instance, while a government can rather quickly authorize universities to offer online
programs (formal rule change), it is a slower process to convince society and employers to value an online
micro-certification equally to a traditional degree (informal norm change). Indeed, as noted by multiple
institutional scholars, informal institutions often change more slowly than formal ones (Minbaeva et al.,
2023). However, research also documents cases where informal norms shift rapidly in response to major
events or systematic shocks (Douarin and Schnyder, 2025). The pandemic-related sudden acceptance of
online learning by many previously skeptical professors and students might be an example of accelerated
normative change induced by necessity.
Furthermore, Elinor Ostrom (see Ostrom, 1990; Ostrom, 2010) introduced the useful concept of
polycentric governance and institutional diversity that can be applied to higher education. It shows
how multiple overlapping authorities can sometimes govern more effectively than a single hierarchy,
as local actors craft rules that fit their context. In higher education, one can see the appearing complex
governance in the way credentialing is now distributed among traditional universities, professional
associations, and private platforms. Rather than a single authority defining educational value, there
might be a more complex system where, for example, a coding bootcamp’s certificate, a Coursera
micro-credential, and a university degree would coexist, each with its own legitimacy claims and
stakeholder networks. The emphasis on communities of users shaping rules is relevant in scenarios
such as open online learning communities or open-source educational resources, where informal user
norms (e.g. peer learning etiquette, open credentialing via community badges) can play a significant
role (Kumar et al., 2022). Ostrom’s perspective is a reminder that not all institutional changes are
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always top-down. Some of them can be experimental, especially in the digital age where new forms of
learning communities can arise naturally across borders.
Finally, Coase (2013) provided some useful insights into transaction costs and the nature of the
firm that offers a foundation for discussion about why educational credentials exist and how new
forms might reduce friction in the labor market. In his classic work, Coase argued that if transaction
costs were zero, parties could bargain against optimal outcomes regardless of initial entitlements
(Chang and Lin, 2024). But in reality, transaction costs (e.g. the cost of obtaining information about
a job candidate’s skills) mean that institutions (in this case, credentials) serve as proxies to facilitate
exchange. Through this lens, a diploma can be seen as a signal that reduces an employer’s uncertainty
(a concept famously formalized by Spence’s job-market signaling theory (see Spence, 2002). From
a Coasian view, the proliferation of micro-credentials could be interpreted as an attempt to lower
transaction costs in more granular ways: instead of relying on a broad (and costly) signal as a 4-year
degree, employers and learners might prefer a set of micro-credentials that more precisely map
to specific skills, thus reducing the measurement costs of identifying who can do what. However,
as Coase would predict whether this improves efficiency depends on the new transaction costs it
introduces. For example, these can be the costs of verifying the quality of myriad micro-credentials or
coordinating standards across providers. If every employer trusts a university diploma but is unsure
about a digital badge, then the informational transaction costs may rise in the short term. This
highlights the importance of institutional trust and standardization.
Main concepts of institutional change in the digital era of higher education
Building on the theoretical insights and the review of the literature, this section outlines a
conceptual framework to analyze how higher education is changing at the institutional level under
the influence of AI tools, micro-credentials, and related innovations. We focus on distinguishing formal
versus informal institutional changes and identifying key triggers of institutional changes. Table 1
below summarizes types of institutional change in higher education and their typical triggers.
Table 1
Types of institutional change in higher education with triggers (formal vs. informal)
Type of
institutional
change
Nature
(formal or
informal)
Policy and regulation
Formal
Governance and
alliances
Formal/Informal
Curriculum
structure
Formal
Pedagogy and
assessment
Informal
Credentialing and
signalling
Formal/Informal
Culture and
legitimacy
Informal
Example in higher education
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Online degree approval;
Micro-credential recognition;
QA updates
University–industry/MOOC
partnerships;
Co-badging
Modular/stackable pathways;
Competency-based credits
Hybrid delivery;
AI-aware assessment;
Portfolio tasks
Digital badges;
Transcript add-ons;
Walleted records
Acceptance of non-degree
pathways;
Value of CPD
Key triggers
•
•
•
•
•
•
•
•
•
•
COVID-19 shock;
AI emergence;
Skills shortages
Competition;
Funding incentives;
Platform scale
Employer input;
Lifelong learning demand
Generative AI;
Faculty development
• Employer verification needs;
• Frameworks/registries
• Success cases;
• Policy narratives;
• Labor market pull
Source: Own results.
The framework presented in Table 1 explains how changes can occur in different layers of the higher
education system. Formal changes often come as explicit responses to clear triggers – e.g., a ministry
W. Strielkowski et al. / Journal of Institutional Studies, 18(1), 63-80
69
of education issuing new regulations after the pandemic allowed emergency remote instruction (the
trigger here is represented by the COVID-19 pandemic), or a university board approving a partnership
with Coursera because competitors have done so (the trigger here is market/competitive pressure).
Informal changes, like shifting pedagogical norms or evolving employer perceptions, might be more
gradual and interlinked with formal changes (for example, legislation enabling micro-credentials over
time lead to greater informal acceptance, as observed historically in other domains).
It is important to note that formal and informal changes interact. A new law or policy (formal) can,
over time, influence norms and expectations (informal). Similarly, widespread informal practices can
prompt formalization. For instance, before the pandemic, many professors were informally skeptical
of online education’s quality. The forced experiment of 2020-2021 made online and hybrid teaching
ubiquitous and that informal norm has shifted to a more accepting attitude in many places (Uleanya,
2022). In turn, universities are formalizing hybrid formats in their program offerings permanently.
On the other hand, the informal trend of employers considering non-degree credentials has pressed
some governments to formally recognize those credentials (as Australia and EU have done). Research
literature provides examples of rapid informal change when formal institutions act decisively: e.g. the
legalization of new practices or rights led to swift changes in public attitudes in some cases (Bigazzi et
al., 2023; Strielkowski et al., 2024; Jing, 2025). In higher education, one might see that if governments
and top universities start endorsing micro-credentials, public and employer attitudes could shift
faster than expected (Varadarajan et al., 2023).
When applying this framework, one should be aware of the multiple triggers that can be observed
in the current era: technological innovation (AI, digital platforms), public health crisis (pandemic),
economic pressures (skill gaps, need for retraining of the workforce), and globalization/competition
(institutions vying in an international education market). Each of these triggers can initiate changes
at different institutional levels. For instance, AI’s emergence might directly trigger pedagogical and
assessment changes (faculty struggling with AI in the classroom). The COVID-19 pandemic triggered
policy and infrastructural changes (governments investing in digital infrastructure, loosening rules
on online instruction). Economic skill-gap concerns triggered the proliferation of micro-credentials as
organizations sensed an opportunity (both universities seeking new markets and private companies
addressing training needs).
Global perspectives of micro-credentials and AI in higher education systems
Higher education systems across the world have responded to the pressures of digital transformation
and changing skill demands in varied ways. In this section, we compare several countries and regions
represented by the United States, European Union (EU), China, Australia and the Russian Federation
to illustrate the acceptance of micro-credentials and integration of AI, highlighting formal policies
and informal trends. Table 2 provides a comparative overview of adoption of micro-credentials at the
country level and AI-related innovations in higher education.
The comparative overview above illustrates that while micro-credentials are a global phenomenon,
the degree and manner of adoption can vary from country to country (Tamoliune et al., 2023). Formal
frameworks appear to be more widespread in Australia and parts of the EU, where governments
proactively set up definitions and effectively incorporate micro-credentials into the national
qualifications’ architecture. In the U.S., there can be observed a somewhat relaxed approach with
innovation going from bottom to top (from institutions and companies to the governmental level) but
lacking a singular policy (leading to a very rich but often confusing landscape of credentials) (Olcott
Jr, 2022). On the contrary, Russian Federation and China have traditionally centralized systems
and are cautiously opening to micro-credentials through pilots and state-run platforms suggesting a
dominating hybrid approach (Fang and Xu, 2025). When it comes to employer acceptance, there are
also some differences. Some EU countries see an extensive industry-university collaboration where
micro-credentials are co-designed with employers (Varadarajan et al., 2023). In the U.S., certain
sectors (IT, manufacturing, healthcare) were faster in accepting micro-credentials, whereas in more
conservative fields (e.g. law or academia), the traditional degrees untouched (Alenzi et al., 2024).
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Table 2
Country-level adoption of micro-credentials and AI innovations in higher education
Country/
Region
•
•
United States
•
European
Union
China
Australia
Russian
Federation
•
•
•
•
•
•
•
•
•
•
•
•
•
Micro-credentials
(policy & uptake)
Market-led proliferation;
Mixed employer recognition;
Growing stackability (storing and
safekeeping)
2022 Council Recommendation;
ECTS portability;
national portals growing
Strong CE/vocational certificates;
Major MOOC platforms;
State-steered
National framework + marketplace;
Creditable short courses;
Public funding pilots
State-backed platforms (e.g., University
2035) serve as a national digital
platform offering short, industry-aligned
programs with micro-credentials;
Pilots toward recognition;
Rising corporate certificates (major
universities and large companies
(Rosatom, Yandex, or Sberbank)
issue their own certificates;
Concept of “micro-qualification” is
promoted in discourse as a way to
continuously update skills
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
AI in higher education
(use & governance)
Institutional policies vary;
Chatbots/tutoring;
Rapid curricular integration, light national
regulation
Ethics-first stance;
AI Act (high-risk edtech);
broad pilots in teaching/assessment
Domestic LLMs;
“Smart Education” infrastructure;
Extensive adaptive systems under tight data rules
Wide student-support chatbots;
analytics for retention;
sector guidelines on integrity
Domestic LLMs (e.g., GigaChat) tested
in education (confirmed ability to pass a
standardized exam and erudition in social
sciences);
Hybrid post-COVID (adoption of online
learning platforms (e.g. “Russian Electronic
School” for secondary education and
widespread use of LMS in universities);
Sovereignty-focused rollout (parallel domestic
EdTech ecosystem is being built due to
restrictions on Western AI tools)
Source: Own results.
Integration with traditional education is also an important aspect. The notion of stackability (the
possibility to collect and safely store credentials, often in a digital format) represents a bridge between
micro and macro credentials (Gamage and Dehideniya, 2025). Some EU countries or Australia with its open
framework, explicitly allow accumulation of micro-credits toward degrees. Many U.S. universities also have
started granting credit for MOOCs or certificates upon evaluation. In Russia, inclusion might come through
continuing education units or professional development that could later be recognized (Alekhina et al., 2024).
When it comes to AI tools, a key insight is that all major systems are exploring AI, but under
different governances. Western contexts stress guidelines and ethical use (since many tools are from
private sector, the role of institutions is to set usage policy), whereas China and to an extent Russia
emphasize sovereign control of AI tools and use them as part of national strategies (education as one
domain of asserting technological leadership) (Roberts et al., 2021).
A peculiar example of AI integration is the High School of Economics (HSE) experiment in Russia
where GigaChat (the Russian own domestic LLM) took a university entrance exam in social studies and
scored above the passing threshold (HSE, 2023). This is not just a novelty but serves as a signal to the
public and academia that home-developed AI can meet educational standards and perhaps be trusted to
assist learning in those domains. It demonstrated the model’s understanding of basic laws of society and
moral sense, implying it could be a helpful knowledge tool for students. The symbolic legitimacy such an
event confers on AI in education is important – it frames AI not as a threat but as a useful tool that can
handle complex knowledge tests.
In global terms, sustainable development goals (SDGs) and the debate over sustainability also
reflect these changes. SDG 4 (Quality Education) emphasizes inclusive, equitable quality education
and promoting lifelong learning (Adipat, S., & Chotikapanich, 2022). Many countries link microcredentials to lifelong learning by arguing they make education more accessible to non-traditional
learners (working adults, marginalized groups) and by updating skills for sustainable industries
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71
(for example, micro-credentials in green technologies, digital skills). Some researchers even see
micro-credentials as a tool to address skills for the green economy (De Rosa et al., 2024). However,
cautionary voices warn that while micro-credentials can quickly fix talent supply issues, they should
not replace the foundational layer of deeper education needed for fields that require long-term study
(e.g. philosophy or science basics) (McGreal and Olcott Jr, 2022). This highlights a common thread in
global discussions which focuses on the scope and limits of micro-credentials.
Another global trend is the role of platforms and consortia. Credential portability is prompting
collaborations such as Credential Engine in the U.S., or the Common Micro-credential Framework in Europe
by groups such as the European MOOC Consortium. These are building registries and taxonomies so that
an employer or institution can validate a micro-credential’s value. In addition, there are risks such as lack
of employer understanding and potential deepening of inequity if only those who can pay or navigate the
system get the benefit (Hunt et al., 2022). Many G20 countries put public funding into micro-credentials to
alleviate that risk after COVID-19 pandemic. For instance, Canada and several EU states subsidized shortcourse enrollments for unemployed or unemployed workers in 2020–2021 as part of recovery plans.
Therefore, the global perspective suggests that institutional change is underway everywhere,
but its speed and direction are mediated by existing institutional environments. Traditional degreecentric cultures are being nudged (or in some cases jolted) toward more open credential ecosystems,
often by a combination of top-down policy (EU, Australia) and bottom-up innovation (U.S. as well
as India with its massive online programs). AI’s integration seems more promising but is rapidly
accelerating, often profiting from the digital surge induced by the pandemic.
Thence, a focus on a particular national context of Russia appears to be an interesting case study of
a country where global currents intersect with local institutional dynamics. Russia’s case is instructive
due to its deliberate strategies to modernize education while maintaining strong central control as
well as due to the geopolitical factors that influence its adoption of technology and partnership choices.
AI-driven post-pandemic transformation in higher education: a case of Russia
Russian Federation presents a very interesting study subject of institutional change in higher
education in the era of AI and micro-credentials. Russian higher education has long been characterized
by a strong state role in defining curricula, qualifications, and institutional hierarchies (Kurbatova et al.,
2021b; Prikhodko et al., 2024; Strielkowski et al., 2024). The classic model was based on state standards
(federal educational standards that prescribed learning outcomes for degrees), a clear difference between
formal degree education and other forms of training, and a high cultural regard for diplomas from
prestigious universities. However, over the past decade and especially after the COVID-19 pandemic,
Russia has launched successful reforms and initiatives that follow global digitalization trends while,
at the same time, also reflecting the country’s unique context of seeking technological self-reliance and
addressing internal skills gaps (Gorina et al., 2024; Lavrov et al., 2024; Kleiner et al., 2025).
When COVID-19 pandemic struck in the beginning of 2020, Russian universities, had to switch to
online learning with little time left to prepare which was a stress test for the whole system of education. As
a result of that, Russian universities had to take colossal measures to provide uninterrupted educational
services during the emergency (Valeeva and Kalimullin, 2021). Challenges ranged from unequal access
to technology (especially in remote regions) to faculty unfamiliarity with online pedagogy. The Ministry
of Science and Higher Education facilitated this transition by creating centralized resources. One major
development was the expansion of the Russian Electronic School (RES) platform, initially designed
for secondary education, and increased use of platforms like Open Education - a consortium of top
universities offering online courses (Pesterva et al., 2019). The Russian government’s commitment to a
modern and safe digital educational environment by 2024 was reinforced. This push included integrating
digital solutions across universities and a move to make various systems interoperable (e.g., linking
university admission systems digitally and creating the “SuperService Online University Admission”).
The pandemic experience had a lasting impact in Russian higher education: it demonstrated to both
administrators and faculty that online and hybrid education could be done at scale, albeit with quality
issues to manage. Despite initial problems, there was recognition that the experience gained has left its
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mark and that a sustainable digital environment was needed so universities would be prepared for future
challenges. Interestingly, some Russian researchers highlighted skepticism about fully replacing faceto-face education post-pandemic, noting that socialization, a key component of university life, was hard
to replicate online (Avetisyan et al., 2022). Nevertheless, the consensus was that a hybrid model would
continue, combining the best of online and offline, which indeed is happening. For example, by the end of
pandemic in 2022 many Russian universities had formalized hybrid formats: some lectures remain online,
some seminars in person, and students have more flexibility (this mirrors the global hybrid trend).
In the aftermath of COVID-19, Russian policymakers grew more receptive to innovations in
credentials. One prominent program is “University 2035” established in 2018 as part of the National
Technology Initiative (NTI). University 2035 is not a university in the traditional sense, but a platform
and operator for digital education and training. Its mission is to develop the competencies required
for “future markets” and the digital economy. By 2020, University 2035 was launched publicly, and it
rapidly scaled up – by 2023 it had over 250,000 “graduates” from its programs, despite not granting
degrees (Universitet 2035, 2025). It offers short courses, many online, in areas like data science,
AI, cybersecurity, project management and similar content, often in partnership with industry or
other educational institutions. These results in digital certificates (micro-credentials) attesting to
those skills. Crucially, University 2035’s approach employs modern technology as it uses an AI-driven
personalized learning platform to tailor content to each learner and maintains a digital portfolio for
each learner that tracks competencies. In effect, it is creating an ecosystem where Russian employers
can trust that a certificate from University 2035 represents a standardized, assessed skill set.
The case of University 2035 shows a clear institutional innovation: a state-affiliated non-university issuer
of credentials that complement traditional universities. It addresses Russia’s skills shortage by aligning
programs with industry needs and focusing on providing skills in high-demand fields. The strategy reflects
an understanding that the fast-moving tech sectors cannot rely solely on the slower university procedures. It
is also a response to a brain-drain and talent development challenge because by creating a local mechanism
for continuous learning, Russia aims to grow own talents internally for its digital economy.
In addition to the case of University 2035, there is also the “Professionalitet” program launched in
2022 to reform vocational education. While not a micro-credential in the digital sense, Professionalitet
shortens certain college programs and deeply involves employers in designing curricula for applied
fields. It represents another example of breaking traditional molds (where vocational college was
often 4 years, now some pilot programs are 2 years with industry internships which is closer to a
micro-credential or associate degree concept) (Maltseva et al., 2025). This shows formal institutional
change in how credentials are structured at sub-degree level.
The Russian government also participated in G20 discussions and reportedly launched a microcredentials pilot in higher education in 2023. While details are sparse, this likely involved selecting
a few universities to experiment with offering micro-credentials and perhaps recommending how to
integrate them into the National Qualifications Framework. It aligns with a broader trend of the
government acknowledging alternative credentials. Indeed, HSE experts predict pedagogies of microcredentials would grow in Russia and globally, with Russian innovators quite attuned to this trend.
One way how micro-credentials can be used informally is through MOOCs. Russian universities, via
the National Platform for Open Education, offer MOOCs and some have begun to accept certificates from
those MOOCs for credit in on-campus programs (especially since many of those MOOCs are created by
the same universities). For example, a student might take an online course from MIPT (Moscow Institute
of Physics and Technology) in programming and later get that recognized as part of their degree at a
regional university – a small but notable shift from the previous closed system. This indicates an informal
institutional change where the value of learning, not just the mode or location, is starting to be recognized.
Furthermore, Sberbank, Russia’s largest bank (and a major technology player), has emerged
as a significant actor in education innovation. Sberbank’s investments in AI led to the creation of
GigaChat (domestic LLM) which is positioned as a domestic alternative foreign LLMs. Sberbank has
not only developed this technology but is actively promoting its adoption across sectors, including
education. The collaboration with HSE University to test GigaChat on the Unified State Exam (USE)
W. Strielkowski et al. / Journal of Institutional Studies, 18(1), 63-80
73
for social studies demonstrated that AI could meet Russian educational standards. GigaChat scored
67 out of 100, above passing and even above average student scores. HSE’s Institute of Education
independently evaluated the answers and confirmed the model’s capability in knowledge and logical
reasoning. This achievement was publicized to build trust among educators that Russian AI can be a
useful tool. It also implicitly suggests that GigaChat could be used to help students prepare for exams
or to provide tutoring in social sciences and other subjects, since it has demonstrated competence.
In addition, Sberbank’s ecosystem includes specific educational services such as SberUniversity
(corporate university) and partnerships in K-12 (a platform called “SberClass”). It also runs initiatives
such as training orphaned children in AI skills, reflecting a social dimension to AI education. Moreover,
Sberbank is actively promoting AI usage in academia, teaching students how to use GigaChat in
science, business as well as arts projects. Providing free courses on generative AI for students means
the next generation of Russian graduates may enter the workforce with a comfort in using domestic
AI tools which is in accord with national strategic goals of technological sovereignty.
Institutionally, this reflects new alliances: corporations working with universities to bring cutting-edge
tools into the learning process, which historically was not common in Russian higher education beyond
certain technical fields. It also indicates a slightly blurred line between formal education and corporate
training which represents a key feature of the new landscape where learning can occur in multiple settings.
Even though on the formal side, Russia’s education ministry has not yet modernized degree
accreditation to a competency-based or micro-credential-friendly model, smaller steps are noticeable.
For instance, digital diplomas and transcripts are being introduced (digital portfolio concepts). A
digital portfolio could eventually include micro-credentials alongside degrees, making them formally
visible in state repositories of qualifications.
Another development includes the Priority 2030 program (an initiative funding universities to
advance specific strategic projects). Some universities under this program can chose lifelong learning and
digital education as their focus. For example, a university might receive funding to create a suite of microprograms for the local industry workforce. This shows a policy-driven encouragement for institutions to
engage in non-traditional educational offerings as part of demonstrating their societal relevance.
Additionally, it is worth noting that in Russia, as elsewhere, there is an ingrained belief in the
importance of higher education – the country historically has a high tertiary education attainment
rate. This cultural embeddedness as described by Oliver Williamson (see Sent and Kroese, 2022) does
not change instantly. Many parents and students still see the bachelor’s or specialist degree as the
main pathway to success. Therefore, micro-credentials are currently positioned not as replacements
but as supplements. Education is adapting rapidly and noticing trends (such as micro-credentials) in
time and aligning them with one’s strategy is crucial. This approach can help framing micro-credentials
in Russia to add to one’s education for career development, not to skip university altogether. This
framing is important for legitimacy since it reassures stakeholders that these innovations serve to
enhance human capital without throwing away the rigorous training associated with degrees.
Another informal factor is language and international recognition. In order for Russian microcredentials to gain full legitimacy, some alignment with international standards might be needed
(so they are recognized abroad or by multinational companies in allied and friendly countries).
University 2035 and other similar initiatives surely consider this by referencing frameworks such as
the European Qualifications Framework when designing their courses.
Challenges and opportunities for transformation: a SWOT analysis
The AI-driven post-pandemic transformation in higher education in the Russian Federation faces
some specific challenges. The ongoing geopolitical situation (post-2022 sanctions and reduced academic
collaboration with Western institutions) creates barriers for but also fosters domestic innovation.
On one hand, access to some global platforms or collaboration is restricted. On the other hand, this
incentivizes Russia to develop its own solutions (such as GigaChat instead of relying on ChatGPT or
domestic MOOC platforms instead of Coursera). This could accelerate internal institutional changes
as gaps need to be filled domestically. It aligns with North’s idea that new organizations emerge to
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seize opportunities. Here, the opportunity is also a necessity, because there might be a need to replace
foreign educational content and providers with local ones that operate under local institutional rules.
Another challenge is ensuring quality across a proliferation of new programs. The Russian higher
education system is hierarchical since top universities have more autonomy, whereas many others
rely on ministry direction. If micro-credentials are to take off broadly, a quality assurance mechanism
is needed. Possibly the ministry or Rosobrnadzor (education oversight body) might create an official
registry or accreditation for micro-credentials providers, to ensure employers know which ones are
trustworthy. In absence of that, University 2035 and a few big players might effectively set the
benchmark.
In terms of governance, if Russian universities embrace micro-credentials, they may need
structural changes: e.g., offices for lifelong learning, new incentive structures for faculty (developing
short courses might need to count in workloads), and new partnerships. Non-university providers such
as corporate academies might seek formal recognition to grant courses awarded with credits. Already,
some non-rooted institutions have significant influence since they can shape innovation and outcomes
despite not being traditional players. In Russia’s case, state corporations’ academies and initiatives
(internships for students, classes for schoolchildren) are becoming integrated into the education-towork process. This could recalibrate governance making universities to coordinate more with industry
curricula. Table 3 that follows presents a table with a SWOT analysis for digital transformation of
Russian higher education led by the institutions.
Table 3
SWOT analysis for institution-driven digital transformation of Russian higher education
Strengths
• Strong state coordination and funding for digital/AI
initiatives (e.g., national platforms, priority programs).
• Domestic AI capacity (e.g., GigaChat) enabling digital
sovereignty and continuity despite external constraints
(geopolitical tensions, economic sanctions);
• Scalable, state-affiliated upskilling infrastructure (e.g.,
University 2035) aligned with industry needs.
• Large state corporations (Sberbank, Rosatom, etc.) as
credible co-issuers and adopters of micro-credentials.
• Pandemic-era hybrid/online “digital revolution” created
lasting capacity for flexible delivery
•
•
•
•
•
•
•
Opportunities
Build national micro-credential frameworks (stackability,
registries, credit transfer) to mainstream uptake.
Rapid reskilling for priority sectors (AI, cybersecurity,
engineering) and regional labour needs.
Export/extend domestic EdTech solutions to EAEU and
friendly markets;
Sectoral partnerships;
Embed AI literacy and ethics across curricula;
Leverage AI for student support and retention;
Co-design with employers to raise employer trust and
shorten graduate-to-job transition
Weaknesses
• Centralization can slow experimentation;
• Uneven autonomy across universities;
• Quality assurance and comparability of
micro-credentials still under construction;
• Limited common standards;
• Variable digital and AI readiness across
regions and institutions;
• Persisting digital divide;
• Faculty workload/incentives and assessment
norms lag AI-aware pedagogy;
• Integrity risks;
• International recognition and portability of
Russian micro-credentials is still limited
Threats
• Sanctions/geopolitics can restrict collaboration,
content access, hardware, and global signalling.
• Brain-drain of top students and faculty;
• Talent attraction and retention challenges.
• Credential inflation and employer confusion
if standards remain fragmented.
• Cybersecurity, data governance, and AI bias
risks could erode trust and legitimacy.
• Funding pressures and demographic decline
may reduce system resilience to reform costs
Source: Own results.
When it comes to strengths, Russia’s state coordination has accelerated digital infrastructure
and AI adoption across HEIs, creating shared platforms and funding streams that smaller systems
struggle to muster. Domestic AI (e.g., GigaChat) mitigates dependence on foreign models and enables
Russian-language, context-aware tools for learning, tutoring, and assessment. University 2035 and
W. Strielkowski et al. / Journal of Institutional Studies, 18(1), 63-80
75
similar operators show that scaled, industry-aligned upskilling is feasible, particularly when large
state corporations co-design content and recognize credentials. The COVID-driven hybrid pivot left
behind LMS capacity, faculty familiarity with online modalities, and student acceptance of blended
formats - all being the prerequisites for micro-credential delivery.
However, there are also weaknesses. A centralized governance style can dampen local
experimentation and slow diffusion of bottom-up pedagogical innovation. Quality assurance for microcredentials (taxonomy, workload/level descriptors, verification) is still maturing; without shared
rubrics and registries, credit transfer and employer comparability remain limited. Regional disparities
in bandwidth, devices, and instructional design capacity create uneven student experiences. Faculty
incentive systems often don’t reward micro-course development or AI-aware assessment redesign,
complicating integrity management in the generative-AI era. Finally, international portability of
credentials is constrained by limited alignment with widely used meta-frameworks and by geopolitics.
When it comes to the opportunities, there is unique possibility to codify a national micro-credential
framework (stackable pathways, issuer standards, public registries/wallets) that integrates with
degree programs and professional licensing. Targeted short courses can rapidly improve critical
sectors (AI, security, advanced manufacturing), reduce regional skill gaps, and support smart
specialization. Russian EdTech and AI stacks could be exported or federated to EAEU/Global South
partners, building ecosystem heft. System-wide AI literacy and ethics can be embedded into general
education to future-proof graduates. Deep co-design with employers (state and private) can raise
employer trust, tighten signaling, and shorten time-to-productivity for graduates.
Finally, there are also some threats to be considered. Sanctions and geopolitical constraints
may limit international collaboration, access to cutting-edge components/content, and the external
validation channels that bolster credential signaling. Brain-drain risks undercut capacity to deliver
and continuously update high-quality programs. Without clear and transparent standards, credential
inflation could be confusing for employers and devalue their role and mission. Cybersecurity and
data-governance issues (or biased AI outputs) could also trigger public distaste thus undermining the
legitimacy of micro-credentialing. Finally, demographic decline and budget pressures could create
obstacles for investments in keeping the platforms up and running or facilitating faculty development
and quality assurance bodies that are essential for sustained reform.
Overall, a case of Russian Federation illustrates a special case among global trends: shockinduced openness to change (pandemic), strategic use of technology (AI) to quickly come up with novel
solutions, experimentation with new credentials under state guidance, and a careful negotiation
between old and new institutional logics. All of that demonstrates that even in a system known for
formality and centralization, there is movement towards flexibility and multiple learning pathways.
Conclusions
Overall, this paper demonstrates that today’s higher education is at an inflection point where
long-standing institutional arrangements are being redesigned. Using the principles of institutional
economics, one can assess fundamental changes marked by the shifts due to technology and crises. The
paper finds that while digitalization and AI introduce new possibilities for delivering and certifying
education, the fundamental institutional questions remain centered on reducing uncertainties
(transaction costs) and aligning incentives for individuals and organizations (universities, employers,
learners, and governments).
The key theme for the discussion of the outcomes of this research appears to be the interaction
between formal and informal institutions. Formal structures (laws, official programs, funding schemes)
around higher education are adapting, sometimes rapidly, as with emergency pandemic measures or
new micro-credential frameworks. Yet, the informal cultural valuation of higher education evolves
more gradually. The distrust that once existed about online education diminished significantly due to
COVID-19 pandemic which represented an extraordinary acceleration of norm changes in academia.
It suggests that external shocks can compress multi-decade changes into a much shorter period
when there is no alternative. Similarly, employer attitudes that may have taken a generation to shift
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(regarding non-degree credentials) might change within a few years if faced with acute skill shortages
and proven outcomes from alternate pathways.
Another important point is that institutions rarely die – instead they layer or blend. We are not
witnessing the abolition of traditional degrees. Instead, we see how traditional degrees coexist with
micro-credentials. In new institutionalist terms, there is path dependence but also path adjustment.
The existing path (degree-centric system) is growing into a new path (hybrid credential ecosystem)
rather than being completely replaced. For practitioners and policymakers, this means strategies
should be about integration and complementarity. They should focus on how to integrate microcredentials into the system of education or how to use AI as a complement to human teaching, rather
than view them as hostile and redundant or try to get rid of them.
The case of the digital transformation of education in the Russian Federation presented in this paper
highlighted how one country is negotiating this path. It shows that even within a relatively centralized
system, multiple actors can bring innovation (ministries, universities, state corporations) and that formal
endorsement (e.g. pilots and national platforms) can lend credibility to novel approaches. It also shows the
reason why local context matters since Russia’s emphasis on domestic platforms and tools is partly driven
by external pressures (sanctions, technological sovereignty), an example of how institutional change can
also be influenced by geopolitical factors, not just technological or economic ones.
An important issue for discussion that emerges from this analysis is the risk of inequality in the new
digital and AI-driven era. Digital divides could result in educational divides if not addressed properly.
Those people or countries with better Internet access, digital skills, and time for continuous learning
can accumulate micro-credentials and use AI effectively, while others may be left behind. This is a
classic institutional issue whether new rules can exclude some groups. North’s framework would remind
us that institutions can have distributional consequences since they are not neutral. A formal policy
implication is to invest in digital infrastructure (as many countries are doing, including Russia’s efforts
to extend online access to villages) and digital skills training, so that many people can take part in these
new opportunities. Additionally, institutions should monitor who is benefiting from micro-credentials
and assess whether they are reaching the unemployed, the rural, and the older workers (and not just
being used by the already privileged professionals to further boost their prospects).
In addition, quality assurance also represents a pressing and ubiquitous concern. Some combination
of market mechanisms and oversight would likely determine the quality equilibrium. If employers
make qualified hiring decisions, low-quality credentials will get filtered out (market discipline).
However, because of information asymmetry (one cannot easily tell a good credential from a bad on
sight), some oversight or signaling of quality (such as accreditation badges for micro-credentials)
could be very helpful to avoid a race to the bottom.
The role of AI in higher education raises not only practical questions but ethical ones that are
related to institutional legitimacy. Issues of academic integrity, data privacy, and the potential
for AI to reinforce biases in educational content or evaluation need to be actively managed. If left
unaddressed, they could destroy trust in digital education. Institutional responses have started
(e.g., ethics guidelines, AI fairness audits for educational software), but keeping human oversight
and values is crucial. This resonates with Ostrom’s principle that no panacea exists, and complex
problems require context-specific combinations of solutions, and communities (in this case, academic
communities) need to create new norms for these tools.
In addition, the impact on faculty and jobs on higher education also needs to be discussed. This
paper focused on institutions broadly, but a micro-level institutional change is the role of the academic
profession. If courses can be taken from anywhere or generated by AI, the professor’s role needs
to be redefined as professors are likely to act more as mentors and curators. However, there may
also be an upheaval since some faculty roles might shift to more research or mentoring focus, while
routine lecturing could diminish. Universities might hire more “facilitators” for learning rather than
traditional lecturers for some content. This could potentially casualize some of the academic labor (as
we see with adjunct growth in many places, now possibly “tutors” for MOOC support). This workforce
aspect is an internal institutional change that universities need to handle carefully to maintain
morale and quality. Hence, the policy makers should consider some actionable insights such as:
W. Strielkowski et al. / Journal of Institutional Studies, 18(1), 63-80
77
Developing integrated frameworks that allow movement between micro-credentials and
traditional degrees.
• Investing in platforms and public-private partnerships to ensure quality and wide access to
micro-credentials.
• Updating regulatory and funding mechanisms to accommodate new forms (e.g., recognition in
hiring, inclusion in financial aid, ensuring accreditation covers new modes).
• Emphasizing digital and AI literacy in education at all levels, so that both educators and
learners can effectively use and not misuse AI.
• Monitoring outcomes and collecting data on how these innovations affect employment, learning
outcomes, and various demographics, to continuously refine policies (an institutional feedback
loop, similar to learning as North described human learning shaping change).
The theoretical contribution of this discussion is demonstrating the utility of institutional
economics in making sense of technological disruption. It provides a structured way to differentiate
types of changes (formal vs informal), identify actors and incentives, and foresee potential unintended
consequences (like the inequality aspect or signaling problems). It reminds us that institutions are
not just reactive but can be proactively shaped if we understand how they function. For instance,
knowing that informal norms can lag, one might invest in “change management” for faculty and
students to embrace the new (workshops, success stories, champions).
One limitation of our analysis is that it is largely conceptual and qualitative. While we referenced
emerging studies and data, the landscape is so new that robust empirical evaluations (like long-term
outcomes of micro-credential earners or learning gains from AI tutors) are still limited. The pathways
for future research in this field should aim to fill these gaps. For example, they can examine whether
micro-credential holders would experience better career mobility or whether the use of AI in a course
significantly improves or worsens learning outcomes compared to traditional methods. It would also
be interesting to see whether these effects differ by discipline or student background. In addition,
cross-country comparative studies could identify best practices in governance or integration models.
We should also note that not all fields are equally impacted. STEM and vocational fields might find it
easier to define micro-credentials (specific technical skills), whereas fields like humanities or theoretical
sciences face more challenges in modularization. Thus, institutional change may be asymmetric within
universities as some faculties race ahead, and others remain more cautious. This internal dynamic
would be worth exploring with institutional theory, perhaps considering each academic discipline as
having its own institutional norms and how they change (the idea of institutional logics could be relevant
and represented here as the professional logic of academia versus market logic of industry credentials).
All in all, it becomes quite clear that the transformation driven by AI and micro-credentials can be
seen as part of the broader historical evolution of higher education institutions responding to societal
needs and technological capabilities. Just as universities expanded in the 20th century with mass
education to meet industrial society’s needs, they are now transforming to meet the digital knowledge
society’s needs. Institutional theory suggests that those who adapt their rules (formal and informal)
to new realities, while holding onto core purposes (e.g. knowledge creation, critical thinking, social
development), would prosper. Those who cling to old structures without meeting new demands risk
decline. The process is path-dependent but not path-determined – there is choice and agency in how
higher education navigates this era. What one can essentially observe (and participate in) today,
represents an institutional reconfiguration that aims to make higher education more sustainable
(in the sense of resilient and relevant) in a world of lifelong learning, high demand for skills, and
ubiquitous and encompassing AI that penetrates all spheres of economy and society.
•
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