The Extended Education 4.0: Lifelong Learning in Times of Artificial Intelligence

Abstract

Lifelong learning has become a central axis in the debate on education and innovation, especially in contexts where technological transformations and the integration of artificial intelligence are reshaping the ways individuals acquire, update, and apply knowledge. Despite the growing relevance of this field, research on lifelong learning remains dispersed across different perspectives, highlighting conceptual diversity and methodological fragmentation. This article presents a systematic review aimed at identifying how lifelong learning has been studied in relation to artificial intelligence, focusing on definitions, benefits, and limitations discussed in the literature. The review followed a rigorous methodological process, including a probabilistic sampling strategy, systematic screening and eligibility assessment, and the application of both qualitative and quantitative analyses supported by triangulation to ensure reliability. The findings indicate that research on lifelong learning in relation to artificial intelligence remains fragmented. While many studies emphasize conceptual definitions and highlight potential benefits, relatively few examine limitations, challenges, or empirical evidence of impact. By systematically synthesizing and analyzing the available literature, this review contributes to a more integrated understanding of how AI is shaping lifelong learning, offering both theoretical insights and practical implications for educational practice and policy.

1. Introduction

Education 4.0 refers to the educational paradigm shaped by the technologies of the Fourth Industrial Revolution, including Artificial Intelligence, virtual reality, robotics, the Internet of Things, and data analytics [1,2]. These advancements are transforming how societies work and learn, requiring the development of skills and competencies that enable individuals to adapt to constant change and uncertainty. In this context, Artificial Intelligence has emerged as a central driver of transformation in teaching and learning, offering possibilities for personalisation, predictive analytics, and enhanced feedback [3,4].

Broadly speaking, there are different types of AI, each with specific characteristics and uses. One of the most common forms of AI is machine learning, which relies on algorithms that allow machines to learn autonomously from data [5]. Another form of AI, possibly the most common of all, is rule-based AI, used to perform specific tasks following a set of predefined rules [6].

In the educational context, the use of AI has begun to be studied in-depth more intensively over the past decade, given the evolution and consolidation of generative AI, which shows enormous potential for improving the quality of teaching and assessment, as well as for personalizing learning [4]. Considering the above, it is worth mentioning that these topics have been broadly and increasingly addressed in educational research over the past two decades, particularly since 2020, as a consolidated trend following the COVID-19 pandemic. The growth of research in these areas is shown in Figure 1.

In the context of Education 4.0, there has been an identified growing need for a continuous learning approach due to constant changes in the labour market and technology caused by the transformations brought about by the incorporation of Fourth Industrial Revolution technologies into various aspects of human life [7]. As previously mentioned, technological advancements and globalisation are transforming the way we work and live, meaning the skills and knowledge needed to succeed in the labour market are constantly changing.

In other words, according to Lang [8], this means that to remain relevant and competitive in today’s labour market, workers need to be willing to continuously learn and adapt to new technologies and skills through processes known as “upskilling” or “reskilling”, which are developed and consolidated in what is known as Lifelong Learning (LLL).

To further clarify, LLL refers to the idea that learning should not be confined to a specific stage of life but should be a continuous and constant practice. According to Kim [9], Lifelong learning aims to foster the acquisition of new knowledge, skills, and competencies throughout life, enabling individuals to adapt to changes and challenges and stay updated in an ever-changing world.

Although UNESCO coined the term Lifelong learning in the 1970s, the concept has deep historical roots. From ancient Greek philosophies to the pedagogical ideas of John Dewey and Paulo Freire, the belief that education extends beyond formal schooling has been widely addressed [10]. In the 21st century, LLL has gained renewed importance as societies face rapid technological advances and changing labour markets, making it essential for personal and professional development.

Furthermore, Vorhaus [11] describes LLL as a social phenomenon that gives rise to a new educational order, framed in an economic and political context that pursues objectives such as competitiveness, employability, and adaptability of the workforce. As a result of this vision, the concept of Continuing Education emerges, defining a philosophy of life that recognises learning as a continuous process extending from childhood to the end of life [12].

Within the framework of Education 4.0, the use of AI to support lifelong learning is becoming increasingly relevant due to its potential to personalise learning and make data-based predictions [13]. From this perspective, Jurkova and Guo [14] indicate that as Lifelong learning is a pedagogical approach closely linked to the individual, AI can contribute to its development in several ways. This includes its ability to analyse student-related data, such as their strengths, weaknesses, learning patterns, and preferences, and use this information to tailor learning content and activities to each student’s needs and pace.

Additionally, AI can help identify areas where students need improvement and provide personalised feedback to help them overcome challenges during their learning process [15]. Finally, as AI can make data-based predictions, it can help students make informed decisions about their learning and career development [16]. This can be achieved by analysing available data on labour market trends and making recommendations on developing certain skills and available courses to achieve them [17].

1.1. Ontological and Ethical Implications of AI in Lifelong Learning

The integration of Artificial Intelligence into lifelong learning environments raises not only pedagogical and technological questions but also profound ontological and ethical concerns. At its core, AI-driven education reshapes the relationship between humans and knowledge, challenging traditional conceptions of autonomy, authorship, and the role of educators. As educational systems increasingly rely on data-intensive technologies to personalize learning, critical scholars have drawn attention to the epistemic and ontological shifts these systems generate.

Barbierato et al. [18] argue that machine learning reconfigures educational spaces into algorithmically governed environments where learners’ trajectories are inferred rather than negotiated, potentially eroding the human capacity for self-directed meaning-making. This resonates with Selwyn [19], who warns that the rapid adoption of AI in education often proceeds without sufficient interrogation of the social, political, and ethical assumptions embedded in these technologies. From this perspective, AI does not merely support learning; it participates in constructing what counts as knowledge, which behaviors are considered desirable, and how educational success is defined and measured.

Moreover, Knox [20] emphasizes the ontological implications of AI by showing how datafication processes reduce learners to predictable profiles, limiting the emergence of unexpected or transformative learning experiences. Similarly, Williamson and Eynon [21] caution against the “pedagogical reductionism” inherent in AI systems, where the richness of human cognition and emotion is often flattened into quantifiable inputs and outputs.

Ethical concerns also abound regarding algorithmic opacity, surveillance, and data ownership. Zuboff’s [22] theory of “surveillance capitalism” provides a broader framework to understand how AI systems in education may commodify student data and reinforce asymmetrical power relations between technology providers and educational institutions. In the specific context of lifelong learning, these issues are particularly acute, given that adult learners often operate in informal or workplace-based environments with fewer regulatory safeguards.

These critiques do not suggest abandoning AI in education but rather underscore the need for responsible and reflexive integration. By foregrounding the ontological and ethical dimensions of AI, this review aims to complement the empirical and practical findings with a deeper understanding of what is at stake when education becomes increasingly mediated by intelligent technologies. This perspective is essential to ensure that lifelong learning, as a human-centred endeavour, remains aligned with the broader goals of personal agency, social justice, and epistemic plurality.

1.2. Integrating AI, Lifelong Learning, and Education 4.0: A Conceptual Convergence

The conceptual foundation of this review rests on the interplay between three key constructs: Education 4.0, lifelong learning, and artificial intelligence (AI). Rather than treating these as parallel or loosely connected domains, we propose an integrative perspective in which each concept reinforces and operationalizes the others within the context of contemporary educational transformation.

Thus, Education 4.0, understood as a response to the demands of the Fourth Industrial Revolution, emphasizes learner-centred approaches, personalization, digital competence, and the cultivation of twenty-first-century skills. This paradigm shift is both enabled and challenged by artificial intelligence, which introduces new forms of instructional mediation, adaptive learning systems, and real-time data analytics. AI, in this sense, becomes a core enabler of Education 4.0, while simultaneously reshaping the roles of educators, learners, and institutions.

At the same time, the integration of AI into educational systems aligns with the evolving vision of lifelong learning, which demands flexible, scalable, and personalized learning opportunities across the lifespan. AI tools, including recommender systems, intelligent tutoring systems, and automated feedback mechanisms, can support the delivery of lifelong learning at scale—provided that such tools are implemented ethically and inclusively. From this perspective, lifelong learning is not merely an individual endeavour but a systemic imperative for resilience and equity in knowledge societies.

Also, we frame AI not only as a technological artifact but as a transformative driver within an Education 4.0 ecosystem that prioritizes continuous, personalized, and context-aware learning trajectories. The convergence of these three constructs forms the analytical basis for this review and shapes the interpretation of both barriers and opportunities in the implementation of AI in lifelong learning contexts.

Recognizing the importance of LLL within the framework of Education 4.0, and the potential educational applications of AI in this context, along with the current state of research on their interrelationships, represents an emerging and pertinent area of study. Consequently, it necessitates a thorough and comprehensive examination. To this end, a systematic literature review has been undertaken to identify the principal trends, barriers, and benefits associated with these developments. Based on the findings, this paper proposes recommendations for the effective and ethical advancement of LLL and AI in education, particularly considering the uncertainties and continuous changes characteristic of the contemporary world.

2. Method

The review was conducted based on the recommendations of Page et al. [23], considering the main guidelines of the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The details of how this method was executed in this literature review are shown in Figure 2.

2.1. Review Protocol Design

2.1.1. Determining the Review’s Purpose

To conduct this review in an orderly and consistent manner, three guiding questions were formulated, which are:

RQ1. What have been the ways of understanding LLL?

RQ2. What are the main barriers to its implementation?

RQ3. What are its most representative benefits for the framework of education 4.0?

The literature search covered publications from January 2000 to December 2024, a period selected to capture both the early developments of the Education 4.0 concept and the most recent trends related to the use of Artificial Intelligence in Lifelong Learning.

2.1.2. Define Eligibility Criteria

To make decisions about the studies that were selected and those that were excluded from the review, two main criteria for inclusion of documents were formulated:

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Only articles presenting research results are included.

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Articles must mention, either tacitly or explicitly, in the title or abstract, the review topic.

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Articles written in English and Spanish will be included; other languages are excluded.

These criteria were established to ensure that the selected texts had solid academic grounding, direct relevance to the focus of this review, and sufficient methodological detail to support the subsequent analyses.

2.1.3. Information Sources

Due to its various analytical tools, data filtering and visualization capabilities, as well as its global academic recognition in terms of reliability, coverage, and rigor in editorial and peer-review processes [24,25], Scopus was used as the main source of access to the articles subject to review. In addition to these technical benefits, Scopus was selected as the sole database for this study to ensure methodological consistency in the retrieval, filtering, and comparison of data. This decision also minimized the risk of duplication of records and heterogeneity in metadata formats, which can occur when combining results from multiple databases. Furthermore, previous systematic reviews in the field have demonstrated that Scopus offers sufficiently broad and multidisciplinary coverage to capture the relevant literature on Lifelong Learning, Education 4.0, and Artificial Intelligence, thus meeting the scope and objectives of this review.

2.1.4. Search Strategy

To advance the search and appropriately respond to the guiding questions of the review, the following search string was established: TITLE-ABS-KEY (“lifelong learning” AND (benefits OR advantages OR limitations OR definitions OR perspectives OR concepts OR “artificial intelligence”)). This combination of terms was selected to ensure the retrieval of studies directly addressing Lifelong Learning in conjunction with its conceptual, practical, and technological dimensions, including those linked to Artificial Intelligence. The use of Boolean operators allowed for the inclusion of synonymous or conceptually related descriptors, thus broadening the scope while maintaining thematic precision.

2.2. Literature Search and Study Selection

The process of literature identification, screening, eligibility assessment, and final selection followed a systematic and transparent approach to ensure reproducibility and methodological rigor. All steps were documented to provide a clear audit trail of decisions made during the review process.

2.2.1. Identification

From the application of the search string in Scopus, an initial set of 5262 documents was generated. This initial corpus reflected the broad scope of Lifelong Learning research in connection with Artificial Intelligence and Education 4.0. To align the results with the educational and sociotechnical focus of the study, a subject area filter was applied to retain only publications indexed under Social Sciences, reducing the dataset to 3230 items.

To ensure that the set of documents subjected to in-depth analysis was both representative of the population and manageable for qualitative and quantitative synthesis, a probabilistic sampling strategy was implemented. The sample size was calculated at a 95% confidence level and a 5% margin of error, resulting in a working set of 344 documents. This statistical approach minimized selection bias and maintained proportional representation of the thematic diversity present in the larger dataset.

2.2.2. Screening and Eligibility

The 344 documents obtained through probabilistic sampling were then subjected to a two-step screening process based on the inclusion and exclusion criteria established in Section 2.1.2. In the first step, titles and abstracts were reviewed to ensure that each study explicitly addressed, either directly or indirectly, Lifelong Learning in the context of Artificial Intelligence or Education 4.0. This stage reduced the set to 153 articles.

In the second step, only documents with complete full-text access were retained. This ensured that the analysis would be based on comprehensive and verifiable data. The resulting 80 articles were deemed eligible for inclusion and were subjected to an in-depth reading process. This close reading was aimed at extracting the information relevant to the three guiding research questions, while also identifying contextual and methodological details not evident in abstracts alone.

2.3. Data Extraction

For each of the 80 articles included, relevant information related to the three guiding research questions (RQ1, RQ2, and RQ3) was systematically extracted and entered into a structured documentation matrix. This matrix included bibliographic metadata, category of Lifelong Learning addressed, and key findings in relation to the understanding, barriers, and benefits of LLL in the context of Education 4.0. The structured format of the matrix allowed for both thematic coding and quantitative tallying of recurring patterns.

2.4. Data Analysis

The data compiled in the documentation matrix underwent two complementary and interconnected analysis processes: one automated and one manual.

The automated analysis was conducted using the Elicit application, which facilitated the identification of thematic patterns, co-occurrence of keywords, and relationships between concepts within the corpus. This stage generated an initial computational mapping of the literature, highlighting clusters of terms and topics that informed subsequent manual interpretation.

The manual analysis was divided into two methodological strands:

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Qualitative analysis:

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An inductive thematic coding approach was applied, allowing categories to emerge from the data without imposing pre-existing frameworks.

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Codes were developed iteratively and refined through constant comparison across studies.

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Special attention was given to identifying context-specific nuances, such as regional variations in LLL conceptualization or culturally embedded barriers to AI adoption.

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Data segments were grouped into higher-order themes aligned with the three research questions (understanding, barriers, benefits), while also noting any emergent themes that extended beyond the initial RQs.

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The process followed the steps of familiarization, open coding, axial coding, and selective coding, ensuring depth and conceptual clarity.

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Quantitative analysis:

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A frequency count was conducted for each code and thematic category to determine their relative prevalence in the dataset.

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Co-occurrence matrices were generated to identify how different categories (e.g., types of barriers and benefits) appeared together across studies.

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Descriptive statistics (absolute and relative frequencies, percentages) were used to highlight the most common perspectives and recurring challenges.

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Where possible, the data were disaggregated by geographic region, type of study, and category of LLL to reveal differential emphases and trends.

The integration of these two approaches was achieved through a triangulation process, where automated outputs were cross-checked against manual thematic coding. This not only validated the patterns identified but also enriched them with interpretative depth, allowing the synthesis to combine computational rigor with human-led contextual analysis.

3. Results

In line with the principles of methodological transparency and data accessibility that guide systematic reviews, a supplementary dataset has been made available containing detailed metadata on the 80 studies included in this review. This dataset includes variables such as year of publication, geographic region, methodological approach, and key findings, among others. While incorporating this information in tabular format within the main body of the article would make readability more difficult, it is accessible through the following permalink: https://tinyurl.com/4p5ujjym (accessed on 24 August 2025). Additionally, the dataset provides complementary bibliometric indicators that, although beyond the specific scope of this review, may serve as a valuable resource for researchers interested in exploring lifelong learning and artificial intelligence through a scientometric lens.

In order to address the three guiding questions of the review, the results are presented in a structured manner that mirrors the analytical dimensions: (i) conceptualizations, definitions, categories and key aspects that underpin this field of study; (ii) barriers, limitations and challenges associated with its implementation; and (iii) benefits of lifelong learning in the context of artificial intelligence. This structure not only facilitates direct alignment with the objectives of the review but also enhances comparability with previous studies, providing a coherent narrative from data extraction to interpretative synthesis.

3.1. Understanding LLL: A Multifaceted Approach

Regional Perspectives on LLL

As shown in Figure 3, digital literacy and adaptation to technological change emerge as cross-regional priorities in LLL research, reflecting a global recognition of the skills required for participation in digitally mediated economies. Notably, the emphasis on social inclusion and equity is more pronounced in South American and African contexts, where socio-economic disparities shape educational priorities. This distribution underscores the interplay between technological imperatives and the specific socio-economic realities that influence the design of LLL initiatives worldwide.

Firstly, there is a widespread recognition of the importance of digital literacy and the need to adapt to technological changes in an increasingly digitalized world. This theme is closely linked to the focus on continuous professional development and workforce competitiveness, as nations strive to maintain economic relevance in a rapidly evolving global marketplace.

Moreover, the role of LLL in addressing social inclusion and inequality through education emerges as a recurring theme across different regions. This emphasis on educational equity is complemented by a growing recognition of the value of self-directed and flexible learning approaches, which cater to diverse learner needs and circumstances. Furthermore, there is a consistent acknowledgement of the importance of formal, non-formal, and informal learning contexts in the LLL journey. Lastly, researchers across continents emphasize the dual role of LLL as a tool for personal growth and societal development, highlighting its potential to foster both individual and collective progress.

Despite these shared themes, notable differences in emphasis exist among researchers from various regions. European and North American perspectives tend to place greater emphasis on personal growth and the development of critical thinking skills, viewing LLL as a means of individual empowerment and intellectual development. In contrast, Asian researchers focus more strongly on economic growth and workforce development, reflecting the region’s prioritization of human capital development as a driver of economic prosperity.

South American and African researchers, on the other hand, highlight social inclusion and the mitigation of inequalities more prominently in their discussions of LLL. This emphasis likely stems from the pressing socioeconomic disparities present in many countries within these continents. Lastly, Oceanian researchers distinguish themselves by placing a greater emphasis on experiential and problem-based learning approaches, potentially reflecting a cultural preference for practical, hands-on education.

These varying emphases across continents underscore the influence of diverse economic, social, and cultural contexts on LLL research and practice. They also highlight the specific challenges and priorities in education and workforce development that shape regional perspectives on LLL. By understanding these commonalities and differences, policymakers and educators can develop more nuanced and culturally sensitive approaches to promoting LLL on a global scale.

3.2. Categories of Lifelong Learning

The review identified three main categories of LLL, which are described below.

3.2.1. Continuing Education

Lifelong learning understood as continuing education is bifurcated into formal, non-formal, and adult continuing education, whose key characteristics include integration into daily and professional activities, self-directed learning, extension of professional development beyond traditional educational infrastructures, and focus on updating knowledge in various fields such as basic education, vocational training, and personal development.

Furthermore, adult continuing education specifically addresses the need to remain competitive in an ever-changing labour market while fostering lifelong personal development. For example, the Open University’s programs for adults in the United Kingdom or the initiatives of The University of Olivet have integrated learning platforms powered by AI and other technologies to tailor study plans for adult learners returning to formal education, offering real-time feedback and personalized pacing. This approach has enabled working professionals to update their qualifications without compromising employment commitments. Other examples of the above can be found in Tong et al. [26], Taylor et al. [27] or Boesi et al. [28].

3.2.2. Vocational and Occupational Training

A second way of understanding LLL includes professional development, on-the-job training, internships with tutors and advisors, development of digital competencies and job updating and retraining. This aspect of LLL is essential for maintaining and enhancing skills in the work environment. A notable case is Singapore’s SkillsFuture initiative, which incorporates AI-driven career guidance systems to recommend personalized training pathways aligned with labour market forecasts. Such tools enable workers to identify skill gaps and engage in targeted upskilling or reskilling activities, improving employability in rapidly changing industries. Other examples of the above can be found in Cort [29], Thwe and Kálmán [30] or Salajan and Roumell [31].

3.2.3. Digital Technology Training

A third way of understanding LLL has become increasingly important in the context of the use of digital technologies and includes online learning, participation in digital knowledge-sharing communities and the development of digital competencies crucial for modern professional environments. In Spain, the “Digital Skills for All” program has leveraged intelligent tutoring systems to support learners in acquiring advanced digital competencies. These systems analyse learner progress data to adapt instructional content, providing tailored exercises and resources that address individual needs while promoting self-directed learning. Other examples of the above can be found in Machado et al. [32], Ashaari et al. [33] or Mohammed and Kinyo [34].

3.3. Key Aspects of Lifelong Learning

The conceptualization of LLL encompasses several crucial aspects that contribute to its comprehensive nature. This section explores two primary dimensions: personal and social development, and learner autonomy and flexibility.

3.3.1. Personal and Social Development

Figure 4 shows the frequency of appearance of the main terms associated with this key aspect.

Lifelong learning is conceptualized as a vehicle for personal growth and social integration. This understanding incorporates the development of emotional and social competencies, which are crucial for effective interpersonal interactions and self-management in various life contexts. Moreover, it emphasizes the cultivation of reflective practices, enabling individuals to critically analyze problematic situations and devise appropriate solutions. Additionally, the application of LLL principles extends to specific contexts, such as prison education, thereby demonstrating its potential for facilitating societal reintegration and reducing recidivism. Examples of the above can be found in Alberici and Di Rienzo [35] or Bjursell [36].

3.3.2. Learner Autonomy and Flexibility

Figure 5 highlights the prominence of concepts related to learner autonomy and flexibility, signalling their centrality in contemporary understandings of LLL. The frequency of autonomy-associated terms indicates a pedagogical shift towards approaches that empower learners to take ownership of their educational journeys, adapt learning strategies to personal circumstances, and engage in self-regulated learning.

Recent scholarly work [37,38,39] has highlighted several key factors that are integral to the LLL paradigm. Firstly, learner autonomy empowers individuals to take control of their learning processes and outcomes. Secondly, an individualized focus recognizes and accommodates diverse learning needs and preferences. Thirdly, methodological flexibility allows for the adaptation of learning approaches to suit various contexts and learner characteristics. Lastly, motivation plays a critical role as a driver in sustaining engagement with lifelong learning endeavours.

3.3.3. Multidisciplinary Perspectives

Figure 6 shows the frequency of appearance of the main terms associated with this key aspect.

The conceptualization of LLL is inherently interdisciplinary, drawing insights from a range of academic fields. This multifaceted approach reflects the complex nature of lifelong learning and its far-reaching implications across various aspects of human development and societal progress. Key disciplines contributing to the understanding of LLL include pedagogy, which informs the theoretical foundations and practical applications of lifelong learning strategies. Psychology offers insights into cognitive processes, motivation, and behavioural change associated with continuous learning.

Furthermore, sociology provides perspectives on the societal implications and cultural contexts of lifelong learning. The integration of these diverse disciplinary viewpoints enables a more comprehensive and nuanced understanding of LLL, highlighting its multidimensional nature and its potential impact across different societal domains. Examples of the above can be found in Boeren [40], Franco and De Deus Lopes [41] or Fernández-Rodrigo et al. [42].

3.3.4. Methodological Approaches

Figure 7 shows the frequency of appearance of the main terms associated with this key aspect.

The literature review has identified several methodological approaches that are particularly conducive to fostering lifelong learning. Problem-based learning situates learning within real-world contexts, encouraging the development of critical thinking and problem-solving skills that are transferable across various life situations [43]. In addition, methodologies that promote autonomous learning enable individuals to take ownership of their educational journey, a crucial skill for sustained engagement in lifelong learning [44]. Furthermore, flexible learning approaches accommodate diverse learning styles and life circumstances, making continuous education more accessible and adaptable to individual needs [45]. These approaches are recognized for their efficacy in cultivating intrinsic motivation and sustaining long-term engagement in learning activities. By emphasizing active participation, real-world relevance, and learner agency, these methodologies align closely with the core principles of lifelong learning [46].

3.4. Barriers to Lifelong Learning Implementation

As depicted in Figure 8, technological and institutional barriers appear with the greatest frequency, suggesting that both access to digital infrastructure and supportive policy frameworks are foundational for successful LLL implementation. The relative prevalence of socio-cultural and economic barriers further illustrates the multifaceted nature of the challenges, highlighting the need for integrated strategies that address both systemic and contextual constraints.

3.4.1. Technological Barriers

One of the primary barriers to the implementation of LLL is the digital divide. This divide manifests through various technological barriers, primarily centred around internet access issues. These problems include connection difficulties, service restrictions, and a lack of essential digital skills necessary for online education. Consequently, these factors create a significant disparity in the use of and access to technology among potential learners, hindering the widespread adoption of LLL initiatives. An effective strategy to reduce the digital divide is the implementation of AI-powered offline learning platforms, such as Kolibri, which allow users to access adaptive educational content without constant internet connectivity. By providing localised, device-based access, such tools mitigate the dependence on broadband infrastructure. Examples of the above can be found in Anastasiades [47], Ram et al. [48] or Daineko et al. [49].

3.4.2. Institutional and Policy Barriers

The absence of specific LLL policies and lack of promotional efforts present significant institutional challenges. Furthermore, there exists a notable mismatch between educational practices and students’ needs, which is exacerbated by insufficient guidance for students during their internships. These institutional and policy-related barriers collectively impede the effective implementation and acceptance of LLL across various regions. Thus, AI-based analytics can help institutions align curricula with learner needs by continuously tracking performance patterns and engagement data. Policymakers can leverage these insights to design evidence-based interventions, ensuring that institutional practices and resource allocations are responsive to actual learner requirements. Examples of the above can be found in O’keefe [50].

3.4.3. Socio-Cultural Barriers

Socio-cultural factors play a crucial role in hindering LLL implementation. Age discrimination leads to the marginalization of older adults, creating a significant barrier to their participation in lifelong learning activities. Additionally, cultural preferences that prioritize academic education over professional education contribute to this obstacle. The prevalence of passive learning practices in some cultures further compounds these socio-cultural barriers, making it challenging to promote and implement more active, LLL approaches. In this regard, AI-facilitated translation and cultural adaptation tools, such as natural language processing-based platforms, can bridge linguistic gaps and adapt content to culturally relevant contexts, reducing resistance to LLL programs among underrepresented groups. Examples of the above can be found in Lui et al. [51] or Suyono [52].

3.4.4. Economic Barriers

Financial constraints pose a substantial challenge to LLL implementation. The high costs associated with digital courses, and the necessary technology are prohibitive for many potential participants. This economic barrier significantly limits access to LLL opportunities, particularly for individuals from lower-income backgrounds. Considering the above, AI-enabled micro-credentialing systems can help learners access affordable, modular learning opportunities that stack toward full qualifications. This reduces upfront costs and allows learners to progress at their own pace. Examples of the above can be found in Cumberland et al. [53] or Al-Yaseen et al. [54].

3.4.5. Health-Related Barriers

Health issues present another set of challenges for LLL implementation. Participation in lifelong learning activities is affected by general health conditions, with ageing and senility contributing to reduced engagement. Moreover, the specific needs of older adults, such as those with mobility problems, create additional hurdles. These health-related barriers are often intertwined with age discrimination, further complicating access to LLL for certain demographic groups. Examples of the above can be found in Leicester [55] or Begoray et al. [56].

3.4.6. Pedagogical and Methodological Barriers

Several pedagogical and methodological issues hinder the effective implementation of LLL. These include organizational difficulties related to infrastructure and class management. Additionally, insufficient levels of learner autonomy and independence pose challenges to the self-directed nature of many LLL initiatives. Perhaps most critically, the lack of ability to reflect effectively on reality emerges as a significant barrier, impeding the deep learning and critical thinking essential to successful lifelong learning. Considering this, it is interesting to note that Intelligent tutoring systems can foster learner autonomy by providing personalised learning paths and scaffolding strategies. By gradually reducing support as learner competence increases, these systems build the self-regulation skills essential for LLL.

3.4.7. Emotional and Psychological Barriers

Emotional and psychological factors also play a role in obstructing LLL implementation. A lack of confidence among learners can significantly impact their willingness to engage in ongoing learning activities. Furthermore, various emotional impacts can affect the learning process, creating additional psychological barriers to participation in LLL programs. Examples of the above can be found in Fu [57] or Dirin et al. [58].

3.4.8. Interdependencies and Regional Manifestations of Barriers

Although prior categorizations of barriers—technological, institutional, and sociocultural—provide a useful starting point for analysis, they may obscure the complex interdependencies that shape how these obstacles manifest and are experienced in lifelong learning ecosystems. Rather than being isolated or additive, these barriers often interact dynamically, forming compound obstacles that amplify exclusion and undermine the benefits of AI-driven educational innovation.

For instance, institutional gaps in teacher training and regulatory frameworks can amplify technological barriers. Thus, when educators are not adequately prepared to integrate AI tools into their pedagogical practices, the presence of advanced infrastructure alone becomes insufficient. This is evident in several Latin American countries where pilot AI-based learning platforms have failed to scale due to a lack of sustained professional development programs and fragmented policy support [59]. Similarly, in Sub-Saharan Africa, even where cloud-based adaptive learning platforms are available, low bandwidth and intermittent connectivity—technological limitations—interact with institutional constraints, such as limited funding and centralized curriculum control, to restrict adoption and impact.

Besides the above, it is noteworthy to mention that sociocultural barriers further complicate the picture. In certain regions, there exists resistance to AI in educational settings due to cultural conceptions of learning as a fundamentally human and relational process. This perception is particularly pronounced in Indigenous communities or in contexts with a strong oral learning tradition, where AI-driven feedback systems may be viewed with skepticism or even distrust. For example, in rural Colombia, teacher interviews conducted as part of a digital inclusion project revealed reluctance to adopt AI-based tools that depersonalize instruction, particularly when such tools lacked cultural and linguistic contextualization [60].

Now, in technologically advanced countries such as Germany or South Korea, the interplay between ethical, legal, and institutional dimensions has tempered the pace of AI integration in public education, despite the availability of robust digital infrastructure. Ethical concerns—particularly those related to data privacy, algorithmic opacity, and student profiling—are compounded by stringent regulatory frameworks, such as the General Data Protection Regulation (GDPR), which impose significant constraints on experimentation with adaptive AI systems. These legal structures intersect with broader sociopolitical sensibilities, contributing to a form of regulatory inertia that slows the adoption of innovative educational technologies, regardless of their technical feasibility.

At the same time, the nature and impact of cross-barrier interactions tend to vary across educational levels, producing differentiated constraints on AI adoption. In higher education, although students often demonstrate sufficient digital literacy to engage with AI-powered platforms, structural limitations—such as rigid curricular frameworks, restricted pedagogical flexibility, or conservative institutional governance—frequently obstruct the implementation of personalized learning models. In contrast, within primary and secondary education (K–12), even in systems that allow a degree of curricular autonomy, sociocultural resistance from parents, guardians, or local communities may lead to the rejection or cautious deployment of AI-based tools, particularly those that monitor student behaviour or generate algorithmic learning pathways.

These examples suggest the need to move beyond static barrier typologies and adopt a systems-thinking approach that recognizes the fluid, contingent, and context-dependent nature of these challenges. Rather than addressing each barrier independently, it is essential to understand how they mutually reinforce one another across geographic, institutional, and cultural domains. Future empirical studies could benefit from developing typologies of barrier constellations—composite profiles of interrelated barriers—that guide more targeted and context-aware interventions.

3.5. Benefits of LLL in the Context of Education 4.0

These findings are consistent with prior literature highlighting the transformative role of lifelong learning in fostering adaptability and innovation when integrated with artificial intelligence systems (e.g., studies in higher education and corporate training environments have similarly emphasized its capacity to bridge skill gaps and enhance employability in rapidly evolving sectors). By synthesizing evidence across diverse contexts, this review reinforces the notion that the benefits are multidimensional, extending from individual cognitive development to systemic organizational resilience.

3.5.1. Personal and Professional Development

Lifelong Learning offers significant benefits in personal and professional growth within the Education 4.0 framework. The continuous improvement of competencies enables individuals to adapt and remain competent in diverse learning environments. This adaptability is crucial in the rapidly evolving landscape of Education 4.0, where learners must constantly update their skills. Furthermore, LLL facilitates the acquisition of specific competencies in professional fields, enhancing employability and reducing unemployment. This process of ongoing skill development contributes to self-realization, allowing individuals to pursue their passions and interests continuously. Consequently, improved knowledge and job skills lead to a reduction in income inequality and promote greater social harmony. Examples of the above can be found in Bjursell [36] or Hachoumi et al. [61].

3.5.2. Economic and Social Benefits

The economic impact of LLL extends beyond individual benefits, providing direct economic benefits and supporting government programs. In the context of Education 4.0, where technological advancements are reshaping the job market, LLL plays a crucial role in workforce adaptation. Moreover, LLL contributes significantly to social inclusion by reducing marginalization and social exclusion. It promotes digital and social inclusion, which are essential components of the Education 4.0 paradigm, ensuring that diverse populations can participate in and benefit from advanced educational technologies and methodologies. Examples of the above can be found in Kourtoumi [62] or Waller et al. [63].

3.5.3. Enhanced Educational Outcomes

Lifelong Learning significantly improves educational results within the Education 4.0 framework. It fosters increased participation and collaboration between students and instructors, leveraging digital platforms and innovative teaching methods. This collaborative approach leads to better ratings, higher satisfaction levels, and improved retention rates. Additionally, LLL supports the personalization of learning, offering flexibility and accessibility, which are key features of Education 4.0. These elements collectively contribute to a more engaging and effective learning experience, aligned with the goals of modern educational paradigms.

3.5.4. Development of Cognitive Competencies

In the context of Education 4.0, LLL plays a crucial role in developing critical and reflective thinking skills. It strengthens the mechanism and effectiveness of reflection, which is essential in a rapidly changing technological landscape. The development of critical thinking and clinical reasoning capabilities through LLL aligns well with the demands of Education 4.0, where problem-solving and analytical skills are highly valued. Examples of the above can be found in Perdanasari et al. [64] or Culver et al. [65].

3.5.5. Innovative Learning Environments

Lifelong Learning promotes the creation and utilization of innovative learning environments, which are central to Education 4.0. Digital environments facilitate digital inclusion and encourage active participation and reflection in the learning process. These environments support the dissemination of best practices and the implementation of inclusion programs and projects. The early integration of digital media in LLL programs prepares learners for the technologically advanced educational landscape of Education 4.0. Examples of the above can be found in Heredia-Sánchez [66] or Susnea et al. [67].

4. Discussion

The results presented in this systematic literature review offer significant insights into the nature, challenges, and benefits of Lifelong Learning within the context of Education 4.0. These findings underscore the critical importance of LLL in today’s rapidly evolving educational and professional landscapes, particularly as we navigate the complexities of the Fourth Industrial Revolution.

4.1. AI in Education: Multifaceted Implications

Central to our analysis is the multifaceted approach to understanding LLL and its adaptability across different cultural contexts. While the core principles of continuous learning remain consistent, the implementation and emphasis of AI in LLL can vary significantly based on cultural and societal norms. This diversity in approach suggests that AI strategies in LLL should be tailored to specific regional and cultural contexts to maximize their effectiveness and relevance. This makes a lot of sense since there is undoubtedly a need to move nimbly from fixed curricula to adaptive learning experiences, with an emphasis on education as a continuous journey and not as a static destination, where AI emerges as a catalyst for change, providing support resources to respond to the multifaceted needs required to redefine today’s education, with a personalized character to each society [68].

4.2. Categorization of LLL in the AI Context

Building on this cultural diversity, the review revealed a comprehensive categorization of LLL into continuing education, vocational and occupational training, and digital technology training. This tripartite classification underscores the importance of addressing not only formal academic learning but also practical skills development and digital literacy. As AI continues to reshape the job market and societal interactions, the importance of digital competencies in LLL cannot be overstated. This coincides with recent studies that have highlighted the impact of AI on teachers’ professional performance and have started to test the effectiveness of some training initiatives for teachers, to promote their educational competence in AI and consequently achieve efficient career paths in coherence with the accelerated changes and demands of the current educational context [69,70].

4.3. Ethical and Ontological Considerations of AI in LLL

While Artificial Intelligence offers significant potential to enhance Lifelong Learning, it also prompts profound ethical and ontological questions. AI systems capable of influencing learning pathways inevitably engage with issues of learner autonomy, agency, and epistemic authority. In this regard, the evolution of machine learning systems challenges traditional conceptions of human decision-making and the very nature of knowledge creation. In the context of LLL, this raises questions about the extent to which learning processes should be delegated to algorithms, and how to safeguard the learner’s ability to critically assess and direct their own learning trajectory.

Furthermore, AI-mediated personalisation must be balanced with transparency and explainability to avoid epistemic opacity, ensuring that learners and educators can understand the rationale behind system recommendations. The ethical imperative extends to data privacy, equitable access, and the prevention of algorithmic bias, particularly in global contexts where socio-cultural diversity is a defining factor of learning needs. Addressing these concerns requires a framework in which AI serves as a facilitator rather than a determinant of learning, preserving the inherently human character of education while leveraging technological affordances.

4.4. Barriers to AI Integration in LLL

Despite the clear benefits and importance of LLL, the review identified several significant barriers to its implementation, particularly in the integration of AI. These obstacles range from the digital divide, technological barriers, and lack of essential digital skills to institutional and policy barriers. Additionally, socio-cultural, economic, health-related, and emotional and psychological barriers present a complex web of challenges. Addressing these challenges requires a holistic approach, necessitating collaboration between educational institutions, policymakers, technology providers, and community organizations. Overcoming these barriers is crucial to ensuring equitable access to continuous learning opportunities and realizing the full potential of AI in education. This coincides with previous studies, which have focused their attention on AI within educational contexts, due to its growing social importance and pedagogical value. For example, one study demonstrated the existence of first-order barriers (Obstacles that are extrinsic to the teacher) and second-order barriers (Obstacles that are intrinsic to the teacher) that are interconnected, thus suggesting that educational institutions use differential, multilevel but convergent strategies to address the barriers [71]. Therefore, although AI promises a powerful transformation and revolution in learning and teaching, significant challenges must first be solved to fully integrate it and take advantage of its benefits [72].

4.5. Benefits of AI in LLL

The benefits of AI-enhanced LLL in the context of Education 4.0 are far-reaching and multidimensional. AI offers significant benefits in personal and professional development, economic and social benefits, enhanced educational outcomes, cognitive competency development, and the creation of innovative learning environments. Importantly, these benefits align closely with the goals of Education 4.0, particularly in fostering adaptability, critical thinking, and continuous skill development in response to rapid technological changes.

4.6. Limitations and Future Research Directions

Despite the contributions of this review, some limitations must be acknowledged. First, the decision to use Scopus as the sole database, although justified by its breadth and indexing quality, may have excluded relevant publications indexed in other databases, such as Web of Science or ERIC. Future reviews may benefit from triangulating multiple databases to enhance comprehensiveness. Second, although the sampling procedure ensured representativeness, the reliance on abstracts for the first stage of screening might have led to the omission of nuanced insights present only in full texts. Expanding the screening process or combining it with citation chaining could strengthen future analyses. Third, while the combination of qualitative and quantitative analyses provided triangulated insights, the interpretation of patterns inevitably involved a degree of subjectivity, which underscores the importance of replication with alternative methods.

Considering the above, several key areas for further research and development emerge from our findings. Firstly, there is a need for more in-depth studies on the integration of AI and other advanced technologies in LLL practices. Future research could explore specific AI applications in different LLL contexts and their long-term impacts on learning outcomes and employability. Additionally, developing more effective strategies to overcome identified barriers to AI integration in LLL should be a priority. This could involve interdisciplinary research combining insights from education, technology, sociology, and psychology to create comprehensive solutions that address multiple barriers simultaneously.

4.7. Longitudinal Studies and AI

Furthermore, longitudinal studies tracking the long-term effects of AI-enhanced LLL participation on career trajectories, personal well-being, and societal development would provide valuable insights into the broader implications of LLL in the context of Education 4.0. Such studies could help refine LLL policies and practices to maximize their positive impacts. As the concept of Education 4.0 continues to evolve, ongoing research is needed to ensure that LLL strategies remain aligned with emerging educational paradigms and technological advancements. This could include exploring new forms of digital credentialing, investigating the potential of virtual and augmented reality in LLL, and examining the role of LLL in fostering global citizenship and cross-cultural competencies in an increasingly interconnected world.

As a conclusion, this review underscores the critical role of Lifelong Learning in preparing individuals and societies for the challenges and opportunities presented by Education 4.0 and the broader Fourth Industrial Revolution. By addressing the identified barriers and leveraging the potential benefits of AI, LLL can serve as a powerful tool for personal empowerment, social inclusion, and economic development in an era of rapid technological change. Continued research and innovation in AI-enhanced LLL practices will be essential to ensure that education systems can effectively prepare learners for the dynamic and unpredictable future that lies ahead.

5. Conclusions and Implications

The present review has provided a comprehensive synthesis of the scientific literature on Lifelong Learning in relation to Artificial Intelligence, responding to the three guiding questions established at the outset. First, regarding the definitions and conceptualisations of LLL, the findings reveal a convergence towards its recognition as a continuous and adaptive process that extends beyond formal education, yet significant divergences persist in the scope and operationalisation of the concept. AI emerges as a potential catalyst for bridging these divergences by enabling personalised learning pathways, supporting adaptive assessment, and facilitating the recognition of non-formal and informal learning experiences.

Second, in terms of the benefits and opportunities, the analysis indicates that AI can enhance learner autonomy, improve access to resources, and strengthen decision-making in educational and professional contexts. The integration of AI into LLL offers the capacity to support predictive learning analytics, early detection of skills gaps, and the development of highly individualised training plans. Nevertheless, the opportunities are contingent upon overcoming the technological, pedagogical, and socio-economic barriers identified in this review.

Third, with respect to the limitations and challenges, the review confirms that ethical, technical, and institutional issues remain critical. These include the risks of algorithmic bias, concerns over data privacy, the potential erosion of human agency in learning processes, and the persistence of digital divides. Addressing these issues requires a deliberate and ethically grounded approach to AI design and deployment in educational settings.

From a theoretical standpoint, this study contributes by integrating AI-specific considerations into the broader discourse on LLL, offering an updated conceptual map that aligns emerging technological capabilities with pedagogical imperatives. By synthesising perspectives from social sciences, educational technology, and ethics, the review not only reaffirms existing theoretical positions but also extends them by introducing a multidisciplinary framework for understanding AI’s role in lifelong learning ecosystems.

From a practical perspective, the findings have direct implications for educators, policymakers, and technology developers. For educators, the integration of AI should be pursued as a complement to, rather than a replacement for, human facilitation, ensuring that the pedagogical intent guides technological adoption. For policymakers, the results underscore the need to design regulatory frameworks that balance innovation with ethical safeguards, promote equitable access, and foster intersectoral collaboration. For technology developers, the review highlights the importance of transparency, explainability, and adaptability in AI systems intended for educational use.

Beyond addressing existing gaps, this review offers a distinctive contribution by providing an integrated, evidence-based perspective that bridges conceptual discourse with applied strategies. This dual orientation enhances its relevance for both academic inquiry and policy-oriented decision-making, ensuring that the discussion of lifelong learning in the age of artificial intelligence remains both theoretically grounded and pragmatically actionable.

While the review has yielded meaningful insights, its scope is bounded by certain limitations. The exclusive reliance on the Scopus database may have excluded relevant studies indexed elsewhere; future research should therefore expand the database coverage and explore complementary methodologies such as meta-analyses or mixed-methods reviews. Additionally, the rapidly evolving nature of AI applications in education calls for longitudinal studies to assess the sustainability and long-term impact of these technologies on LLL.

5.1. Policy and Practice Recommendations

In response to the identified barriers and challenges, this review underscores the need for more nuanced and operational strategies to guide the implementation of AI in lifelong learning systems. The following recommendations are intended to move beyond general prescriptions and offer actionable pathways for educational institutions, policymakers, and practitioners, while acknowledging potential trade-offs and implementation complexities.

5.1.1. Institutional Capacity Building and Pedagogical Alignment

Educational institutions must invest in sustained professional development programs that support educators in understanding, selecting, and meaningfully integrating AI tools into diverse learning environments. Training initiatives should not only focus on the technical use of AI platforms, but also address pedagogical coherence, ethical concerns, and context-sensitive adaptation. Such efforts require institutional leadership that bridges digital innovation with long-term curriculum planning and teacher agency.

5.1.2. Multilevel Governance and Regulatory Innovation

Policy frameworks should evolve to accommodate the specificity of AI applications in education, especially regarding data protection, algorithmic transparency, and equity. This includes establishing adaptive governance models that combine national-level standards (e.g., ethical AI principles, privacy protections) with localized mechanisms for school-level accountability and community participation. Regulatory sandboxes may serve as transitional environments to test AI-based solutions without full-scale deployment.

5.1.3. Cultural Responsiveness and Stakeholder Engagement

The design and implementation of AI in education must actively consider the cultural, linguistic, and social characteristics of target populations. Co-creation strategies—involving teachers, students, families, and local communities—are essential to mitigate resistance and ensure that AI tools are perceived as enhancing rather than displacing human-centred learning. In contexts with low digital trust, special attention should be given to transparency, explainability, and participatory design processes.

5.1.4. Infrastructure and Interoperability

Efforts to scale AI in lifelong learning require investments not only in connectivity and hardware, but also in interoperable systems that allow integration across platforms and learning modalities. Special attention must be paid to marginalized regions or populations, where infrastructural gaps amplify existing educational inequities. Public–private partnerships may play a strategic role in addressing these deficits if aligned with open standards and public interest objectives.

It is noteworthy to mention that each of these recommendations implies potential tensions—for example, between innovation and privacy, or between personalization and curriculum coherence—that must be addressed transparently. Rather than proposing universal solutions, this review advocates for adaptive strategies grounded in local realities and aligned with broader human development goals.