Regional Perspectives on Service Learning and Implementation Barriers: A Systematic Review

Abstract

Service learning (SL) is at a pivotal moment as education systems worldwide confront the challenges and opportunities posed by artificial intelligence (AI) and digital technologies. This scoping review synthesizes regional perspectives on SL and examines the barriers to its implementation in higher education. This study adopts a methodological approach widely used in prior educational research, enriched with selected PRISMA processes, namely identification, screening, and eligibility, to enhance its transparency and rigor. A total of 101 peer-reviewed articles were analyzed, using a mixed methods approach. Results are presented for six regions, Africa, Asia, Latin America, Europe, North America, and Oceania, revealing context-specific constraints, such as technological infrastructure, policy frameworks, linguistic diversity, and socio-economic disparities. Common barriers across regions include limited faculty training, insufficient institutional support, and misalignment with community needs. AI is explored as a potential enabler of SL, not as an empirical outcome, but as part of a reasoned argument emerging from the documented complexity of SL implementation in the literature. Ethical considerations, including algorithmic bias, equitable access, and the preservation of human agency, are addressed, alongside mitigation strategies that are grounded in participatory design and community engagement. This review offers a comparative, context-sensitive understanding of SL implementation challenges, providing actionable insights for educators, policymakers, and researchers, aiming to integrate technology-enhanced solutions responsibly.

1. Introduction

In its most recent report on the Future of Education, UNESCO presents a sour outlook regarding the challenges to achieving quality education that is rethought, equitable, and, above all, solidary and collaborative. According to the report, there are several challenges to achieving these goals, such as a lack of access to education in some areas of the world, the digital divide, and inequality in regard to access to quality educational resources [1].

Additionally, the report indicates that the current education system is not adequately preparing students to face the challenges present in today’s world, such as climate change, job automation, and growing economic inequality. Therefore, it is considered relevant at the level of public policy formulation in education to encourage a clear focus on developing skills and competencies that enable students to confront these challenges and contribute to the construction of a more just and sustainable future [2].

Given this context, many institutions of higher education have developed strategies in recent decades to promote spaces of collaboration and solidarity with communities through teaching, research, or outreach.

Firstly, one of the strategies that stands out the most is the one that focuses on implementing volunteer and community service programs, where students can apply the knowledge acquired in the classroom in real situations and, at the same time, contribute to the development of the communities in which they are inserted [3,4]. These programs can be developed both within the curriculum and in regard to extracurricular activities and can be designed in collaboration with community organizations and other local actors.

Another common strategy is the promotion of research and outreach projects that address relevant social and environmental issues for communities [5]. These projects are usually developed in collaboration with community organizations and groups and can involve students, teachers, and other researchers. The goal of these projects is to generate knowledge and practical solutions that can be applied in communities, while promoting collaboration and solidarity among the different actors involved [6,7].

In addition, many institutions have begun to incorporate sustainability and social responsibility-focused courses and programs into their curricula, with the aim of training leaders capable of understanding and addressing the global challenges facing the world today [8]. This trend has even led to the emergence in educational discourse of a “third mission” for universities: to contribute through their formative and scientific responsibilities and their academic community (students, professors, and alumni) to the social, economic, and environmental development of communities and their territories.

The idea of the “third mission” of universities has become an increasingly important topic in the world of higher education. Universities have realized that they cannot limit themselves solely to the transmission of knowledge and academic research, but also have a responsibility to contribute to the social, economic, and environmental development of the communities and territories in which they operate [4]. This “third mission” implies that universities must work in collaboration with other organizations, companies, and social actors, rather than only focusing on the interests of the academic community [9].

To carry out this mission, universities have established partnerships with different actors, such as governments, companies, and non-governmental organizations, among others, to address social, economic, and environmental problems in communities. According to García-Gutiérrez & Corrales Gaitero [10], these partnerships are not only beneficial for communities, but also for universities, as they provide them with the opportunity to expand their networks and collaborations in academic, research, and social responsibility fields.

In addition, this “third mission” has also led universities to reconsider the way they educate and prepare their students. Now, universities seek to include practical and socio-emotional skills in their study programs, so that students can apply their knowledge in the real world and contribute to solving problems within their communities [11]. In this way, universities not only educate more well-rounded students, but also generate a positive impact on society.

In this regard, Truong et al. [12] indicate that many educational projects at universities have focused on the integration, in curricula and extracurricular spaces, of training activities with “social meaning” that have a dual purpose: on the one hand, to transform student learning towards the acquisition of new, more practical and socio-emotional competencies and skills and, on the other hand, to generate an impact on communities through these learning dynamics, which has a social aspect.

Thus, in the last two decades, the promotion of experiential social learning has emerged with more force than ever, in which student learning occurs through service and immersion in communities, and aims to reconcile theory with practice, also known as “service learning” [13,14]. According to Zainuri & Huda [15], this learning modality has gained greater attention and importance in higher education in recent decades, and its focus seeks to provide students with practical and meaningful opportunities to apply their knowledge and skills in solving real problems and challenges in communities.

Service learning is an educational methodology that connects theory with practice through community service, gaining relevance in various parts of the world. Known as “social service” in Mexico, “Semesters of Practical Social Experience” in Colombia, “community service learning” in Brazil, “community service learning” in Turkey, “communal work” in Costa Rica, “service learning” in Bolivia, and in the United States as “service learning,” it is a practical methodology aimed at enabling students to apply their knowledge to address real social challenges [16].

Originating in North America in the late 1970s, service learning promotes active and experiential learning, wherein students engage in projects that benefit their communities. This methodology not only enables students to consolidate their theoretical knowledge, but also fosters the development of social skills that are essential for nurturing critical and reconstructive thinking. In other words, it enables students to see others from a close and empathetic perspective, learning and unlearning the true meaning of citizenship.

When students participate in service projects, they have the opportunity to establish direct and vivid contact with the real needs of the community; from this perspective, participants can contribute and act to improve the quality of life of people within their communities, strengthen social bonds, and develop a lasting commitment to the common good. In this sense, Jones et al. [17] assert that service learning is a solidarity-based service led by students, aimed at meeting real needs within a community, integrated with the curriculum, and centered on student learning.

In regard to the development of service learning, the University of Cambridge emerged as a pioneer in this field by introducing the concept of “outreach” and disseminating scientific findings from service-learning experiences. Likewise, as early as 1869, various German universities, such as those in Leipzig, Berlin, Hamburg, and Munich, adopted similar practices, thereby extending knowledge beyond academic walls and demonstrating the transformative impact of higher education on society [18].

Service learning goes beyond theory, inviting students to apply their knowledge in real-world contexts. This practical experience fosters meaningful, collaborative, and enduring learning and, most importantly, it transcends academic spaces. Students become agents of change by identifying needs, together with communities, in a way that enables them to design unique projects that are necessarily aligned with the reality of community spaces.

This set of participatory and direct intervention activities provides students with better opportunities to become true leaders and to design projects that address the needs of communities, as well as to develop teamwork skills of a participative nature. Approaching the community, and listening to the issues and needs of that community, leads students to reflect on their actions and to strengthen their impact within the community. It is equally important for students involved in service learning to acquire the necessary skills to analyze the root causes of social problems and to assess the effectiveness of their interventions [16].

On the other hand, given the current technological developments and evolution that characterize our time, especially with the application of technologies from the fourth industrial revolution, it is necessary to reflect on the challenges related to the implementation of service learning in this complex and constantly evolving context.

In this sense, the emergence of technology in education has created new opportunities and challenges in the way teaching and learning methods are implemented. Thus, the implementation of service learning in the current context of the fourth industrial revolution represents an opportunity for students to learn not only the skills necessary for them to integrate into society, but also to acquire knowledge that allows them to understand and apply emerging technologies in real contexts [19]. However, this also involves the challenge of adapting service learning to the digital era and ensuring that education adapts to the constant changes that technological evolution proposes. For this purpose, it is necessary to develop new pedagogical and technological strategies that enable the effective implementation of service learning and its adaptation to the context of the fourth industrial revolution [20].

Taking this into consideration, it is worth highlighting the role of artificial intelligence (AI) in the development of what is known as “education 4.0.”

Artificial intelligence is playing an increasingly important role in education, which has led to the emergence of what is known as “education 4.0” [21]. In this context, AI can be used to improve teaching and learning in various ways, including generating personalized learning and adapting teaching and evaluation processes to the individual needs of each student [22]. This is proposed based on the analysis of data and the tracking of individual learning progress, automatic evaluation and real-time feedback, all supported by the identification of patterns and trends in student behavior and performance [23].

To further reinforce the idea of personalization through the use of AI, it can also be used to improve accessibility and the inclusion of students with disabilities or special educational needs, so that they can have access to tools and resources adapted to their specific needs [24].

To conceptually ground the intersection between SL and AI, this review draws on experiential learning theory, situated learning, and constructivist pedagogies that emphasize the role of context, reflection, and active engagement in shaping meaningful learning. From this perspective, artificial intelligence is not seen as a replacement for human interaction, but rather as a mediating technology that can support reflection, personalization, and the scalability of SL initiatives. This review does not present AI as an empirical outcome of the analyzed studies, but rather as an element of a reasoned argument that emerges from the existing literature on technology-supported educational practices. While not derived from empirical data, the assessment contained in this review of the potential alignment of technological solutions, including, but not limited to, AI, along with the relevant contextual constraints, offers a framework for future research and practice. By embedding AI within established pedagogical frameworks, we aim to move beyond technological determinism and instead explore how these tools may enhance the transformative, ethical, and social dimensions of SL when implemented with intentionality.

Currently, from the point of view of educational research, both service learning and education 4.0 show a growing trend, as shown in Figure 1.

However, considering the scarcity of knowledge about the relationship between these two topics, it has been deemed appropriate to carry out a systematic literature review on this subject to identify the challenges, implementation difficulties, and outcomes of these socially meaningful experiential learning strategies.

2. Method

This study was conducted as a scoping review, following a methodological approach that has been consistently applied in previous educational research [25]. To enhance transparency and methodological rigor, this review incorporated some specific processes from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, namely identification, screening, and eligibility. These processes were integrated into the review design to strengthen the traceability of the literature selection, without transforming the study into a full systematic review. The application of these steps ensured greater clarity in reporting the search and selection procedures, while preserving the broader exploratory nature of the scoping review. Specific details on how this method was implemented in regard to the literature review are presented in Figure 2.

2.1. Review Protocol Design

2.1.1. Determining the Review’s Purpose

To ensure a structured and consistent review process, two guiding questions were formulated:

How is service learning perceived in different contexts?

What have been the main barriers to the implementation of service learning regarding the use of AI in education?

These questions helped establish the focus of the review and guided the selection and analysis of the studies included.

Figure 2. Review process.

Figure 2. Review process.

2.1.2. Eligibility Criteria

Specific inclusion and exclusion criteria were developed to determine which studies to include and exclude from the review, as shown in

Table 1. Document inclusion and exclusion criteria.

Inclusion Criteria	Exclusion Criteria
Articles presenting research findings.	Other document types, such as reviews, letters to the editor, editorials, reflective texts, books, and book chapters.
Articles that explicitly or implicitly mention the review topic in the title or abstract.	Articles that do not mention the review topic in the title or abstract, or for which relevance cannot be inferred.
Approach taken from an educational perspective.	Approach taken from perspectives other than educational.
Articles published in English or Spanish.	Articles published in other languages.

.

The inclusion and exclusion criteria were applied independently by two reviewers during the screening phase. In cases of disagreement, a third reviewer participated to reach a consensus. For documents categorized under “inferential theme,” inclusion was determined based on the abstract’s relevance to service-learning principles or educational goals, even if the term was not explicitly mentioned. This interpretive decision was guided by a coding manual and predefined thematic descriptors that were aligned with the research questions.

2.1.3. Information Sources

Scopus was selected as the sole source for accessing articles due to its comprehensive indexing of peer-reviewed educational research, its advanced filtering capabilities, and its strong coverage of service learning publications across diverse regions. Although the use of only one database represents a limitation, it ensured methodological consistency, language uniformity, and a manageable scope for this review. Future studies may expand such coverage through the inclusion of complementary sources, such as the Web of Science or ERIC [26].

2.1.4. Search Strategy

A preliminary search string was established: TITLE-ABS-KEY “service learning”. After conducting this initial search, a second, complementary search was performed to assess whether the use of semantically related terms would produce significant variations in the results. This step aimed to contrast the retrieval scope of a single-term query with that of a broader search strategy incorporating synonyms and related concepts identified from educational thesauri and academic databases, such as ERIC, UNESCO’s IBE Thesaurus, and the Thesaurus of ERIC Descriptors. The additional search string was TITLE-ABS-KEY (“service learning” OR “community-based learning” OR “community service learning” OR “academic service learning” OR “learning through service”).

All of the searches maintained the same filters (subject area: Social Sciences; document type: Article; language: English or Spanish). The comparative analysis of the two search strategies showed that the broader set of terms produced an increase of less than 6% in the global results and less that 2% in terms of the relevant records (a value specified in the abstract results), with most additional articles either being duplicates of content already retrieved or lacking a clear educational orientation. This confirmed that the single-term strategy captured most of the peer-reviewed articles that were directly relevant to the research questions, while the complementary search enabled us to check for potential omissions.

2.2. Literature Search and Study Selection

2.2.1. Identification

The identification process followed the structure proposed by the PRISMA 2020 statement and is visually represented in Figure 2, which highlights the three initial phases, identification, screening, and eligibility, together with the corresponding number of records retrieved at each stage. The search in Scopus using the initial query retrieved 9525 records, which were then filtered by subject area (Social Sciences) and document type (Article), resulting in 4208 records. To generate a dataset that was manageable yet faithfully representative of the total amount of results obtained, a statistically representative sample was calculated using a 95% confidence level and a 5% margin of error, yielding a subset of 353 articles for the screening phase. These figures, as well as the specific filtering steps, are annotated in the figure for clarity. The inclusion of these elements in Figure 2 ensures that the reader can trace each decision made during the search and filtering process.

2.2.2. Screening

During the screening phase, the titles and abstracts were reviewed independently by two researchers, who applied the inclusion and exclusion criteria described in Section 2.1.2. Disagreements were resolved through discussions and consensus. While no formal inter-rater reliability coefficient was calculated, a shared rubric guided the decision-making process. As shown in Figure 2, the screening process reduced the initial filtered set from 4208 to 174 articles. The reasons for exclusion at this stage included the lack of an explicit or inferential reference to service learning in the title or abstract and the absence of an educational perspective in the study. The number of exclusions for each criterion is indicated in the notes accompanying Figure 2.

2.2.3. Eligibility

The eligibility phase involved a preliminary full-text review to confirm the relevance of the remaining 174 articles. This process led to the exclusion of 73 articles for reasons such as insufficient methodological detail, a lack of focus on service learning as an educational practice, or the absence of content related to implementation barriers or conceptualizations. As depicted in Figure 2, this resulted in a final set of 101 articles for the in-depth analysis. The figure specifies the exact numbers of articles included at each stage, following PRISMA conventions, to enhance transparency.

2.3. Data Extraction

The selected articles were then read in detail. Pertinent data related to the two guiding questions in the review were systematically extracted and recorded in a documentation matrix for subsequent analysis.

2.4. Data Analysis

The data obtained and recorded in the documentation matrix were subjected to two complementary types of analysis: qualitative and quantitative.

2.4.1. Qualitative Analysis

A coding process was developed in which the data were grouped and categorized into relevant themes and sub-themes to capture the diversity in the approaches, perceptions, and barriers related to service learning across different educational contexts. This analysis involved the systematic identification of patterns, the creation of interpretive categories, and the definition of emerging themes. Each category was reviewed and iteratively refined to ensure that it adequately reflected the variety of perspectives observed in the articles. Thematic coding was conducted using Microsoft Excel. The coding process was performed by two researchers, who independently reviewed the articles and then refined the thematic categories through iterative discussions. The categories were derived inductively, with reference to the guiding research questions. No external software was used. Discrepancies in the coding were discussed until an agreement was reached, and emerging categories were cross-validated across a sample of 20 articles to ensure consistency.

2.4.2. Quantitative Analysis

The quantitative component consisted of a frequency analysis of the themes and subthemes identified during the qualitative coding phase. For each selected article, the presence or absence of each category was recorded in the coding matrix, generating numerical data that could be aggregated by region and theme. This process enabled the identification of dominant patterns, regional contrasts, and recurrent barriers to SL implementation. The results were summarized in frequency tables to support the descriptive statistics presented in the Results section. This integration of qualitative coding with quantitative frequency analysis ensured that the findings captured both the depth of the thematic interpretation and the breadth of distribution across the dataset.

3. Results

Service learning (SL) has evolved since the 1960s as an educational strategy that enables students to address community needs, while developing leadership skills, social engagement, and civic competencies. Various studies have demonstrated the positive impact of SL projects, not only on the immediate social environment, but also on the comprehensive development of students. However, the literature reveals a diversity of conceptualizations and barriers to SL implementation that vary across regions, as shown in Figure 3.

Below is a comparison of the conceptualizations and challenges in regard to SL implementation, grouped by geographic region. To start with, the geographic distribution of the studies analyzed is shown in Figure 4.

3.1. Service-Learning Conceptions: A Mosaic of Regional Perspectives

3.1.1. Latin America: Service Learning as a Vehicle for Transformation and Social Justice

In Latin America, service learning is an educational approach fostering social justice and challenging inequality. However, as Lesser [27] highlights, social justice is complex and resists a precise definition. SL initiatives in the region emphasize active participation and shared responsibility between students and communities, promoting sustainability, community empowerment, and environmental justice to drive structural change. These programs cultivate civic and democratic responsibility through experiential education [28].

Service learning also addresses social challenges, while enhancing school coexistence and fostering civic and ethical skills, especially in contexts marked by inequality. For instance, in Mexico and Chile, SL encourages active citizenship, enabling collaborative efforts between students and educators to combat issues like poverty and inequity. It is recognized as a transformative educational resource aimed at societal improvement through education and action. One example is the SL experience analyzed by Condeza et al. [29] at Pontificia Universidad Católica de Chile, wherein professors and students addressed communication, poverty, and inequality. This project integrated marginalized populations’ right to communication and information into higher education. Similarly, Kennedy and Tilly [30] documented a model in San Miguel Analco, Mexico, wherein students participated in community planning, blending transformative learning with community development. These examples underscore SL’s role in linking education with efforts to create a more equitable society.

3.1.2. North America: Inclusion, Multidisciplinary Learning, and Civic Responsibility in Multicultural Contexts

In the United States and Canada, SL involves a multidisciplinary approach that fosters social justice, equity, and cultural inclusion in educational systems. It seeks to modify curricula to integrate these elements as core competencies [31]. While research has yet to confirm its full impact, SL facilitates interdisciplinary projects, enabling students to engage in real-world experiences and foster civic engagement in diverse cultural settings. This approach positions SL as vital in terms of global citizenship education, addressing the needs of multicultural and Indigenous communities, while encouraging civic responsibility through community service [32].

North American SL emphasizes ethical and social skills development through community service projects designed collaboratively with educational and social institutions. It focuses on sustainable student–community engagement, utilizing cultural sensitivity, experiential learning, and digital tools to achieve its objectives. By bridging educational practice with real-world applications, SL contributes to civic responsibility and social innovation, aligning with the goals of inclusive, equitable education [32,33,34,35].

3.1.3. Europe: Service Learning as a Tool for Active Citizenship and Ethical Commitment

In Europe, particularly in Spain and Italy, service learning (SL) emphasizes active citizenship, ethical values, and social responsibility to improve teaching quality and student engagement [36]. This experiential learning model promotes civic participation and encourages students to address societal challenges. SL is seen as a transformative tool for building just and sustainable communities by fostering critical awareness of inequalities and inspiring altruistic behaviors and a commitment to social justice [37].

European SL projects encourage leadership and collaboration with communities to tackle social issues and structural inequalities. Unlike other models, the European approach focuses on developing civic competencies within structured institutional frameworks, promoting social change through community action. Grounded in constructivist–experiential methodologies and the “learning by doing” paradigm, it underscores the role of education in empowering citizens and advancing societal transformation [16,38,39].

3.1.4. Asia: SL as a Strategy for Professional and Social Development and Community Cohesion

In Asia, service learning (SL) integrates technical and vocational education with professional skills development and social responsibility. It enriches traditional curricula through extracurricular activities that address community needs [40]. Since the 1990s, SL has gained traction in countries like Japan and Korea, where higher education institutions have adopted this model. Similarly, nations such as China, India, and the Philippines have embraced SL to engage youths in their communities, while fostering essential 21st century skills and values [41]. SL in Asia often combines community service and experiential learning with reflective practices, connecting students’ training to local needs. This method adds value by addressing real-world challenges and encouraging critical reflection. Despite its diverse applications, SL faces hurdles, including the absence of a unified framework, which hinders its regional expansion and coherence.

Nevertheless, SL remains a critical strategy for promoting social cohesion and personal development. By engaging students in practical, meaningful applications of their learning, SL contributes to both workforce readiness and social integration. Its interdisciplinary and collaborative approaches help align education with societal needs, enabling young people to connect their academic knowledge with real-life issues [15,42,43].

3.1.5. Oceania: Volunteering, Experiential Learning, and Community Engagement

In Oceania, particularly in Australia, service learning emphasizes volunteering and experiential learning, engaging students in community projects to address local needs [44]. This approach fosters hands-on learning and ethical commitment, enhancing their interpersonal skills and social responsibility. However, research on SL in the region remains limited, and challenges persist due to the absence of a strong institutional framework and adequate resources for program expansion.

While SL offers benefits for students, communities, and universities, it does not always meet its objectives. Students often experience frustration due to ambiguous and unclear tasks, and some critics argue that SL can dilute the curriculum, reducing time for traditional academic subjects [45]. Despite these issues, Australian SL shares similarities with the European and North American models, focusing on civic engagement and learning through community interaction.

Positive outcomes are more apparent when SL closely integrates course concepts with students’ learning experiences, balancing the development of academic and professional skills, with a strong emphasis on social responsibility. This approach highlights the potential for SL to create meaningful educational and social impacts when thoughtfully implemented [44,45].

3.1.6. Africa: Contextual Misunderstandings, Poor Communication, and Distrust

Regarding the experiences and conceptualization of service learning in some African regions, Matambanadzo [46] highlights that SL implementation in Africa faces significant challenges related to poor communication and the perception of some projects as experimental endeavors, which can create distrust within communities. In Africa, service learning is designed as a collaborative methodology to empower communities through active student participation in service projects. It integrates experiential learning into courses to broaden perspectives, enhance cultural competence, deepen the understanding of social justice, and reinforce course content [47]. For example, in low-connectivity contexts, the Ushahidi platform, originally developed in Kenya for crisis mapping, has been adapted in educational settings to coordinate community projects via SMS and offline tools, offering a precedent for low-tech, data-driven solutions within SL programs. While this case does not directly involve AI, it illustrates how contextually appropriate technology can enhance communication, coordination, and data collection in regard to service learning, potentially paving the way for the integration of lightweight AI applications.

In countries like South Africa, SL emphasizes collaborative research and experiential learning to address local needs and reduce social inequalities, fostering social cohesion and community development. However, SL in Africa faces significant challenges. Poor communication and perceptions of projects as experimental endeavors often lead to community distrust, hindering effective cooperation. Additionally, corruption, ineffective governance, and inadequate infrastructure constrain higher education systems, limiting their ability to adapt to evolving societal challenges [48].

Despite these barriers, the African SL approach prioritizes sustainable relationships between students and communities, aligning projects with local priorities to create meaningful learning opportunities. This model emphasizes addressing immediate needs, while fostering long-term community development and student engagement. With continued effort, SL in Africa remains a vital tool for promoting cultural understanding and empowering communities through education and collaborative action [46,47,48].

3.2. Barriers and Challenges in the Implementation of Service Learning

3.2.1. Latin America: Lack of Resources and Institutional Support

Ochoa et al. [28] identify several challenges in regard to implementing service learning (SL) in Latin America, including barriers to access for students from marginalized ethnic groups or low socio-economic backgrounds. Financial constraints and limited institutionalization further hinder SL expansion, with inadequate public policy support and insufficient funding threatening project continuity. Resistance from teachers and students to adopt new methodologies and the lack of a clear conceptual framework complicate the integration of SL into formal curricula. Additionally, there is a shortage of research on the unique educational values and community identities in the region, limiting SL’s potential for transformative impacts [49].

In Latin America, adaptive learning and management platforms have been piloted in higher education to coordinate community-based practice, such as the WorldStrides project implemented in Chile and the Global Citizen Adventure Program implemented in Mexico. Although these systems were not AI driven, their architecture could be augmented with AI tools for scheduling, progress monitoring, and personalized feedback, offering a pathway to improve SL coordination in complex institutional contexts.

In response to these issues, artificial intelligence offers solutions to some of these challenges. AI-driven tools can optimize resource allocation, reduce economic barriers, and facilitate curriculum integration by identifying critical needs and opportunities. Intelligent learning platforms and data analytics could improve program management and be used to assess SL’s social impact, generating robust data to secure funding and institutional backing. However, AI’s adoption in Latin America raises concerns about job insecurity, security breaches, and inequalities affecting those without AI skills [49,50]. While AI holds promise for enhancing SL programs, addressing these socio-economic and technological challenges is critical to its successful implementation.

Considering the above, the implementation of educational AI projects in this region would require alignment with national education policies, robust faculty training in digital pedagogy, and adaptations to address the socio-economic disparities that affect both students and community partners.

3.2.2. North America: Cultural Adaptation Challenges and Resource Limitations

According to Hildenbrand & Schultz [51], despite North America’s advanced educational infrastructure, service learning faces challenges such as resource scarcity, scheduling conflicts, fluctuating enrollment, and the cost of materials. The region’s cultural diversity requires flexible SL practices to address the specific needs of multicultural and Indigenous communities, often causing cohesion and communication issues. The pandemic exacerbated these challenges by shifting SL to online modalities, limiting the direct interaction between students and communities. Insufficient resources for teacher training and online coordination have further hindered SL’s effectiveness, emphasizing the need for greater investment [52].

Given these factors, AI offers solutions by personalizing SL to meet diverse cultural needs. Adaptive AI systems can tailor SL content and strategies, providing personalized materials for students and teachers to enhance program impact. AI tools for online learning, such as sentiment analysis and content recommendations, can improve virtual interactions and foster inclusivity, ensuring active participation. Integrating digital technologies into SL could expand and deepen civic and humanistic outcomes, enabling students to reflect intentionally on their experiences. This integration enhances student education, supports community service, and encourages broader participation in democratic society, fostering equity and justice [33,52,53].

Considering the above, in the context of North America, proposals for AI integration should leverage the region’s advanced technological infrastructure and strong research–practice networks, while also addressing issues of equity and access for underrepresented communities. This includes ensuring that AI tools do not exacerbate existing educational disparities between urban and rural settings.

3.2.3. Europe: Resistance to Change and Funding Shortages

In Europe, the implementation of service learning faces challenges such as resistance from teachers and students favoring traditional methods, insufficient funding, and high faculty workloads, which limit program adoption and development [54]. Methodological difficulties in assessing students’ civic and ethical competencies further hinder SL’s integration into educational systems. Despite its potential to promote active citizenship, the expansion of SL is constrained by a lack of financial and human resources, necessitating a rethink of educational policies [55]. In this regard, AI could support SL adoption by providing training tools to ease the transition to active methodologies. Simulations and virtual models could help teachers and students experiment with SL without overburdening limited resources. AI-powered analytics can demonstrate SL’s tangible benefits, strengthening the case for funding and increasing acceptance. Moreover, AI-driven research could explore students’ perceptions of SL and its potential to address educational challenges and foster best practices.

The integration of digital technologies, particularly through virtual service learning (VSL), provides an opportunity for ethical and civic learning in digitally mediated contexts. VSL aligns technology use with SL’s cognitive and community-focused objectives. However, rigorous quality control and a robust theoretical foundation are essential for achieving its goals and ensuring SL’s relevance in a rapidly virtualizing educational landscape [16,54,56].

Regarding the aspects mentioned above, it becomes interesting to note that in Europe AI integration into SL should comply with the region’s robust data protection frameworks, such as the General Data Protection Regulation (GDPR), and align with institutional policies on ethical technology use. Furthermore, proposed applications must be adaptable to the diverse higher education systems and funding models that characterize the region.

3.2.4. Asia: Cultural Challenges and Coordination Issues

Cultural and political barriers complicate the implementation of service learning in Asia, due to the diverse educational models implemented across the region. A lack of understanding and acceptance of SL, coupled with poor coordination among institutions and faculties, limits its adoption. Additionally, the absence of supportive frameworks and challenges in regard to online communication and collaborative project management further hinder SL development [57]. To address these barriers, tailored SL models that consider specific cultural and educational contexts are essential for achieving sustainable community impacts [15]. Thus, AI offers promising solutions by enhancing collaboration through the use of management platforms and tools for automatic translation and cultural analysis. These technologies can improve communication and adapt SL strategies to local cultural nuances.

Similar needs for bridging linguistic and cultural gaps have been addressed in regard to other educational initiatives. For instance, virtual service-learning projects in the Philippines (GECC3230 program) and China (Guizhou Clothes Drive) have integrated real-time translation and transcription tools to facilitate collaboration between students and community partners across different languages. These cases suggest that the adaptation of existing AI-based language technologies to enhance intercultural communication in SL settings is plausible. These examples emphasize that any AI integrations in an Asian context should be sensitive to linguistic diversity, data privacy regulations, and local pedagogical traditions, ensuring that the technology complements rather than overrides culturally grounded practices.

AI can also centralize the tracking and evaluation of SL projects, fostering a standardized framework to promote regional collaboration. As face-to-face teaching shifts to online education, e-service learning (e-SL) emerges as a viable alternative, offering advantages in terms of sustainability over traditional SL [23,42,58]. By leveraging AI, SL can overcome logistical and cultural challenges, ensuring its effectiveness in diverse Asian educational systems.

3.2.5. Oceania: Research and Institutional Support Limitations

In Oceania, particularly in Australia, service learning faces challenges due to limited research, resources, and infrastructure to support its long-term implementation. While volunteering and experiential learning are valued, insufficient institutional frameworks and funding hinder the sustainability and coordination of SL programs. These constraints limit the scope of SL, often reducing its impact to individual student experiences, despite its effectiveness in fostering job readiness and employability [59]. The SL model in Oceania resembles the European and North American approaches by emphasizing civic engagement and practical learning, but lacks the structural support necessary for sustained community impacts. To address these challenges, AI can enhance SL scalability and efficiency by optimizing resource management and coordination. AI-driven platforms for online learning and volunteering could be used to attract and organize a larger participant base, fostering a broader network of engagement.

By automating administrative and evaluation tasks, AI can free up time and resources, enabling stronger community collaboration and training. This approach not only enhances program sustainability, but also supports academics in managing diverse stakeholder needs, fostering shared values among students, community partners, universities, and industry experts [45,59,60].

In addition to the above, potential educational AI projects in Oceania, particularly in countries with geographically dispersed populations, should be designed to enhance remote collaboration and support flexible learning modalities. Integration strategies must also respect Indigenous knowledge systems and involve community stakeholders in the co-design of technological interventions.

3.2.6. Africa: Need for Community Empowerment and Social Cohesion

According to DeMarais et al. [61], SL implementation in Africa faces significant challenges related to poor communication and the perception of some projects as experimental endeavors, which can create distrust within communities. Critical reflection is essential to achieving the desired outcomes of service learning, including transformative learning. Moreover, the lack of resources for training and the absence of mutual understanding between communities and universities also hinder effective collaboration.

In some cases, communities feel their local contexts are used as “laboratories” for experimentation, which complicates cooperation and openness toward SL projects. Despite these barriers, the African approach to sustainable livelihoods continues to prioritize community empowerment and social cohesion, generating meaningful learning experiences for both students and the communities involved. SL experiences in this region highlight the role of collaborative education and community engagement, contributing to addressing local issues and reducing social inequalities [48].

From this perspective, AI could help build trust through the use of culturally adapted communication tools, such as virtual assistants with automatic translation functions and context recognition to mediate between communities and universities. Additionally, AI-based relationship management systems can track ongoing interactions among stakeholders, fostering more open and transparent communication. AI can also rapidly and accurately analyze community concerns and needs, enabling the alignment of SL programs with participant expectations, reducing the perception of being “experimental,” and strengthening community engagement and empowerment. Another factor in regard to addressing existing barriers to SL is the need to overcome the disciplinary silos that make it difficult for stakeholders to collaborate beyond specific issues [48,62].

Ethical considerations emerge as a transversal challenge when envisioning the integration of AI into service-learning contexts. The reviewed literature, although limited in terms of direct empirical evidence, points to potential risks, such as bias in algorithmic decision making, reduced human agency in regard to reflective learning processes, and inequitable access to AI-enhanced tools. In community-based educational settings, these risks are amplified by the relational and trust-based nature of SL partnerships. For instance, deploying AI tools to match students with community projects could inadvertently reinforce existing social inequalities if the datasets used are incomplete or skewed. Likewise, automated feedback systems, while potentially efficient, may fail to capture the nuanced socio-cultural dimensions of students’ community engagement. Addressing these concerns requires not only technical safeguards, such as transparency in regard to algorithmic processes and bias mitigation, but also a participatory design approach, ensuring that both educators and community partners are actively involved in the decision making about AI integration. This alignment between ethical principles and SL’s community-centered ethos is essential to maintain the integrity of its pedagogical and civic missions. Also, these potential applications must be designed for low-bandwidth environments, prioritizing offline functionality and minimal data consumption to ensure equitable participation in SL activities.

4. Discussion

This systematic review identified and compared the diverse conceptions of service learning and the main barriers to its implementation across global regions, offering a contextualized and comparative understanding of the phenomenon. Thus, the reflections on the role of AI in regard to SL presented here are grounded on the regional patterns and barriers identified throughout this review, and on prior research on technology integration within experiential learning. The reflections contained in this paper are intended not as speculative propositions, but as informed arguments derived from the intersection of the reviewed literature and the documented complexity of SL implementation across diverse contexts. In addition to the above, this section discusses the main findings based on the reviewed literature, emphasizing their contribution to the field, and exploring the potential and limitations of artificial intelligence in regard to enhancing service learning. It also reflects on the methodological contributions and constraints of the study.

Additionally, it is important to note that the analysis presented in this review does not aim to establish definitive causal relationships between the identified barriers and the implementation of SL, nor to provide prescriptive, one-size-fits-all solutions. Instead, the discussion is intended to highlight patterns and contextual factors emerging from the literature, offering a comparative perspective that recognizes the diversity of institutional, cultural, and technological environments across different regions. This approach allows for a nuanced understanding of the complexity inherent to integrating SL and AI-related considerations, encouraging locally adapted strategies that respond to the specific needs and conditions of each context.

4.1. Core Contribution: Conceptions and Barriers to SL from a Comparative Perspective

This study’s central contribution lies in providing a regionally differentiated synthesis of how service learning (SL) is conceptualized and implemented across the globe. While previous research has often focused on national case studies or thematic reviews, this work offers a comparative perspective that highlights both the diversity of SL models and the common obstacles that pose challenges to their adoption. In doing so, it deepens our understanding of SL as a culturally situated and context-dependent pedagogical practice.

For instance, Latin American models of SL emphasize social justice and community transformation, often arising from histories of inequality and political activism. In contrast, Asian implementations tend to focus on employability and civic development through institutional partnerships. European models reflect a more structured integration of civic engagement into formal curricula, while African SL practices, although they face considerable infrastructural and socio-political barriers, offer insights into the resilience and adaptability of community-based learning in low-resource settings. This regional mosaic of SL approaches challenges universalist assumptions about SL and reinforces the need for locally grounded frameworks when designing or evaluating such programs.

Beyond mapping these differences, the study reveals persistent implementation barriers shared across different regions, particularly limited funding, low institutional commitment, teacher resistance, and the lack of robust mechanisms to assess civic or ethical learning outcomes. These findings suggest that, while SL holds transformative potential, its success is highly contingent on institutional capacity, cultural alignment, and policy support.

These insights have several practical implications. First, universities seeking to scale or internationalize their SL initiatives must avoid “one-size-fits-all” models and instead engage in co-designing such processes with communities, ensuring that SL projects respond to specific social and cultural realities. Second, the persistent absence of reliable evaluation frameworks underscores the need to invest in the development of indicators and tools that go beyond academic performance and capture the social, emotional, and civic dimensions of learning. Finally, policymakers and institutional leaders must recognize that strengthening SL requires not only pedagogical innovation, but also structural support, including funding mechanisms, professional development, and cross-sector partnerships.

In this way, the regional mapping presented here serves as both a diagnostic tool and a strategic resource for educators, researchers, and decision makers seeking to design more effective, equitable, and contextually relevant SL programs.

4.2. Projecting AI Integration: Opportunities and Constraints

Although the reviewed studies did not offer empirical evidence of AI use in SL contexts, the analysis revealed emergent opportunities for AI to support SL implementation, particularly in overcoming systemic barriers. For instance, in regions facing communication or coordination challenges, such as parts of Africa and Asia, AI-powered tools like automatic translation, virtual assistants, or culturally adapted communication systems could improve interactions between universities and communities. In North America and Europe, where the challenges involve resource limitations and the assessment of civic competencies, AI analytics and learning management systems could be used to support better tracking of outcomes and for the personalization of experiences.

Moreover, AI offers pathways to scale up and sustain SL programs, particularly through the automation of administrative processes, the monitoring of stakeholder participation, or the use of predictive models for resource allocation. In contexts where direct interaction is limited, such as during global pandemics or in remote regions, AI could be used to support virtual or hybrid SL experiences that preserve meaningful engagement, as seen in the growing literature on virtual service learning (VSL).

However, these projections must be approached with critical caution. The risk of technological determinism, wherein AI is perceived as a solution to all educational challenges, needs to be countered by a deeper reflection on how these tools align with the human-centered, relational, and ethical principles of SL. If not properly contextualized, AI could reinforce existing inequities or erode the experiential depth that defines SL. It is essential to anchor AI integration on pedagogical frameworks, such as Kolb’s experiential learning model or Lave and Wenger’s situated learning theory, ensuring that AI supports, rather than replaces, the reflective and relational dimensions of SL.

4.3. Ethical Considerations and the Human-Centered Nature of SL

The ethical dimension of AI integration into SL deserves special attention. Although concerns such as data privacy, algorithmic bias, and community distrust were mentioned briefly in the reviewed literature, this study highlights the need to move toward context-specific ethical guidelines. This includes principles such as informed consent, data sovereignty, and community co-design, which are essential when introducing digital technologies into settings where trust and reciprocity are foundational.

Moreover, SL is rooted in human relationships and civic responsibility. As such, the integration of AI must be designed in a way that does not compromise the transformative, emotional, and ethical learning that occurs through direct human interaction. Emerging technologies like virtual and augmented reality may enrich engagement with the community, but only if they are pedagogically justified and ethically sound. Future implementations should draw upon ethical frameworks such as UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adapting them to educational contexts where social justice and human dignity are at the core.

4.4. Methodological Contributions and Study Limitations

From a methodological perspective, this review uses a mixed methods strategy that combines qualitative thematic coding with quantitative frequency analysis. This approach enabled the identification of patterns in SL conceptions and implementation barriers, offering both descriptive richness and interpretive depth. However, the review process also has several limitations. The exclusive use of the Scopus database limited access to gray literature and publications in other languages. The language restrictions to English and Spanish, as well as the absence of inter-coder reliability measures (e.g., Kappa coefficient), could have introduced selection and interpretive biases.

In addition, the random sample of 353 articles, although statistically justified, lacked stratification by region or publication year, which may have masked trends over time or regional nuances. The PRISMA protocol was followed in general terms, but its formal implementation could be improved by incorporating a detailed flowchart and a supplementary checklist. Despite these limitations, the regional comparative design and the focus on the AI–SL intersection offer a valuable conceptual scaffold for future studies.

Future research should prioritize the empirical validation of AI use within SL, the co-creation of technological solutions with community stakeholders, and the development of critical digital pedagogies that respect the unique epistemologies and needs of each region.