Digital competencies of students for the use of artificial intelligence in distance higher education: a systematic literature review

ABSTRACT

Introduction:  Artificial Intelligence (AI) has been increasingly incorporated into distance higher education, especially in the Brazilian context, where the number of students in this modality has grown significantly, coexisting with strong asymmetries in access to and mastery of digital technologies. This scenario highlights the need to understand which digital competencies are essential for Brazilian students to use AI critically and effectively.

Objective:  This study aims to identify the digital competencies necessary for the critical and productive appropriation of AI by distance higher education students, as well as to map challenges and opportunities in applying these competencies to learning.

Methodology:  A Systematic Literature Review was conducted following the guidelines of Kitchenham and Charters (2007), considering articles published between 2020 and 2025. The research included searches in recognized databases and applied rigorous inclusion and exclusion criteria for the selection of analyzed studies.

Results:  The findings highlight digital literacy, critical thinking, self-learning, and adaptation to emerging technologies as essential competencies. Additionally, tools such as intelligent tutoring systems, learning analytics, and educational chatbots were identified. The challenges include a lack of infrastructure, difficulties in technological adaptation, and the need for continuous training.

Conclusion:  AI has great potential to enhance distance education, but its implementation requires investment in digital training and infrastructure. Future research should further explore the impact of digital competencies on learning and investigate pedagogical strategies to maximize AI’s benefits in higher education.

KEYWORDS:
Artificial intelligence; Higher education; Digital competencies; Distance education; Systematic literature review

RESUMO

Introdução:  A Inteligência Artificial (IA) tem sido cada vez mais incorporada ao ensino superior a distância, especialmente no contexto brasileiro, onde o número de estudantes nessa modalidade cresce de forma significativa e coexistem fortes assimetrias no acesso e domínio de tecnologias digitais. Esse cenário evidencia a necessidade de compreender quais competências digitais são essenciais para que os estudantes brasileiros utilizem a IA de modo crítico e com qualidade.

Objetivo:  O estudo busca identificar as competências digitais necessárias à apropriação crítica e produtiva da IA por estudantes do ensino superior a distância, bem como mapear desafios e oportunidades na aplicação dessas competências no aprendizado.

Metodologia:  Foi conduzida uma Revisão Sistemática da Literatura seguindo as diretrizes de Kitchenham e Charters (2007), considerando artigos publicados entre 2020 e 2025. A pesquisa incluiu buscas em bases de dados reconhecidas e aplicou critérios de inclusão e exclusão para seleção dos estudos analisados.

Resultados:  Os achados destacam a alfabetização digital, o pensamento crítico, a autoaprendizagem e a adaptação a tecnologias emergentes como competências essenciais. Além disso, foram identificadas ferramentas como sistemas tutores inteligentes, análise de aprendizado e chatbots educacionais. Os desafios incluem a falta de infraestrutura, dificuldades na adaptação tecnológica e a necessidade de formação contínua.

Conclusão:  A IA apresenta grande potencial para aprimorar a educação a distância, mas sua implementação requer investimento em capacitação digital e infraestrutura. Pesquisas futuras devem aprofundar o impacto das competências digitais na aprendizagem e explorar estratégias pedagógicas para maximizar os benefícios da IA no ensino superior.

PALAVRAS-CHAVE:
Inteligência artificial; Ensino superior; Revisão sistemática da literatura; Competências digitais; Educação a distância

1 INTRODUCTION

Distance learning (DL) has proven to be an effective way to expand access to higher education, particularly in areas where geographical and economic barriers make face-to-face education difficult to access (Alves & Souza, 2025).

In Brazil, for example, DL has been a primary driver of democratization in higher education, accounting for over 60% of new enrollments since 2020 (INEP, 2025). However, regional disparities in internet and technology access still pose a significant challenge, particularly in the northern and northeastern regions. This scenario underscores the importance of assessing students' digital proficiency in distance learning environments, given the variability in technological resources and students' prior experience with digital literacy.

As technologies advance, artificial intelligence (AI) resources have been integrated into distance learning, enabling the personalization of students' learning experiences and improving the efficiency of educational processes. According to Bezerra et al. (2024), AI enables adaptive learning by providing real-time feedback and adjusting to students' individual performance and needs. This contributes to a more personalized and dynamic learning experience.

In addition to personalizing teaching, AI requires students to develop digital skills to efficiently interact with these technologies. AI tools in higher education not only automate processes but also require students to understand and interpret algorithmic feedback, use adaptive learning platforms, and make data-driven decisions. According to Silva and Behar (2023), digital skills in education entail using digital technologies critically, ethically, and creatively to solve problems and build knowledge in an educational context. These competencies encompass technical skills and an understanding of the social and cultural impacts of technology. This enables students to navigate the digital environment critically and responsibly.

Interacting with intelligent systems in education requires skills that go beyond technological knowledge. The growing integration of AI in education necessitates the development of social and emotional skills, also known as soft skills, which play a central role in Education 5.0. This educational model emphasizes not only mastery of technologies but also individuals' ability to work collaboratively, manage emotions, and make ethical decisions in digital environments. Communication, problem-solving, teamwork, diversity, empathy, and ethics are essential skills that enable students to use technology in a healthy and productive way, creating solutions that are relevant to themselves and society (Felcher, Blanco, & Folmer, 2022).

Powered by AI, adaptive learning requires students to develop the ability to adapt to personalized learning systems. These systems adjust content and pace based on individual needs, promoting a more meaningful and inclusive educational experience. Students must be able to interact with digital platforms and understand automated AI responses to take full advantage of these tools. According to Souza et al. (2024), AI has the potential to personalize teaching, boost student engagement, and assist teachers with administrative tasks, enabling them to devote more time to pedagogical guidance.

Furthermore, implementing AI in education offers significant opportunities to personalize learning and optimize administrative processes. However, it should be used in balance with social interaction and human pedagogical mediation to ensure that technology supports, rather than replaces, the teacher's role in guiding, monitoring, and building knowledge (Ferreira, Silva, & Bezerra, 2025).

Based on this, the objective of this study is to identify the digital skills necessary for effectively using AI in distance higher education, as well as to map the related challenges, tools, and opportunities in developing these skills in the teaching-learning process.

While international studies offer various insights into the development of digital skills, it is crucial to grasp how these skills are cultivated and required within the Brazilian context of distance learning, which is marked by significant regional disparities in access to technology, connectivity, and digital literacy. Thus, this research begins with the acknowledgment that the reality of distance learning in Brazil necessitates a critical analysis of students' preparedness to interact with AI-based systems and utilize their resources in an ethical, autonomous, and productive manner.

The RSL was conducted between December 2024 and February 2025, covering articles published in journals and at national and international scientific events, with the aim of identifying knowledge gaps and suggesting opportunities for future research.

This article is organized into four sections. Section 2 describes the research methodology, including the definition of the search string, inclusion and exclusion criteria, and the stages of the study selection and analysis processes. Section 3 presents the obtained results, accompanied by a critical analysis and discussion of the identified main trends, limitations, and contributions in the literature. Section 4 concludes the article by presenting the study's findings and pointing out the research's limitations. It also suggests avenues for future research in the field of AI applied to distance education.

2 LITERATURE REVIEW

In contemporary education, digital skills involving critical, ethical, and creative abilities in the use of technology have become essential (Tomczyk, 2024). In higher education, these skills are indispensable for active participation in complex virtual environments (Jin & Ryu, 2025). As artificial intelligence (AI) expands, the need for these skills grows, requiring students to not only master tools and algorithms (Morze et al., 2024; Sengsri & Khunratchasana, 2024) but also assess the social and cultural impacts of these technologies (Scarci, Teixeira, & Dal Forno, 2024). Therefore, the development of digital skills is understood to go beyond instrumental learning and is established as a prerequisite for critical and responsible action in digital society.

These skills encompass multiple interdependent dimensions. These include the use and management of digital information, communication in collaborative networks, content creation and sharing, and digital security. These dimensions also involve solving problems in technology-mediated environments (Nguyen et al., 2024; Yu, Zhang, & Sun, 2024) and are constantly present in everyday academic life (Ng et al., 2023; Zheng et al., 2023). Thus, research indicates that technical mastery of these skills is insufficient; it is also necessary to understand their ethical, social, and cognitive implications, especially when students interact with AI platforms. These platforms require not only access, but also discernment regarding the use of their functionalities and the consequences thereof.

The integration of AI into higher education has significantly transformed teaching and learning practices (Al Ka'bi, 2023; Borah & Borah, 2024). Intelligent tools can personalize content, analyze student performance, and suggest activities based on students' needs (Sari, Tumanggor, & Efron, 2024). These changes alter the traditional educational model, which is centered on uniformity and teacher control (Madhu, Latha, & Savatha, 2024; Cai, Msafiri, & Kangwa, 2024). In this new configuration, the relationship between students and knowledge is mediated by automated systems, which requires new attitudes, skills, and responsibilities regarding technology use (Ayeni et al., 2024; Cai, Msafiri, & Kangwa, 2025). Thus, AI redefines not only pedagogical roles and processes but also increases the demand for critical, ethical, and reflective digital skills. These skills are indispensable for students to remain protagonists in learning environments mediated by algorithms.

In distance learning, AI plays an even more central role (Badshah et al., 2023; Dogan, Goru Dogan, & Bozkurt, 2023) since face-to-face contact is absent and mechanisms are needed to monitor students' progress and engagement. Common solutions include recommendation systems (Dhananjaya et al., 2024), chatbots (Luckyardi et al., 2024), and virtual tutors (Aggarwal, Sharma, & Saxena, 2023). These solutions offer real-time responses and adapt learning to each student's profile. However, the effectiveness of these technologies depends on students' digital preparedness and self-regulation capacity (Yu, Zhang, & Sun, 2024). Therefore, rather than replacing human mediation, AI in distance learning increases the need for critical and reflective digital skills that enable students to ethically and autonomously understand, interpret, and direct automated recommendations.

The Education 5.0 model emphasizes comprehensive training focused on balancing human development and technological advancement (Nurdiansyah & Wahab, 2025). In this paradigm, technology enhances and complements human processes rather than replacing them (Adel, 2024; Chakraborty, 2024). Therefore, the use of AI needs to be guided by values such as empathy, ethics, and responsibility (Ortega-Bolaños et al., 2024; Pandya, 2024). From this perspective, digital skills extend beyond technical proficiency to encompass a critical awareness of technology's role in society and its relationship with the human dimension of learning (Nurdiansyah & Wahab, 2025). Education 5.0 thus proposes integrating technology and humanism, linking the development of digital skills to the ethical and emotional training of students, and preparing them to act responsibly in AI-mediated learning ecosystems.

Digital literacy is a fundamental skill in contemporary education (Tinmaz et al., 2022; Reddy, Chaudhary, & Hussein, 2023). It is the first step for students to engage with educational technologies and is influenced by family and institutional practices (Shin & Park, 2024). Digital literacy involves skills such as navigating platforms, using search engines, and interpreting data. In AI-mediated environments, digital literacy must also encompass an understanding of algorithms and their ethical implications to ensure critical interaction with intelligent systems (Pinski & Benlian, 2024; Stolpe & Hallström, 2024). Thus, digital literacy is not merely instrumental learning; it constitutes the basis for developing cognitive and ethical abilities that empower students to act autonomously and conscientiously within complex digital ecosystems.

Other skills are gaining prominence, such as self-learning, which is defined as the ability to manage one's own training process (Bembenutty, 2023). AI enables this autonomy by offering personalized learning paths, allowing students to progress at their pace and according to their interests (Abbasi, Wu, & Luo, 2024; Shahzad, Xu, & Zahid, 2024). However, this flexibility requires discipline, organization, and initiative - fundamental aspects of success in digital environments. Therefore, digital autonomy becomes a differentiator in distance higher education (Shamsutdinova, 2022). It is not just an individual attribute; it is a skill that combines self-regulation and critical thinking. This allows students to play an active role in knowledge construction and the ethical use of AI.

Adaptability is an essential skill in the face of constant technological volatility. Studies indicate that the ability to learn new interfaces and integrate heterogeneous platforms favors the pedagogical appropriation of digital technologies (Puckett, 2020; Reddy, Chaudhary, & Hussein, 2023). Nevertheless, resistance to innovation, associated with institutional and individual factors (Saiz-González et al., 2024), limits educational potential by restricting the use of available resources. As Li, Li, and Wang (2022) point out, adaptability acts as a critical mediator in the transition between technology and meaningful learning. From this perspective, developing this competence requires adjusting to new tools and cultivating a flexible, investigative, and reflective attitude toward the transformations driven by AI in education.

Critical thinking is one of the indispensable digital competencies in the contemporary educational context (Dumitru et al., 2023). It allows students to evaluate information, particularly in automated systems that generate data-based recommendations (Mancin et al., 2023). This conscious judgment prevents superficial interpretations and hasty decisions (Zhao & Zhang, 2024), while also expanding analytical capacity through processes such as problem decomposition and evidence validation (González-Cacho & Abbas, 2022). Thus, critical thinking becomes a key element in helping students understand the limits and potential of AI and adopt a reflective and ethical stance toward information mediated by algorithms.

Digital communication is a fundamental skill in technology-mediated education. In virtual learning environments, students interact in forums, chats, and collaborative platforms, which requires communication skills integrating clarity, empathy, and respect (Schäfer, Reis, & Stricker, 2022; Cui et al., 2023). These interactions presuppose mastery of practices such as netiquette and online identity management (Roh, Yoo, & Ok, 2024), which are essential for maintaining ethical and constructive relationships in online spaces. Additionally, technology-mediated teamwork (Johler, 2022) requires specific collaborative skills, such as active listening, negotiation of meanings, and digital tool proficiency (Correia, 2020). In this context, communication and collaboration represent cognitive and social practices that support collective learning in intelligent digital environments, not just exchanges of information.

However, the development of digital skills does not happen automatically. It depends on pedagogical intentionality and institutional planning (Zancajo, Verger, & Bolea, 2022). To this end, educational institutions must develop systematic strategies to support this process, which include continuous teacher training, curriculum review geared toward the critical integration of technologies, and consistent investment in digital infrastructure. Without these structural conditions, AI's transformative potential in education tends to perpetuate existing inequalities, marginalizing some students from innovation and digital culture (Chiu et al., 2024; Li et al., 2024).

Unequal access to technology is one of the main barriers to developing digital skills (Osabutey & Jackson, 2024). Not all students have adequate devices or stable internet connections, particularly in vulnerable socioeconomic contexts (Wrigley, Wollifson, & Matthews, 2020). Additionally, significant differences in digital repertoire persist among students, influenced by factors such as family environment and educational background (Assefa et al., 2024; Nkansah & Oldac, 2024). These inequalities directly impact the use of AI in higher education, limiting access to personalized learning opportunities and exacerbating existing disparities (Dalgıç, Yaşar, & Demir, 2024). Therefore, promoting digital inclusion and technological support policies is essential for AI to equitably contribute to the democratization of higher education.

Another challenge relates to understanding AI critically. Many students use available tools without knowing how they work (Yue Yim, 2024). However, it is important to recognize that AI is not neutral. It operates based on data that can reproduce or intensify structural biases (Varsha, 2023; Singh et al., 2024). Therefore, digital literacy should incorporate ethical and socio-technical considerations regarding the limitations of automation and its impact on the educational process (Saklaki & Gardikiotis, 2024; Saheb & Saheb, 2024). Developing this critical awareness is an indispensable step in training students to interact with intelligent systems autonomously, responsibly, and ethically.

Teachers play a decisive role in this process of building digital skills. They need to be familiar with technological tools and know how to meaningfully integrate them into teaching practices (Fang et al., 2024). Students receive more consistent guidance and develop greater confidence in using technologies when this occurs (Parker, Mantei, & Kervin, 2024). Teachers then cease acting solely as transmitters of content and begin playing the role of mediators of digital experiences. In this role, they articulate the technical, pedagogical, and ethical dimensions of the educational process (Mayer & Schwemmle, 2023; Chiu et al., 2024). This qualified mediation links technology to meaningful learning by ensuring that AI is used with pedagogical intent and a focus on education.

The use of AI expands the possibilities for personalized teaching. Algorithms can identify students' difficulties and suggest adapted learning paths (Madhu, Latha, & Savitha, 2024). Students must take an active role by interpreting AI-generated reports, reflecting on their performance, and making autonomous decisions (Li et al., 2024; Wang et al., 2024). Digital skills such as information literacy and proficiency in interactive tools form the basis for autonomous, interactive, and reflective learning. In this scenario, students become co-authors of their own educational process (Horváth et al., 2024).

METHODOLOGY

This research study employed a RSL approach based on Kitchenham and Charters' (2007) guidelines to map the digital skills necessary for effectively using AI in distance learning higher education. The review, conducted between December 2024 and February 2025, sought to identify the main required skills, the most commonly used tools and technologies, and the impact of these skills on the teaching and learning processes. Additionally, the study examined the challenges and barriers students face in developing these skills, offering a critical overview of preparing for AI use in an educational context.

3.1 Research Questions

To guide the research in a meaningful way, four central questions were formulated, as presented in Chart 1.

Chart 1
Research questions

Identifier	Research question
Q1	What are the key digital skills needed for distance learning students to use AI effectively in their learning?
Q2	Which AI technologies and tools are most frequently used by distance learning students in higher education?
Q3	What impact do digital skills for using AI have on the academic performance and learning experience of distance learning students in higher education?
Q4	What challenges and barriers do students face in building and applying these digital skills in AI-mediated educational environments?

• Source: the authors.
• Description: Chart 1 presents the research questions in two sections, with the first column containing the descriptive term “identifier” and the second column containing the description “Research questions.” The chart has four rows. The border of the chart is red with a salmon pink background.

3.2 Research Sources

The search in research sources was conducted using keywords related to the themes “digital skills,” “artificial intelligence,” “higher education,” and “distance learning,” as shown in Chart 2.

Chart 2
Keywords used in the RSL

Code	Theme	Keywords
Q1	Digital Skills	Digital Competencies
		Digital Skills
Q2	Artificial Intelligence	Artificial Intelligence
		AI
Q3	Higher Education	Higher Education
		University Students
		College Students
Q4	Distance Learning	Distance Education
		Online Learning
		E-learning

• Source: the authors.
• Description: Chart 2 describes the keywords used in the Systematic Literature Review, with three divisions, the first column containing the descriptive term for “Code.” The second column contains the description for “Theme,” and finally, the third column contains the description “Keyword.” The chart has four rows. The border of the chart is red with a salmon pink background.

To formulate the search string, the keywords for each topic were combined using the Boolean operator “OR,” while the relationship between the different topics was established using the operator “AND.” The databases consulted included:

• a) ACM Digital Library (https://dl.acm.org/);

• b) Emerald Insight (https://www.emerald.com/insight/);

• c) IEEE Xplore (https://ieeexplore.ieee.org/Xplore/home.jsp);

• d) Scopus (https://www.scopus.com);

• e) SpringerLink (https://link.springer.com/);

• f) Web of Science (https://www.https://webofscience.com/) e

• g) Wiley Online Library (https://onlinelibrary.wiley.com/).

To perform the search, the keywords C1, C2, C3, and C4 were combined in English, using the logical operators conjunction (AND) and disjunction (OR) to improve the relevance of the results, as illustrated in Chart 3. The selection process considered full scientific articles published between January 2020 and February 2025 in Spanish, English, and Portuguese.

Chart 3
Search string used during database searches

Search string
(“digital competence” OR “digital skills”) AND (“Artificial Intelligence” OR ‘AI’) AND (“higher education” OR “university students” OR “college students”) AND (“distance learning” OR “online learning” OR “e-learning”)

• Source: the authors.
• Description: Chart 3 shows the search string used during database searches. Only one column and one row. The chart border is red with a salmon pink background.

3.3 Inclusion and Exclusion Criteria

The inclusion and exclusion criteria adopted in the RSL are described in Chart 4.

Chart 4
Inclusion and exclusion criteria used in the SLR

Inclusion criteria (IC)

Critérios de exclusão (CE)

CI1 - Primary studies CI2 - Studies that answer research questions

CE1 - Secondary or tertiary studies. CE2 - Studies published before January 2020. CE3 - Books, book chapters, notes, editorials, manuals, reports, theses, and dissertations, or other types of documents. CE4 - Studies published in a language other than Spanish, English, or Portuguese. CE5 - Duplicate studies, only one of which was used. CE6 - Paid studies or studies without institutional access. CE7 - Studies that do not address students' digital skills for the use of AI in distance higher education (outside the scope).

• Source: the authors.
• Description: Chart 4 presents the inclusion and exclusion criteria used in the Systematic Literature Review, with the first column labeled “Inclusion criteria (IC)” and the second column labeled “Exclusion criteria (EC).” The chart has a single row, with two criteria in the first column and seven criteria in the second column. The border of the chart is red with a salmon pink background.

In addition, the analysis of the articles was conducted in three main stages: (i) initial screening, considering titles, abstracts, and keywords, to exclude those that did not address the research questions; (ii) full reading of the previously selected articles; and (iii) organization of the final articles in an auxiliary spreadsheet¹ for recording and control

3.4 Data Extraction

Information from the selected articles was recorded in a pre-structured form. Data such as the title, authors, publication event or journal, year, abstract, and keywords were collected for each study. Additionally, all articles were categorized according to the classification system proposed by Zelkowitz and Wallace (1998).

To optimize the extraction and organization of the information, Parsifa², an online tool developed specifically to support systematic literature reviews, was used. A quantitative analysis was then performed to evaluate the distribution of articles in the databases and the evolution of publications over time. These analyses provided relevant insights, which will be detailed in Sections 4 and 5, in line with the objectives and research questions presented in Subsection 3.1.

A total of 523 articles were returned by the search performed in the databases using the search string adapted for each platform. Initially, three duplicate articles were removed. Then, based on an analysis of titles, abstracts, and keywords, 514 articles were discarded for not meeting the inclusion criteria. This left six studies for complete reading. After a full analysis, all six studies were included in the research.

Figure 1 illustrates the stages of the RSL process and the number of studies excluded at each stage, along with the exclusion criteria applied.

Figure 1
Steps performed in SR

Table 1 shows the number of studies excluded, categorized according to the exclusion criteria adopted and the databases consulted.

Table 1
Works excluded from the RSL x Database

Data Base	CE1	CE2	CE3	CE4	CE5	CE6	CE7	Excluded
SpringerLink	28	0	17	0	0	0	156	201
Emerald Insight	17	0	3	0	0	0	167	187
Wiley Online Library	9	0	14	0	0	0	44	67
ACM Digital Library	3	0	1	0	0	0	42	46
IEEE Xplore	1	0	0	0	0	0	6	7
Scopus	0	1	0	0	3	0	1	5
Web of Science	0	0	0	0	0	0	4	4
Total	58	1	35	0	3	0	420	517

• Source: the authors.
• Description: Table 1 presents an overview of the studies excluded from the Systematic Literature Review x databases. The first row contains the names of the databases, followed by eight columns with the results, the last of which shows the total number of data excluded. Below is the total results bar. The border of the table is red with a salmon pink background.

Table 2 shows the distribution of studies throughout the review process, detailing the number of studies removed due to duplication, those excluded in the initial screening based on titles, abstracts, and keywords, and those discarded after full-text reading. In addition, the table indicates the number of primary studies selected from each database during the search stages.

Table 2
Distribution of tasks throughout the review process

Data Base	Articles Found	Excluded Studies			Selected primary studies
		Duplicates Reading stage of title, abstract, and keywords	Duplicates Reading stage of title, abstract, and keywords	Full reading stage
SpringerLink	203	0	201	0	2
Emerald Insight	187	0	187	0	0
Wiley Online Library	67	0	67	0	0
ACM Digital Library	46	0	46	0	0
Scopus	8	3	2	0	3
IEEE Xplore	7	0	7	0	0
Web of Science	5	0	4	0	1
Total	523	3	514	0	6

• Source: the authors.
• Description: Table 2 presents an overview of the work carried out throughout the review process. The first row contains the names of the databases, followed by five columns with descriptions of: Articles found; Duplicates; Excluded studies; Full-text reading stage; and Selected primary studies. Below is the bar with the total results for each column. The border of the table is red with a salmon pink background.

4 ANALYSIS AND DISCUSSION OF RESULTS

This section presents the results and discussions of the RSL, conducted according to the protocol described in Section 3.

Figure 2 shows that the number of published primary studies varied over the years analyzed. In 2021, one study was identified, showing an initial interest in the topic of this study. However, in 2022 and 2023, there was stability, with only 1 study published in each year. On the other hand, 2024 saw a significant increase, reaching 3 studies, indicating a growth in interest in the topic over time. These data suggest a growing trend in research in the area, especially in the last year analyzed.

Figure 2
Number of primary studies published per year

The six studies analyzed in this RSL are available in the “Further Reading” section, located after the References section. Each study was identified by a unique code in the format “EP” followed by a number (EP1, ..., EP3, ...), differentiating them from the main studies cited throughout the text. In addition, a document was prepared consolidating the information extracted from the selected primary studies. The characteristics of these studies are presented below, aligned with the research questions established in Subsection 2.1.

Q1 - What are the main digital skills necessary for distance higher education students to use AI effectively in their learning?

Chart 5 presents the main digital skills identified in the primary studies analyzed. Among these skills, the five most frequent and essential for the use of AI in education stand out and demonstrate their relevance in the digital context.

Chart 5
Digital skills identified by primary studies

Digital skills	Primary studies
Digital literacy	[EP1, EP2, EP3, EP4, EP5, EP6]
Self-learning	[EP1, EP5]
Critical thinking	[EP2, EP6]
Adaptation to emerging technologies	[EP3, EP4]
Collaboration and communication in digital environments	[EP4, EP6]
Navigation of online platforms	[EP1]
Interaction with AI-based systems	[EP2]
Complex problem solving	[EP4]
Digital information management	[EP4]
Digital content creation	[EP4]
Data analysis	[EP5]
Personalization of learning	[EP5]

• Source: the authors.
• Description: Chart 5 shows the digital skills identified by the primary studies. In the first column, entitled “Digital skills,” there are 12 rows with the respective skills. In the second column, entitled “Primary studies,” there are also 12 rows with the respective primary studies from 1 to 6, represented by the capital letters EP and the numbering. The border of the chart is red with a salmon pink background.

The most frequently cited skill is described below. Chart 5 shows the digital skills identified by the primary studies. The first column, labeled "Digital Skills," contains 12 rows with the corresponding skills. The second column, "Primary Studies," also has 12 rows, each representing a primary study from 1 to 6, represented by the capital letters EP and numbering. The chart has a red border and a salmon pink background. The most frequently mentioned skill was digital literacy. This skill emphasizes the importance of acquiring basic knowledge about digital technologies, tools, and platforms to enable more intentional and productive interaction with AI-based systems. Studies such as EP1, EP2, EP3, EP4, EP5, and EP6 discuss this skill, highlighting its importance for adapting to the digital environment. This emphasis aligns with the findings of Tomczyk (2024) and Reddy, Chaudhary, and Hussein (2023), who emphasize that digital literacy is essential for critical participation in technology-mediated educational environments. Similarly, Pinski and Benlian (2024) and Stolpe and Hallström (2024) emphasize that understanding algorithms and the ethical implications of AI is an integral part of this process.

Next are self-learning, critical thinking, adaptation to emerging technologies, and collaboration and communication in digital environments, each with two mentions.

Self-learning, mentioned in [EP1 and EP5], highlights the need to develop autonomy in digital learning, allowing individuals to seek knowledge independently and continuously. This evidence converges with the perspective of Bembenutty (2023), who highlights self-regulation as a core competency in technology-mediated contexts, and with Abbasi, Wu, and Luo (2024), who associate autonomy with the ability to critically interpret feedback generated by intelligent systems. Thus, self-learning is not limited to operational autonomy, but implies a reflective attitude towards AI, which is essential for student protagonism in distance learning.

Critical thinking, addressed in studies [EP2 and EP6], is a fundamental skill for analyzing and interpreting information generated by AI systems, enabling more informed and conscious decisions. This finding is in direct dialogue with Dumitru et al. (2023) and Mancin et al. (2023), who defend critical thinking as a necessary cognitive filter for dealing with the biases and automated inferences of AI. By recognizing this analytical role, the primary studies reinforce that the training of critical students is an indispensable condition for the ethical and responsible use of technology.

The adaptation to emerging technologies, mentioned in [EP3 and EP4], highlights the ability to deal with new technological solutions, an essential aspect in the face of rapid changes in the digital educational landscape. This competency is consistent with Puckett (2020) and Reddy, Chaudhary, and Hussein (2023), who relate adaptability to the progressive mastery of new educational interfaces and tools. From this perspective, the findings demonstrate that the ability to adapt acts as a mediator between technological innovation and meaningful learning, confirming the argument of Li, Li, and Wang (2022) about the integrative role of this competency in the educational process.

Finally, collaboration and communication in digital environments, identified in studies [EP4 and EP6], highlight the need to develop skills to interact productively in virtual spaces, promoting knowledge exchange and collective work. This statement is in line with Roh, Yoo, and Ok (2024), who highlight the need for netiquette and online identity management, and with Correia (2020), who points to active listening and mastery of collaborative tools as pillars of digital teamwork. Thus, the findings reaffirm that AI-mediated collaboration is not only a technical skill but also a socio-affective one, essential to the collective construction of knowledge.

It is worth noting that the digital skills identified reflect the growing need to prepare students for an educational environment increasingly mediated by AI. Digital literacy, autonomy in learning, critical thinking, adaptation to new technologies, and collaboration in digital spaces are key elements for building a more dynamic and innovative teaching environment. In this sense, fostering the development of these skills is essential to ensure that AI is used strategically, promoting more accessible, personalized, and interactive learning.

Q2 - Which AI technologies and tools are most frequently used by distance learning higher education students?

Chart 6 presents the main AI technologies and tools identified in the primary studies analyzed. Among these technologies, the five most frequent stand out, reflecting the impact of AI on education and its contribution to personalization, monitoring, and learning support.

Chart 6
AI technologies and tools used according to primary studies

AI technologies and tools	Objective	Primary studies
Learning Analytics Tools	Collect and process data to provide insights into learning patterns. Monitor student progress..	[EP2, EP3, EP4, EP5, EP6]
Intelligent tutoring systems	Provide automated and personalized support to students, adjusting content based on individual performance and needs.	[EP1, EP2, EP3, EP5]
Educational chatbots	Answer frequently asked questions and facilitate communication between students and teachers, promoting greater autonomy in the teaching-learning process.	[EP2, EP4, EP5, EP6]
Recommendation systems	Assist students in choosing materials and activities that are more aligned with their interests and difficulties. Offer personalized recommendations based on AI algorithms.	[EP2, EP3, EP5]
AI tools for automated assessment	Automatically assess student performance, reducing the need for direct intervention by teachers.	[EP1, EP5]
AI-enhanced learning management systems (LMS)	Auxiliar na organização do ensino remoto e híbrido, oferecendo funcionalidades como geração automática de testes, exportação de resultados e personalização do aprendizado.	[EP4, EP6]
Virtual reality and gamification	Increase student engagement.	[EP1]
Massive open online courses (MOOCs)	Enabling flexible and accessible education.	[EP1]
Digital credentialing tools	Enable the validation and certification of learning achieved through transnational courses.	[EP3]
Natural language processing (NLP) tools	Enable the creation of educational content and efficient communication between students and teachers.	[EP4]
Augmented Reality (AR) and AI	Develop a resilient educational ecosystem for remote teaching and digital assessments.	[EP6]

• Source: the authors.
• Description: Chart 6 on AI technologies and tools used according to primary studies presents a chart with three columns. The first column is titled “AI technologies and tools.” The second column is titled “Objective,” and the last column focuses on “Primary studies,” symbolized by the acronym EP and the numbers one through six next to the acronym. The chart has eleven rows. The border of the chart is red with a salmon pink background.

The most cited technology, with five mentions, was the use of learning analytics tools. This approach allows the collection and processing of educational data to provide insights into learning patterns, as well as enabling continuous monitoring of student progress. This approach, present in studies [EP2, EP3, EP4, EP5, EP6], is in line with Bezerra et al. (2024) and Ferreira, Silva, and Bezerra (2025), who highlight data analysis as a pillar of personalization and adaptive monitoring in distance learning. Thus, the use of learning analytics reinforces the transition to evidence-based education, in which pedagogical decisions are based on performance metrics and engagement indicators.

Next, with four mentions, are intelligent tutoring systems, which offer automated and personalized support to students, adjusting content according to their performance and individual needs. This technology, present in studies [EP1, EP2, EP3, EP5], converges with what Sari, Tumanggor, and Efron (2024) and Cai, Msafiri, and Kangwa (2024) discuss when defending the role of intelligent tutors as cognitive mediators that stimulate autonomous learning. From this perspective, these systems expand the potential for formative feedback, promoting dynamic and responsive learning experiences.

Also, with four mentions, educational chatbots appear as an essential technology for communication and learning support. Tools of this type, analyzed in [EP2, EP4, EP5, EP6], are in line with the discussion by Luckyardi et al. (2024) and Badshah et al. (2023), who point to chatbots as interaction tools that reduce the feeling of isolation in virtual environments. This evidence suggests that conversational AI contributes to student engagement and sense of belonging in distance learning, strengthening the socio-affective dimensions of learning.

Next, with three mentions, are recommendation systems, which help students choose materials and activities aligned with their interests and difficulties, in addition to offering personalized recommendations based on AI algorithms. This technology confirms what Dhananjaya et al. (2024) and Yu, Zhang, and Sun (2024) point out about the role of AI in curating personalized trajectories. Thus, recommendation systems are consolidating themselves as strategic tools for adaptive teaching, allowing for more flexible and targeted learning.

Finally, with two mentions, AI tools for automated assessment stand out, allowing for automatic evaluation of student performance, reducing the need for direct intervention by teachers. This application, observed in [EP1, EP5], corroborates the analysis by Ayeni et al. (2024), which relates assessment automation to time optimization and increased objectivity in assessment processes. However, as Ferreira, Silva, and Bezerra (2025) point out, automation needs to be accompanied by critical teacher mediation to prevent the use of AI from reducing assessment to a purely technical process.

Chart 7
Digital skills identified by primary studies

Impacts of digital skills on the use of AI	Primary studies
Improved academic performance, autonomy, and motivation	[EP1, EP2, EP3, EP4, EP5, EP6]
Personalized teaching and adaptive feedback	[EP1, EP2, EP5]
Optimization of the teaching-learning process and improvement of the student experience in distance higher education	[EP2, EP5, EP6]
Development of critical and creative thinking	[EP4, EP5, EP6]
Greater engagement and satisfaction	[EP1, EP2]
Strengthening collaboration and communication	[EP5, EP6]
Improving analytical and decision-making skills	[EP5, EP6]
Facilitating the acquisition of advanced technological skills	[EP5, EP6]
Facilitating the application of knowledge in different contexts and strengthening employability in high-tech sectors	[EP3]
Efficiency in learning and adapting to hybrid and remote scenarios	[EP4]

• Source: the authors.
• Description: Chart 7, which presents a chart with two columns identifying digital skills as determined by primary studies, is titled "Digital Skills Identified by Primary Studies." The first column, titled "Impacts of Digital Skills on the Use of AI," contains ten rows. The second column is titled "Primary Studies" and is represented by the acronym EP and the numbers one through six. The chart has a red border and a salmon pink background.

The most cited impact, with six mentions, was improved academic performance, autonomy, and motivation. This result demonstrates that acquiring digital skills in the use of AI can enhance student learning, making them more autonomous and motivated, which favors engagement and knowledge retention. This finding converges with the analyses of Bezerra et al. (2024) and Abbasi, Wu, and Luo (2024), which highlight the role of AI in promoting self-regulation and active learning by allowing students to monitor their progress and receive immediate feedback. The primary studies [EP1, EP2, EP3, EP4, EP5, EP6] reinforce this perspective, demonstrating that AI, when integrated pedagogically, contributes to strengthening intrinsic motivation and student autonomy.

Secondly, with three mentions, three essential impacts stand out: personalization of teaching and adaptive feedback, optimization of the teaching-learning process in distance higher education, and development of critical and creative thinking.

The personalization of teaching and adaptive feedback, addressed in studies [EP1, EP2, EP5], highlight the potential of AI to adjust content and offer individualized feedback. This finding is in line with Madhu, Latha, and Savitha (2024) and Sari, Tumanggor, and Efron (2024), who describe how educational algorithms make learning more responsive and student-centered. Such evidence confirms that AI-mediated personalization represents a step toward more inclusive and formative pedagogical practices.

The impact of optimizing the teaching-learning process and improving the student experience in distance higher education, evidenced in studies [EP2, EP5, EP6], highlights how digital skills associated with AI can make remote learning more dynamic, interactive, and accessible. This result agrees with Borah and Borah (2024), who associate the pedagogical use of AI with increasing the efficiency of educational processes without reducing the centrality of the teacher as a mediator.

The development of critical and creative thinking, mentioned in studies [EP4, EP5, EP6], reinforces the importance of encouraging students not only to consume information generated by AI but also to analyze it critically and apply it in innovative ways. This perspective is in line with Dumitru et al. (2023) and Mancin et al. (2023), who point to critical thinking as a key competence for interpreting and questioning automated inferences, avoiding uncritical uses of technologies.

In third place, with two mentions, are four additional impacts: greater engagement and satisfaction, strengthened collaboration and communication, improved analytical and decision-making skills, and easier acquisition of advanced technological skills. These impacts, identified in studies such as [EP1, EP2, EP5, EP6], confirm what Roh; Yoo and Ok (2024) and Horváth et al. (2024) maintain about the role of digital skills in building collaborative and innovation-oriented environments. In summary, these results reinforce that AI, when linked to the development of critical, socio-affective, and technological skills, expands the conditions for meaningful and participatory learning in distance higher education.

Given these findings, it is clear that the development of digital skills plays a fundamental role in maximizing the benefits provided through AI in education. The combination of these skills with emerging technologies enhances the teaching and learning experience and prepares students for an increasingly digital and interconnected future.

Q4 - What challenges and barriers do students face in building and applying these digital skills in AI-mediated educational environments?

Chart 8 presents the main challenges and barriers identified in the primary studies analyzed in relation to the use of digital skills in AI-mediated education. Among these challenges, the five most frequent stand out, reflecting the difficulties faced by students in adopting these technologies.

Chart 8
Challenges and barriers faced by students in building and applying digital skills for the use of AI, according to primary studies

Challenges and barriers	Primary studies
Gap in digital literacy	[EP1, EP2, EP4, EP5]
Difficulty adapting to new technologies and teaching methodologies	[EP2, EP4, EP5, EP6]
Lack of adequate technological infrastructure	[EP2, EP4, EP5, EP6]
Need for autonomy in digital learning	[EP1, EP2, EP3]
Lack of ongoing support and real-time feedback	[EP3, EP6]
Interoperability issues between educational systems	[EP4, EP6]
Lack of direct contact with instructors and peers may affect student motivation and engagement	[EP1]
Accuracy of automated assessment and necessary support for students with difficulties	[EP1]
Over-reliance on AI tools and uncritical use	[EP2]
Accessibility to specialized teaching	[EP3]
Challenges in curriculum integration and credit recognition	[EP3]
Need to develop interpersonal and social-emotional skills to function effectively in AI-mediated learning environments	[EP4]
Cognitive overload due to rapid technological evolution	[EP4]
Lack of personalization in remote teaching	[EP5]
Risk of plagiarism and lack of academic integrity	[EP5]

• Source: the authors.
• Description: Chart 8 on the challenges and barriers faced by students in building and applying digital skills for the use of AI, according to primary studies, presents a chart with two columns. The first column is titled “Challenges and barriers” and consists of 15 rows. The second column is titled “Primary studies,” symbolized by the acronym EP and the numbers one through six next to the acronym. The border of the chart is red with a salmon pink background.

The most frequently cited challenge, with four mentions, was the gap in digital literacy, which highlights the difficulty many students have in dealing with digital technologies that are essential for AI-mediated learning. This limitation reflects what Tomczyk (2024) and Reddy; Chaudhary and Hussein (2023) have already pointed out: digital literacy is a basic requirement for the critical and functional use of educational technologies. The absence of basic skills can compromise the use of available tools, as pointed out in studies [EP1, EP2, EP4, EP5], and reinforces the need for inclusive digital training policies focused on distance higher education.

Also mentioned four times, the difficulty in adapting to new technologies and teaching methodologies was a recurring challenge. This finding converges with Puckett (2020) and Saiz-González et al. (2024), who associate technological resistance with both institutional factors and individual barriers. Studies [EP2, EP4, EP5, EP6] show that, without support strategies and continuous training, the transition to innovative educational approaches tends to generate insecurity and disengagement among students.

Another significant obstacle, also mentioned in four studies, was the lack of adequate technological infrastructure. The effective implementation of AI in education depends on access to devices, quality internet, and appropriate software, elements that are still unevenly distributed in the Brazilian context. This finding is in line with the analyses of Nkansah and Oldac (2024) and Suardi (2024), who highlight that infrastructure is a prerequisite for digital equity. Studies [EP2, EP4, EP5, EP6] reinforce that the lack of technological resources compromises not only learning but also the principle of digital inclusion in distance learning.

With three mentions, the need for autonomy in digital learning appears, a challenge that reflects the difficulty some students have in managing their own educational path in AI-mediated environments. This barrier, observed in [EP1, EP2, EP3], is in line with Bembenutty (2023) and Abbasi, Wu, and Luo (2024), for whom self-regulation and a sense of agency are fundamental conditions for success in virtual contexts. Thus, the absence of these skills limits the student's ability to take an active and reflective stance toward technology.

In fifth place, with two mentions, are two important challenges: the lack of continuous support and real-time feedback and interoperability problems between educational systems. The absence of teacher monitoring and personalized feedback, discussed in studies [EP3, EP6], reduces the effectiveness of pedagogical mediation, an aspect already highlighted by Mayer and Schwemmle (2023). In turn, the difficulties of integration between platforms, identified in [EP4, EP6], confirm what Ferreira; Silva and Bezerra (2025) observe about the technical and pedagogical limits of digital fragmentation, which can compromise the fluidity of the educational experience.

Overall, these results demonstrate that, despite the opportunities provided by AI in education, its implementation still faces structural and pedagogical obstacles. To maximize the benefits of this technology, it is essential to invest in infrastructure, digital training, and methodologies that favor users' adaptation to the new educational ecosystem.

5 CONCLUSION

This study conducted a systematic literature review (SLR) to identify and analyze the digital skills necessary for effectively using AI in distance learning higher education. The results emphasized the importance of these skills in enabling students to interact critically, reflectively, and productively with AI-based technologies, thereby optimizing the teaching and learning process.

The most frequently cited digital skills in the analyzed studies were digital literacy, critical thinking, self-learning, and adaptation to emerging technologies. These findings demonstrate the need to prepare students for a dynamic digital environment in which AI plays a central role in personalizing educational experiences and analyzing academic performance.

The most commonly used AI technologies and tools in the analyzed studies include intelligent tutoring systems, learning analytics tools, and educational chatbots, which provide adaptive support to students. However, implementing these technologies faces significant challenges, including a lack of technological infrastructure, difficulty adapting to new methodologies, and the need for students and teachers to receive greater training in the conscious and skilled use of these tools.

The RSL covered six primary studies, mainly conducted in university contexts in Asia and Europe between 2022 and 2024. These studies focused on undergraduate and graduate students in Education, Computing, and the Social Sciences. This regional and thematic predominance indicates a scarcity of research in Latin American countries, particularly Brazil. This underscores the importance of future investigations exploring local realities, digital inequalities, and contextualized pedagogical practices.

Such reflections are particularly relevant in the Brazilian context. Although the rapid expansion of distance learning has increased access to higher education, regional inequalities in connectivity, technological infrastructure, and digital literacy still pose structural challenges. Therefore, developing students' digital skills is not merely a technical requirement; it is a condition for educational inclusion and equity in the country. Strengthening these skills is essential for AI to effectively contribute to reducing asymmetries and promoting meaningful learning in different regional contexts.

Despite the challenges, it can be concluded that AI has great potential to transform distance higher education by promoting greater accessibility, personalization, and efficiency. To maximize these benefits, it is crucial to invest in digital training for students and teachers, develop inclusive educational policies, and create innovative pedagogical approaches that integrate AI into teaching in an ethical and responsible manner.

Acknowledgements:

The lead author would like to thank the organization where he works for allowing him time to study.

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• 

  
  
• 

  
  
• Funding:
  This study was funded by the Coordination for the Improvement of Higher Education Personnel (CAPES) for scholarships and financial support.
• 1
  Available at: https://docs.google.com/spreadsheets/d/1Rhu- dLN68o8fVrZOGa1yHn0YPKdcjXuw33OabFB8 WA/edit?usp=sharing
• 2
  Available at: https://parsif.al.
• Ethical approval:
  Not applicable.
• Availability of data and material:
  https://doi.org./10.6084/m9.figshare.30524498
• Image:
  Extracted from the Lattes platform.
• JITA:
  LP. Intelligent agents | Artificial inteligence
• SDG:
  4. Quality education

Edited by

• Editor:
  Gildenir Carolino Santos https://orcid.org/0000-0002-4375-6815

Data availability

https://doi.org./10.6084/m9.figshare.30524498

Publication Dates

• Publication in this collection
  27 Apr 2026
• Date of issue
  2026

History

• Received
  14 Mar 2025
• Accepted
  10 Sept 2025
• Published
  31 Oct 2025