Advancing Accessibility, Personalization, and User Engagement in Smart Educational Portals for High Schools in Gauteng Province, South Africa: A Systematic Literature Review of Natural Language Processing and Machine Learning Driven Approaches

Abstract

The evolution of smart educational platforms has been significantly driven by the global development of ML (Machine Learning), particularly through the application of NLP (Natural Language Processing) to personalize student learning experiences. However, high schools in Gauteng Province face significant challenges in adopting smart educational portals. This is due to inadequate accessibility to resources, insufficient personalization mechanisms, and low student engagement frameworks caused by the failure to adapt to proven teaching methods. Despite the successful adoption of NLP and ML in global case studies and implementations, local gaps persist. Specifically, pretrained LLMs (Large Language Models) such as BERT (Bidirectional Encoder Representations from Transformers) require fine-tuning for African languages, yet little testing of these tools in Gauteng’s public high schools has been done. This review uses a structured literature review methodology, which examines peer-reviewed studies, case reports, and technical documents published between 2014–2026. Findings indicate that multilingual NLP resources for South African languages remain severely underdeveloped. Furthermore, it demonstrates that current smart-learning portals lack inclusive design adjustments for multilingual and low-resource contexts. Based on these findings, the paper recommends strategies for enhancing accessibility, personalization, and engagement. This includes the development of multilingual NLP resources, optimization of ML architectures for constrained infrastructure, and context-aware pedagogical adaptations. The review follows a two-layer design, consisting of (i) a global systematic synthesis of NLP and ML applications in education, and (ii) a contextual interpretation of these findings for Gauteng high schools using regional policy documents, infrastructure reports, and educational statistics. The conclusions pertain to implications and recommendations for Gauteng high schools, rather than evaluation of an existing local portal. This paper highlights the potential of NLP and ML to transform education in Gauteng but highlights the urgency of localized research and ML implementation.

1. Introduction

In Gauteng, where only 35% of public schools meet basic digital learning standards (Gauteng Department of Education, 2023), NLP and ML could revolutionize education equity. South Africa’s education system faces persistent inequalities in access and quality, worsened in the post-COVID-19 era as digital adoption accelerates. Initiatives like the Gauteng Online Learning Programme aim to bridge historical gaps, but challenges endure (Kgosi et al., 2023). While global e-learning platforms leverage NLP/ML for personalization, their designs often ignore low-resource, multilingual classrooms. Most tools remain inflexible and monolingual, which is not optimal for Gauteng’s multicultural student population.

South Africa’s education system is inherently multilingual, with learners in Gauteng speaking a diverse range of African languages such as isiZulu, Sesotho, Setswana, and isiXhosa. Existing research shows that multilingual educational technologies significantly improve comprehension and learner engagement when they align with local language practices (Matellio, 2025; Mabokela et al., 2023). However, most digital learning platforms remain predominantly English-centric, resulting in language barriers and increased cognitive load for learners. Recent advances in transformer-based architectures and transfer learning techniques offer promising pathways to overcome the province’s dual challenges of linguistic diversity and infrastructure constraints. Where traditional systems require extensive labelled datasets for African language support, modern few-shot learning approaches using models like NLLB (No Language Left Behind) have achieved up to a 44% BLEU (Bilingual Evaluation Understudy) improvement over previous state-of-the-art systems across 200 languages, including many African low-resource languages (NLLB Team et al., 2022). No empirical ML/NLP studies from Gauteng high schools were identified; therefore, the provincial focus of this review is attained through contextualization of global evidence using local policy documents, infrastructure reports, and educational statistics.

In Africa and particularly in South Africa, we face the problem where most South African languages spoken are not supported in software solutions that often are made in North America or Europe (IITPSA, 2025). As a result, language barriers hinder comprehension as most platforms lack robust translation tools for African languages despite their dominance in Gauteng’s classrooms (Samuel et al., 2025). This gap demonstrates the need for multilingual NLP tools tailored to African languages to support inclusive learning in the Gauteng context. Furthermore, poor personalization is the result of generic ML models that are rarely trained on South African student data. The challenges introduced where NLP resources supporting African languages, remain severely underdeveloped resulting in a corpus scarcity, despite voice-enabled AI assistants that have achieved near-human interaction quality in English (IITPSA, 2025).

Methodologically, this study adopts a two-layer review design. Firstly, a global systematic literature review synthesizes empirical evidence on NLP- and ML-driven educational technologies addressing accessibility, personalization, multilingualism, and learner engagement. Secondly, these findings are interpreted and contextualized for high schools in Gauteng Province using local policy documents, infrastructure reports, and educational statistics. This approach is particularly suitable in contexts where localized empirical evidence remains limited and explicitly addresses the scarcity of local studies, ensuring that the review provides both evidence-based insights from international research and contextualized implications for Gauteng’s multilingual, resource-constrained high schools.

1.1. Objectives

This review paper aims to evaluate NLP and ML techniques that enhance accessibility, personalization, and student engagement in educational technology. It seeks to bridge the divide between global technological advancements and local needs, contributing to the development of ML-driven education portal tools and sustainable digital infrastructure for education accessibility in South Africa. Specifically, the review aims to:

• Identify and summarize current NLP and ML techniques applied in educational technology, with a focus on accessibility, personalization, and student engagement.
• Assess the extent and effectiveness of these techniques in South African high schools, particularly in Gauteng Province.
• Determine gaps and limitations in existing smart learning portals in multilingual and resource-constrained settings.
• Provide evidence-based recommendations for improving ML-driven educational tools and digital infrastructure to support inclusive and adaptive learning in Gauteng high schools.

This review performs a dual function: synthesizing the global ML/NLP evidence base while identifying gaps specific to Gauteng high schools, where empirical studies remain scarce.

1.2. Research Questions

The following research questions guided this review:

Main Research Question: How can natural language processing and machine learning be utilized to improve accessibility, personalization, and engagement in smart learning portals for high schools in Gauteng, South Africa?

Research Sub-Questions:

• How effective are current NLP tools in supporting multilingual learning for Gauteng high school students, particularly for isiZulu, Sesotho and isiXhosa?
• What ML approaches are most feasible for implementing adaptive personalization within resource-constrained Gauteng school environments?
• How do NLP/ML-driven smart learning portals influence student engagement and learning outcomes in Gauteng compared to traditional LMS platforms?
• What technological and linguistic limitations must be addressed to successfully develop and deploy AI-enhanced educational portals in Gauteng high schools?

2. Materials and Methods

This study adopts a systematic literature review (SLR) approach to examine how NLP and ML techniques can enhance accessibility, personalization, and user engagement in smart educational portals for high schools in Gauteng, South Africa. This study is structured according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The integration of PRISMA ensures a structured, transparent, and replicable review process while providing a comprehensive synthesis of current research trends and gaps.

2.1. Research Design

This study employs a two-stage review design. Stage 1 constitutes a PRISMA-aligned global SLR synthesizing empirical and applied conceptual studies on NLP and ML in education, with explicit thematic focus on accessibility, personalization, multilingualism, adaptive learning, and student engagement. Stage 2 involves a contextual interpretation of Stage 1 findings for Gauteng high schools, using policy documents, infrastructure reports, and educational statistics. This separation ensures that claims based on empirical studies are clearly distinguished from contextually derived insights, maintaining methodological transparency throughout the manuscript.

A systematic review methodology, guided by PRISMA, was selected to identify, evaluate, and synthesize existing academic literature related to NLP and ML applications in educational technology (Mengist et al., 2020). This design allows for an evidence-based understanding of how these technologies are currently being used to address educational challenges, particularly those related to accessibility, language diversity, and learner engagement. This study employs a two-layer systematic review design guided by PRISMA guidelines.

• Layer 1 consists of a global SLR of empirical and applied conceptual studies on NLP and ML applications in educational technology. This layer synthesizes evidence related to accessibility, personalization, multilingualism, adaptive learning, and learner engagement across diverse international contexts.
• Layer 2 involves the contextual interpretation of the synthesized global findings for public high schools in Gauteng Province, South Africa. This interpretive layer draws on regional education policy documents, infrastructure reports, and official statistics to assess feasibility, relevance, and contextual constraints.

2.2. Search Strategy

A structured search was conducted across multiple reputable academic databases, including IEEE Xplore, ScienceDirect, Google Scholar and Web of Science (WoS). This was done to ensure inclusion of high coverage indexing for interdisciplinary technology research. The search terms combined relevant keywords such as: “Natural Language Processing”, “Machine Learning”, “education”, “e-learning”, “smart portals”, “accessibility”, “multilingualism”, “personalization” or “engagement”. Google Scholar was included to ensure comprehensive retrieval of grey literature and region-specific studies that may not be indexed elsewhere. WoS was included to expand the breadth of indexed interdisciplinary research and reduce the risk of missing relevant studies. Although Web of Science was included to strengthen interdisciplinary coverage, Scopus was not searched. This may have resulted in the omission of some relevant studies indexed exclusively in that database. Scopus was not included due to overlap with coverage provided by Web of Science and ScienceDirect (this decision and its implications for potential coverage gaps are acknowledged in the Limitations section). The search was limited to peer-reviewed articles published between 2014 and 2026 to ensure that the reviewed materials reflect the most recent technological advancements and educational applications. For the purposes of this review, “peer-reviewed” refers to journal articles and full conference papers that underwent formal editorial and peer-review processes. Preprint materials (e.g., arXiv and SSRN) were screened for contextual relevance but were not included in the final 72-study systematic corpus unless a corresponding peer-reviewed version was available. The initial, broad WoS search yielded over 422,000 records. To improve relevance and ensure that the results accurately reflected the targeted topics and context, the search was refined using the specific set of keywords mentioned above. This resulted in a manageable and focused set of 347 records. The exact query used was:

TS = (“Natural Language Processing” OR NLP)

AND TS = (“Machine Learning” OR ML)

AND TS = (Education OR “e-learning” OR “smart portals”)

AND TS = (Accessibility OR Personalization OR Engagement OR
Multilingual* OR “adaptive learning”)

The search terms did not impose any restrictions on educational level or geographic region, ensuring broad coverage of ML and NLP applications in education across global contexts.

These results were merged with other database results for screening. To ensure methodological clarity, the selection of databases was guided by relevance, accessibility, and alignment with the study’s interdisciplinary scope. They provide strong coverage of core domains in this review, namely NLP, ML and educational technology research.

The systematic search did not identify empirical NLP or ML studies conducted directly within high schools in Gauteng Province, highlighting a significant local research gap. The search strategy was intentionally broad and not restricted by educational level or geographic region. This approach was adopted to capture the full scope of NLP and ML applications in education. The focus on high school (secondary education) was operationalized during the screening and eligibility stages rather than at the search stage.

2.3. Contextual Data Sources for Gauteng Province

To support the contextual interpretation of the global systematic review findings, a complementary set of contextual and grey literature sources was consulted focusing specifically on high schools in Gauteng Province. These sources were not included in the systematic review corpus but were used to situate the synthesized evidence within the regional infrastructure, policy, and linguistic environment. Contextual sources included official South African government and provincial education policy documents, publicly available infrastructure and connectivity reports, and national and provincial education statistics. These materials were retrieved from official government portals and institutional repositories, including the Department of Basic Education and Statistics South Africa. Only documents published between 2014 and 2026, or the most recent versions available at the time of review, were considered.

Inclusion criteria for contextual sources required that documents be official, publicly accessible, and directly relevant to secondary education, digital infrastructure, language policy, or ICT integration in Gauteng schools. These sources were used solely to inform the contextual layer of analysis and to support interpretation of implications and recommendations, rather than to evaluate or aggregate empirical ML or NLP interventions.

2.4. Inclusion and Exclusion Criteria

The inclusion and exclusion criteria were determined using purposive sampling, a non-probabilistic method commonly applied in systematic reviews to selectively identify studies that are most relevant to the research objectives (Tajik et al., 2024). The structured database search initially yielded 312 records from IEEE Xplore, ScienceDirect, and Google Scholar, with an additional 347 records retrieved from WoS. After merging all records and removing duplicates, 609 unique articles remained for title and abstract screening. Duplicates were removed at the identification stage following the merging of records retrieved from multiple databases. Of these, 500 were excluded for lacking relevance to NLP/ML in educational contexts or failing to address accessibility, personalization, or engagement. 109 studies proceeded to full-text assessment, from which 72 met the methodological requirements and were included in the final synthesis. To operationalize the “high school” criterion, studies were screened at title, abstract, and full-text level for explicit reference to secondary education, high school learners, or equivalent grade ranges (typically Grades 8–12). Studies reporting mixed educational levels (e.g., combined primary and secondary samples) were retained only when results were disaggregated for secondary learners or when the reported outcomes, system designs, or pedagogical mechanisms were explicitly applicable to secondary education contexts. Studies focusing exclusively on higher education, early childhood education, or adult training were excluded at screening unless they presented transferable design principles relevant to multilingual, resource-constrained secondary school environments. Of the 72 included studies, most addressed mixed educational levels with outcomes transferable to secondary education. A smaller subset focused directly on secondary or high school education. No studies conducted exclusively in Gauteng high schools were identified. Studies were excluded if they were duplicate publications, purely theoretical without applied relevance or lacked sufficient methodological detail.

To provide transparency in the review process, a flow diagram was used to illustrate the identification, screening, eligibility, and inclusion of studies (Figure 1). The diagram outlines the stepwise selection of relevant literature. This visual summary ensures clarity and reproducibility in the systematic review methodology. Explicit exclusion categories, including non-secondary education focus, are reflected in the PRISMA flow diagram. Exclusion based on educational level (non-secondary focus) was explicitly recorded as a screening exclusion category and is reflected in the PRISMA flow diagram.

Eligibility was operationalized during the title, abstract, and full-text screening stages. The “high school” criterion was applied at screening. Studies were included if they explicitly addressed secondary education or reported findings transferable to high school contexts. Furthermore, studies were included if they reported empirical or applied conceptual research on NLP or ML in educational technology and explicitly addressed secondary or high school education. Studies with a global or mixed educational scope were included only if their findings were directly transferable to secondary education or to multilingual, resource-constrained school environments similar to those in Gauteng. Studies focusing exclusively on early childhood education or corporate training were excluded unless they presented clearly applicable design principles relevant to high school learning contexts. Studies were also excluded if they were purely theoretical without applied relevance or lacked sufficient methodological detail. Selected conceptual studies were retained where they provided foundational frameworks or design principles relevant to multilingual and low-resource educational settings.

2.5. Inclusion and Exclusion Quality Assessment

Each study included in the review was evaluated using a structured PRISMA-aligned quality assessment checklist focusing on clarity of research questions, methodological rigor, validity of findings, relevance to the research objectives, and transparency of results reporting. Studies failing to meet a minimum quality threshold were excluded at the full-text assessment stage. The final corpus of 72 studies therefore represents research meeting predefined quality standards appropriate for evidence synthesis. To enhance transparency, an overview of quality score distribution across the included studies is provided in the Appendix E.

2.6. Data Extraction and Analysis

A total of 72 articles met the methodological quality standards and were included in the data extraction phase. Data extraction was conducted manually by reviewing each selected article and capturing relevant quantitative and qualitative findings into structured summary notes. Studies were grouped using narrative synthesis, a method in which themes are identified through the comparison of findings from multiple studies (Rai et al., 2020). Thematic analysis was performed to group studies under core themes, including:

• NLP for accessibility and multilingual translation,
• ML-driven personalization and adaptive learning, and
• student engagement enhanced through intelligent feedback and recommendation systems.

This process also enabled the identification of research gaps, technical limitations in low-resource educational settings, and opportunities for future NLP/ML integration.

To ensure transparency throughout the review, peer-reviewed studies identified through the systematic search were used exclusively to answer the research questions. While contextual and grey literature, such as Gauteng policy documents, infrastructure reports, and departmental statistics, was used solely to interpret and localize those findings. This separation was applied consistently across the manuscript to distinguish empirical evidence from contextual framing. This ensures that claims grounded in the systematic corpus remain methodologically correct while still being contextualized within Gauteng’s educational landscape.

2.7. Quality Assessment

To ensure the reliability of the review, each study was evaluated using a structured checklist aligned with PRISMA guidelines. The assessment focused on methodological rigor, clarity of research questions, validity of findings and relevance to the study objectives. Only studies meeting a predefined quality threshold were retained for synthesis, ensuring that the review is based on reliable and evidence-based sources.

2.8. Ethical Considerations

As this review relies solely on secondary data from publicly available sources, no direct human participation or data collection occurred. Ethical integrity was maintained by ensuring accurate citation and proper acknowledgment of all referenced works, as well as adherence to transparent SLR and PRISMA reporting standards.

3. Results

The following section presents the key findings of this review, structured according to the main research question and sub-questions. Of the 72 studies included, 18 studies (S1, S6, S10, S17, S23, S32, S36, S41, S45, S48, S51, S53, S55, S56, S58, S61, S65, S70) directly address RQ1, 20 studies (S1, S3, S6, S10, S17, S19, S23, S26, S32, S41, S48, S51, S56, S58, S61, S65, S70, S72) address RQ2, 15 studies (S7, S9, S20, S28, S31, S36, S41, S43, S44, S49, S52, S54, S56, S61, S65) address RQ3, and 10 studies (S9, S23, S28, S31, S44, S45, S54, S56, S61, S70) address RQ4. Each subsection below separates empirical evidence from contextual/grey literature to clearly indicate the source of each claim.

Of the 72 studies included in the final synthesis, 14 focused exclusively on secondary or high-school contexts, 37 examined mixed educational levels (e.g., primary, secondary and tertiary), and 21 were retained for their transferable design principles, such as general online education strategies or universal ML/NLP frameworks, relevant to high-school educational portals, as detailed in Appendix D. Studies exclusively focused on higher education were excluded unless their findings were directly transferable to high school contexts.

3.1. ML and NLP Concepts for an Education Context (Supporting RQ1 & RQ2)

NLP is a sub-category of AI (Artificial Intelligence) allowing computers the ability to understand, interpret and generate human language (Muhammad, 2025). Empirical studies suggest that NLP supports multilingual learning and adaptive personalization, which are key to RQ1 and RQ2 (S12, S36, S45, S53). Furthermore, empirical studies suggest that AI-driven smart portals offer dynamic personalization and multilingual support, improving engagement relative to traditional LMSs (S1, S6, S10, S23). Contextual evidence confirms these features align with local policy requirements around mother-tongue instruction and access equity.

• Contextual/Grey Evidence

Table 1. Language Distribution of High School students in Gauteng (Statistics South Africa, 2024; McKellar & Puttkammer, 2020). (Contextual/grey literature data using official demographic and educational statistics; this table does not draw on the 72 reviewed studies but provides essential contextual background for the Gauteng educational setting).

Language	Total of Gauteng Students (%)	NLP Tool Availability
isiZulu	32	Limited (no GPT-4 support)
English	28	Extensive
Afrikaans	16	Moderate (e.g., Google Translate)
Sesotho	12	None
Xhosa	8	Minimal (basic NMT models)
Other	4	-

below shows the language distribution of high school students in Gauteng and the availability of current NLP translation tools. One of the supporting factors to the importance of this situation is the policy mandate of the South African Schools Act (1996), which requires mother-tongue instruction. However, most platforms only support English/Afrikaans.

Sentiment Analysis is an NLP technique which is commonly used to identify emotions in text (Gillis & Barney, 2024).

• Empirical Evidence

The following table shows how smart portals powered by NLP and ML has been proven to differ from a traditional Learning Management System (LMS).

Table 2. Traditional LMS Capabilities compared to NLP/ML Driven Smart Educational Portals (Paik et al., 2024; Farshad et al., 2024; see S12, S23, S41). (Conceptual synthesis grounded in peer-reviewed literature from the 72-study corpus. Highlights the differences in functionality relevant to personalization, multilingualism support and learner engagement).

Feature	Traditional LMS Capabilities	Smart Portal Capabilities
Personalization	Static content for all students	Dynamic adjustments based on real-time data
Language Support	Monolingual	Multilingual with NLP translation
Engagement Tools	Basic quizzes	AI chatbots, gamification, sentiment analysis

compares the capabilities of traditional LMSs (Learning Management Systems) with smart educational portals powered by NLP and ML. It highlights how AI-driven portals offer dynamic personalization, multilingual support and advanced engagement tools (Bousalem et al., 2025; Lundström et al., 2024; see S1, S6, S10, S23). This contrasts with the static and monolingual nature of conventional LMS platforms (Kunicina et al., 2025).

• Contextual/Grey Evidence

This comparison underscores the potential of AI technologies to address accessibility, personalization, and engagement challenges in Gauteng’s high schools.

Global research has made substantial progress in multilingual NLP, low-resource language modelling, and adaptive ML-driven personalization; however, these advancements remain unevenly distributed across linguistic contexts.

While models such as NLLB, mBERT (multilingual BERT), and transformer-based adaptive fine-tuning demonstrate measurable gains for African languages, their efficacy deteriorates sharply when applied to South Africa’s morphology-rich languages (Masethe et al., 2024). Studies consistently highlight severe data scarcity, tokenization errors, and limited curriculum alignment as persistent barriers to effective deployment in multilingual educational settings.

Furthermore, research on smart learning portals for African schools shows that most existing systems are designed for monolingual, high-income environments, creating a pronounced algorithmic imprint that fails to account for local sociocultural and infrastructural realities (Ehsan et al., 2022; Bentley et al., 2024; Sabiteka, 2025).

This gap underscores the need for localized NLP and ML approaches tailored to Gauteng’s diverse linguistic landscape, resource constraints, and policy requirements around mother-tongue instruction and digital learning (see S66).

This section addresses research sub-questions 1 and 2 by demonstrating that ML and NLP concepts, including adaptive learning, sentiment analysis, and multilingual support form the foundation of smart educational portals. It addresses research sub-question 1 by highlighting the current effectiveness of NLP tools for multilingual learning in Gauteng. It addresses research sub-question 2 by showing that ML approaches can enable adaptive personalization, though limited local language support and data scarcity remain challenges.

3.2. ML for Gauteng’s Secondary Education Sector (Supporting RQ2)

RQ2 is addressed by 20 studies (S1, S3, S6, S10, S17, S19, S23, S26, S32, S41, S48, S51, S56, S58, S61, S65, S70, S72), which examine ML-driven personalization in secondary education. Empirical studies suggest that lightweight ML architectures and adaptive recommendation algorithms can increase course completion rates by up to 30% (S17, S19, S26, S51). Contextual evidence indicates that Gauteng’s multilingual and resource-constrained schools face additional challenges (Gauteng Department of Basic Education, 2023).

• Empirical Evidence

Educational platforms leveraging ML offer potential for creating personalized learning experiences in resource-constrained, multilingual contexts such as Gauteng (see S17, S19, S26, S51). While global evidence demonstrates that ML can support adaptive learning and engagement, many solutions are based on the algorithmic imprint described in Section 3.1 (Tennakoon et al., 2021; see S3, S4, S19). Evidence also indicates that lightweight ML architectures and adaptive recommendation algorithms can increase course completion rates by up to 30% (see S17).

• Contextual/Grey Evidence

This divide disproportionately affects multilingual regions like Gauteng, where educational inequality manifests through three interconnected challenges: linguistic barriers, poor personalization, and low user engagement. Recent data from South Africa’s Department of Basic Education underscores the severity of these challenges as a high percentage of students in Gauteng do not have an English home language. The urgency of this research stems from a constitutional paradox (Gauteng Department of Education, 2023). While Section 29 of South Africa’s Constitution mandates mother-tongue instruction, national education policy increasingly emphasizes digital learning through initiatives like the Gauteng Online Learning Programme (Gauteng Department of Basic Education, 2023).

Studies of Current Natural Language Processing Tools

RQ2-focused empirical studies suggest that smart portals enhanced by NLP and ML provide adaptive personalization and multilingual support for Gauteng high schools (S17, S19, S26, S51).

• Empirical Evidence

In Gauteng Province, smart learning portals enhanced by AI technologies offer a potential solution to longstanding educational challenges. Unlike traditional LMSs, these next-generation portals are characterized by adaptive personalization through ML algorithms and NLP-enabled multilingual support.

• Contextual/Grey Evidence

The importance of this technological approach becomes clear when considering Gauteng’s educational landscape, where ±946,000 high school students navigate a complex linguistic environment of at least six primary languages, while curriculum content remains predominantly English-based (Janks, 2014; Gauteng Department of Education, 2023; see S66).

• Empirical Evidence

Recent advancements in transformer-based NLP models and lightweight ML architectures provide the technological foundation for this research. Studies like Mkhize’s work on isiZulu language processing demonstrate both the promise and limitations of current technologies (Parliament of South Africa, 2025). Google’s NLLB and Meta’s mBERT models prove successful in controlled environments, however their practical application in South African classrooms faces substantial barriers. Techniques like multilingual adaptive fine-tuning can reduce model size by approximately 50% while maintaining robust performance across African languages (Nasir & Mchechesi, 2022). On the other hand, data scarcity remains acute: isiZulu corpora contain only a fraction of the token counts compared to English (typically between 1–3 million tokens) highlighting the severe imbalance (Alabi et al., 2022).

Appendix B.1 demonstrates how transformer-based NLP architecture is used for multilingual adaptive learning portals. From an educational perspective, this research intersects with Vygotsky’s sociocultural theory, particularly the concept of the Zone of Proximal Development (ZPD). In theory, NLP tools like intelligent tutoring systems (ITS) could serve as the “more knowledgeable other” in Vygotsky’s framework, providing scaffolding for student learning. The appendix demonstrates how the LMS adapts content to student needs using ML through lightweight ML architecture for adaptive personalization in AI-enhanced smart learning portals. Appendix B.2 and Figure 2 demonstrates how this is achieved as to how ITS leverages NLP for scaffolding student learning. However, this potential remains unrealized when tools fail to accommodate local languages and curriculum requirements. Current studies found that AI-driven interfaces had 25.13% higher engagement rates than human tutors, while prototype isiZulu interfaces showed promise but lacked proper alignment with the national CAPS curriculum (Eiselen & Puttkammer, 2014).

This section addresses research sub-question 2 by confirming that ML approaches are feasible for implementing adaptive personalization within resource-constrained Gauteng schools. Evidence shows that lightweight ML architectures and transformer-based NLP models can dynamically adjust content to meet diverse student needs, supporting this research’s sub-question while acknowledging infrastructural and curriculum-related barriers.

3.3. NLP for Multilingual Education (Supporting RQ1 & RQ3)

RQ1 and RQ3 are addressed by 15 studies (S32, S36, S45, S53, S61), which examine multilingual NLP performance and engagement impacts. Empirical studies suggest substantial disparities across languages, with BLEU scores dropping for English–isiZulu and English–Sesotho translations (S36, S45, S53). Contextual evidence highlights limited local language resources, which impede implementation in Gauteng classrooms (Gauteng Department of Basic Education, 2023).

• Empirical Evidence

The application of NLP in South African education must address complex multilingual realities where the average classroom contains speakers of three or more languages (Khaled, 2014). Current research reveals significant disparities in NLP tool performance across languages (see S32, S36, S45, S53). While state-of-the-art models achieve BLEU scores of 0.68 for English-French translation, performance drops to 0.41 for English-isiZulu and there are no reliable benchmarks for Sesotho or Xhosa (Shaik et al., 2022; Blocker et al., 2025; Wikipedia Contributors, n.d.; see S36, S45). This “NLP divide” stems from the scarcity of training data (isiZulu Wikipedia contains just 10,500 articles compared to English’s 6.5 million). Furthermore, the lack of computational linguistic research on African languages’ unique morphological structures contribute to the divide (Esmail et al., 2022). Recent attempts to bridge this gap include the South African Centre for Digital Language Resources’ (SADiLaR) development of isiZulu part-of-speech taggers and the Masakhane project’s community-driven translation efforts (see S36, S45). However, studies highlight that NLP initiatives for African languages face significant sustainability challenges, with many projects discontinued within a few years due to limited funding, insufficient digital resources, and a lack of trained computational linguists (Masakhane Project, 2025).

This section addresses research sub-questions 1 and 3 by showing how NLP tools perform across different South African languages through the BLEU score differences identified. Findings reveal disparities in tool performance between English and local languages, emphasizing the importance of context-specific NLP systems to enhance engagement and comprehension.

3.4. ML-Driven Personalization (Supporting RQ2 & RQ3)

RQ2 and RQ3 are addressed by 18 studies (S20, S23, S26, S41, S48, S49, S56, S65). Empirical studies suggest that ML-driven personalization improves concept mastery, writing skills, and student engagement in secondary education (S13, S23, S41, S48, S56, S62).

• Empirical Evidence

ML’s potential to personalize education is well-documented in global literature (Öncel et al., 2021; see S23, S48, S56), with a meta-analysis showing median effect size of 0.66 standard deviations, indicating a substantial improvement in student learning outcomes over traditional methods (Muhammad, 2025).

• Contextual/Grey Evidence

However, these results derive primarily from high-resource contexts with stable internet, 1:1 device ratios, and standardized curricula.

• Empirical Evidence

Emerging research from universities suggests students actively leverage AI tools to overcome systemic learning barriers (S20, S26, S65), particularly in these key areas relevant to Gauteng’s high schools (Ahmed, 2021; Kulik & Fletcher, 2015). DBE reports indicate these tools must align with CAPS and local language curricula (Gauteng Department of Basic Education, 2023).

• Concept Mastery: Thousands of students use generative AI tools such as ChatGPT to clarify misunderstood lecture content, with non-native English speakers reporting higher rates of reliance (Bosch et al., 2023; see S20, S41). This aligns with Gauteng’s need for curriculum-aligned explanations in isiZulu/Sesotho (Kulik & Fletcher, 2015).
• Writing Scaffolding: Tools such as QuillBot, among other ML-based writing assistants, can be useful for school alumni to support English writing development (Bensalah, 2024; Liang et al., 2023; see S49, S56), suggesting ML models should incorporate CAPS-aligned grammar checking and code-switching support for informal settlement dialects (Kulik & Fletcher, 2015). Appendix B.3 demonstrates ML-based writing scaffolding incorporating grammar correction, paraphrasing, and code-switch support.

This section addresses research sub-questions 2 and 3 as it demonstrates that ML-driven personalization supports RQ2 and RQ3 by improving student concept mastery and writing skills. Personalized interventions, including ITSs and writing scaffolding tools, increase learning outcomes and engagement compared to traditional LMS platforms.

3.5. Importance of AI Technologies in Education (Supporting RQ3 & RQ4)

RQ3 and RQ4 are addressed by 15 studies (S9, S20, S27, S41, S43, S50, S52, S61). Empirical studies suggest AI tools improve learning outcomes, engagement, and grading efficiency. While contextual evidence highlights technological and linguistic limitations in Gauteng schools (S20, S27, S41, S43, S50, S52, S61; Gauteng Department of Basic Education, 2023).

• Contextual/Grey Evidence

In Gauteng Province, smart learning tools enhanced by AI technologies offer a potential solution to longstanding educational challenges. Unlike traditional LMSs, these next-generation educational tools are characterized by adaptive personalization through ML algorithms and NLP-enabled multilingual support (Bernasconi et al., 2025; Docebo, 2025). Provincial education frameworks like Gauteng’s Education 4.0 explicitly call for personalized learning technologies. Internationally, the United Nations Sustainable Development Goal 4 (United Nations, 2025) targets equitable quality education for all. Together, these policy connections emphasise the needed bridge between local needs with global educational priorities.

• Empirical Evidence

Educationally, second-language learners face substantial cognitive challenges when studying STEM concepts in English (Rodríguez-Ortiz et al., 2025; see S52). Research in cognitive science indicates that learning in a non-native language increases cognitive load (Mabokela et al., 2023; see S50, S61). This is particularly due to the additional demands on working memory and language processing, which can hinder comprehension and knowledge retention (Kormos, 2023). GPT-based tools (GPT-3.5, GPT-4) improve accessibility for neurodiverse students and multilingual learners (see S20, S27, S41). AI-DSS (AI-driven Decision Support Systems) systems have been shown to enhance grading efficiency (up to 70%) and learning outcomes (see S43).

• Contextual/Grey Evidence

While NLP-powered translation tools could theoretically alleviate this burden, existing solutions like Google Translate fail to handle curriculum-specific terminology accurately (Akallouch et al., 2025). This gap highlights the need for specialized educational NLP systems designed for South Africa’s multilingual classrooms. Digital literacy is a key strategy for enhancing youth employability and participation in the digital economy. Projections suggest that South Africa’s digital economy could nearly double its GDP contribution by 2030, potentially creating up to 1.2 million jobs in AI, data analytics, and digital services (Kormos, 2023). However, this potential remains unrealized without educational technology that accounts for local realities.

Global Innovations in AI for Education

The past decade has seen exponential growth in AI applications for education, with three dominant concepts emerging (Haering et al., 2021), as summarized in

Table 3. Conceptual matrix of NLP/ML-enabled Educational Technologies: Examples, Impacts, and Challenges. (Author-generated evidence-based summary derived directly from the 72-study corpus (S49, S65, S72, S35, S41), summarizing empirical findings and recurring design challenges).

Concept	Example	Impact	Challenge
Intelligent Tutoring Systems	Carnegie Learning’s MATHia	Consistent learning gains through cognitive tutor models (Global Citizen, 2025); provided personalized, step-by-step problem-solving guidance and real-time misconception detection with an average of ±2.5% in accuracy per opportunity (Simpson et al., 2024).	Training data mostly originates from Western education contexts, creating substantial relevance gaps when considering African curricula and educational approaches (Koedinger et al., 2023).
Automated Writing Evaluation	Grammarly	Leveraged transformer models.	Frequently exhibit cultural bias against African English variants, struggle with code-switched texts, and provide inadequate subject-specific feedback (Zawacki-Richter & Loglo, 2023).
Adaptive Learning Platforms	Dreambox Learning’s math software	Utilize sophisticated algorithms to enable competency-based progression tailored to individual learners (Deas et al., 2023).	Limitations such as assumption of 1:1 device access as well as training primarily on Western educational content (Simon & Zeng, 2024).

.

A 2025 meta-analysis of ITS implementations found zero studies conducted in African schools (Strielkowski, 2025), highlighting a critical research gap. Furthermore, recent industry findings prove the growing role of ML in personalizing learning experiences, automating administrative tasks, and identifying at-risk students (Di Pietro & Castaño Muñoz, 2025). ML algorithms enable adaptive learning by analyzing individual student performance and engagement data to tailor educational content, as demonstrated by platforms like Khan Academy (X. L. Chen et al., 2025; Di Pietro & Castaño Muñoz, 2025).

This section addresses research sub-questions 3 and 4 by highlighting how AI-enhanced smart learning portals influence engagement and learning outcomes (RQ3) while exposing technological and linguistic limitations (RQ4). Existing platforms can scaffold learning and provide multilingual support, but gaps in curriculum alignment, digital literacy, and contextual relevance must be addressed for effective deployment.

3.6. African Context and Case Studies (Supporting RQ4)

RQ4 is addressed by 10 studies (S9, S23, S28, S31, S44, S45, S54, S56, S61, S70). Empirical studies suggest AI-driven portals can provide scaffold learning and increased engagement, but practical adoption is challenged by curriculum alignment, mother-tongue support, and resource constraints (S9, S28, S31, S45, S54). Contextual evidence demonstrates how case studies from Kenya, Rwanda, and South Africa highlight both successes and failures in low-resource African contexts (Eneza Education, 2016; Andela, 2025; ICDL Africa, 2025; Jimes et al., 2013).

• Empirical Evidence

South African research on smart portals indicates that AI tools can offer scaffold learning yet struggle with curriculum alignment and mother-tongue support (see S9, S28, S31, S45, S54). Studies in Namibia, Oman, and Kenya highlight the implementation of generative ML models, adaptive learning, and LLM-based feedback systems to enhance personalization and engagement in resource-constrained settings (see S23, S24, S44, S59). Across these contexts, ethical considerations and bias mitigation are key for successful deployment (see S7, S54).

• Contextual/Grey Evidence

The African context presents a complex landscape of educational technology implementation, marked by both inspiring success stories and valuable lessons from failures. Successful implementations include Kenya’s Eneza Education. One of the notable success stories, having reached over 500,000 students through its innovative SMS-based quiz system designed for basic feature phones (Grigalashvili & Olaresko, 2025). Another success case study is Rwanda’s Andela Learning Platform (Andela, 2025). This platform demonstrated the effectiveness of offline-first design principles coupled with culturally localized STEM content, currently serving over 100,000 learners (Eneza Education, 2016; Kuuire, 2021).

These successful implementations share common characteristics including operating effectively across different types of devices, aligning with local educational and cultural practices, as well as designed with cost structures that are feasible for the communities they serve (ICDL Africa, 2025). Failed implementations include Nigeria’s Tutor.ng Platform. Challenges faced included high data costs for users, which limited accessibility and adoption of its educational content, particularly for video-based lessons (Olaogun, 2014). Another failed case study is South Africa’s Siyavula Platform. The platform’s limited adoption in township schools has been attributed to its English-only interface, which failed to accommodate the multilingual reality of these learning environments (Jimes et al., 2013). These cases suggest that in some LMIC (Low- or Middle-Income Country) contexts, higher technical sophistication does not necessarily translate to greater real-world impact, with accessibility, localization, and affordability often playing a decisive role.

South Africa’s academic research on educational technology has clustered around two themes, language technologies and policy frameworks. Masakhane’s work on isiZulu NLP remains one of the only published research on African language educational applications and created processors for translating African languages (Masakhane Project, 2025; GitHub, 2025). This research gap is particularly striking given South Africa’s constitutional recognition of eleven official languages. The POPIA Compliance Guidelines for EdTech (South African Government, 2021) established critical data privacy parameters for learner data protection and set standards for age-appropriate content in digital learning tools. Despite these important thematic developments, there is a notable absence of studies successfully bridging these topics through integrated solutions.

This section addresses research sub-question 4 by illustrating the practical challenges and successes of implementing AI-driven educational technology in African contexts. Case studies highlight that culturally aligned and low-resource-conscious designs are essential. Furthermore, language, infrastructure, and policy constraints must be addressed to ensure successful adoption in Gauteng high schools (Semi et al., 2025).

Research Gap and Added Value of This Review

Despite rapid global advancements in ML and NLP for education, very few empirical studies have examined the deployment of AI-enhanced smart learning portals in African secondary schools, particularly in multilingual, resource-constrained settings such as Gauteng. Most existing evidence derives from high-income, monolingual contexts where digital infrastructure and teacher training differ substantially from local realities. This review bridges two critical dimensions: it synthesizes global evidence on ML/NLP applications in education and contextualizes these findings against the unique linguistic, infrastructural, and policy challenges of Gauteng high schools. By doing so, it explicitly identifies where international innovations can inform local solutions, and where gaps in research remain.

Consequently, the review not only consolidates the current state of knowledge but also provides a context-driven blueprint for implementing AI-enhanced smart portals in Gauteng, highlighting priorities for research, development, and policy interventions. This dual approach underscores that the Gauteng-specific empirical evidence is currently scarce, and the added value of this work lies in combining international best practices with a rigorous local gap analysis.

Overall, empirical evidence addressing RQs in multilingual, resource-constrained settings such as Gauteng is scarce, particularly for RQ2–RQ4. This review integrates 72 studies to synthesize global best practices while contextualizing them for Gauteng’s unique linguistic, infrastructural, and policy landscape (Information Regulator SA, n.d.).

4. Discussion

This Discussion section is structured according to the Main Research Question and its four Sub-Questions. Each subsection below directly addresses one of the research questions by synthesizing relevant findings from the literature review.

4.1. Sub-Question 1: How Effective Are Current NLP Tools in Supporting Multilingual Learning for Gauteng High School Students, Particularly for isiZulu, Sesotho and isiXhosa?

Overcoming Language Barriers Through NLP Adaption

An analysis of current multilingual NLP tools reveals major disparities in their effectiveness across South Africa’s languages. BLEU scores are defined as a quality score for measuring the similarity between a machine translation (MT) output and a human translation, by the MT system (Vashee, 2019; Litman, 2016). As shown in Figure 3, while transformer models achieve 0.71 BLEU scores for English-Afrikaans translation, performance drops to 0.08 for isiZulu and 0.38 for Sesotho (Litman, 2016).

This gap is stemmed from the following three factors—corpus limitations, morphological complexity and curriculum misalignment. This is transparent by the isiZulu Wikipedia corpus containing approximately 10,500 articles (Gilfillan, 2022) compared to English’s over 6 million (Wikipedia Contributors, 2020; Wikipedia Contributors, n.d.), creating fundamental training data shortages. Furthermore, South African languages like isiZulu requires specialized tokenizers. Recent research highlights the difficulty of applying generic NLP tools to morphology-rich languages such as isiZulu (Iyamu, 2024; Esmail et al., 2022). Standard tokenizers like those provided in SpaCy, which are trained primarily on English and other high-resource languages, tend to underperform when faced with isiZulu’s structure and complex noun class system. Whereas English tokenization generally reaches accuracies above 90%, isiZulu performance is evidently lower. The literature suggests a consistent 25–30% performance gap between the two languages when standard tokenization methods are used, underscoring the importance of developing language-specific models and resources (Eiselen & Puttkammer, 2014; Mncube, 2023).

4.2. Sub-Question 2: What ML Approaches Are Most Feasible for Implementing Adaptive Personalization Within Resource-Constrained Gauteng School Environments?

Personalization in Resource-Constrained Environments

In resource-constrained environments, algorithm selection requires balancing predictive performance with memory and computational demands (Mesham et al., 2021; Mncube, 2023; see S47). Gradient-boosted tree models, such as LightGBM in ML.NET, provide robust accuracy on tabular datasets while maintaining low memory requirements (<500 MB) (Microsoft, 2023). Transformer-based models, including BERT and MobileBERT, achieve higher predictive accuracy (85–90%) but create substantially greater memory costs, with full models exceeding 1 GB (Z. Sun et al., 2020). Classical decision trees are highly memory-efficient (<100 MB) and computationally inexpensive, though they exhibit lower accuracy (70–78%) relative to contemporary models (Tan et al., 2018).

The benchmark patterns in

Table 4. ML Technique Feasibility Analysis using Microsoft ML.NET Benchmarks; TensorFlow Lite Case Studies (Mncube, 2023; Mesham et al., 2021; Microsoft, 2023). (Evidence-informed conceptual summary; draws on studies included in the corpus and additional grey literature).

Algorithm	Accuracy (%)	Memory Usage/Footprint 1
LightGBM (ML.NET)	80–85	Low (~small to medium dataset, <500 MB)
BERT (HuggingFace)	85–90	High (~1–2 GB typical for base model)
Decision Trees	70–78	Very Low (<100 MB typical)

¹ Memory usage is illustrative and based on typical device deployment scenarios. Accuracy ranges are literature-informed, reflecting typical performance on standard datasets. (Accuracy ranges and memory profiles are literature-based, but the comparative structure and feasibility framing were authored for the review).

highlight practical considerations for adopting ML techniques in South African high schools, particularly those operating with limited digital infrastructure (see S69). Lightweight models such as Decision Trees and LightGBM, which have small memory footprints and modest computational requirements, are better suited for use in environments where learners and teachers rely on older devices, shared computer labs, or low-cost tablets (see S62). Their efficiency makes them more feasible for educational applications that require quick feedback, low data consumption, or the possibility of offline or low-connectivity operation. Conversely, high-capacity transformer models such as BERT offer stronger accuracy but require substantial memory and processing power, meaning they would typically need to be deployed on remote servers or cloud-based educational platforms rather than on school devices. Understanding these feasibility differences is important for policymakers, developers, and educators (Chung et al., 2022). It provides clarification on which ML techniques are realistically implementable within resource-constrained schooling contexts and which may require more advanced digital infrastructure to be used effectively.

4.3. Sub-Question 3: How Do NLP/ML-Driven Smart Learning Portals Influence Student Engagement and Learning Outcomes in Gauteng Compared to Traditional LMS Platforms?

Engagement Metrics and Learning Outcomes

Gauteng online education platforms have demonstrated consistent growth in user engagement over time, reflecting the province’s investment in digital learning initiatives (Gauteng Department of Education, 2023). Figure 4 shows this and how weekly active users have increased steadily following platform implementation, with the most pronounced growth occurring during the initial months. Assignment completion rates have also shown improvement over the same period, indicating enhanced learner participation (Almeida et al., 2025). After-hours engagement has increased as well, though growth appears to plateau, suggesting limits to voluntary usage under current platform designs. These trends establish useful benchmarks for evaluating digital learning solutions in resource-constrained, multilingual contexts (see S46).

This data provides an additional key education insight about multimedia balance (see S30). It was found that students exposed to either interactive videos or traditional video lectures, demonstrated a 25% improvement in learning outcomes, as measured by post-test scores (Tan et al., 2018). These findings suggest that NLP/ML-driven portals can significantly increase both engagement and learning outcomes, provided they incorporate multimodal and interactive content tailored to learners’ contexts (J. Chen et al., 2025).

4.4. Sub-Question 4: What Technological and Linguistic Limitations Must Be Addressed to Successfully Develop and Deploy AI-Enhanced Educational Portals in Gauteng High Schools?

Security and Adoption Considerations

The security and adoption data shown in Figure 5 and Figure 6 reveal critical insights into both technological and human factors affecting educational portal development and design. Teacher adoption patterns showed a strong preference (70%) for digital training modules over in-person sessions (30%), reflecting both COVID-19 pandemic-era adaptation to e-learning (Gauteng Department of Education, 2023; Cale et al., 2024; see S66). Cost analysis demonstrated that on-premises solutions were 2.1 times more feasible than cloud alternatives (68% vs. 32% adoption), primarily due to Azure’s prohibitive data costs and schools’ unreliable internet connectivity (Cale et al., 2024). These findings highlight that successful edtech adoption in resource-constrained environments requires flexible training accommodating teacher schedules, hybrid infrastructure options aligned with school capabilities, and transparent security measures to build institutional trust (UNESCO, 2021).

The outcomes of security implementations indicate that blockchain attribution mechanisms increased teacher-generated content uploads by 40–50%, addressing intellectual property concerns and encouraging sharing of educational resources (Gauteng DBE, 2023; Grech & Camilleri, 2017). Similarly, integration of Microsoft Authentication Library (MSAL) with mandatory multi-factor authentication reduced unauthorized access attempts by 85–90%, providing critical protection for student data and supporting compliance with POPIA regulations (Grech & Camilleri, 2017; Microsoft, 2022; GB Hackers, 2023).

4.5. Limitations, Future Directions and Conclusions

NLP and ML technologies hold substantial promise for improving accessibility, personalization, and engagement in multilingual educational contexts such as Gauteng’s high schools.

Limitations: While this review provides a comprehensive synthesis of the literature, several methodological limitations should be acknowledged. First, the database search, though structured and covering IEEE Xplore, ScienceDirect, Google Scholar, and Web of Science, did not include Scopus, which may have excluded some relevant studies. Second, although the review followed PRISMA principles, including structured search strategies, screening procedures, and a flow diagram, it did not employ a fully specified PRISMA protocol with pre-registered items. Third, some of the Gauteng-specific information relied on grey literature and contextual sources, such as provincial statistics and conceptual syntheses, which may introduce bias despite efforts to triangulate with peer-reviewed work.

It is important to note that much of the empirical evidence comes from high-resource international settings. Consequently, the applicability of these findings to Gauteng’s multilingual, resource-constrained high schools is limited. Factors such as language coverage, curriculum alignment, and infrastructure may affect the transferability of global insights, meaning some conclusions are necessarily more speculative in the local context.

Furthermore, no empirical ML- or NLP-enabled educational portal studies conducted directly within public high schools in Gauteng were identified in the systematic review. As a result, the evidence base informing this study is derived primarily from international contexts that are generally better resourced in terms of digital infrastructure, language support, and data availability. This creates an inherent limitation regarding the transferability of findings to Gauteng’s multilingual, resource-constrained school environments and necessitates cautious interpretation of applicability at the provincial level.

Future Directions: Future research should focus on expanding linguistic resources for underrepresented South African languages, improving code-switching support, and designing lightweight ML models suitable for low-end devices. In particular, there is a clear need for pilot studies that implement and evaluate selected ML- and NLP-based educational interventions within Gauteng public high school classrooms to empirically assess feasibility, effectiveness, and contextual constraints in real-world conditions. There is also a need for longitudinal studies examining the real-world effectiveness of adaptive ML and NLP applications in multilingual, resource-constrained educational settings. Additionally, greater integration of conceptual, policy, and technical research could help bridge gaps between global innovations and local classroom realities (Mahamad et al., 2025).

Conclusion: This discussion has highlighted both technological advancements and contextual challenges, including low-resource African languages, device constraints, and the uneven availability of educational content aligned with local curricula. By synthesizing evidence from empirical studies, conceptual frameworks, and grey literature, the discussion has provided insights into how ML-driven personalization, intelligent tutoring systems, and NLP-based writing scaffolding can support learners in complex multilingual settings.

5. Conclusions and Future Work

This review has established both the transformative potential and significant challenges of implementing NLP and ML technologies in Gauteng’s high school education portals. Through the analysis of journal articles and published web documents, three critical insights emerge—linguistic limitations, infrastructure constraints and requirements for success. Current smart education solutions remain underdeveloped for South African languages, particularly isiZulu and Sesotho, due to limited annotated corpora and linguistic complexity. As a result, NLP systems for these languages perform significantly worse than for European languages (du Toit & Puttkammer, 2021). Infrastructure constraints require model optimization as conventional ML architectures often exceed the memory capacity of devices available in many Gauteng schools. Additionally, a significant digital divide remains, with 87% of Gauteng public schools lacking internet connectivity, further complicating the deployment of data-intensive ML applications (Gaustad & Puttkammer, 2022). Successful implementations in comparable LMICs demonstrate that cultural alignment and teacher involvement are equally as important as technical sophistication for adoption (Democratic Alliance Gauteng Provincial Legislature, 2025; see S42).

This review explicitly maps its findings to the main and sub-research questions, clarifying both the existing knowledge gaps and the unique contribution of this work. By examining the performance of NLP and ML tools in multilingual educational contexts, their feasibility in resource-constrained Gauteng schools, and their impact on student engagement and learning outcomes, the study identifies clear gaps: limited development of NLP resources for isiZulu, Sesotho, and isiXhosa, insufficient adaptive ML applications for local school infrastructures, and a lack of engagement data for multilingual learners. Consequently, this review contributes a focused, context-specific synthesis that informs the design of AI-enhanced educational portals tailored to the needs of Gauteng high schools.

As this research transitions from theory to practice, it represents more than a technical solution; it embodies a shift in how educational technology can be designed to serve rather than exclude (Gleason, 2018). By centring Gauteng’s linguistic diversity, infrastructure realities, and cultural context in every design decision, this work pioneers an equity-driven model for educational portals. The potential extends beyond improved test scores to something far more profound, a future where every South African learner, regardless of language or postal code, can engage with technology that recognizes and responds to their unique voice and potential.

While global ML/NLP research offers valuable insights, the lack of African secondary school studies highlights the importance of local validation and adaptation. This review uniquely positions Gauteng’s contextual challenges alongside international evidence to guide future interventions. The recommendations emerging from this review are evidence-based strategies. These include the development of NLP resources for isiZulu, Sesotho, and isiXhosa, deployment of ML architectures suitable for constrained school infrastructure, adaptive personalization and engagement features tailored to the Gauteng context, and the integration of cultural and pedagogical considerations to ensure effective adoption. These strategies reflect the current state of the evidence and provide actionable guidance for future research and practical implementations.

To visually underscore the potential of advanced NLP architectures in overcoming the linguistic and contextual challenges discussed above, Appendix B presents the Bidirectional Encoder Representations from Transformers (BERT) model which is a foundational deep learning architecture that enables contextual understanding of language by processing text bidirectionally. Its self-attention mechanism allows the model to capture relationships between words across entire sentences, making it especially powerful for morphologically rich and low-resource languages such as isiZulu and Sesotho (see S21). Integrating such transformer models into Gauteng’s educational NLP pipelines could therefore represent a decisive step toward inclusive, context-aware, and linguistically intelligent learning platforms.