Machine Learning in Education

Abstract

This narrative review examines the historical evolution, current applications, and major challenges of machine learning (ML) in education, positioning ML as a transformative yet deeply contested force in contemporary teaching and learning. Tracing developments from early computer-assisted instruction and intelligent tutoring systems to contemporary deep learning, natural language processing, and generative AI, the review shows how these technologies have expanded education’s capacity for personalization, prediction, automation, content generation, and large-scale data-driven decision-making. It synthesizes evidence across key domains, including student performance prediction, early warning systems, adaptive learning, intelligent tutoring, automated assessment, learning analytics, curriculum design, and inclusive education. In addition, the review critically highlights persistent limitations and risks, particularly algorithmic bias, data privacy concerns, limited interpretability, uneven pedagogical value, infrastructure constraints, and the disruption of conventional assessment by generative AI. Rather than treating ML as a purely technical innovation, the paper argues that its educational significance depends on how responsibly it is designed, implemented, and governed. The review concludes that the future of ML in education will be shaped not only by advances in computational methods but also by ethical judgment, pedagogical alignment, and institutional commitment to equity, transparency, and human-centered educational practice across diverse learning contexts worldwide.

1. Introduction

Machine Learning (ML), a core subfield of artificial intelligence (AI), has emerged as a transformative force with the potential to fundamentally reshape educational practices, pedagogical methodologies, and institutional administration [1]. Unlike traditional software systems that follow explicitly programmed instructions, ML enables computers to learn from data, identify patterns, and make predictions or decisions without being explicitly programmed for every conceivable scenario [2]. In the educational context, this capability translates into systems that can analyze vast quantities of learner data, adapt instructional content in real-time, predict student outcomes, and provide personalized support at scale [3]. To avoid conceptual ambiguity, this review distinguishes among three related but non-identical terms. AI is used as the broad umbrella term for computational systems designed to perform tasks commonly associated with human intelligence, such as reasoning, language processing, perception, decision support, and problem solving. ML refers more specifically to the data-driven subset of AI used for prediction, classification, recommendation, knowledge tracing, adaptive learning, and early warning systems. Generative AI, by contrast, refers to AI systems, often based on large language models (LLMs) or other foundation models that generate new text, images, code, explanations, feedback, or learning materials.

The integration of ML into education represents a paradigm shift that has been accelerating over the past decade. The global AI in education market was valued at USD 5.88 billion in 2024 and is projected to expand at a compound annual growth rate (CAGR) of 31.2% from 2025 to 2030 [1]. This remarkable growth trajectory is driven by several converging factors, including rising demand for personalized learning solutions, the rapid shift toward e-learning platforms accelerated by the COVID-19 pandemic, increasing investments in educational technology (EdTech) startups, and growing recognition of ML’s potential to address long-standing challenges in educational access and quality [4,5]. By technology segment, ML led the market with a 64.7% revenue share in 2024, underscoring its central role in enabling adaptive and personalized learning experiences [1].

The significance of ML in education extends beyond market metrics. For decades, educators have grappled with the fundamental tension between the need to provide individualized attention to each student and the practical constraints of classroom settings with limited resources [6]. ML-powered systems offer a pathway to address this tension by automating routine administrative tasks—such as class planning and grading—thereby freeing educators to focus on high-impact activities, including mentorship, discussion facilitation, and personalized student support [7]. Furthermore, ML enables data-driven decision-making at unprecedented scales, allowing educators, administrators, and policymakers to derive actionable insights from the large volumes of student data now being generated through digital learning platforms [8,9].

Contemporary ML applications in education span the entire educational lifecycle, from student admissions and course scheduling to content generation, instructional delivery, performance assessment, and outcome prediction [10]. Intelligent tutoring systems, powered by ML algorithms, can now imitate human tutors by providing real-time feedback and adapting instruction to individual learning preferences without human intervention [11]. Learning analytics platforms leverage ML to identify at-risk students, predict dropout probabilities, and enable timely interventions [12]. Natural language processing (NLP) techniques, particularly transformer-based models such as BERT and GPT, are increasingly being deployed to provide automated feedback on student writing, power conversational agents, and support language learning [13,14].

However, the integration of ML into education is not without challenges and ethical considerations. Concerns regarding data privacy, algorithmic bias, the “black-box” nature of many ML models, and the potential for technology to undermine rather than enhance learning outcomes have been raised by researchers and practitioners alike [15,16]. The recent proliferation of generative AI tools, in particular, has introduced new complexities around academic integrity and the assessment of authentic student learning [17]. These challenges underscore the need for careful, ethically grounded approaches to ML deployment in educational contexts (see [18]).

This study adopts a narrative review design intended to provide a broad historical, conceptual, and critical synthesis of ML in education rather than a fully systematic review or meta-analysis. The review focuses on major developments in ML-driven educational technologies, including intelligent tutoring systems, learning analytics, adaptive learning, automated assessment, generative AI, and ethical/governance challenges. Literature searches were conducted between January and March 2026 using Google Scholar, Scopus, Web of Science, IEEE Xplore, and ERIC. Literature was identified through iterative searches using broad terms related to the historical development, applications, and challenges of ML in education. The review prioritized: (a) foundational and historically influential publications; (b) highly cited empirical and conceptual studies; (c) recent post-2020 scholarship reflecting the rapid expansion of generative AI and post-pandemic digital learning; and (d) representative technical, pedagogical, and critical perspectives across educational contexts. Purely commercial or narrowly application-specific studies without broader conceptual relevance were generally excluded unless they illustrated a particularly influential technological development. Because the review is narrative rather than systematic, the aim was not exhaustive coverage of every subdomain, but the synthesis of major trajectories, methodological developments, debates, and challenges shaping the field.

2. Historical Development of ML in Education

2.1. Early Foundations: Computer-Assisted Instruction and Intelligent Tutoring Systems

The history of applying computational methods to education long predates the emergence of ML as a distinct field. The 1960s witnessed the development of computer-assisted instruction (CAI), which leveraged technology to deliver learning materials and provide feedback to users [19]. The Programmed Logic for Automatic Teaching Operations (PLATO), developed in 1960 at the University of Illinois, stands as the first large-scale CAI system, serving a diverse range of learners from school pupils to university students and even prison inmates across subjects including mathematics, Latin, and the sciences [20]. PLATO’s innovations included personalized learning paths, immediate feedback, and sophisticated student tracking capabilities, features that would later become central to ML-based educational systems [21].

The late 1960s and 1970s saw the emergence of more sophisticated intelligent tutoring systems (ITSs). The Time-shared, Interactive Computer-Controlled Instructional Television (TICCIT) system, developed in 1968, delivered individualized, multimedia-based content to users and allowed learners to progress at their own pace [22]. These early systems were grounded in contemporary learning theories, particularly the work of B.F. Skinner on programmed instruction and Benjamin Bloom on mastery learning, which emphasized the importance of individualized tutoring and immediate feedback [23]. Bloom’s influential “2 sigma” study, which demonstrated that one-on-one tutoring produced learning outcomes two standard deviations above conventional instruction, provided a compelling rationale for developing computer-based systems that could approximate the benefits of human tutoring at scale [6].

2.2. The Emergence of AI and ML in Education (1980s–2000s)

The 1980s and 1990s witnessed the gradual integration of artificial intelligence (AI) techniques into educational systems. Early intelligent tutoring systems, such as SCHOLAR (teaching South American geography) and WHY (teaching causes of rainfall), incorporated knowledge representation and reasoning capabilities that allowed them to engage in dialogue with students and answer questions [24]. The development of cognitive tutors at Carnegie Mellon University, grounded in John Anderson’s Adaptive Control of Thought (ACT) theory, represented a significant advance by incorporating cognitive models that could trace student problem-solving steps and provide targeted hints and feedback [25,26].

Despite their conceptual importance, many early intelligent tutoring systems and expert-system approaches failed to scale beyond controlled or highly specialized contexts. Systems such as SCHOLAR and WHY demonstrated the potential of AI-driven instructional dialogue, but they depended heavily on handcrafted domain knowledge, rigid rule-based representations, limited computational resources, and costly maintenance requirements [27]. Their pedagogical interactions were also often constrained, brittle, and difficult to generalize across subjects, learners, or institutional settings [28]. The subsequent AI Winters reflected broader disillusionment with inflated expectations, limited computational power, insufficient data, and the gap between laboratory demonstrations and sustainable real-world implementation [29].

The invention of the World Wide Web in 1989 fundamentally transformed the landscape for AI in education [30]. Web-based learning environments could collect unprecedented amounts of data on user interactions, which could then be used to train software agents and improve system performance [31]. The 1990s also saw the emergence of learning management systems (LMS) such as FirstClass and Blackboard, which organized content, tracked student progress, and managed online education [32]. While early LMS platforms relied primarily on basic automation rules, they laid the groundwork for the data collection infrastructure that would later enable sophisticated ML applications [33].

The turn of the millennium brought advances in hardware capabilities, data mining techniques, and ML algorithms that accelerated the development of AI in education. The emergence of educational data mining (EDM) as a distinct research field in the early 2000s formalized the application of data analytics to educational questions [34]. Researchers began applying ML techniques to predict student performance, identify at-risk learners, and uncover patterns in learning behaviors [35,36]. The launch of Massive Open Online Courses (MOOCs) in the late 2000s created new imperatives for automated assessment and personalization, as courses with tens of thousands of students rendered traditional instructional methods infeasible [37].

2.3. The Deep Learning Revolution and Modern Era (2012–Present)

The deep learning revolution, catalyzed by AlexNet’s landmark performance in the 2012 ImageNet competition, profoundly impacted educational applications [38] (Deep neural networks offered new capabilities for analyzing complex, high-dimensional educational data, including student writing, discussion forum posts, and multimodal learning interactions [39]. Related advances in speech-and-language ML—such as the use of biomarkers for early neurological detection—illustrate the broader maturation of ML methods that are increasingly transferable to educational language data and analytics [40]. The introduction of the Transformer architecture in 2017, with its self-attention mechanism, fundamentally transformed NLP and opened new possibilities for AI in education [41].

The release of BERT (Bidirectional Encoder Representations from Transformers) by Google in 2018 demonstrated the power of pre-trained language models for understanding context-dependent language, enabling more sophisticated analysis of student writing and more natural conversational agents for tutoring [13]. OpenAI’s GPT series, introduced between 2018 and 2020, progressively expanded the capabilities of generative language models, with GPT-3 demonstrating remarkable few-shot learning abilities across diverse tasks [42,43]. These developments laid the foundation for the integration of generative AI into educational applications, from automated feedback generation to intelligent tutoring [44].

The COVID-19 pandemic served as a powerful accelerant for ML adoption in education. The rapid shift to remote learning created urgent demand for technologies that could support online instruction, maintain student engagement, and provide continuity in assessment [45]. Educational institutions that had previously been hesitant to adopt AI-powered tools found themselves compelled to explore digital solutions [46]. The pandemic also generated vast new datasets of online learning interactions, providing rich material for training and refining ML models [47].

The release of ChatGPT in November 2022 marked a watershed moment for AI in education [48]. For the first time, generative AI capabilities became accessible to the general public through an intuitive interface, sparking widespread experimentation by students and educators alike [49]. Subsequent developments, including GPT-5 and specialized educational tools, have continued to expand the possibilities for ML in education while also raising profound questions about academic integrity, assessment practices, and the nature of learning in an AI-augmented world [50,51,52]. Figure 1 presents a timeline of ML-in-Education milestones.

2.4. Evolution of Research Themes

The research literature on ML in education has evolved significantly over the past two decades. Early work focused primarily on prediction tasks, forecasting student performance, identifying at-risk learners, and modeling knowledge acquisition [53]. This trajectory was strongly shaped by the Educational Data Mining and Learning Sciences communities, which emphasized not only prediction but also student modeling, knowledge tracing, metacognition, self-regulated learning, adaptive feedback, and the design of learning environments grounded in cognitive and pedagogical theory. As ML techniques matured, researchers increasingly turned their attention to personalization and adaptation, developing systems that could dynamically adjust content and feedback based on learner characteristics [54]. More recently, the focus has expanded to include affective computing (detecting and responding to student emotions), multimodal learning analytics (integrating data from multiple sources such as clickstreams, facial expressions, and physiological sensors), and the application of generative AI for content creation and feedback [55,56,57].

A systematic analysis of research published between 2003 and 2022 reveals the growing diversity of ML applications in education [10]. Performance assessment has been the most extensively studied category, accounting for a substantial portion of published work, followed by content generation, learning content design, and student admission logistics [58]. The COVID-19 pandemic period (2020–2022) saw increased attention to online learning contexts and the challenges of maintaining educational quality in remote settings [59].

3. Applications of ML in Education

3.1. Student Performance Prediction and Early Warning Systems

One of the most extensively developed applications of ML in education is the prediction of student performance and the identification of learners at risk of academic difficulty or dropout [60]. Educational institutions at all levels face the challenge of identifying students who may need additional support before they fall irreparably behind [61]. ML models trained on historical student data, including demographics, prior academic performance, engagement metrics, and clickstream data from learning management systems, can predict outcomes with accuracy that often exceeds traditional methods [62,63,64].

A wide range of ML algorithms has been applied to performance prediction tasks. Logistic regression and decision trees provide interpretable models that can identify key risk factors [65,66]. Random forests and gradient boosting machines often achieve high predictive accuracy by capturing complex, non-linear relationships in the data [67]. Moreover, support vector machines and neural networks have been employed for more challenging prediction tasks involving high-dimensional data [68,69]. Finally, deep learning approaches, particularly recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, have proven effective for modeling temporal patterns in student learning trajectories [70,71]. More recent educational sequence models have moved beyond conventional RNN/LSTM approaches toward attention-based knowledge tracing, including self-attentive and context-aware models that can better weight relevant prior learner interactions in sparse or irregular temporal data [72]. Comparable temporal-attention methods in biomedical informatics also show how longitudinal indicators can be aligned with later outcomes, offering useful methodological parallels for modeling knowledge retention, semester-long engagement, and dropout risk [73].

Early warning systems based on these predictive models enable timely interventions. When a model identifies a student as at-risk, institutions can trigger automated alerts to advisors, instructors, or the students themselves, prompting targeted support [74]. Research has demonstrated that such systems, when properly implemented, can significantly reduce dropout rates and improve academic outcomes [75,76]. However, the effectiveness of early warning systems depends critically on the quality of the underlying data, the appropriateness of the prediction horizon, and the availability of effective interventions to offer at-risk students [77].

3.2. Personalized and Adaptive Learning

Personalization stands at the core of ML’s promise for education. The principle that instruction should be tailored to individual learner needs has deep roots in educational theory, but practical constraints have historically limited its implementation [78]. ML offers the potential to deliver personalized learning at scale by continuously adapting content, pacing, and pedagogical strategies based on individual learner characteristics and performance [79,80].

Adaptive learning systems employ ML algorithms to build and continuously update models of student knowledge, often referred to as knowledge tracing [81]. Bayesian Knowledge Tracing (BKT), one of the earliest and most influential approaches, models student mastery of individual knowledge components and updates probability estimates based on performance on related tasks. More recent deep learning approaches, such as Deep Knowledge Tracing (DKT), use recurrent neural networks to capture more complex patterns of knowledge acquisition and forgetting [70]. Work in the Learning Sciences has also emphasized that adaptive learning should not be understood merely as algorithmic personalization, but as the design of learning environments that connect student modeling, feedback, instructional sequencing, and theory-informed pedagogy.

Content recommendation represents another key dimension of personalization. Collaborative filtering techniques, similar to those used by e-commerce and streaming platforms, can recommend learning resources based on what similar learners have found helpful [82]. Content-based recommendation systems analyze the characteristics of learning materials and match them to learner profiles [83]. Hybrid approaches combining multiple techniques often achieve the best results [84].

Personalized learning systems have been developed across diverse educational contexts and subject areas. In Sri Lanka, researchers developed an ML-powered personalized learning system for secondary mathematics that creates individualized learning paths, implements chatbot-based tutoring, and provides real-time feedback on student progress [79]. In Hong Kong, researchers applied recurrent neural networks to detect mindset states—including concentration, motivation, perseverance, engagement, and self-initiative—among secondary students in online courses, demonstrating the potential for real-time adaptation to affective as well as cognitive states [57].

3.3. Intelligent Tutoring Systems and Conversational Agents

Intelligent tutoring systems (ITSs) represent one of the most mature applications of AI in education, with research spanning more than four decades. Modern ITSs integrate multiple ML capabilities: they model student knowledge, track progress through curricula, generate appropriate problems and examples, provide targeted feedback, and offer hints when students struggle [85,86]. Cognitive Tutors, widely used in mathematics classrooms, exemplify this approach, having demonstrated significant learning gains in rigorous evaluations [87,88]. However, ITS effectiveness remains highly domain- and context-dependent. Reported gains may be stronger in well-structured domains and controlled settings than in under-resourced or complex classroom environments.

Recent advances in NLP have enabled the development of increasingly sophisticated conversational agents for education [89,90]. These agents can engage students in dialogue, answer questions, and provide explanations in natural language [91]. Early systems such as AutoTutor demonstrated the feasibility of tutorial dialogue systems, showing that conversational agents could produce learning gains comparable to human tutors in some domains [92]. Contemporary systems leverage LLMs to engage in more flexible and contextually appropriate dialogue [93,94].

Chatbots have emerged as a scalable approach to providing on-demand support to learners. In higher education, chatbots are being deployed to answer administrative questions, provide academic advising, and offer course-specific tutoring [95,96]. Research on chatbot effectiveness has shown positive effects on student engagement and learning outcomes, though results vary considerably depending on design and implementation [89]. The RASA framework, an open-source platform for building conversational AI, has been employed to develop educational chatbots with natural language understanding capabilities [97].

3.4. Automated Assessment and Feedback

Assessment represents both a significant opportunity and a significant challenge for ML in education. Automated assessment offers the promise of reducing the grading burden on educators, providing students with more immediate feedback, and enabling more frequent formative assessment [98]. However, assessing complex student work—particularly writing and other open-ended responses—requires sophisticated natural language understanding [99].

Automated essay scoring (AES) has been an active area of research and development since the 1960s [100]. Modern AES systems employ a variety of ML techniques to evaluate essays on dimensions including organization, argumentation, grammar, and style. While these systems have achieved sufficient reliability for use in high-stakes assessments such as the GRE and GMAT, concerns remain about their ability to validly assess authentic writing ability and their susceptibility to gaming [101,102]. In particular, AES scores may correlate strongly with essay length and shallow lexical or syntactic features rather than substantive argument quality, as highlighted in Perelman’s critique of automated writing evaluation.

ML is also being applied to provide more fine-grained feedback on student work. Systems can identify specific errors, suggest improvements, and provide explanatory comments [103,104]. In programming education, automated feedback systems can evaluate code correctness, efficiency, and style, providing students with detailed guidance for improvement [105,106]. In mathematics, intelligent assessment systems can analyze student problem-solving steps, identify misconceptions, and generate personalized practice problems [107].

The emergence of generative AI has created new challenges for assessment. Students can now use tools like ChatGPT to generate convincing text, code, and problem solutions, potentially undermining the validity of traditional assessment formats [108,109,110]. This has prompted calls for assessment reform, including a shift toward process-oriented assessment, oral examinations, and tasks that require integration of AI tools rather than their prohibition [111,112]. Some institutions are exploring “AI-resilient” assessment designs that emphasize higher-order thinking skills and authentic performance [113].

3.5. Learning Analytics and Institutional Decision-Making

Learning analytics, defined as the measurement, collection, analysis, and reporting of data about learners and their contexts for purposes of understanding and optimizing learning, has emerged as a distinct field at the intersection of education, data science, and ML [9]. Learning analytics platforms integrate data from multiple sources—learning management systems, student information systems, library usage, and other institutional data sources—to provide dashboards and reports that inform decision-making by educators, administrators, and students themselves [114,115].

ML plays an increasingly central role in learning analytics. Predictive models identify students who may benefit from additional support [116]. Clustering algorithms reveal patterns in student learning strategies and engagement [117]. Social network analysis maps patterns of interaction in online learning communities [118]. NLP analyzes discussion forum content to identify topics of interest, confusion, or concern [119,120].

At the institutional level, ML-powered analytics support strategic planning and resource allocation [121]. Enrollment management, curriculum planning, and program evaluation can all be informed by predictive models and pattern analysis [122]. However, researchers caution against over-reliance on analytics without appropriate human interpretation and contextual understanding [123]. Ethical considerations, including student privacy, algorithmic fairness, and transparency, are central to responsible learning analytics implementation [124,125].

3.6. Content Creation and Curriculum Design

ML is increasingly being applied to the creation of educational content and the design of curricula [126]. Natural language generation techniques can produce instructional texts, practice problems, and assessment items [127]. In language learning, ML-powered systems can generate exercises tailored to individual learner proficiency levels and interests [128]. In science and mathematics education, systems can generate problems with specified parameters and difficulty levels [129].

Generative AI, particularly LLMs, has dramatically expanded the possibilities for automated content creation [130]. Educators can use tools like GPT-4 to generate lesson plans, explanations, examples, and assessment items, significantly reducing preparation time [131]. However, concerns about accuracy, appropriateness, and pedagogical quality require careful review and adaptation of AI-generated content [132,133].

Curriculum design, traditionally a labor-intensive process requiring extensive expertise, can be informed by ML analysis of learning trajectories, prerequisite relationships, and effectiveness data [134]. Sequence mining techniques can identify optimal ordering of learning activities [135], and reinforcement learning approaches can optimize curriculum sequencing to maximize learning outcomes [136,137].

3.7. Supporting Diverse Learners and Inclusive Education

ML has significant potential to support diverse learners and advance inclusive education [138]. For students with disabilities, ML-powered tools can provide alternative means of accessing content and demonstrating learning [139]. Text-to-speech and speech-to-text technologies, enhanced by deep learning, have become increasingly accurate and accessible. Computer vision systems can describe visual content to students with visual impairments [140].

For students learning in a second language, ML-powered tools can provide real-time translation, simplified language, and vocabulary support [141]. Adaptive systems can adjust linguistic complexity based on individual proficiency levels [142]. For students with learning disabilities, personalized learning systems can provide additional practice, alternative explanations, and multi-modal representations of content [143,144].

However, researchers caution that ML systems must be carefully designed to avoid exacerbating existing inequities [145]. If training data reflects historical biases—for example, if certain demographic groups are underrepresented or if assessment instruments contain cultural biases—ML models may perpetuate or amplify these biases [146,147]. Ensuring that ML applications serve all learners equitably requires attention to diverse data sources, fairness-aware algorithms, and inclusive design processes [148]. Figure 2 displays a circular dendrogram of ML applications in Education.

4. Challenges of ML in Education

4.1. Ethical Challenges and Algorithmic Fairness

The deployment of ML in education raises profound ethical questions that must be addressed to ensure responsible innovation [15,149]. Perhaps the most fundamental concern is algorithmic fairness, that is, the risk that ML systems may produce systematically different outcomes for different demographic groups, potentially exacerbating existing educational inequities [150]. Research has documented instances where predictive models for student success exhibited bias based on race, socioeconomic status, or gender [151,152,153]. These biases often originate in training data that reflects historical patterns of inequality, but they can be amplified or obscured by algorithmic processing [154].

Beyond measurable algorithmic bias, generative AI also raises concerns about epistemic injustice and the coloniality of data [155,156]. Because LLMs are trained predominantly on English-language and Western-centric corpora, AI-generated educational content may reproduce Anglo-American cultural assumptions as if they were universal. Lesson plans, examples, classroom dialogues, or social scenarios generated for diverse educational contexts may therefore marginalize local, multilingual, minority, or non-Western perspectives. This creates risks of cultural and linguistic homogenization.

Fairness in educational ML is complicated by the multiple, sometimes conflicting, definitions of what constitutes a fair algorithm [157]. Should a model aim for demographic parity (equal outcomes across groups), equal opportunity (equal true positive rates), or counterfactual fairness (outcomes that would be the same if sensitive attributes were different)? The choice among these definitions has substantive implications for system behavior and requires careful consideration of educational values and goals [158,159]. Personalized learning creates a specific tension for algorithmic fairness because adaptive systems are designed to treat learners differently. Metrics such as demographic parity may be difficult to reconcile with systems that vary in content, pacing, feedback, or interventions according to learner needs. However, personalization can still be inequitable if some groups receive lower-quality recommendations, fewer challenging tasks, or less human support. Fairness in educational ML should therefore be assessed not only statistically, but also pedagogically, in terms of opportunity, quality of support, learner agency, and whether personalization reduces or reinforces existing inequalities.

Beyond fairness, ethical challenges include transparency and explainability [160]. Many of the most powerful ML models, particularly deep neural networks, operate as “black boxes” whose internal workings are opaque even to their developers [161]. When these models influence consequential decisions about students—such as identifying at-risk learners or determining course placements—the inability to explain how decisions are reached undermines accountability and trust [162]. Explainable AI (XAI) techniques that provide interpretable explanations for model predictions are an active area of research with particular importance for educational applications [163,164].

4.2. Data Privacy and Security

ML in education depends on data—often large volumes of detailed data about students, their behaviors, and their performance [165]. This data dependency creates significant privacy and security concerns [166]. Educational data may include personally identifiable information, academic records, behavioral traces from learning platforms, and increasingly, biometric data from facial analysis or other sensors [167]. The collection and analysis of such data raises questions about consent, data ownership, and the potential for misuse [168].

Student privacy is protected by legal frameworks in many jurisdictions, including the Family Educational Rights and Privacy Act (FERPA) in the United States and the General Data Protection Regulation (GDPR) in the European Union [169]. These frameworks impose obligations on educational institutions and technology providers regarding data collection, storage, and sharing [170]. However, the rapid pace of technological change often outstrips legal frameworks, creating ambiguities about how existing regulations apply to novel ML applications [171].

Privacy-preserving ML techniques offer potential approaches to reducing privacy risks, but their applicability in education should not be overstated [172]. Federated learning enables model training across distributed data sources without centralizing raw student data [173,174]; however, it does not eliminate privacy risks, as model updates may still leak information about individual participants, particularly in small or distinctive educational datasets. Such systems may therefore remain vulnerable to membership inference attacks or related privacy threats [175]. Differential privacy reduces these risks by adding calibrated noise to data or model outputs [176], but this protection involves important trade-offs in educational contexts. In small classes, specialized programs, or low-incidence learner populations, such as students with specific learning disabilities, added noise may obscure precisely the rare signals that educators need to identify and support vulnerable learners. Homomorphic encryption allows computation on encrypted data without decryption [177], yet it remains computationally demanding and difficult to implement in many educational infrastructures. Thus, privacy-preserving ML should be treated not as a technical fix, but as a set of imperfect tools whose educational value depends on context, dataset size, model purpose, and the consequences of missed or distorted signals [171].

A related privacy concern is the risk of surveillance creep from students to teachers. Learning analytics dashboards and platform-generated metrics, such as engagement scores, response times, course activity levels, or feedback frequency, may be repurposed from pedagogical support tools into instruments of managerial oversight and performance evaluation [178]. This reflects the broader datafication of teaching, whereby educational work becomes increasingly measurable, comparable, and governable through digital traces [179]. When such indicators are treated as objective measures of teaching quality, they can narrow teacher autonomy, encourage metric-driven compliance, and undervalue less visible forms of educational labor, including mentoring, emotional support, curriculum adaptation, and inclusive pedagogy. Human-centered ML in education should therefore protect educators as well as learners by ensuring transparency, teacher participation in analytics governance, and clear limits on the punitive use of dashboard-based metrics [180].

4.3. Pedagogical Appropriateness and Effectiveness

A fundamental challenge for ML in education is ensuring pedagogical appropriateness—that systems are designed in ways that align with sound educational principles and actually enhance learning [181]. Too often, ML applications are developed with primary attention to technical sophistication rather than pedagogical effectiveness [123]. Systems may optimize for easily measurable outcomes (such as quiz completion or time-on-task) while neglecting deeper learning goals [182]. They may provide feedback that is technically accurate but pedagogically unhelpful [183].

The evidence base for ML effectiveness in education remains uneven. While some applications, particularly intelligent tutoring systems in well-defined domains like mathematics, have demonstrated significant learning gains in rigorous studies [184], others have been subject to minimal evaluation [185]. Many published studies report positive results but suffer from methodological limitations, including small sample sizes, short intervention periods, and lack of appropriate control groups [186,187]. The rapid pace of technological change means that evidence often lags behind deployment [188].

Integrating ML systems into existing educational practices presents additional challenges. Systems designed without adequate attention to teacher workflows and classroom contexts may be underutilized or misused [189,190]. Teachers may lack the training and support needed to interpret ML-generated insights and translate them into effective action [191,192]. The most successful implementations treat ML systems as tools to augment, not replace, teacher expertise, and involve educators deeply in design and implementation [193].

4.4. The “Black Box” Problem and Interpretability

The opacity of many ML models poses particular challenges in educational contexts where decisions affect students’ educational trajectories and opportunities [194]. When a model identifies a student as at-risk or recommends a particular intervention, educators and students need to understand the basis for these determinations to respond appropriately [195]. Without interpretability, it is difficult to verify that decisions are fair, to identify potential errors, or to build trust in the system [196].

Interpretability needs vary by stakeholder group [197]: educators benefit from knowing which features most influenced a prediction to guide pedagogical responses [198]; students and families need understandable rationales to participate and advocate for support [199]; developers and researchers rely on interpretability to audit, debug, and refine models [200]; and regulators/policymakers use explanations to assess legal and ethical compliance [201]. Designing ML systems that can meet these diverse interpretability needs is an ongoing research challenge [202].

Post hoc explanation methods, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), can provide approximations of model reasoning after the fact [203,204]. However, these explanations have limitations and may not faithfully represent actual model decision processes [205]. In education, this limitation is especially consequential because explanations must be actionable for teachers and learners. Rather than merely showing which variables influenced a prediction, educational XAI should increasingly provide contrastive or counterfactual guidance, such as what a student would need to change to alter an at-risk prediction. Some researchers advocate for inherently interpretable models—such as decision trees, logistic regression, or attention-based neural networks—over black-box models, even if they achieve slightly lower predictive accuracy. The choice between accuracy and interpretability involves trade-offs that must be made with educational values in mind [206].

4.5. Infrastructure and Implementation Barriers

Implementing ML in education requires substantial infrastructure that remains lacking in many contexts. Reliable internet connectivity, adequate computing devices, and robust data systems are prerequisites for most ML applications [207]. Schools and institutions serving under-resourced communities often lack this infrastructure [208], raising concerns that ML may exacerbate rather than reduce educational inequities [209]. The “digital divide” encompasses not only access to technology but also the capacity to use it effectively [210].

Data quality presents another significant barrier [211]. ML models are only as good as the data they are trained on, and educational data is often messy, incomplete, or inconsistently recorded [212]. Different systems may use incompatible data formats; historical data may be unavailable or of questionable quality; privacy concerns may limit data access for model development [213]. Building the data infrastructure needed for effective ML requires significant investment in data governance, cleaning, and integration [214]. Yet these infrastructure challenges are not only technical. They are also tied to the political economy of educational technology. Increasing reliance on large LMS platforms and AI vendors may lock institutions into proprietary ecosystems in which student data, analytics pipelines, and ML models are inaccessible to independent auditing or public scrutiny.

Implementation challenges extend beyond technical infrastructure to include organizational capacity and culture [215]. Educational institutions may lack personnel with the data science and ML expertise needed to develop, deploy, and maintain systems [216]. Existing staff may require substantial professional development to use ML tools effectively [217], while institutional policies and procedures may need updating to address novel issues raised by ML deployment [218]. Sustained commitment from leadership is essential but not always present [219].

A related challenge concerns the financial and environmental sustainability of ML deployment in education. Advanced ML systems, especially LLM-based tools, require substantial computational resources, energy, infrastructure, and maintenance costs [220]. These costs raise questions about whether the return on investment, in terms of demonstrable student learning gains, justifies large-scale adoption [221]. Critical evaluation of educational ML should therefore consider not only accuracy and scalability, but also cost-effectiveness, carbon footprint, and long-term institutional sustainability.

4.6. Academic Integrity and Evolving Nature of Learning

The emergence of generative AI has fundamentally disrupted traditional conceptions of academic integrity [222]. Students can now use tools like ChatGPT to generate essays, solve problems, and complete assignments in ways that are difficult to detect [223]. This has created what some describe as an “AI arms race” between students using generative tools and institutions attempting to detect misuse [224]. Traditional plagiarism detection systems, designed to identify copied text, are largely ineffective against AI-generated content [225,226].

Institutions are grappling with how to respond. Some have attempted to ban or restrict AI tool use, though enforcement is challenging [227]. Others have chosen to embrace generative AI as a reality to be worked with rather than against, redesigning assessments to assume AI access and focus on higher-order skills [228,229]. These approaches include process-oriented assessment (evaluating drafts and revisions), oral examinations, collaborative projects, and authentic tasks that require integration of AI tools with original thinking [230]. However, these proposed responses are themselves contested. Oral examinations, process-oriented assessment, and continuous supervision may be difficult to scale in large or under-resourced educational systems and may increase workload burdens on faculty. Such approaches may also create inequities for students with anxiety, language-related challenges, disabilities, or limited access to sustained mentoring support. More broadly, critics argue that heavy reliance on generative AI for drafting, summarizing, and idea generation risks encouraging cognitive offloading and the deprofessionalization of writing, potentially weakening foundational literacy practices, sustained attention, and independent argument development over time [231].

More fundamentally, the availability of powerful AI tools raises questions about the nature of learning and the goals of education [16]. If AI can perform many of the tasks traditionally used to assess learning, what does it mean to be educated? How should curricula and pedagogy evolve to prepare students for a world where AI capabilities are ubiquitous? These questions lack simple answers and require ongoing dialogue among educators, students, policymakers, and the broader public [232,233]. The evidence synthesized in this review suggests that education may increasingly need to emphasize capacities that extend beyond the production of correct answers alone, including critical judgment, ethical reasoning, epistemic responsibility, creativity, collaboration, and the ability to evaluate, contextualize, and appropriately use AI-generated knowledge. Figure 3 shows the main challenges of ML in Education.

Figure 4 illustrates a suggested ML-in-Education pipeline with governance checkpoints. The framework presented in the figure also aligns with emerging FAccT (Fairness, Accountability, and Transparency) approaches that conceptualize ML governance as an ongoing socio-technical process rather than a single stage of technical validation. In educational contexts, this perspective emphasizes continuous documentation, stakeholder participation, auditing, and post-deployment review throughout the ML lifecycle. Tools such as model cards, dataset documentation, impact assessments, and participatory governance workflows can help make educational ML systems more transparent by clarifying model purpose, training data characteristics, performance limitations, intended users, fairness considerations, and potential harms [234,235].

5. Conclusions

ML has emerged as a transformative force in education, offering powerful tools for personalizing instruction, predicting student outcomes, automating assessment, and supporting institutional decision-making. The historical trajectory from early computer-assisted instruction to contemporary generative AI systems reflects decades of technological advancement and pedagogical innovation. Today, ML applications span the entire educational lifecycle, from admissions and course planning through instruction and assessment to outcome prediction and alumni engagement.

The potential benefits of ML in education are substantial. Personalized learning at scale can help address the long-standing challenge of providing individualized attention to every student. Routine administrative and assessment tasks can also be automated, allowing educators to devote more time to high-value work such as mentorship, discussion facilitation, and targeted student support. In addition, analysis of large-scale educational data can generate actionable insights that strengthen evidence-based decisions about curricula, pedagogy, and institutional policy. For learners, ML-enabled tools can deliver timely feedback, adapt the level of challenge, and offer support aligned with individual needs and circumstances.

Yet, realizing this potential requires navigating significant challenges. Ethical concerns, including algorithmic fairness, data privacy, and transparency, demand careful attention. The pedagogical appropriateness of ML applications must be rigorously evaluated, with attention paid to whether they genuinely enhance learning rather than merely optimizing easily measurable proxies. Infrastructure and capacity barriers must be addressed to ensure that ML benefits all learners, not only those in well-resourced contexts. The disruption of generative AI to traditional assessment practices requires fundamental rethinking of how we evaluate and certify learning.

Beyond technical and ethical challenges, a fundamental tension exists between how ML is theorized in computer science and how education is practiced as a profession. For classroom educators, ML is often presented as an opaque system that produces predictions or recommendations without transparent reasoning. This contrasts sharply with pedagogical traditions that value professional judgment, contextual awareness, and relational trust. In practical terms, educators do not need to understand gradient descent or transformer architectures. However, they do need to grasp several core concepts to evaluate ML tools critically: (a) that ML models learn patterns from historical data, which may embed past inequities; (b) that prediction is not causation—a student flagged as at-risk may simply reflect what the model was trained on, not a deterministic outcome; (c) that personalization algorithms optimize for measurable behaviors (clicks, time, correct answers), which may not align with deeper learning goals like curiosity, persistence, or critical thinking; and (d) that generative AI produces plausible but not necessarily true or pedagogically sound output.

Several directions for future development merit attention, but these should be framed not only as technical improvements but also as part of a broader research agenda on the possibilities and limits of ML in education. First, advancing explainable AI techniques tailored to educational contexts can enhance transparency and trust, enabling educators and learners to understand and appropriately rely on ML systems. Second, developing privacy-preserving ML approaches, including federated learning and differential privacy, can enable valuable analysis while protecting student data. Third, creating robust evidence bases through rigorous evaluation of ML applications in diverse contexts can guide effective implementation. Fourth, fostering interdisciplinary collaboration among ML researchers, educators, learning scientists, and ethicists can ensure that technical development is guided by pedagogical wisdom and ethical reflection.

A more specific research agenda should examine which educational problems resist ML solutions, especially if model scaling plateaus. These include normative questions about the meaning, value, and ethical purpose of learning; relational aspects such as trust, care, motivation, identity formation, and teacher judgment; and forms of authentic understanding expressed through dialogue, creativity, uncertainty, embodied practice, or long-term development. ML also cannot fully address educational equity, which is shaped by historical, institutional, linguistic, economic, and cultural conditions beyond the model. Moreover, generative AI complicates assessment by shifting attention from correct answers to students’ ability to justify, critique, transfer, and take responsibility for AI-assisted knowledge. Future research should therefore identify the boundaries of ML in education and develop pedagogical approaches grounded in human judgment, ethical deliberation, and institutional change.

Ultimately, the integration of ML into education is not primarily a technical challenge but a human one. The question is not whether ML will be used in education—it already is, and its role will surely grow—but how. Will ML systems be designed to augment and empower educators, or to replace them? Will they serve all learners equitably, or exacerbate existing disparities? Will they promote deep learning and authentic understanding, or incentivize strategic compliance and gaming? The answers to these questions will be determined not by technology alone but by the values, choices, and actions of educators, researchers, policymakers, and technologists.