Deep Learning and Reinforcement Learning for Assessing and Enhancing Academic Performance in University Students: A Scoping Review

Abstract

University students often face challenges in managing academic demands and difficulties like time management, task prioritization, and effective study strategies. This scoping review investigates the application of Deep Learning (DL) and Reinforcement Learning (RL) in evaluating and enhancing academic performance, focusing on their practical applications, limitations, and future potential. Using PRISMA guidelines, 27 empirical studies published between 2014 and 2024 were analyzed. These studies utilized advanced DL and RL technologies, including neural networks and adaptive algorithms, to support personalized learning and performance prediction across diverse university contexts. Key findings highlight DL’s ability to accurately predict academic outcomes and identify at-risk students, with models achieving high accuracy in areas like dropout prediction and language proficiency assessments. RL proved effective in optimizing learning pathways and tailoring interventions, dynamically adapting to individual student needs. The review emphasizes significant improvements in grades, engagement, and learning efficiency enabled by AI-driven systems. However, challenges persist, including scalability, resource demands, and the need for transparent and interpretable models. Future research could focus on diverse datasets, multimodal inputs, and long-term evaluations to enhance the applicability of these technologies. By integrating DL and RL, higher education can foster personalized, adaptive learning environments, improving academic outcomes and inclusivity.

1. Introduction

University students face intricate and multifaceted challenges throughout their academic path, with learning difficulties constituting one of the most significant obstacles to success. A primary issue lies in the increasing cognitive demands of higher education, which necessitate advanced critical thinking, analytical reasoning, and problem-solving skills. Unlike the structured learning environment of secondary education, university settings demand a high degree of independence and self-regulated learning (SRL), requiring students to autonomously manage their academic progression. However, a significant portion of students exhibit limited proficiency in SRL, which hinders their ability to meet academic expectations [1]. The ineffective application of SRL strategies impacts essential features such as time management, task prioritization, and adaptability in learning techniques, all of which are crucial for mastering the complexities of higher education [2].

This gap leaves many students struggling to effectively manage their studies, particularly in disciplines that require independent learning and self-assessment. Commonly reported difficulties include organizing information, understanding complex academic material, and translating theoretical concepts into practical applications [3]. These issues are exacerbated by a lack of metacognitive awareness, which limits students’ capacity to identify effective study strategies and make necessary adjustments to their learning approaches [4].

A critical consequence of insufficient SRL skills is the inability to efficiently manage time and tasks. Time management is a cornerstone of academic success, yet many students exhibit procrastinatory behaviors that exacerbate stress and diminish productivity. Procrastination is often associated with low self-discipline and volition, both of which are integral to SRL. Students who fail to establish structured schedules or allocate sufficient time for study tend to adopt surface learning strategies, relying on rote memorization rather than engaging in deeper cognitive processing. Such approaches not only compromise academic performance but also hinder the acquisition of transferable skills essential for professional growth [5].

Cognitive difficulties are particularly pronounced in fields that require the integration of theoretical and practical knowledge. For instance, students of the faculties of Human Sciences often struggle to synthesize diverse perspectives and construct coherent arguments. These challenges are exacerbated when students lack the skills to decompose complex tasks into manageable steps, recognize gaps in their understanding, and seek appropriate resources for improvement [6].

Motivational factors play a pivotal role in academic performance. Students with low intrinsic motivation or poor self-efficacy are less likely to engage deeply with their studies, often perceiving academic tasks as insurmountable rather than as opportunities for growth. This mindset, coupled with external pressures such as competitive academic environments or unrealistic familial expectations, further undermines academic success. Additionally, the absence of well-defined goals frequently results in a lack of direction and persistence [7].

Given the multifaceted challenges faced, this paper surveys the application of emerging technological solutions, particularly Deep Learning (DL) and Reinforcement Learning (RL), in addressing these obstacles and supporting the academic performance of university students. DL and RL were chosen for their unique capabilities in handling complex, high-dimensional data and their proven success in creating adaptive, personalized educational experiences. Unlike other AI algorithms, DL excels in uncovering intricate patterns in student interactions and performance metrics, enabling precise predictions and tailored interventions. RL complements this by offering dynamic feedback mechanisms and optimizing learning strategies through iterative improvement [8].

The motivation for this survey lies in the need to consolidate and analyze the growing body of research on DL and RL applications to assess and promote learning in university students. By focusing on these two approaches, the paper aims to highlight their transformative potential and provide educators and policymakers with actionable insights into their implementation. The survey’s contributions include identifying trends, evaluating the effectiveness of DL and RL systems, and addressing current limitations. This focused approach is justified by the scalability, adaptability, and real-time responsiveness that DL and RL offer, making them particularly well-suited to addressing the challenges of SRL and enhancing higher education outcomes [9].

2. A Background Literature of RL and DL Based Solution

DL, a transformative branch of Machine Learning (ML), operates through artificial neural networks inspired by the human brain to process vast amounts of data and learn hierarchical representations. This approach allows DL to excel in tasks such as image recognition, Natural Language Processing (NLP), and predictive modeling, where it identifies and refines meaningful patterns from raw inputs. The versatility of DL applications spans across diverse domains, including healthcare, agriculture, and education. By leveraging its capability to identify trends and extrapolate insights, DL has proven to be an invaluable asset for addressing complex challenges in these fields [10,11].

A pivotal application of DL resides in its potential to personalize learning experiences, particularly within higher education. DL algorithms analyze data streams from varied sources—such as student interactions, performance metrics, and individual preferences—to construct adaptive models tailored to specific learning needs. These models dynamically recommend resources, assessments, or activities, aligning with the learner’s pace, strengths, and areas requiring improvement. As a result, these AI-driven systems optimize educational outcomes by customizing the learning trajectory [12].

In higher education, adaptive learning systems, increasingly powered by DL, utilize predictive analytics to guide students toward success. By analyzing historical performance data, these systems anticipate areas of difficulty, proactively delivering supplementary materials or alternative instructional strategies. This preemptive approach mitigates potential frustration and disengagement, ensuring a more seamless and effective learning process [13].

Furthermore, DL-driven platforms often incorporate NLP tools to enhance interaction and engagement. Chatbots, underpinned by sophisticated neural networks, provide real-time assistance by addressing inquiries, guiding students through complex content, and elucidating concepts. Such advancements democratize education, offering ubiquitous support and facilitating accessibility irrespective of geographical or temporal constraints [14].

In university contexts, where theoretical knowledge is commonly integrated with practical applications, DL technologies offer interactive and engaging learning opportunities. Virtual laboratories and simulation-based environments, powered by AI, replicate real-world scenarios, enabling students to conduct experiments in risk-free settings. For instance, medical students can simulate surgical procedures with a complexity akin to real-life operations, fostering confidence and refining decision-making capabilities [15].

The integration of DL in academic settings also addresses scalability challenges inherent in traditional education systems. Institutions accommodating large student populations often struggle to provide personalized attention. DL tools bridge this gap by automating routine processes such as grading and scheduling, thus allowing educators to concentrate on mentorship and critical teaching interactions. For example, intelligent grading systems assess extensive volumes of submissions with precision and consistency, alleviating the administrative burden on instructors while maintaining academic standards [16].

RL, another paradigm of ML, optimizes agent behaviors through iterative interactions within defined environments. Guided by feedback mechanisms in the form of rewards or penalties, RL agents develop adaptive strategies to achieve specific objectives: it enables adaptive learning and dynamically adjusts task difficulty based on user performance [17,18]. This framework, based on Markov Decision Processes (MDPs), systematically model states, actions, and transitions, enabling dynamic decision-making in evolving contexts. Unlike other ML approaches, RL emphasizes sequential learning, rendering it particularly suited to domains requiring continuous adaptation and feedback-based optimization, such as education [19,20].

The application of RL in education has facilitated notable advancements, especially in the realm of Intelligent Tutoring Systems (ITS). These systems employ RL algorithms to tailor instructional content to individual learner profiles, optimizing engagement and efficacy [21]. RL is a pivotal key in game-based learning environments, where adaptive engines dynamically adjust task difficulty and feedback, thus promoting active and personalized learning experiences [22]. This alignment with constructivist pedagogical models underscores RL’s relevance in contemporary educational frameworks [23].

In online learning platforms, RL advances methodologies such as adaptive experimentation and instructional sequencing. For instance, multi-armed bandit algorithms optimize resource allocation by adapting learning materials to individual needs, while task sequencing maximizes learning efficiency [24].

Deep Reinforcement Learning (DRL), an advanced integration of RL with neural networks, has broadened educational applications by addressing the complexities of high-dimensional data. DRL facilitates dynamic difficulty adjustment mechanisms within e-learning platforms, tailoring challenges based on real-time learner assessments. Furthermore, incorporating Explainable AI (XAI) within RL ensures transparency and equity, addressing ethical concerns in AI-driven education technologies [25].

Simulation-based learning environments, driven by RL, effectively bridge theoretical constructs and practical applications. In fields such as engineering and medicine, RL-powered simulations offer risk-free procedural training, enhancing both competence and confidence. Similarly, RL algorithms refine Massive Open Online Courses (MOOCs) by personalizing course recommendations and adapting learning pathways, thereby promoting inclusivity and accommodating learner variability [26].

Current studies on DL and RL in education face several limitations, including narrow datasets, restricted generalizability, and an over-reliance on controlled environments. Many approaches lack scalability and are often constrained by computational resource demands, limiting their applicability in diverse academic settings. Additionally, the absence of longitudinal research leaves uncertainties about sustained impacts on learning outcomes. This review aims to address these challenges by providing a synthesis of empirical evidence in university contexts. By analyzing studies on DL and RL to assess and improve academic performance of university students, the review might identify trends, gaps, and best practices. This approach can provide concrete insights for educators and policymakers, promoting the development of optimized AI-based solutions in higher education [27].

In line with the above, a scoping review was conducted to map the research carried out in this area, as well as to identify potential applications and current limitations in using RL- and DP-based solutions to support academic performance in university students. The research question of the scoping review aims to explore the main RL- and DP-based solutions developed to evaluate and improve academic performance. Furthermore, it aims to investigate the possible practical implications, the limitations of these solutions and the perspectives for future studies.

3. Related Studies

Several recent studies demonstrate the potential of AI in adaptive learning, with methodologies originally developed for object detection, saliency prediction, and perceptual video compression playing a crucial role in refining DL and RL approaches for academic performance assessment. For instance, the Asymmetric Light-Aware Progressive Decoding Network for RGB-Thermal Salient Object Detection [28] has informed the development of personalized educational pathways by identifying key learning features. Similarly, the Efficient Perceptual Video Compression Scheme Based on Deep Learning-Assisted Video Saliency and Just Noticeable Distortion [29] has contributed to optimizing real-time assessments through feature extraction and attention mechanisms. Additionally, the Adaptive Differentiation Siamese Fusion Network for Remote Sensing Change Detection [30] and the Full-Scale Feature Aggregation and Grouping Feature Reconstruction-Based UAV Image Target Detection [31] demonstrate how cross-layer feature aggregation can enhance predictive analytics in academic settings.

The CFANet model [32] and the VSS-Net: Visual Semantic Self-Mining Network for Video Summarization [33] further illustrate how AI-driven methodologies can support dynamic and personalized learning experiences. These approaches align with the core principles of RL, where AI systems dynamically adjust learning strategies based on ongoing feedback. Moreover, the Multi-Scale Spatiotemporal Feature Fusion Network for Video Saliency Prediction [34] highlights how spatiotemporal analysis can contribute to Intelligent Tutoring Systems, improving student engagement and personalized content delivery.

Despite these advancements, the current literature lacks a comprehensive synthesis of how these AI methodologies can be tailored specifically for academic performance enhancement in university settings. This review aims to bridge that gap by mapping existing research, identifying key trends, and addressing current limitations. By integrating insights from diverse fields, this study offers an innovative perspective on AI-driven academic interventions, ensuring that future research maximizes the benefits of DL and RL while addressing issues of scalability, interpretability, and inclusivity.

4. Method

The review followed the PRISMA guidelines [35]. A structured methodology was applied to ensure rigor in identifying, screening, and selecting relevant studies. The initial search was conducted on Scopus using the following Boolean expression:

(“University Students” AND “Artificial Intelligence”) AND (“Deep Learning” OR “Reinforcement Learning”)

This query ensured precise retrieval by combining terms relevant to the focus on DL and RL solutions for academic performance in university students. Boolean operators were explicitly ordered using parentheses to clarify precedence rules.

4.1. Inclusion Criteria

Studies were included if they met all of the following conditions:

• Keywords: “University Students”, “Artificial Intelligence”, “Reinforcement Learning”, or “Deep Learning”.
• Publication period: 2014–2024.
• Study type: Empirical studies.
• Language: English.
• Relevance: Direct alignment with the research question focusing on DL and RL applications for assessing and enhancing academic performance.
• Participants: University students.

4.2. Exclusion Criteria

Studies were excluded if they matched any of the following conditions:

• Document type:

  -

  Reviews and conference papers.

  -

  Preprints or articles still in press.

• Data integrity:

  -

  Retracted papers.

• Relevance:

  -

  Research addressing DL and RL for purposes unrelated to academic performance evaluation.

  -

  Studies involving participants other than university students.

4.3. Search and Screening Process

The initial search retrieved 311 results. After filtering for studies conducted between 2014 and 2024, 300 documents were identified. Further screening excluded non-empirical studies and non-English language papers, resulting in 128 documents. Among these, 7 were removed due to retraction, and 5 were excluded because they were still in press. This step narrowed the selection to 116 documents. After excluding studies not relevant to DL and RL for academic performance in university students, the final dataset included 27 studies (Figure 1).

The screening and selection process was performed independently by two judges with expertise in psychology and educational technology to ensure reliability. These judges underwent formal training, which included instruction on the study inclusion/exclusion criteria, data extraction methods, and consensus-building strategies. A 13% disagreement rate was resolved through consultation with a third judge, who re-evaluated the selected studies, systematically reviewing the reasoning behind the discrepancies. This process involved a structured discussion, where judges presented their justifications, and the third judge acted as an arbitrator to ensure consistency and adherence to the eligibility criteria. Additionally, studies flagged as ambiguous were subjected to further scrutiny using a predefined decision framework to ensure methodological rigor and minimize selection bias. This iterative process strengthened the accuracy and alignment of the final dataset with the review’s objectives.

5. Results

The findings of this review are divided into two main areas: (1) the application of DL and RL for assessing academic performance and predicting student outcomes and (2) their use in enhancing learning processes and optimizing educational outcomes. Together, these studies provide a comprehensive overview of the transformative role that AI technologies can play in higher education, highlighting both their strengths and areas for further development.

5.1. DL and RL for Assessing Academic Performance

This category focuses on the use of DL and RL to evaluate academic performance, predict outcomes, and identify students at risk of underperforming or dropping out. Eight studies were reviewed in this category, collectively analyzing data from 49,210 students across diverse university contexts. These studies emphasize the potential of predictive models to support decision-making and early interventions, which are crucial for academic success.

Hybrid DL models could be used to monitor educational progress and classify students based on their risk of discontinuation. Kadhim and Hassan [36] developed a predictive model using Recurrent Neural Networks (RNNs) optimized with the Adaptive Momentum (ADAM) algorithm to analyze data from 1000 students in virtual classrooms at the University of Technology, Baghdad. The model achieved a remarkable accuracy of 99.1% in predicting students’ academic continuity, significantly outperforming traditional methods such as Multi-Layer Perceptrons (97.99%), Decision Trees (64.78%), and Random Forests (77.96%).

DL could also be used to develop classification models for complex educational assessments. Liu, Wang, and Yuan [37] introduced a feedforward Spiking Neural Network (SNN) designed to process data from 55 students over six semesters, spanning 62 courses. The SNN excelled in categorizing academic performance into three levels: high, medium, and low. Its accuracy, particularly in predicting low-grade categories, surpassed traditional methods by integrating synaptic weight adjustments and time delay encoding.

Similarly, Ujkani et al. [38] utilized Explainable AI (XAI) techniques, incorporating SHapley Additive exPlanations (SHAP), to analyze a dataset of 32,593 students from the Open University Learning Analytics Dataset (OULAD). Their research identified pivotal factors, such as engagement levels and registration timelines, in predicting student success. With a prediction accuracy of 94%, their custom neural networks demonstrated the value of combining DL models with explainability tools to foster trust and transparency among educators.

In the domain of dropout prediction, Tsai et al. [39] and Sayed [40] explored the application of DL-based architectures. Tsai et al. employed a multilayer perceptron model trained on data from 3552 university students, achieving an accuracy of 77% in identifying at-risk individuals based on variables such as academic performance, loan applications, and attendance records. Sayed, on the other hand, utilized a Convolutional Neural Network (CNN) model to analyze behavioral data from 12,000 students at the Arab Open University. With a predictive accuracy of 98.6%, this model integrated dropout mechanisms to minimize overfitting and identify critical retention factors, emphasizing the potential of CNNs in early intervention strategies.

In the context of language learning assessments, Jing et al. [41] applied a hybrid model combining fuzzy logic with neural networks to evaluate oral English proficiency among 10 participants. Their approach achieved an accuracy of 86.16% in assessing pronunciation quality, fluency, and accuracy, offering consistent and data-driven feedback. Similarly, Li and Mohamad [42] developed the Latent Dirichlet Integrated DL (LDiDL) framework to assess English oral proficiency. By combining Latent Dirichlet Allocation (LDA) with DL, this framework analyzed 500 spoken English samples, achieving a 99% accuracy in proficiency categorization and providing tailored feedback on fluency, grammar, and vocabulary.

These studies collectively demonstrate that DL and RL technologies are powerful tools for assessing academic performance, offering accurate predictions, nuanced insights, and explainable results. Their ability to process vast datasets and adapt to specific educational contexts ensures their relevance in addressing diverse student needs.

5.2. DL and RL for Enhancing Academic Performance

The second category of results examines how DL and RL technologies are used to optimize teaching strategies, personalize learning experiences, and improve educational outcomes. A total of 19 studies were analyzed, involving data from 4,545 university students across various disciplines and educational settings. These studies underscore the capacity of AI-driven solutions to foster engagement, enhance learning efficiency, and promote academic success.

AI-based solution could adapt dynamically to students’ needs. Naseer et al. [43] demonstrated the effectiveness of integrating DL techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), into adaptive learning platforms. Their study involved 300 students divided into experimental and control groups. The experimental group, which used the AI-driven platform, exhibited a 25% improvement in grades and engagement compared to the control group following traditional instruction.

Expanding on these findings, Francisco and Silva [44] focused on developing software maintenance education through an Intelligent Tutoring System (ITS) powered by Q-learning, which is an RL algorithm. The system adaptively recommended activities based on students’ prior performance, achieving optimal recommendations after fewer iterations than traditional methods. Their findings demonstrated reduced learning time and increased accuracy in task selection, highlighting the potential of RL in creating targeted and efficient educational interventions.

Similarly, Xu and Yu [45] explored the application of DL in online learning platforms by integrating blockchain-supported decision tree algorithms and fuzzy CNNs. Their platform, which served 1212 students over six months, optimized resource allocation and personalized learning paths. Results showed a 30% increase in resource utilization efficiency and improved interaction levels, emphasizing the role of AI in enhancing the scalability and efficiency of online education.

Language learning received significant attention, with studies highlighting the transformative impact of AI-driven tools. Wang, Zou, and Xue [46] implemented the EAP TALK system, which improved fluency and pronunciation among non-English major university students. Similarly, Shao et al. [47] introduced an AI-based Arabic Language and Speech Tutor (AI-ALST), which utilized Mel Frequency Cepstrum Coefficients (MFCC) and BiLSTM models to detect pronunciation errors. The tutor provided real-time feedback, enabling students to improve their speaking proficiency effectively.

Smart teaching models also emerged as an area of focus. Yin, Peng, and Liu [48] validated the E-GPPE-C model, which integrated DL technologies to construct personalized learner profiles and recommend tailored learning paths. Their study, involving 103 students, demonstrated significant improvements in learning strategies (β = 0.286), classroom engagement (β = 0.211), and participation (β = 0.20). Liu [49] employed a Q-learning-based system to optimize teaching in Civics and Political Science, achieving notable increases in assessment scores and engagement levels among first-year university students.

Several studies also explored the integration of DL and RL into blended teaching models. For instance, Ou [50] combined Bayesian Knowledge Tracing with RL to enhance English learning outcomes through the BOPPPS framework. This blended approach significantly improved listening, reading, and speaking skills among 105 university students, demonstrating its effectiveness in fostering cognitive, skill-based, and affective learning outcomes. Qiao and Fu [51] applied the Ant Colony Optimization (ACO) algorithm in university mathematics courses, optimizing resource allocation and sequencing tasks. Their AI-based model achieved accuracy rates of up to 98.87% for specific optimization tasks, showcasing its potential for scalable and efficient educational applications.

Beyond specific disciplines, AI technologies also enhanced broader aspects of teaching and learning. He and Wang [52] implemented a blended teaching model in public administration courses, combining AI with big data analytics to personalize content and adapt teaching strategies. Their approach improved teaching quality, student engagement, and learning efficiency. Similarly, Li, Wang, and Wang [53] developed a personalized teaching system using face recognition and Natural Language Processing (NLP) technologies, significantly increasing participation and attention levels among university students.

These studies illustrate the wide-ranging applications of DL and RL technologies for enhancing academic performance. From optimizing learning pathways and personalizing instruction to fostering engagement and developing critical skills, AI-driven systems provide educators with powerful tools to address diverse educational challenges.

Table 1. Synoptic table of reviewed studies. DL and RL for assessing academic performance.

Authors	Objective	Method	Participants	Results
Kadhim and Hassan	Enhance e-learning systems by predicting students’ learning continuity.	Utilized a Recurrent Neural Network (RNN) optimized with the Adaptive Momentum (ADAM) algorithm to process grades and behavioral data, achieving superior accuracy in predictions.	One thousand rows collected from students enrolled in virtual classrooms	RNN-ADAM achieved 99.1% predictive accuracy.
Liu, Wang, and Yuan	Predict academic performance.	Designed a feedforward Spiking Neural Network (SNN) that encoded input data into spiking sequences, adapted synaptic weights, and decoded outputs to classify grades across three categories (high, medium, low).	Fifty-five students over six semesters	Achieved highest accuracy; improved low-grade prediction.
Jing, Zhao, Ren, Chen, and Gaowa	Enhance oral English assessment.	Combined fuzzy logic and neural networks with advanced speech recognition algorithms to analyze pronunciation quality, fluency, and emotional expression in oral English assessments.	Ten participants	Model achieved 86.16% accuracy, standardizing oral evaluation.
Yuhua	Assess English language teaching.	Designed a Back Propagation Neural Network (BPNN) to analyze nonlinear relationships between instructional factors and outcomes, using multi-layer neural networks with adaptive learning rates.	Classified sample data. Number of participants not specified	The BPNN-based model improved assessment accuracy by 22.64% compared to traditional instructional systems.
Li and Mohamad	Assess English oral proficiency.	Combined Latent Dirichlet Allocation (LDA) for topic modeling with a Deep Learning (DL) framework to analyze linguistic and acoustic features for proficiency categorization.	Five hundred spoken English samples	Achieved 99% accuracy in proficiency assessment.
Tsai, Chen, Shiao, Ciou and Wu	Predict university student dropouts using a multilayer perceptron model.	A logistic regression model (statistical learning) and a Deep Learning model using a multilayer perceptron algorithm trained with the TensorFlow framework to predict dropout probabilities.	A total of 3552 university students in Taiwan (2093 females, 1459 males) with data from their first academic year.	The DL model achieved a 77% accuracy rate (and higher specificity), while the logistic regression achieved 68% accuracy.
Sayed	Develop dropout prediction model.	Convolutional Neural Network (CNN) with a feature-weighting method, Nadam optimizer, and pooling layers for dropout prediction using AOU-LMS and AOU-SIS datasets.	A total of 12,000 students from the Arab Open University, diverse in age (below 20 to 29+), with data on demographics, GPA, and blended learning engagements.	Achieved 98.6% prediction accuracy for dropout rates.
Ujkani, Minkovska, and Hinov	Predict course success and early identification of at-risk students.	ML models (Random Forest, Gradient Boosting, k-NN, Neural Networks) and SHAP for explanation.	A total of 32,593 students (Open University Learning Analytics Dataset), diverse demographics and academic backgrounds.	Achieved 94% prediction accuracy; engagement identified as a critical factor.

and

Table 2. Synoptic table of reviewed studies. DL and RL for enhancing academic performance.

Authors	Objective	Method	Participants	Results
Xu and Yu	Enhance online learning platform.	Integrated a DL model with blockchain-supported decision tree algorithms and fuzzy Convolutional Neural Networks (CNNs) to optimize resource scheduling and adapt learning paths in real-time.	A total of 1212 students over six months.	Improved scores, interaction levels, and resource efficiency by 30%.
Wang, Zou, and Xue	Enhance oral English proficiency.	Created the EAP TALK system, integrating AI, DL, and big data to evaluate pronunciation, fluency, and comprehension using real-time scoring with speech recognition algorithms.	A total of 110 university students in China (27 males, 83 females) aged 17–29, mostly freshmen and sophomores, with middle-level English entrance scores.	Improved fluency and pronunciation; 65.5% satisfaction rate.
Naseer, Khan, Tahir, Addas and Aejaz	Evaluate AI-driven adaptive learning platforms for personalized pathways.	Leveraged Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze student data and dynamically adapt learning materials and assessments based on performance patterns.	Three hundred students (control + experimental).	A 25% improvement in grades and engagement; significant p-value.
Jia and Zhang	Integrate AI into psychology and pedagogy teaching modes.	Weighted Evaluation Algorithm for cognitive ability, combining AI with traditional teaching methods.	A total of 290 teachers and students (age not specified).	In total, 90% reported improvement in teaching quality and learning outcomes.
Francisco and Silva	Enhance Software Maintenance teaching.	Employed a Q-Learning algorithm, a type of Reinforcement Learning (RL), to model states and actions in educational tasks, refining content recommendations dynamically based on prior outcomes.	Ten virtual students.	Optimal recommendations with fewer iterations; improved activity selection.
Yin, Peng, and Liu	Enhance personalized learning.	Developed the E-GPPE-C model using CNNs and subject knowledge mapping to construct learner profiles and recommend personalized learning paths based on engagement and performance data.	A total of 103 college students in smart teaching classes, tracked over one semester.	The E-GPPE-C model showed significant improvement in learning engagement (β = 0.286), participation (β = 0.203), and creativity (β = 0.424). AI-driven tools effectively promoted personalized learning and collaboration, enhancing the overall smart learning environment.
Liu, Chen, and Yao	Promote learning.	Applied the YOLOv3 Convolutional Neural Network (CNN) to analyze classroom behavior data, providing real-time feedback to inform teaching strategies and address learning gaps.	Forty first-year university students.	Significant improvements in learning and emotional engagement.
Liu	Optimize Civics teaching using adaptive systems.	Utilized RL (Q-Learning) to tailor teaching interventions and resource recommendations, dynamically adjusting strategies based on student feedback and performance.	Fifty first-year university students majoring in Ideology and Politics at University D.	Post-intervention scores improved by an average of 6.25 points; personalized recommendation accuracy exceeded 92.5%, and satisfaction ratings for system functionality averaged 4.63/5. The system enhanced learning engagement, outcomes, and adaptability across student groups.
Li, Wang, and Wang	Implement AI-driven personalized teaching system for local universities.	Face recognition, NLP, and virtualization technologies.	Eighty-eight students from two classes, ages not specified, divided into experimental and control groups.	Increased student participation (45.78%) and attention levels (0.6–0.9). Enhanced interaction and teaching outcomes in the AI-supported class.
Ou	Enhance English learning outcomes.	Integrated Bayesian Knowledge Tracing and RL to track learning progress and adapt teaching strategies dynamically, optimizing comprehension and addressing gaps.	A total of 105 students from School Z (52 in experimental group, 53 in control group).	Significant improvements in English proficiency (listening, reading, writing, translation, speaking) and interest in English for the experimental group. Enhanced effectiveness of AI-supported blended teaching.
Qiao and Fu	Enhance university mathematics learning.	Applied Ant Colony Optimization (ACO) and Apriori algorithms to allocate resources and sequence tasks dynamically based on real-time learner data.	The study does not explicitly mention the number of participants but analyzes data from online self-paced mathematics microcourses conducted between 2011 and 2020, encompassing multiple student interactions over a decade.	Proposed algorithms achieved 98.87% accuracy, enhancing microcourse customization and efficiency in meeting learning outcomes.
Liu and Ren	Improve English learning efficiency using AI and genetic algorithm.	Used a genetic algorithm-based framework analyzing English course performance using UCI repository data	A total of 1046 students; higher education.	Significant improvement in learning and engagement.
Li and Wu	Develop embedded voice teaching system.	Hybrid HMM-LSTM model for voice recognition integrated with a cloud computing platform	One hundred university students.	High voice recognition rate (96.25%) with robust noise immunity; improved learning engagement and satisfaction; efficient feedback on exercises; enriched course content fostering independent study skills.
He and Wang	Enhance public administration teaching using AI and blended learning.	Blended learning model integrating AI technologies for personalized learning pathways, knowledge tracking, and assessment.	Students enrolled in Public Administration courses at a Chinese university (number unspecified).	The AI-enhanced blended model improved learning outcomes, student engagement, and teaching effectiveness. Personalized pathways and automated assessments were pivotal in addressing diverse learner needs.
Wang, Y., Wu, Chen, Wang, Z., Li, and Wang, L.	Evaluate AI-powered tools for vocabulary acquisition in EFL.	Apriori algorithm for analyzing survey data on AI-driven language platforms	A total of 110 s-year university students from diverse majors (ages 19–21).	Enhanced vocabulary learning by identifying effective strategies; personalized learning experiences led to better language acquisition.
Wang and Zheng	Enhance English communication.	Deep Neural Network (DNN) incorporating cognitive psychology principles for English grammar detection and communication training.	Sixty university students from North China University of Water Resources and Electric Power, divided into experimental and control groups.	Experimental group improved significantly: reading comprehension (+13.33%), question answering (+15.19%), situational dialog (+17.39%), topic description (+28.3%). Overall, Class A’s average score rose by 17.75% compared to a minimal 3.25% improvement in the control group.
Koć-Januchta et al.	Investigate AI-enriched biology textbooks’ impact on learning.	AI-enriched textbook leveraging NLP and a knowledge base.	Forty-two university students (69% female, ages 17–44, M = 26.28).	Germane cognitive load significantly higher than intrinsic and extraneous loads, indicating meaningful engagement and learning.
Chen, Yu, and Wu	Improve English vocabulary acquisition using DL-based system.	The study used DL neural network models for student behavior detection, facial orientation recognition, and personalized recommendation.	Eighty-one university students in School Y, divided into two classes (A and B) with similar initial abilities. Ages and detailed characteristics not specified.	Class A (using the system) showed significant improvement in vocabulary test scores (average increase from 75 to 93) compared to Class B (traditional teaching). Improved engagement and efficiency.
Shao, Alharir, Hariri, Satam, Shiri and Mbarki	Develop AI-based Arabic tutor for pronunciation teaching.	The AI-ALST system used Mel-Frequency Cepstrum Coefficient (MFCC) for feature extraction and an attention BiLSTM model to process audio data and detect pronunciation errors. A cost-based learning strategy addressed class imbalance.	Twelve participants learning Moroccan Arabic at the University of Arizona.	The system achieved high accuracy in detecting mispronunciations. Attention BiLSTM outperformed BiLSTM for precision, recall, and F1-score across most words. It successfully provided tailored feedback, enabling learners to improve pronunciation effectively.

provide a comparative overview of AI applications in university education, focusing on predictive analytics and adaptive learning methodologies.

Table 1 primarily emphasizes DL and RL techniques for predicting academic performance, student retention, and language proficiency. These studies predominantly utilize neural networks such as RNN, CNN, SNN, and MLP, with optimization techniques like ADAM, Q-Learning, and SHAP for model explainability [38,42].

The Table 2, in contrast, extends the scope beyond predictive models, encompassing AI-driven personalized learning systems, cognitive assessment, resource optimization, and real-time feedback mechanisms [46]. It includes applications of blockchain, fuzzy logic, Bayesian models, and genetic algorithms to improve educational experiences [39,50].

While the first table highlights the accuracy and effectiveness of AI models, with dropout prediction reaching up to 99.1% [36], the second table focuses more on engagement, participation, and learning effectiveness, reporting improvements in fluency, comprehension, and cognitive learning processes [48].

Additionally, the first table relies on large datasets from virtual learning platforms and university records, whereas the second table incorporates smaller experimental studies and real-time interactions, making findings more context-dependent [44].

Both perspectives are complementary: predictive models help in early risk detection, while AI-enhanced teaching tools actively improve learning outcomes and student engagement. However, challenges persist, including scalability, computational costs, and the need for extensive labeled datasets in predictive models, whereas adaptive learning methods require infrastructure improvements and broader empirical validation [37,40,48].

The dendrogram presented in the analysis visually represents the relationships between different studies based on their methodological approaches (Figure 2). The distances in the dendrogram were calculated using hierarchical clustering with the Ward linkage method, which minimizes the variance within clusters as new data points are merged.

The x-axis represents the Euclidean distance between studies, which quantifies how different their methodologies are. A small distance (near 0–10) indicates closely related studies, employing similar AI architectures or algorithms. A large distance (above 30–40) indicates important methodological differences, i.e., the use of different techniques (e.g., CNN vs. Q-learning).

The dendrogram reveals relationships between studies and methodologies, showcasing clusters based on shared approaches to educational enhancement. Kadhim and Hassan [36] and Naseer et al. [43] employ advanced neural network models, including RNN-ADAM and CNNs, to predict academic performance and personalize learning pathways. These studies underscore a common focus on predictive accuracy and the dynamic adaptation of resources to improve student outcomes. Similarly, Liu, Wang, and Yuan [37] utilize Spiking Neural Networks to classify academic performance, aligning with this predictive precision cluster.

Another prominent grouping involves reinforcement learning (RL). Francisco and Silva [44] apply Q-Learning algorithms, dynamically tailoring content and strategies to optimize educational processes. These approaches emphasize personalization and iterative learning, adapting continuously to students’ needs.

Li and Mohamad [42] integrate Deep Learning with topic modeling to assess linguistic features, paralleling Ou’s [50] use of Bayesian Knowledge Tracing and RL to refine teaching strategies. These works highlight the synergy between language analysis and adaptive educational tools.

Lastly, studies like He and Wang [52] and Wang et al. [54] blend AI with traditional teaching to create personalized and engaging environments. Their methodologies showcase the transformative role of AI in real-time feedback and interaction, fostering enhanced student engagement and learning outcomes.

6. Discussion

DL and RL approaches have demonstrated utility in predicting academic performance and addressing student retention. Studies such as Kadhim and Hassan [36] and Liu, Wang, and Yuan [37] exemplify this by leveraging neural networks, including Recurrent Neural Networks (RNN) and Spiking Neural Networks (SNN). These models surpass traditional predictive techniques, achieving unprecedented accuracy levels. RNN-ADAM classified dropout risks with 99.1% accuracy underscores the potential of DL algorithms to improve decision-making in e-learning platforms. SNNs addressed performance disparities among students, significantly enhancing predictions for underperforming categories. Together, these studies underscore the pivotal role of DL in optimizing student success metrics and providing actionable insights for educators. However, while RNN-ADAM provides high accuracy, its reliance on large, labeled datasets and computationally expensive training processes poses challenges for widespread adoption. Conversely, SNNs offer advantages in terms of real-time processing but require more specialized hardware and complex parameter tuning.

The application of RL further refines personalized learning pathways and content recommendations. Francisco and Silva’s [44] integration of Q-Learning to develop an Intelligent Tutoring System highlights the adaptability of RL in tailoring educational activities to individual needs. The iterative nature of RL algorithms ensures the continuous refinement of teaching strategies, reducing time to mastery and enhancing educational efficiency. This is evident in the study on Civics and Political Science education, where Q-Learning dynamically optimized teaching interventions, resulting in improved assessment scores and engagement levels. However, while Q-Learning provides a structured way to personalize learning, its dependence on extensive reward feedback can lead to slower convergence and suboptimal policy learning when applied to highly variable educational settings. The success of these RL-based methodologies suggests a shift towards learner-centric educational models that prioritize adaptability and customization [49].

Another critical theme emerging from the reviewed studies is the integration of DL and RL with NLP and other AI-driven technologies to enhance language education. For instance, Li and Mohamad [42] and Ou [50] utilized DL frameworks to assess and improve English proficiency through personalized feedback mechanisms. These studies emphasize the importance of analyzing acoustic and linguistic features to provide targeted support in language learning. Moreover, Wang, Zou, and Xue [46] developed the EAP TALK system, combining speech recognition with DL to enhance oral fluency. The 65.5% satisfaction rate reported in their study highlights the practicality and acceptance of such technologies in real-world educational contexts. This pattern suggests that integrating DL and RL into language education can address common barriers to learning, such as lack of personalization and inconsistency in assessment. However, these models often require significant computational resources and large annotated datasets, making their implementation challenging in resource-constrained institutions.

The reviewed studies also reveal significant advancements in using DL and RL to enhance engagement and collaboration in smart learning environments. Yin, Peng, and Liu [48] validated the E-GPPE-C model, which employed CNNs and knowledge mapping to construct personalized learning paths. Their findings demonstrated positive correlations between the model’s implementation and improvements in classroom engagement, participation, and creativity. Similarly, Liu, Chen, and Yao [55] applied the YOLOv3 framework to optimize classroom behaviors, demonstrating that DL-driven real-time analysis can significantly enhance teaching strategies. These results underscore the transformative impact of AI technologies on fostering dynamic, interactive learning spaces that promote collaboration and active participation. However, although CNN-based models provide reliable performance for pattern recognition tasks in smart classrooms, their high computational cost and the need for large, labeled training data present implementation challenges.

The studies collectively highlight the importance of explainability and transparency in AI-driven educational interventions. Ujkani et al. [38] emphasized the role of Explainable AI (XAI) through SHAP analysis to identify critical success factors in online learning. By providing interpretable insights into student engagement and registration timelines, their research addressed a key barrier to adopting AI in education: the opacity of complex models. These findings align with broader trends advocating for the integration of explainability into AI systems to build trust and facilitate informed decision-making among educators and policymakers. However, while XAI methods enhance trust and adoption by educators, they also introduce computational overhead and may not always provide fully intuitive explanations for non-technical users.

Despite these advancements, several gaps and challenges persist. One notable limitation is the scalability of AI models in diverse educational contexts. While studies like Sayed [40] demonstrated high accuracy in dropout predictions across large datasets, such as those from the Arab Open University, the applicability of these models to smaller, resource-constrained institutions remains uncertain. Furthermore, studies often emphasize accuracy and performance metrics without fully addressing the broader implications of AI deployment, including ethical concerns, data privacy, and potential biases inherent in algorithmic decision-making. Addressing these issues is critical for ensuring equitable and responsible use of AI in education.

Another challenge lies in the integration of AI-driven tools into existing educational frameworks. While many studies report significant improvements in engagement and outcomes, the practicalities of implementing such systems, including training for educators and infrastructure requirements, are often underexplored. For example, Convolutional Neural Networks (CNNs) have demonstrated high effectiveness in predicting student progression and dropout rates, but their high computational cost can limit adoption [39]. Future research should prioritize developing scalable, cost-effective solutions that are accessible to a broader range of institutions, including those in underprivileged regions [52].

The discussion also highlights the potential of AI to redefine assessment and evaluation practices. Traditional methods often fail to capture the complexities of student learning. By contrast, DL and RL models offer nuanced insights into student performance, enabling real-time feedback and continuous improvement. These approaches align with contemporary educational paradigms that emphasize formative assessment and individualized learning trajectories, moving away from one-size-fits-all evaluation models [39].

In conclusion, the reviewed studies demonstrate the transformative potential of DL and RL in revolutionizing educational practices. By enabling personalized learning, improving predictive accuracy, and fostering engagement, these technologies address critical challenges in modern education. However, their widespread adoption requires addressing scalability, ethical concerns, and practical implementation barriers. Future research should focus on developing inclusive, transparent, and adaptable AI-driven solutions that prioritize the diverse needs of learners and educators, ensuring that the benefits of these technologies are equitably distributed. This synthesis underscores the urgency of leveraging AI to create innovative, learner-centric educational environments that prepare students for an increasingly complex and interconnected world.

7. Limitations and Implications for Future Research

A common limitation across many studies was their reliance on data from single institutions or specific academic settings, restricting the generalizability of their findings [36,45,52]. To address this, future research could validate these models across diverse institutions, disciplines, and populations, incorporating multi-institutional datasets and more heterogeneous samples. Expanding the contexts in which these models are tested could refine their scalability and ensure their adaptability across broader educational landscapes.

Sample size constraints also posed challenges, as seen in studies like Jing et al. [41] and Wang and Zheng [56], which used small participant pools of 10 and 60 students, respectively. Such limited sample sizes reduce the robustness of findings and their applicability to diverse student populations. Future research could engage larger, more varied participant groups to capture linguistic, cultural, and educational diversity. For example, incorporating non-native speakers with diverse accents and dialects into language-focused models could enhance their linguistic versatility and broaden their utility [42].

Several studies, including those by Liu [49], Francisco and Silva [44], and Qiao and Fu [51], concentrated on specific academic disciplines, such as civics, software maintenance, and mathematics. This narrow focus limits the applicability of their models to other fields. Expanding these systems to cross-disciplinary applications could increase their relevance and impact in varied educational contexts. For instance, models tailored for specific domains, such as LDiDL for English proficiency or BPNN for language teaching [42], could be adapted to address challenges in STEM or social sciences.

Computational complexity and resource dependency also emerged as barriers to scalability in studies such as those by Naseer et al. [43], Liu and Ren [50], and Wang, Zou, and Xue [46]. The reliance on advanced technologies like CNNs, RNNs, and genetic algorithms creates challenges for implementation in resource-constrained institutions. Future research could prioritize the development of lightweight, efficient algorithms and explore cost-effective technological solutions to ensure broader accessibility. For example, integrating these systems with open-source platforms or optimizing their architecture for low-resource settings could enhance their feasibility in diverse academic environments.

A notable limitation was the exclusion of multimodal and socio-emotional data, which are critical to understanding the complexity of learning processes [37,48]. Current models often focus narrowly on cognitive metrics, ignoring behavioral, emotional, or contextual factors that significantly influence educational outcomes. Future research could integrate multimodal data streams, including speech patterns, facial expressions, and engagement metrics, to offer a more holistic evaluation of student performance. Such advancements could be particularly beneficial for systems like EAP TALK and the ITS developed by Francisco and Silva [44], which would gain from incorporating additional layers of interaction and contextual analysis.

Another limitation was the lack of interpretability in several AI models, such as the RNN-ADAM model [43] and the SNN model [37]. These black-box systems hinder educators’ ability to understand and trust their outputs. While some studies utilized Explainable AI techniques [38], the computational demands of methods like SHAP limited their scalability. Future research could prioritize the development of transparent, user-friendly explainability tools to enhance trust and usability. This could involve creating intuitive visualizations or interfaces that allow educators to explore how predictions are generated, thereby supporting informed decision-making.

Technological integration and user experience issues were also evident in models like EAP TALK [46] and the personalized teaching system [53]. Participants in these studies reported discomfort with prolonged use and challenges in navigating complex interfaces. Future research could focus on improving usability and reducing cognitive load by designing intuitive, user-centered interfaces. Incorporating user feedback during development could ensure these systems align with the needs of both students and educators, enhancing adoption and effectiveness.

The studies also highlighted limitations in linguistic and cultural adaptability, particularly in language-focused models. For instance, Jing et al. [41] and Shao et al. [47] noted challenges in handling diverse accents and dialects, while Li and Mohamad [42] acknowledged the need to extend their model beyond English proficiency to other languages. Future research could address these issues by expanding datasets to include a wider range of linguistic and cultural contexts and integrating multimodal inputs like gestures and facial expressions. Such advancements could enhance the robustness and inclusivity of these models.

Short-term evaluations were another recurring limitation. Future research could incorporate longitudinal studies to assess the sustained effects of AI-driven systems on learning outcomes and educational practices. For example, evaluating how models influence students’ critical thinking, collaboration, or adaptability over time would provide deeper insights into their efficacy [49].

Several models focused exclusively on academic metrics, neglecting the broader dimensions of learning, such as emotional intelligence, creativity, or collaborative skills. For instance, Jia and Zhang [57] and Yuhua [58] limited their assessments to predefined cognitive categories, which may not capture the full spectrum of student development. Future research could expand these frameworks to include holistic metrics, integrating socio-emotional and interpersonal dimensions alongside academic performance.

Finally, the reliance on simulated or pre-structured datasets may not fully represent real-world educational variability [51]. Future research could validate these models using live classroom data and real-world scenarios, ensuring their outputs are aligned with the complexities of dynamic educational environments. Moreover, expanding assessment formats to include open-ended or case-based questions could enhance their utility in fostering critical thinking and problem-solving skills.

Addressing these limitations could significantly enhance the effectiveness, scalability, and inclusivity of AI-driven educational tools. By integrating diverse datasets, multimodal inputs, efficient algorithms, and explainable techniques, future research could refine these models to meet the evolving needs of higher education. This could pave the way for transformative advancements in personalized learning, academic performance evaluation, and educational innovation.

8. Ethical Considerations in AI-Driven Education

As AI technologies become increasingly integrated into educational settings, it is imperative to address critical ethical concerns related to data privacy, algorithmic bias, and transparency. AI-driven educational tools rely on large datasets that include sensitive student information, necessitating robust data protection mechanisms. Ensuring compliance with established privacy frameworks, such as the General Data Protection Regulation (GDPR) and the Family Educational Rights and Privacy Act (FERPA), is crucial to safeguarding student data and maintaining trust in AI applications [59].

Algorithmic bias is another pressing concern, as AI models may inadvertently reinforce existing educational disparities if not designed with fairness in mind. Bias mitigation strategies, including diverse training datasets, fairness-aware algorithms, and continuous performance evaluations, should be employed to promote equitable learning opportunities for all students [60]. Moreover, transparency in AI decision-making is essential for educators and students to trust and effectively utilize these technologies. Explainable AI (XAI) techniques should be integrated into AI-driven educational systems to provide clear and interpretable insights into how decisions are made, fostering greater accountability and user confidence [61].

Frameworks for ethical AI usage in education should also emphasize inclusivity, ensuring that AI-driven solutions are accessible to diverse student populations, including those with disabilities and socio-economic constraints. Additionally, stakeholder involvement—including educators, students, policymakers, and AI developers—is critical in shaping guidelines that align technological advancements with ethical considerations [62].

By prioritizing privacy, fairness, and transparency, AI-driven education can foster trust and inclusivity, ensuring that technological innovations enhance learning experiences without compromising ethical principles. Future research should further explore the development of standardized ethical frameworks that can guide the responsible implementation of AI in education.

9. Conclusions

AI solutions have demonstrated their ability to address critical challenges in academic performance, engagement, and personalization. By leveraging their capacity for adaptive learning, predictive accuracy, and real-time feedback, DL and RL technologies present promising solutions to the multifaceted difficulties faced by university students.

One of the most significant contributions of DL and RL lies in their application to predictive analytics. Studies like those by Kadhim and Hassan [36] and Liu, Wang, and Yuan [37] underscore the unparalleled accuracy of neural network models in forecasting academic outcomes and identifying at-risk students. These models not only surpass traditional approaches in precision but also provide actionable insights, enabling timely interventions to improve retention and success rates. Similarly, RL methodologies dynamically optimize learning pathways, ensuring that educational content is tailored to individual needs. The iterative feedback mechanisms inherent in RL further refine these pathways, fostering a learner-centric environment that adapts to the evolving requirements of students [44].

The integration of DL and RL into language education highlights another critical advancement. Studies such as those by Li and Mohamad [42] and Ou [50] demonstrate the potential of AI-driven tools in addressing common barriers to language acquisition, including personalization and inconsistent assessments. By combining advanced NLP techniques with adaptive algorithms, these tools deliver precise, real-time feedback, enhancing students’ linguistic competencies and overall engagement. The reported improvements in pronunciation, fluency, and comprehension underscore the efficacy of these systems in bridging gaps in traditional language instruction. Furthermore, innovations like the EAP TALK system and AI-ALST tutor highlight the role of AI in creating interactive, accessible, and culturally adaptable learning experiences.

Smart teaching environments represent a significant leap forward in educational practices. The findings reveal how DL-powered systems, such as the E-GPPE-C model and YOLOv3 framework, enhance classroom dynamics through personalized learning paths and real-time behavior analysis [48,55]. These technologies not only improve engagement and participation but also foster creativity and collaboration, essential skills for the modern workforce. By integrating multimodal data streams, such as facial expressions and speech patterns, these systems offer a holistic approach to understanding and addressing student needs.

Explainability and transparency in AI-driven education emerged as pivotal themes across the reviewed studies. Ujkani et al. [38] demonstrated the importance of Explainable AI (XAI) in building trust among educators and students by providing interpretable insights into predictive models. However, the broader application of XAI remains limited by computational demands and the complexity of implementation. Addressing this gap is crucial for ensuring the responsible and equitable use of AI in education. Transparent models not only enhance trust but also empower educators to make informed decisions based on data-driven insights.

Despite these advancements, challenges persist in the scalability and inclusivity of AI-driven educational tools. Many models rely on advanced computational infrastructures that may not be accessible to resource-constrained institutions. The reliance on narrow datasets and controlled environments further limits the generalizability of these findings. To overcome these limitations, future research should prioritize the development of lightweight, efficient algorithms and explore scalable solutions that extend the benefits of AI technologies to diverse academic settings. Additionally, the integration of multimodal and socio-emotional data streams could provide a more comprehensive understanding of student learning processes, addressing the current overemphasis on cognitive metrics [43,46].

The ethical implications of AI deployment in education also warrant attention. Issues related to data privacy, algorithmic bias, and the potential for misuse must be addressed to ensure the equitable and responsible implementation of AI systems. Transparent and inclusive frameworks are essential for fostering trust and promoting the ethical use of AI in diverse educational contexts. Moreover, involving educators and students in the design and evaluation of these tools can enhance their usability and relevance, aligning technological innovations with real-world needs.

Longitudinal studies are necessary to evaluate the sustained impacts of AI-driven interventions on learning outcomes. While many studies focus on immediate improvements in performance and engagement, the long-term effects of these technologies on critical thinking, adaptability, and collaboration remain underexplored. Future research should investigate how AI influences the broader dimensions of learning, including emotional intelligence and creativity, over extended periods. Such studies could provide valuable insights into the transformative potential of AI in shaping holistic educational practices.

The reviewed studies highlight the potential of AI to redefine assessment and evaluation practices. Traditional methods, which often fail to capture the complexities of student learning, can be augmented by DL and RL models to provide nuanced insights and real-time feedback. These approaches align with contemporary educational paradigms that emphasize formative assessments and individualized learning trajectories, moving away from one-size-fits-all evaluation models. By enabling continuous improvement and fostering a growth mindset, AI-driven assessments can contribute to a more inclusive and effective education system.

In conclusion, the integration of DL and RL technologies in higher education represents a paradigm shift toward personalized, adaptive, and data-driven learning environments. These innovations address critical challenges in academic performance, engagement, and accessibility, offering powerful tools for educators and policymakers. However, realizing the full potential of AI in education requires addressing scalability, ethical considerations, and practical implementation barriers. By focusing on inclusivity, transparency, and sustainability, future research can pave the way for transformative advancements in educational practices, ensuring that the benefits of AI are equitably distributed across diverse learning contexts.