Applied Sciences

Research

24 pages, 1078 KiB

Open AccessArticle

ICT Adoption in Education: Unveiling Emergency Remote Teaching Challenges for Students with Functional Diversity Through Topic Identification in Modern Greek Data

by Katia Lida Kermanidis, Spyridon Tzimiris, Stefanos Nikiforos, Maria Nefeli Nikiforos and Despoina Mouratidis

Appl. Sci. 2025, 15(9), 4667; https://doi.org/10.3390/app15094667 - 23 Apr 2025

Viewed by 231

Abstract

This study explores topic identification using text analysis techniques in Modern Greek interviews with parents of students with functional diversity during Emergency Remote Teaching. The analysis focused on identifying key educational themes and addressing challenges in processing Greek educational data. Machine learning models, [...] Read more.

This study explores topic identification using text analysis techniques in Modern Greek interviews with parents of students with functional diversity during Emergency Remote Teaching. The analysis focused on identifying key educational themes and addressing challenges in processing Greek educational data. Machine learning models, combined with Natural Language Processing techniques, were applied for topic identification, utilizing cross-validation and data balancing methods to enhance reliability. The findings revealed the impact of linguistic complexity on topic modeling and highlighted the educational implications of analyzing qualitative data in this context. Among the models tested, the Naïve Bayes (Kernel) algorithm performed best when combined with lemmatization-based preprocessing, confirming that text normalization significantly enhances classification accuracy in Greek educational data. The proposed framework contributes to the analysis of qualitative educational data by identifying key parental concerns related to Emergency Remote Teaching. It demonstrates how text analysis techniques could support data-driven decision-making and help guide policy development for the inclusive and effective integration of Information and Communication Technology in education. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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21 pages, 4365 KiB

Open AccessArticle

Teaching Artificial Intelligence and Machine Learning in Secondary Education: A Robotics-Based Approach

by Georgios Karalekas, Stavros Vologiannidis and John Kalomiros

Appl. Sci. 2025, 15(8), 4570; https://doi.org/10.3390/app15084570 - 21 Apr 2025

Viewed by 683

Abstract

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) highlights the need for innovative, engaging educational approaches in secondary education. This study presents the design and classroom implementation of a robotics-based lesson aimed at introducing core AI and ML concepts to [...] Read more.

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) highlights the need for innovative, engaging educational approaches in secondary education. This study presents the design and classroom implementation of a robotics-based lesson aimed at introducing core AI and ML concepts to ninth-grade students without prior programming experience. The intervention employed two low-cost, 3D-printed robots, each used to illustrate a different aspect of intelligent behavior: (1) rule-based automation, (2) supervised learning using image classification, and (3) reinforcement learning. The lesson was compared with a previous implementation of similar content delivered through software-only activities. Data were collected through classroom observation and student–teacher discussions. The results indicated increased student engagement and enthusiasm in the robotics-based version, as well as improved conceptual understanding. The approach required no specialized hardware or instructor expertise, making it easily adaptable for broader use in school settings. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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21 pages, 3409 KiB

Open AccessArticle

Integrating Rapid Application Development Courses into Higher Education Curricula

by Urtė Radvilaitė and Diana Kalibatienė

Appl. Sci. 2025, 15(6), 3323; https://doi.org/10.3390/app15063323 - 18 Mar 2025

Viewed by 348

Abstract

As the development of technology and business improvement is rapidly advancing these days, higher education (HE) should continually provide and develop up-to-date knowledge and skills for students. This is crucial for training competitive specialists, addressing digital transformation and enhancing digital readiness of HE [...] Read more.

As the development of technology and business improvement is rapidly advancing these days, higher education (HE) should continually provide and develop up-to-date knowledge and skills for students. This is crucial for training competitive specialists, addressing digital transformation and enhancing digital readiness of HE institutions, as well as increasing students’ employment opportunities. Therefore, this paper explores the development and implementation of the new courses for teaching Rapid Application Development (RAD) on the Oracle Application Express platform at five European universities. Consequently, a new and flexible methodology for the integration of developed courses into existing study programs with different integration strategies is proposed and implemented. The effectiveness of the courses’ integration, implementation and students’ satisfaction were evaluated using Kirkpatrick’s model. The results reveal that students’ knowledge of RAD increased after completing the courses, which can improve students’ employment opportunities and promote digital transformation in HE institutions and studies. In addition, a majority of the students expressed positive feedback for both modules, finding the courses relevant, well delivered and motivating for future study. This study and its results are expected to inspire researchers, teachers and practitioners for further work towards the digital transformation of HE and offer valuable insights for future HE digitalization and research. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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30 pages, 4981 KiB

Open AccessArticle

A Machine Learning Framework for Student Retention Policy Development: A Case Study

by Sidika Hoca and Nazife Dimililer

Appl. Sci. 2025, 15(6), 2989; https://doi.org/10.3390/app15062989 - 10 Mar 2025

Cited by 1 | Viewed by 1126

Abstract

Student attrition at tertiary institutions is a global challenge with significant personal and social consequences. Early identification of students at risk of dropout is crucial for proactive and preventive intervention. This study presents a machine learning framework for predicting and visualizing students at [...] Read more.

Student attrition at tertiary institutions is a global challenge with significant personal and social consequences. Early identification of students at risk of dropout is crucial for proactive and preventive intervention. This study presents a machine learning framework for predicting and visualizing students at risk of dropping out. While most previous work relies on wide-ranging data from numerous sources such as surveys, enrolment, and learning management systems, making the process complex and time-consuming, the current study uses minimal data that are readily available in any registration system. The use of minimal data simplifies the process and ensures broad applicability. Unlike most similar research, the proposed framework provides a comprehensive system that not only identifies students at risk of dropout but also groups them into meaningful clusters, enabling tailored policy generation for each cluster through digital technologies. The proposed framework comprises two stages where the first stage identifies at-risk students using a machine learning classifier, and the second stage uses interpretable AI techniques to cluster and visualize similar students for policy-making purposes. For the case study, various machine learning algorithms—including Support Vector Classifier, K-Nearest Neighbors, Logistic Regression, Naïve Bayes, Artificial Neural Network, Random Forest, Classification and Regression Trees, and Categorical Boosting—were trained for dropout prediction using data available at the end of the students’ second semester. The experimental results indicated that Categorical Boosting with an F1-score of 82% is the most effective classifier for the dataset. The students identified as at risk of dropout were then clustered and a decision tree was used to visualize each cluster, enabling tailored policy-making. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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25 pages, 2837 KiB

Open AccessArticle

Using Tablets to Teach Basic Literacy Skills to Kindergarten Students: A Case Study

by Emmanuel Fokides and Dionysia Klaoudatou

Appl. Sci. 2025, 15(5), 2252; https://doi.org/10.3390/app15052252 - 20 Feb 2025

Viewed by 843

Abstract

Tablets have long been employed across a variety of educational domains. Given the ongoing debate regarding their effect on skill and knowledge development, especially among very young learners, a study was conducted to evaluate their effectiveness against conventional educational materials. The study involved [...] Read more.

Tablets have long been employed across a variety of educational domains. Given the ongoing debate regarding their effect on skill and knowledge development, especially among very young learners, a study was conducted to evaluate their effectiveness against conventional educational materials. The study involved 76 kindergarten students, employing a between-subjects design to investigate basic literacy skills. These skills included letter name identification and letter sound recognition. To measure knowledge acquisition, evaluation tests were administered, and questionnaires were used to collect data on students’ motivation, engagement, and enjoyment. The findings revealed that tablets significantly enhanced the targeted skills compared to conventional materials. Tablets were also rated higher in enjoyment, engagement, and motivation. Importantly, these factors significantly impacted learning outcomes with tablets, while they had no effect in the case of conventional materials. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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24 pages, 2972 KiB

Open AccessArticle

Analysis of Learning Behaviors and Outcomes for Students with Different Knowledge Levels: A Case Study of Intelligent Tutoring System for Coding and Learning (ITS-CAL)

by Chien-Hung Lai and Cheng-Yueh Lin

Appl. Sci. 2025, 15(4), 1922; https://doi.org/10.3390/app15041922 - 12 Feb 2025

Cited by 1 | Viewed by 1118

Abstract

With the rapid development of generative AI technology, programming learning aids have become essential resources for enhancing students’ programming capabilities. This study developed an intelligent tutoring system, ITS-CAL, powered by a large language model (LLM) to provide students with immediate and hierarchical learning [...] Read more.

With the rapid development of generative AI technology, programming learning aids have become essential resources for enhancing students’ programming capabilities. This study developed an intelligent tutoring system, ITS-CAL, powered by a large language model (LLM) to provide students with immediate and hierarchical learning feedback, particularly in scenarios with limited class time and large student populations. The system helps students overcome challenges encountered during the learning process. A mixed-method approach, combining quantitative and qualitative analyses, was employed to investigate the usage patterns of the system’s three primary functions—Hint, Debug, and User-defined Question—and their impact on learning outcomes among students with varying knowledge levels. The results indicated that students with high knowledge levels tended to use the Hint and User-defined Question functions moderately, while those with lower knowledge levels heavily relied on the Hint function but did not achieve significant improvements in learning outcomes. Overall, students who used ITS-CAL in moderation achieved the highest pass rate (72.22%), whereas excessive reliance on ITS-CAL appeared to diminish independent problem-solving abilities. Additionally, students generally provided positive feedback on the system’s convenience and its role as a learning aid. However, they highlighted areas for improvement, particularly in the Debug function and the quality of Hint content. This study contributes to the field by demonstrating the application potential of LLMs in programming education and offering valuable empirical insights for designing future programming learning assistance systems. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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17 pages, 1584 KiB

Open AccessArticle

The Effectiveness of an Authentic E-Learning Environment for Deaf Learners

by Nasim Niksiar, İldeniz Özverir and Ali F. Meghdari

Appl. Sci. 2025, 15(3), 1568; https://doi.org/10.3390/app15031568 - 4 Feb 2025

Viewed by 1274

Abstract

The digitalization of educational systems and the corresponding impact on the learners’ improvement require modern pedagogical approaches to increase motivation among learners in distance learning. According to the literature, authentic e-learning and real-world themes create a dynamic learning setting and enhance learners’ engagement; [...] Read more.

The digitalization of educational systems and the corresponding impact on the learners’ improvement require modern pedagogical approaches to increase motivation among learners in distance learning. According to the literature, authentic e-learning and real-world themes create a dynamic learning setting and enhance learners’ engagement; however, the impact of adopting authentic learning has not been investigated for deaf learners in a sign language e-learning setting. Therefore, this study aims to examine the effect of adopting authentic learning for the design of e-learning for deaf learners. The study employed a mixed-methods approach by conducting a one-group pre-test–post-test with 11 deaf learners and measuring design principles of authentic learning via semi-structured interviews. The statistical portion analyzed the T-test and Cohen’s d-effect size, and the result showed that e-learning through authentic themes was significantly effective. The interview results revealed a positive attitude toward using e-learning based on an authentic learning approach, which increased deaf learners’ motivation. It was found that e-learning based on an authentic sign language e-learning setting and technology enhances deaf learners’ satisfaction and motivation with their first e-learning experience. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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41 pages, 1486 KiB

Open AccessArticle

Digital Teaching Practices and Student Academic Stress in the Era of Digitalization in Higher Education

by Roger Pedro Norabuena-Figueroa, Angel Deroncele-Acosta, Hugo Marino Rodríguez-Orellana, Emerson Damián Norabuena-Figueroa, Milagros Catalina Flores-Chinte, Lourdes Liz Huamán-Romero, Víctor Hilario Tarazona-Miranda and Marybel Esther Mollo-Flores

Appl. Sci. 2025, 15(3), 1487; https://doi.org/10.3390/app15031487 - 31 Jan 2025

Cited by 1 | Viewed by 3047

Abstract

ICT and digitization are transforming higher education, but one aspect that has been little addressed is student academic stress in this context. The objective of this research was to explore the relationship between teaching practices and academic stress in virtual learning environments. A [...] Read more.

ICT and digitization are transforming higher education, but one aspect that has been little addressed is student academic stress in this context. The objective of this research was to explore the relationship between teaching practices and academic stress in virtual learning environments. A quantitative study based on structural equation modeling was conducted using 6605 students from a public university in Lima. Two scales were applied and validated to assess specific dimensions of teaching practices and levels of academic stress. The scales demonstrated robust psychometric properties. Exploratory and confirmatory factor analysis allowed the identification of key underlying factors and the validation of the proposed theoretical model. Findings reveal indirect negative influences between teaching practices and academic stress. External factors like overload, distractions, and technological issues disrupt teacher interaction, collaboration, and learning processes. Model fit indices confirm its robustness, emphasizing the need to optimize resources and improve virtual education environments. ICT in education enhances accessibility and personalization but demands addressing academic stress and optimizing virtual learning environments. Future research is called for to delve deeper into the integration of emerging technologies, particularly artificial intelligence and virtual reality, as well as mindfulness, digital skills, and mobile self-efficacy, to comprehensively address stress and student well-being. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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15 pages, 253 KiB

Open AccessArticle

Effectiveness and Design of PBL-Based Project Approach for Non-Major University Computing Courses

by Jung-In Choi and Sooyeon Yang

Appl. Sci. 2025, 15(1), 50; https://doi.org/10.3390/app15010050 - 25 Dec 2024

Viewed by 935

Abstract

The purpose of this study is to analyze the learning outcomes and educational implications of introducing a new Problem-Based Learning project approach in a computing course for non-major students. The Problem-Based Learning project approach, designed by the research team, provides a teaching and [...] Read more.

The purpose of this study is to analyze the learning outcomes and educational implications of introducing a new Problem-Based Learning project approach in a computing course for non-major students. The Problem-Based Learning project approach, designed by the research team, provides a teaching and learning strategy in which students collaborate to analyze and extend code within real-world problem contexts while systematically exchanging feedback. The study was conducted as a case study involving two groups of students: a control group comprising 30 students following traditional instructional methods and an experimental group consisting of 31 students employing the proposed approach. Educational outcomes were evaluated through both quantitative and qualitative analyses. The findings demonstrate that the Problem-Based Learning project approach facilitated a significantly more effective learning experience, fostering key skills such as communication, collaboration, and problem-solving. Furthermore, substantial improvements were observed in instructional effectiveness and the structure of practical activities. These findings suggest that the Problem-Based Learning project approach mirrors real-world practices, equipping students with essential computational thinking skills and a deeper understanding of foundational topics such as Python, data structures, and algorithms. This study provides valuable insights for the development of instructional strategies in computing courses for non-major students, emphasizing the importance of creating systematic learning environments that bridge theoretical knowledge and practical application through real-world problem-solving activities. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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17 pages, 1114 KiB

Open AccessArticle

Integrating Students’ Real-Time Gaze in Teacher–Student Interactions: Case Studies on the Benefits and Challenges of Eye Tracking in Primary Education

by Raimundo da Silva Soares, Jr., Eneyse Dayane Pinheiro, Amanda Yumi Ambriola Oku, Marilia Biscaia Rizzo, Carolinne das Neves Vieira and João Ricardo Sato

Appl. Sci. 2024, 14(23), 11007; https://doi.org/10.3390/app142311007 - 27 Nov 2024

Viewed by 2252

Abstract

Integrating neuroscience techniques, such as eye tracking, into educational practices has opened new avenues for understanding the cognitive processes underlying learning. This study investigates the feasibility and practicality of using eye tracking as a supportive tool for educators in primary school settings. By [...] Read more.

Integrating neuroscience techniques, such as eye tracking, into educational practices has opened new avenues for understanding the cognitive processes underlying learning. This study investigates the feasibility and practicality of using eye tracking as a supportive tool for educators in primary school settings. By taking into account eye-tracking features in lesson plans and instruction, this study explores the benefits and challenges of this technology from teachers’ perspective. The findings reveal that eye tracking can enhance interactivity, maintain student attention, and provide immediate feedback, thereby aiding in identifying student difficulties that may otherwise go unnoticed. However, the study also highlights concerns related to technical complexities, data privacy, and the need for teacher training to utilize and interpret eye-tracking data effectively. These insights contribute to a nuanced understanding of how eye-tracking technology can be implemented in educational settings, offering potential pathways for personalized teaching and improved learning outcomes. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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31 pages, 2613 KiB

Open AccessArticle

Meta4CBC: Metamodel for Competency-Based Curriculum Design in Higher Education

by Ania Cravero, Dante Álvarez, Samuel Sepúlveda, Maria-Isabel Valdivieso and Lilia Muñoz

Appl. Sci. 2024, 14(22), 10110; https://doi.org/10.3390/app142210110 - 5 Nov 2024

Viewed by 1843

Abstract

Meta4CBC is proposed as an innovative metamodel for competency-based curriculum design in higher education, addressing the critical need to adapt curricula to contemporary challenges driven by globalization, technological advancement, and climate change. Inspired by the Business Motivation Model and Semantics of Business Vocabulary [...] Read more.

Meta4CBC is proposed as an innovative metamodel for competency-based curriculum design in higher education, addressing the critical need to adapt curricula to contemporary challenges driven by globalization, technological advancement, and climate change. Inspired by the Business Motivation Model and Semantics of Business Vocabulary and Business Rules metamodels, Meta4CBC seeks to overcome the limitations of current CBCD models by providing a coherent structure that integrates curriculum components at supra, macro, meso, micro, and nano levels and establishes a common language that facilitates interdisciplinary collaboration. The proposal promotes curriculum alignment and coherence while supporting the development of software tools to assist in curriculum design, automate repetitive tasks, and provide data analysis for informed decision-making. The validation of Meta4CBC was conducted through a multi-step methodology involving proof of concept, expert judgment, and practical application. After defining the components through a systematic literature review, the metamodel was tested in the Computer Engineering program at the University of La Frontera. This testing verified its adaptability and alignment across curriculum levels. Experts from various Chilean universities reviewed the model, providing feedback for refinement. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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27 pages, 1914 KiB

Open AccessArticle

Leveraging Gamification in ICT Education: Examining Gender Differences and Learning Outcomes in Programming Courses

by Rafael Mellado, Claudio Cubillos, Rosa Maria Vicari and Gloria Gasca-Hurtado

Appl. Sci. 2024, 14(17), 7933; https://doi.org/10.3390/app14177933 - 5 Sep 2024

Cited by 6 | Viewed by 4600

Abstract

This study investigates the differential effects of gamification on learning outcomes, motivation, and usability perceptions in an introductory programming course, focusing on gender differences. While gamification has shown promise for increasing student engagement in educational settings, its impact may vary across genders. An [...] Read more.

This study investigates the differential effects of gamification on learning outcomes, motivation, and usability perceptions in an introductory programming course, focusing on gender differences. While gamification has shown promise for increasing student engagement in educational settings, its impact may vary across genders. An experimental study was conducted with 88 university students randomly assigned to gamified and non-gamified groups. Learning gains were assessed through pre- and post-tests, motivational factors were measured via questionnaires, and usability perceptions were evaluated using the Technology Acceptance Model (TAM) questionnaire. Results revealed that women learned significantly more than men in the non-gamified condition, while men outperformed women in the gamified condition. Furthermore, men reported higher enjoyment, usefulness, and comfort with the gamified tool than women. Interestingly, both genders indicated greater satisfaction with the non-gamified version. These findings contribute nuanced insights into how gamification impacts genders differently in programming education, suggesting that gamification may hinder women’s learning while modestly benefiting men. The study highlights the importance for practitioners to carefully consider gender dynamics when implementing gamified approaches, potentially offering customization options or blended techniques to optimize learning outcomes for all students in programming education. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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18 pages, 2634 KiB

Open AccessArticle

Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning

by Shan Wang, Hongtao Wang, Yijun Lu and Jiandong Huang

Appl. Sci. 2024, 14(12), 5020; https://doi.org/10.3390/app14125020 - 8 Jun 2024

Cited by 2 | Viewed by 1264

Abstract

By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases [...] Read more.

By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases the burden on educators. Therefore, this study aims to use a machine learning model to build a model to evaluate students’ learning levels for art education. To improve the prediction accuracy of the model, SVM was adopted as the basic model in this study, and was combined with SSA, ISSA, and KPCA-ISSA algorithms in turn to form a composite model. Through the experimental analysis of prediction accuracy, we found that the prediction accuracy of the KPCA-ISSA-SVMM model reached the highest, at 96.7213%, while that of the SVM model was only 91.8033%. Moreover, by putting the prediction results of the four models into the confusion matrix, it can be found that with an increase in the complexity of the composite model, the probability of classification errors in model prediction gradually decreases. It can be seen from the importance experiment that the students’ achievements in target subjects (PEG) have the greatest influence on the model prediction effect, and the importance score is 9.5958. Therefore, we should pay more attention to this characteristic value when evaluating students’ learning levels. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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Review

Jump to: Research, Other

21 pages, 629 KiB

Open AccessReview

Retrieval-Augmented Generation (RAG) Chatbots for Education: A Survey of Applications

by Jakub Swacha and Michał Gracel

Appl. Sci. 2025, 15(8), 4234; https://doi.org/10.3390/app15084234 - 11 Apr 2025

Viewed by 3046

Abstract

Retrieval-Augmented Generation (RAG) overcomes the main barrier for the adoption of LLM-based chatbots in education: hallucinations. The uncomplicated architecture of RAG chatbots makes it relatively easy to implement chatbots that serve specific purposes and thus are capable of addressing various needs in the [...] Read more.

Retrieval-Augmented Generation (RAG) overcomes the main barrier for the adoption of LLM-based chatbots in education: hallucinations. The uncomplicated architecture of RAG chatbots makes it relatively easy to implement chatbots that serve specific purposes and thus are capable of addressing various needs in the educational domain. With five years having passed since the introduction of RAG, the time has come to check the progress attained in its adoption in education. This paper identifies 47 papers dedicated to RAG chatbots’ uses for various kinds of educational purposes, which are analyzed in terms of their character, the target of the support provided by the chatbots, the thematic scope of the knowledge accessible via the chatbots, the underlying large language model, and the character of their evaluation. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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Other

Jump to: Research, Review

34 pages, 2096 KiB

Open AccessSystematic Review

The Role of Artificial Intelligence in Computer Science Education: A Systematic Review with a Focus on Database Instruction

by Alkmini Gaitantzi and Ioannis Kazanidis

Appl. Sci. 2025, 15(7), 3960; https://doi.org/10.3390/app15073960 - 3 Apr 2025

Viewed by 1314

Abstract

The integration of artificial intelligence (AI) into computer science (CS) education is evolving, yet its specific application in database instruction remains underexplored. This systematic review analyzes 31 empirical studies published between 2020 and 2025, examining how AI applications support teaching and learning in [...] Read more.

The integration of artificial intelligence (AI) into computer science (CS) education is evolving, yet its specific application in database instruction remains underexplored. This systematic review analyzes 31 empirical studies published between 2020 and 2025, examining how AI applications support teaching and learning in CS, with an emphasis on database education. Following the PRISMA methodology, the review categorizes AI applications according to instructional design models, roles, actions, benefits, and challenges. Findings indicate that AI tools, particularly chatbots, intelligent tutoring systems, and code generators, effectively support personalized instruction, immediate feedback, and interactive problem-solving across CS and database-specific contexts. However, challenges persist, including AI inaccuracies, biases, student dependency in AI, and academic integrity risks. The review also identifies a shift in programming education as AI reshapes software development practices, prompting a need to align curricula with evolving industry expectations. Despite growing attention to AI applications in programming education, database-related research remains limited. This review highlights the necessity for further empirical investigations specifically in database instruction, including more extensive studies addressing AI-driven pedagogical strategies and their long-term impacts. The results suggest that careful integration of AI tools can complement traditional instruction, emphasizing the critical role of human educators in achieving meaningful and effective learning outcomes. Full article

(This article belongs to the Special Issue ICT in Education, 2nd Edition)

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