Automated Assessment Tool Research Articles

A Systematic Literature Review on Artificial Intelligence in English Language Education

This systematic literature review explores the application of artificial intelligence (AI) in English language education, focusing on studies published between 2020 until 2024 from Malaysia and international contexts. A total of 15 articles were analysed to examine the impact of AI-driven tools such as tutoring systems, game-based apps, and automated assessment tools on learning outcomes, student engagement, and language proficiency. The findings highlight significant improvements in motivation, personalized learning experiences, and enhanced language skills facilitated by AI platforms. However, ethical considerations and regional disparities in technology access pose challenges. Recommendations for future research emphasize longitudinal studies on AI’s efficacy, the development of advanced AI tools, and comprehensive educator training. This review underscores AI’s transformative potential in shaping modern language education while advocating for continued exploration and ethical integration.

A quality assessment tool for focused abdominal sonography for trauma examinations using artificial intelligence.

Current tools to review focused abdominal sonography for trauma (FAST) images for quality have poorly defined grading criteria or are developed to grade the skills of the sonographer and not the examination. The purpose of this study is to establish a grading system with substantial agreement among coders, thereby enabling the development of an automated assessment tool for FAST examinations using artificial intelligence (AI). Five coders labeled a set of FAST clips. Each coder was responsible for a different subset of clips (10% of the clips were labeled in triplicate to evaluate intercoder reliability). The clips were labeled with a quality score from 1 (lowest quality) to 5 (highest quality). Clips of 3 or greater were considered passing. An AI training model was developed to score the quality of the FAST examination. The clips were split into a training set, a validation set, and a test set. The predicted scores were rounded to the nearest quality level to distinguish passing from failing clips. A total of 1,514 qualified clips (1,399 passing and 115 failing clips) were evaluated in the final data set. This final data set had a 94% agreement between pairs of coders on the pass/fail prediction, and the set had a Krippendorff α of 66%. The decision threshold can be tuned to achieve the desired tradeoff between precision and sensitivity. Without using the AI model, a reviewer would, on average, examine roughly 25 clips for every 1 failing clip identified. In contrast, using our model with a decision threshold of 0.015, a reviewer would examine roughly five clips for every one failing clip - a fivefold reduction in clips reviewed while still correctly identifying 85% of passing clips. Integration of AI holds significant promise in improving the accurate evaluation of FAST images while simultaneously alleviating the workload burden on expert physicians. Diagnostic Test/Criteria; Level II.

Artificial Intelligence (AI) as a Tool to Address Academic Challenges in South African Higher Education

This systematic literature review (SLR) study explores artificial intelligence (AI) as a tool to address academics challenges in South African higher education. Numerous difficulties beset higher education system in South Africa, such as a lack of funding, high student-to-lecturer ratios, and unequal access to high-quality education. Relevant academic databases, such as PubMed, Google Scholar, ERIC, and Web of Science, was searched using a combination of keywords relating to South African higher education, AI in education, academic issues, and AI technologies. A total of twenty (20) documents were identified and used in this study. The examination looks at how AI is currently being used, how it affects how people teach and learn, and the opportunities and challenges that come with using it in this setting. The results show that artificial intelligence (AI) technologies, including automated assessment tools, intelligent tutoring systems, and adaptive learning platforms, have the potential to improve personalized learning experiences, boost student engagement and academic performance, and facilitate effective management of educational resources. However, overcoming several issues, such as a lack of technological know-how, restricted infrastructure, concerns about equity, and ethical implications is necessary for the successful integration of AI in South African higher education. It encourages educators, politicians, and technology experts to work together to establish long-term solutions for incorporating AI into teaching and learning processes while maintaining inclusivity, equity and ethical issues.

The Potential of Automated Assessment of Cognitive Function Using Non-Neuroimaging Data: A Systematic Review.

Background/Objectives: The growing incidence of cognitive impairment among older adults has a significant impact on individuals, family members, caregivers, and society. Current conventional cognitive assessment tools are faced with some limitations. Recent evidence suggests that automating cognitive assessment holds promise, potentially resulting in earlier diagnosis, timely intervention, improved patient outcomes, and higher chances of response to treatment. Despite the advantages of automated assessment and technological advancements, automated cognitive assessment has yet to gain widespread use, especially in low and lower middle-income countries. This review highlights the potential of automated cognitive assessment tools and presents an overview of existing tools. Methods: This review includes 87 studies carried out with non-neuroimaging data alongside their performance metrics. Results: The identified articles automated the cognitive assessment process and were grouped into five categories either based on the tools' design or the data analysis approach. These categories include game-based, digital versions of conventional tools, original computerized tests and batteries, virtual reality/wearable sensors/smart home technologies, and artificial intelligence-based (AI-based) tools. These categories are further explained, and evaluation of their strengths and limitations is discussed to strengthen their adoption in clinical practice. Conclusions: The comparative metrics of both conventional and automated approaches of assessment suggest that the automated approach is a strong alternative to the conventional approach. Additionally, the results of the review show that the use of automated assessment tools is more prominent in countries ranked as high-income and upper middle-income countries. This trend merits further social and economic studies to understand the impact of this global reality.

Utilization of Artificial Intelligence in Strengthening the Pancasila Student Profile Project Integrated with STEM

As the essence of education, the curriculum will persistently undergo changes and renewal to align with societal needs and the ever-expanding global challenges. The integration of STEM and the Pancasila Student Profile Strengthening Project is essential in the context of education in Indonesia. By utilizing STEM, educators can contextually and relevantly teach Pancasila values, enabling students to comprehend scientific and technological concepts and incorporate these noble values into their daily lives. However, STEM learning in schools often faces challenges, such as limited resources, less innovative methods, and students' difficulties understanding abstract concepts. Technological advances have brought significant changes in various aspects of life in recent years. Artificial intelligence extends beyond computer science and informatics, permeating multiple disciplines. Educators can utilize the introduction of artificial intelligence (AI) in the education sector as a tool to enhance learning. AI is designed to process cognitive modeling of human thinking related to how a machine can record information, imitate, and modify automatically. Therefore, this study aims to investigate the integration of AI in P5 with STEM. We conducted this research at SMP Negeri 1 Purwosari using a qualitative approach. The research results demonstrate the use of AI in the integrated STEM P5 activities, beginning with activity planning, implementation, and evaluation. AI guides activity planning, integrates directly into technology, and functions as an automated assessment tool, delivering prompt and precise feedback. Therefore, overall, AI has outstanding potential to help improve the effectiveness of the learning process and introduce students to technology that will play an essential role in their future.

Artificial Intelligence in the Education of Teachers: A Qualitative Synthesis of the Cutting-Edge Research Literature

The integration of Artificial Intelligence (AI) into teacher education has been transformative, offering personalized learning experiences, enhanced professional development, and improved teaching methodologies. AI technologies such as Intelligent Tutoring Systems (ITS), AI-driven analytics, and automated assessment tools have become central to modern educational practices, significantly improving engagement, adaptability, and effectiveness. This study employs a qualitative thematic analysis of current literature on AI in teacher education, examining peer-reviewed articles and reports using thematic coding to identify key patterns, opportunities, and challenges. The findings reveal that AI enhances teacher education by providing personalized learning pathways, fostering critical thinking, and supporting ongoing professional growth. Technologies like ITS, Virtual Reality (VR), and AI-driven analytics have proven effective in promoting motivation and engagement among teachers. However, ethical challenges such as biases in AI systems and concerns regarding data privacy require continuous attention. Furthermore, a gap in teacher preparedness, particularly in developing AI literacy and integrating AI tools into classroom practices, is evident. Despite these challenges, AI offers substantial benefits, transforming teaching practices and enabling personalized, adaptive instruction that supports both teachers and students. The study emphasizes the need for comprehensive teacher training programs focusing on digital literacy and ethical AI use, ensuring educators can navigate an AI-enhanced educational environment effectively. This research contributes to the ongoing discourse by highlighting the need for ethical guidelines and robust teacher training programs, offering actionable insights for educators, policymakers, and institutions aiming to integrate AI into teacher education

"Navigating the complexities of low-Grade glioma treatment: insights into SBT I-125 and novel assessment tools".

Central nervous system tumors, classified by the WHO into four grades based on their aggressiveness, present significant challenges in treatment, particularly low-grade gliomas (LGGs) which, despite their slower growth, can progress to high-grade gliomas. Lucca B. Palavani and colleagues evaluated the efficacy and safety of SBT I-125 brachytherapy for LGMs in a systematic review and meta-analysis of 20 studies involving 988 patients. The analysis revealed an overall complication rate of 10%, with headaches and cyst formation being the most frequent issues. The five-year progression-free survival (PFS) rate was 66%, while the ten-year PFS rate was 30%, and the rate of malignant transformation was 26%. The mortality rate was 33%. Despite these findings, significant limitations were noted, including data insufficiencies, study heterogeneity, lack of randomized controlled trials, and potential publication bias. Inconsistencies in follow-up durations further hindered the evaluation of long-term efficacy and safety. Recent advancements in automated tumor assessment, such as Cheng et al.'s deep learning-based pipeline, are revolutionizing glioma management by enhancing the accuracy and consistency of tumor volume and RANO measurements. These innovations facilitate improved glioma grading, genetic mutation prediction, surgical planning, real-time intraoperative guidance, and histopathological analysis. Integrating such advanced tools into clinical practice can significantly enhance the precision and efficiency of glioma management. In conclusion, while SBT I-125 brachytherapy shows promise, concerns regarding safety and efficacy underscore the need for further research with standardized methodologies. Incorporating advanced automated assessment tools could improve treatment evaluation and patient outcomes.

Exploring predictors of interaction among low-birth-weight infants and their caregivers: a machine learning–based random forest approach

BackgroundQuality caregiver-infant interaction is crucial for infant growth, health, and development. Traditional methods for evaluating the quality of caregiver-infant interaction have predominantly relied on rating scales or observational techniques. However, rating scales are prone to inaccuracies, while observational techniques are resource-intensive. The utilization of easily collected medical records in conjunction with machine learning techniques offers a promising and viable strategy for accurate and efficient assessment of caregiver-infant interaction quality.MethodsThis study was conducted at a follow-up outpatient clinic at two tertiary maternal and infant health centers located in Shanghai, China. 68 caregivers and their 3-15-month-old infants were videotaped for 3–5 min during playing interactions in non-threatening environment. Two trained experts utilized the Infant CARE-Index (ICI) procedure to assess whether the caregivers were sensitive or not in a dyadic context. This served as the gold standard. Predictors were collected through Health Information Systems (HIS) and questionnaires, which included accessible features such as demographic information, parental coping ability, infant neuropsychological development, maternal depression, parent-infant interaction, and infant temperament. Four classification models with fivefold cross-validation and grid search hyperparameter tuning techniques were employed to yield prediction metrics. Interpretable analyses were conducted to explain the results.ResultsThe score of sensitive caregiver-infant interaction was 6.34 ± 2.62. The Random Forest model gave the best accuracy (83.85%±6.93%). Convergent findings identified infant age, care skills of infants, mother age, infant temperament-regulatory capacity, birth weight, positive coping, health-care-knowledge-of-infants, type of caregiver, MABIS-bonding issues, ASQ-Fine Motor as the strongest predictors of interaction sensitivity between infants and their caregiver.ConclusionsThe proposed method presents a promising and efficient approach that synergistically combines rating scales and artificial technology to detect important features of caregiver-infant interactions. This novel approach holds several implications for the development of automatic computational assessment tools in the field of nursing studies.

Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills.

Health professional education stands to gain substantially from collective efforts toward building video databases of skill performances in both real and simulated settings. An accessible resource of videos that demonstrate an array of performances - both good and bad-provides an opportunity for interdisciplinary research collaborations that can advance our understanding of movement that reflects technical expertise, support educational tool development, and facilitate assessment practices. In this paper we raise important ethical and legal considerations when building and sharing health professions education data. Collective data sharing may produce new knowledge and tools to support healthcare professional education. We demonstrate the utility of a data-sharing culture by providing and leveraging a database of cardio-pulmonary resuscitation (CPR) performances that vary in quality. The CPR skills performance database (collected for the purpose of this research, hosted at UK Data Service's ReShare Repository) contains videos from 40 participants recorded from 6 different angles, allowing for 3D reconstruction for movement analysis. The video footage is accompanied by quality ratings from 2 experts, participants' self-reported confidence and frequency of performing CPR, and the demographics of the participants. From this data, we present an Automatic Clinical Assessment tool for Basic Life Support that uses pose estimation to determine the spatial location of the participant's movements during CPR and a deep learning network that assesses the performance quality.

Implementing Privacy Preserving Techniques in Distributed Big Data Framework on Cloud Computing

Cloud-based deployment architectures have emerged as the preferred model for Big Data operations due to their scalability, flexibility, and cost-effectiveness. However, this shift raises new security concerns as data is no longer directly under the user's control. Securing Big Data in cloud environments is crucial for widespread adoption, but developing a comprehensive security plan is challenging without a thorough analysis of potential vulnerabilities.To address this, a novel security-by-design framework for Big Data deployment on cloud computing, termed Big Cloud, is proposed in this article. The framework leverages systematic security analysis methodologies and automated assessment tools to map domain-specific security knowledge to design best practices. Validation of the framework was conducted through an Apache Hadoop stack use case, demonstrating its effectiveness in enhancing security awareness and reducing design time.The study also evaluates the framework's strengths and limitations, identifying key challenges in the Big Cloud domain.

Bridging the Gap between Project-Oriented and Exercise-Oriented Automatic Assessment Tools

In this study, we present the DP Plugin for IntelliJ IDEA, designed to extend the Drop Project (DP) Automatic Assessment Tool (AAT) by making it more suitable for handling small exercises in exercise-based learning environments. Our aim was to address the limitations of DP in supporting small assignments while retaining its strengths in project-based learning. The plugin leverages DP’s REST API to streamline the submission process, integrating assignment instructions and feedback directly within the IDE. A student survey conducted during the 2022/23 academic year revealed a positive reception, highlighting benefits such as time efficiency and ease of use. Students also provided valuable feedback, leading to various improvements that have since been integrated into the plugin. Despite these promising results, the study is limited by the relatively small percentage of survey respondents. Our findings suggest that an IDE plugin can significantly improve the usability of project-oriented AATs for small exercises, informing the development of future educational tools suitable for mixed project-based and exercise-based learning environments.

The Role of AI in Improving Student Learning Outcomes: Evidence in Vietnam

This study delves into the transformative role of AI in enhancing student learning outcomes in Vietnam, examining both the effectiveness of AI-driven educational technologies and their inclusivity across various learning environments. The research reveals that personalized learning systems and automated assessment tools substantially boost student engagement and learning efficiency. However, significant challenges remain, notably the digital divide and the substantial infrastructure and professional development investments to support widespread AI adoption. The implications of these findings underscore the necessity of strategic management in AI integration to ensure equitable access and effective utilization across all educational sectors. Contributing a nuanced perspective to the discourse on AI in education, this study underscores the potential of AI to revolutionize educational practices while highlighting the critical barriers that must be overcome. It offers concrete recommendations for policymakers, educators, and technology providers to optimize AI's benefits while addressing its challenges. This research provides a foundational analysis for future explorations into strategic AI integration, emphasizing the need to balance technological advancements with equitable educational practices in developing contexts like Vietnam.

ProgEdu4Web: An automated assessment tool for motivating the learning of web programming course

AbstractThe expanding software industry has increased the demand for proficient programming professionals. However, more than technical expertise is needed for successful software development; code quality assurance and effective teamwork are also indispensable. Unfortunately, recent graduates often exhibit deficiencies in programming, code quality assurance, and teamwork skills, highlighting the need for improvements in these areas. In response to these challenges, this study presents ProgEdu4Web, an innovative Automated Programming Assessment System designed to evaluate code quality and teamwork proficiency in web programming. The system integrates automatic source code evaluation tools for static code analysis, ensuring timely feedback on coding quality issues. To assess the effectiveness of ProgEdu4Web, a questionnaire and hypothesis model was employed, surveying a cohort of 111 students enrolled in web programming classes. The study's findings demonstrate that the system significantly alleviated instructor workload while motivating students to enhance their web programming with good code quality and teamwork abilities.

Automated Assessment Tool for 3D Computer-Aided Design Models

Computer-aided design (CAD) has become an integral part of engineering education, particularly for those studying mechanical engineering. By providing practical skills that are highly valued in the engineering industry, proficiency in CAD systems enhances students’ employability. Generally, CAD systems provide students with the tools and knowledge necessary to excel in their engineering education and future careers. In order to help teachers to give the best training to their students and to make the right evaluations, an automatized tool is needed to support the evaluation of CAD models during training sessions. After an extensive bibliographical search, this paper proposes a CAD Model Automatized Assessment (MAA) Tool for mechanical courses called the CAD MAA Tool. This tool is mainly based on a developed model that takes into account different aspects of modeling, such as geometric, feature-based, and parametric modeling. To correctly evaluate a given part compared to a reference one, the proposed model uses different coefficients fixed by the teacher according to their teaching strategies or course objectives.

Dampak Kecerdasan Buatan (AI) terhadap Pola Pikir Cerdas Mahasiswa di Pontianak

This research examines the impact of artificial intelligence (AI) on the growth mindset of students in Pontianak. AI has become a revolutionary technology applied in education, such as adaptive learning systems, virtual assistants, and automated assessment tools that provide real-time feedback. The use of AI has the potential to enhance critical and analytical thinking skills, as well as encourage students to develop a more open and adaptive mindset. However, this research also found that excessive reliance on AI can hinder the development of deep thinking and creativity among students, with a tendency to seek quick solutions instead of engaging in exploratory thinking. Through a mixed-methods approach that combines quantitative and qualitative methods, this research identifies the positive and negative impacts of AI on students' mindsets. Quantitative analysis shows that the use of AI enhances thinking flexibility, while qualitative interviews indicate a potential decrease in motivation for independent thinking due to technology dependence. This research offers strategic recommendations for the wise use of AI in education, in order to maximize its benefits without diminishing students' cognitive abilities.

NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations

Image noise and vascular attenuation are important factors affecting image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA). The aim of this study was to develop an algorithm that automatically performs noise and attenuation measurements in CCTA and to evaluate the ability of the algorithm to identify non-diagnostic examinations. The algorithm, “NoiseNet”, was trained and tested on 244 CCTA studies from the Swedish CArdioPulmonary BioImage Study. The model is a 3D U-Net that automatically segments the aortic root and measures attenuation (Hounsfield Units, HU), noise (standard deviation of HU, HUsd) and signal-to-noise ratio (SNR, HU/HUsd) in the aortic lumen, close to the left coronary ostium. NoiseNet was then applied to 529 CCTA studies previously categorized into three subgroups: fully diagnostic, diagnostic with excluded parts and non-diagnostic. There was excellent correlation between NoiseNet and manual measurements of noise (r = 0.948; p < 0.001) and SNR (r = 0.948; <0.001). There was a significant difference in noise levels between the image quality subgroups: fully diagnostic 33.1 (29.8–37.9); diagnostic with excluded parts 36.1 (31.5–40.3) and non-diagnostic 42.1 (35.2–47.7; p < 0.001). Corresponding values for SNR were 16.1 (14.0–18.0); 14.0 (12.4–16.2) and 11.1 (9.6–14.0; p < 0.001). ROC analysis for prediction of a non-diagnostic study showed an AUC for noise of 0.73 (CI 0.64–0.83) and for SNR of 0.80 (CI 0.71–0.89). In conclusion, NoiseNet can perform noise and SNR measurements with high accuracy. Noise and SNR impact image quality and automatic measurements may be used to identify CCTA studies with low image quality.

Artificial intelligence-based diagnosis in fetal pathology using external ear shapes.

Here we trained an automatic phenotype assessment tool to recognize syndromic ears in two syndromes in fetuses-=CHARGE and Mandibulo-Facial Dysostosis Guion Almeida type (MFDGA)-versus controls. We trained an automatic model on all profile pictures of children diagnosed with genetically confirmed MFDGA and CHARGE syndromes, and a cohort of control patients, collected from 1981 to 2023 in Necker Hospital (Paris) with a visible external ear. The model consisted in extracting landmarks from photographs of external ears, in applying geometric morphometry methods, and in a classification step using machine learning. The approach was then tested on photographs of two groups of fetuses: controls and fetuses with CHARGE and MFDGA syndromes. The training set contained a total of 1489 ear photographs from 526 children. The validation set contained a total of 51 ear photographs from 51 fetuses. The overall accuracy was 72.6% (58.3%-84.1%, p<0.001), and 76.4%, 74.9%, and 86.2% respectively for CHARGE, control and MFDGA fetuses. The area under the curves were 86.8%, 87.5%, and 90.3% respectively for CHARGE, controls, and MFDGA fetuses. We report the first automatic fetal ear phenotyping model, with satisfactory classification performances. Further validations are required before using this approach as a diagnostic tool.

Transforming Education: The Evolving Role of Artificial Intelligence in The Students Academic Performance

As technology continues to advance, its integration into various aspects of education has become inevitable. This article delves into the transformative impact of Artificial Intelligence (AI) on students' academic performance. AI's role in education has shifted from a mere supplementary tool to a fundamental component reshaping teaching and learning paradigms. This paper explores how AI-powered educational technologies, such as adaptive learning platforms, intelligent tutoring systems, and automated assessment tools, are revolutionizing traditional educational practices. It examines the benefits AI brings to students, educators, and educational institutions, including personalized learning experiences, enhanced student engagement, and efficient assessment mechanisms. Furthermore, the article discusses the potential challenges and ethical considerations associated with AI integration in education, such as data privacy concerns, algorithmic biases, and the digital divide. By analyzing recent research findings and real-world implementations, this paper provides insights into the evolving landscape of education empowered by AI and underscores the importance of responsible AI adoption to optimize students' academic outcomes in the digital era.

Artificial intelligence for caries detection: a novel diagnostic tool using deep learning algorithms.

The aim of this study was to develop an assessment tool for automatic detection of dental caries in periapical radiographs using convolutional neural network (CNN) architecture. A novel diagnostic model named ResNet + SAM was established using numerous periapical radiographs (4278 images) annotated by medical experts to automatically detect dental caries. The performance of the model was compared to the traditional CNNs (VGG19, ResNet-50), and the dentists. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique shows the region of interest in the image for the CNNs. ResNet + SAM demonstrated significantly improved performance compared to the modified ResNet-50 model, with an average F1 score of 0.886 (95% CI 0.855-0.918), accuracy of 0.885 (95% CI 0.862-0.901) and AUC of 0.954 (95% CI 0.924-0.980). The comparison between the performance of the model and the dentists revealed that the model achieved higher accuracy than that of the junior dentists. With the assist of the tool, the dentists achieved superior metrics with a mean F1 score of 0.827 and the interobserver agreement for dental caries is enhanced from 0.592/0.610 to 0.706/0.723. According to the results obtained from the experiments, the automatic assessment tool using the ResNet + SAM model shows remarkable performance and has excellent possibilities in identifying dental caries. The use of the assessment tool in clinical practice can be of great benefit as a clinical decision-making support in dentistry and reduce the workload of dentists.

CODE, CLICK, LEARN: A SYSTEMATIC REVIEW OF ONLINE ASSESSMENT TOOLS IN 21ST CENTURY PROGRAMMING EDUCATION

This study investigates the contemporary landscape of 21st-century programming education, recognizing the imperative for adaptive pedagogical approaches. Delving into the evolution and impact of automated assessment tools in programming education, the research identifies the problem of enhancing student proficiency through effective assessment methodologies. The dual purpose of the study is to elucidate diverse teaching methods and strategies, highlighting innovative approaches fostering comprehensive comprehension and skill acquisition and to unravel the transformative role of Technology-Enhanced Learning and Assessment Tools in Programming Education, exploring their implications on pedagogical practices and learning outcomes. Employing advanced search techniques, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological framework uses advanced search techniques to meticulously analyse 27 scholarly articles obtained from the Web of Science, ERIC, and SCOPUS databases using keywords such as "programming," "online assessment," "tool," and "feedback," resulting in a synthesis that encapsulates the current state of programming education. The principal results underscore the efficacy of automated assessments in gauging student proficiency and illuminate diverse teaching methods fostering enhanced comprehension. It also highlights three pivotal themes: (1) Automated Assessment in Programming Education, (2) Teaching Methods and Strategies in Programming Education, and (3) Technology-Enhanced Learning and Assessment Tools in Programming Education. The major conclusions drawn from this comprehensive synthesis serve as a guide for teachers, policymakers, and researchers navigating the dynamic intersection of technology and pedagogy in programming education, laying a robust foundation for future advancements in the field.