Knowledge tracing is a well-known problem in AI for education, consisting of monitoring how the knowledge state of students changes during the learning process and accurately predicting their performance in future exercises. In recent years, many advances have been made thanks to various machine learning and deep learning techniques. Despite their…

Providing educators with understandable, actionable, and trustworthy insights drawn from large-scope heterogeneous learning data is of paramount importance in achieving the full potential of artificial intelligence (AI) in educational settings. Explainable AI (XAI)--contrary to the traditional "black-box" approach--helps fulfilling this…

Neural and symbolic architectures are key techniques in AI for learner modelling, enhancing adaptive educational services. Symbolic models offer explanation and reasoning for decisions but require significant human effort. On the other hand, neural architectures demand less human input and yield better predictions, yet lack interpretability. Given…

This study introduces a novel approach for predicting student engagement levels in a language-based AI curriculum. The curriculum was integrated into English Language Arts classrooms, in which 106 students from five classes participated five web-based machine learning and text mining modules for 2 weeks. Sentiment and categorical analyses,…

In order to promote the growth of contemporary music and the reform of music, this article designs an improved collaborative filtering (CF) algorithm to solve the problem of sparse matrix in traditional recommendation algorithms. The data matrix is dimensionally reduced to find the nearest neighbor, so as to realize personalized recommendation of…

Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a…

The influence of Artificial Intelligence on higher education is increasing. As important drivers for student retention and learning success, generative AI-tools like translators, paraphrasers and most lately chatbots can support students in their learning processes. The perceptions and expectations of first-years students related to AI-tools have…

This paper critically examines the transformative potential of Artificial Intelligence (AI) in Zimbabwe's higher education system, focusing on how AI can enhance learning outcomes and optimize administrative processes. The study employs a qualitative research approach, gathering insights from key stakeholders in the educational sector to identify…

This article presents a research investigation focusing on the effects of ChatGPT utilization on sustainable education and development. The study employed five machine learning (XGBoost, RF, SVM, GBDT, and ANN) models for predicting the impacts of ChatGPT usage in education, aiming at identifying the potential benefits of ChatGPT usage on…

Traditional education systems do not provide students with much freedom to choose the right training of study that suits them, which leads on long-term to the negative effects not only on social, economic and mental' well-being of student, but also will have a negative effect on the quality of the work produced by this student in the future. In…

The use of Artificial Intelligence (AI) in education is transforming various dimensions of the education system, such as instructional practices, assessment strategies, and administrative processes. It also plays an active role in the progression of science education. This systematic review attempts to render an inherent understanding of the…

The application of machine learning (ML) has grown and is now used to enhance learning outcomes. In blended classroom settings, ML, emerging smartphones and wearable technologies are commonly used to improve teaching and learning. The combination of these advanced technologies and ML plays a crucial role in enhancing real-time feedback quality.…

Although early behavioral intervention is considered as empirically-supported for children with autism, estimating treatment prognosis is a challenge for practitioners. One potential solution is to use machine learning to guide the prediction of the response to intervention. Thus, our study compared five machine algorithms in estimating treatment…

The recent surge of online language learning services in the past decade has benefitted second language learners. However, there is a lack of understanding of whether learners, especially young learners, are engaged in online learning, and how educators can enhance the engagement of the online learning experience. This study examines an artificial…

This study investigates the potential of automated classification using prompt-based learning approaches with transformer models (large language models trained in an unsupervised manner) for a domain-specific classification task. Prompt-based learning with zero or few shots has the potential to (1) make use of artificial intelligence without…