Adaptive Learning Environment Research Articles

Educational Big Data Analytics for Futuristic Smart Learning Using Deep Learning Techniques

The goal is to use the massive amounts of data created by digital education systems to develop intelligent and adaptable learning environments that are particularly suited to each student’s requirements. The rapid digitization of education systems has led to the proliferation of educational big data, presenting unprecedented opportunities to reshape learning environments into intelligent, responsive spaces that adapt to the needs of individual learners. This paper explores the integration of advanced deep learning techniques with educational big data analytics to forge the path towards futuristic smart learning ecosystems. By leveraging robust datasets derived from a myriad of educational interactions, ranging from student performance metrics to engagement patterns in digital learning platforms, we propose a multi-tiered analytical framework that harnesses the predictive power of deep learning. We commence by elucidating the scope and scale of educational big data, highlighting its potential to provide granular insights into student learning processes. The paper then delineates the architecture of a deep learning-based analytical model designed to process complex, multidimensional educational datasets. This model applies state-of-the-art algorithms to perform tasks such as predictive analytics for student performance, personalized content recommendation, and real-time engagement monitoring. Central to our discussion is the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs) in deciphering patterns and trends that escape traditional analytical methodologies. We emphasize the capacity of these techniques to capture the subtleties of learner behavior and to facilitate the development of adaptive learning pathways. Furthermore, we address the challenges of integrating deep learning with educational big data, including issues of data privacy, computational demands, and the need for robust model interpretation. The paper presents a series of case studies that demonstrate the successful application of our proposed framework in various educational settings, from K-12 to higher education and continuous professional development.

The Digital Shift: Is the Trend of Transferring Developmental Mathematics Coursework to Computerized Adaptive Learning Environment Effective?

The Digital Shift: Is the Trend of Transferring Developmental Mathematics Coursework to Computerized Adaptive Learning Environment Effective?

Modeling Frustration Trajectories and Problem-Solving Behaviors in Adaptive Learning Environments for Introductory Computer Science

Frustration, an inherent part of the classroom, is routinely associated with negative stigmas, however, if appropriately managed it can be a highly effective tool for excelling students. Often referred to as eustress, it is the principle that follows the premise of introducing students to a topic they are not familiar with, causing frustration as they learn the currently unfamiliar topic. In the experiment, a correlating pattern involving behavior can properly monitor frustration in a way that remains productive to the student. This process can be emulated using artificial intelligence in schools (AIED) to create adaptive algorithms that provide students with unfamiliar topics until the point that said student becomes sufficiently frustrated, at which point the AI would slow down the curriculum. Through discernment patterns in learners’ frustration levels and problem-solving strategies, AI can dynamically adjust the difficulty of tasks to accommodate each student’s personal needs, as well as the pacing of instruction, and the provision of support. In doing so, AIED could mitigate the adverse effects of extreme frustration and reaping the benefits of frustration that pushes students to a farther level than our current educational system can provide. With AIED creating the ideal structure of adaptive scaffolded learning, students’ needs can be individually fostered in a way that most preferably suits them. This ability to push students into learning curriculum at such an intricate level will allow students to further develop their independence when solving future problems as the high difficulty will authenticate the student's ability to grasp the concept extensively. The intricate interplay between AIED and frustration models is crucial for advancing the frontiers of human knowledge and pushing students to achieve their full potential.

The role of student motivation in integrating AI into web design education: A longitudinal study

Amidst the current wave studies of artificial intelligence (AI) in education, this longitudinal case study, spanning Spring 2023 to Spring 2024, delves into the integration of AI in the UI/UX web design classroom. By introducing both text-based and image-based AI tools to students with varying levels of skill in introductory web design and user experience (UX) courses, the study observed a significant enhancement in student creative capabilities and project outcomes. The utilization of text-based generators markedly improved writing efficiency and coding, while image-based tools facilitated better ideation and color selection. These findings underscore the potential to augment traditional educational methods, providing students with novel avenues for creativity and innovation. At the same time, the goal of this study was also to ascertain the factors that led to the adoption of AI tools in the educational workflow, specifically focusing on student major and background, thereby illuminating how AI can be tailored to meet diverse educational needs and foster a more adaptive and innovative learning environment. The findings reveal that students were more receptive to integrating AI tasks into their workflows when these tasks did not directly relate to their major field of study. For example, Computer Science students exhibited less resistance to using AI for selecting color palettes, a task outside their primary focus, compared to utilizing AI for coding. Additionally, the study observed a significant growth in both awareness and usage of AI tools among students throughout the duration of the research. This trend suggests an increasing incorporation of AI technologies into their standard toolkit, highlighting a broader acceptance and integration of AI in educational practices.

In-person and Hybrid Learning in the Training of Professionals in Higher Education. An ict-centric Approach

Objective: Creating more adaptive, dynamic, and student-centered learning environments that can meet the diverse needs and learning styles of students in the digital age. Methods: This study employs a qualitative approach through documentary research, enabling us to understand the integration of virtual inquiry into the classroom as a meaningful process for instructing university students. The study population consists of research conducted between 2018 and 2024, with the sample selected based on eligibility criteria. Inclusion criteria involve specific databases such as Scielo, Redalyc, Dialnet, Google Scholar, and Scopus. Conversely, articles lacking categories such as virtual reality, teaching, and university education are excluded. Result: The documentary review matrix analyzes the work of fifteen prominent authors in the field of face-to-face and blended learning in higher education, focusing on information and communication technologies (ICT). Each entry provides the title of the work, the author's name, the year of publication, a brief summary, and the Digital Object Identifier (DOI) to access the full text. These works cover a wide range of topics, from the theoretical foundations of instructional design to the practical application of educational technologies in face-to-face and hybrid environments. The diversity of theoretical and methodological approaches is highlighted, as well as the presence of recognized authors in the field. Additionally, special attention is given to emerging trends and innovative technologies, such as virtual reality, artificial intelligence, and mobile learning. In summary, these resources offer a comprehensive and up-to-date overview of the state of the art in the field, being highly valuable for educators, instructional designers, and researchers interested in advancing their understanding and application of these topics.

Towards believable and educational conversations with virtual patients

Virtual Reality (VR) technology allows the design and application of realistic but adaptive learning environments in medical education. In particular, virtual patient systems have logistical and methodological advantages compared to non-computerized interventions. However, evidence for their effectiveness is fragmented as any educational domain introduces its requirements regarding learning goals, measurements of learning outcomes, and application design. In this context, we present preliminary results of evaluating a VR training application for conducting a clinical interview to diagnose mental disorders in children and adolescents using virtual patients. The evaluation focuses on design elements related to the virtual patient’s appearance and natural language capabilities. Our results indicate that our virtual patient design is highly believable and that our dialog system is satisfying. However, conversational flow requires optimization. We discuss design directions and potential enhancements for learner-virtual patient interactions in VR and address future operations to evaluate the effectiveness of our approach.

Developing an adaptive virtual learning environment for sustainable learning in individuals with Down syndrome

PurposeThe purpose of the paper is to describe the development and implementation of a specialized Virtual Learning Environment (VLE) designed to enhance the knowledge and skills related to sustainability in students with Down syndrome. This VLE serves as a means to make sustainable concepts more accessible and comprehensible to this specific student group, with the aim of promoting their engagement and understanding of sustainability, environmental awareness, recycling, and sustainable construction. The ultimate goal is to empower students with Down syndrome by providing them with a tailored educational tool that facilitates their learning in a manner that is engaging and effective.Design/methodology/approachThe approach outlines the overarching plan for creating the e-learning platform, including the technological choices and design considerations necessary to make it effective and accessible for students with Down syndrome. It's a fundamental component of the methodology, as it sets the direction for the platform's development and aligns with the objectives of the study. And also encompass the strategy for teaching and learning sustainability aspects to students with Down syndrome.FindingsPositive Feedback from Tutors and Professionals: The feedback from tutors and professionals is generally positive, with 91.4% finding the platform to be well-organized and 88.6% considering the content adequate and understandable. This suggests that the VLE met the needs and expectations of educators and professionals involved in the learning process. Utility for Professional Practice: Approximately 80% of tutors and professionals found the platform useful for their professional practice, indicating that it has practical applications beyond student learning. This information highlights the success and potential impact of the VLE for this specific target group.Research limitations/implicationsThe study may not have explored the depth of sustainability concepts covered within the VLE. Future research could delve into the specifics of the content and its effectiveness in teaching complex sustainability topics.Practical implicationsThe incorporation of universal design principles in the VLE development could serve as a model for creating inclusive e-learning platforms. This has broader implications for improving digital accessibility in education. The positive feedback from tutors and professionals suggests the importance of interdisciplinary collaboration in education. Professionals from various fields, including special education and sustainability, can work together to create effective and inclusive learning tools.Social implicationsThis study can contribute to the broader discussion on inclusive education and the effective use of technology to enhance learning experiences for individuals with disabilities.Originality/valueThe study addresses a crucial gap in the field of sustainability education by focusing on students with Down syndrome. It highlights the importance of making sustainability education inclusive and accessible to a diverse range of learners, including those with disabilities. This originality contributes to the broader discourse on inclusive education and environmental awareness. The development of a specialized Virtual Learning Environment (VLE) for this specific target group is an original contribution. It demonstrates the potential for adapting educational technology to meet the unique needs of students with Down syndrome, potentially serving as a model for future educational tool development.

The Role of Artificial Intelligence in Personalizing Learning for Each Student

Artificial Intelligence (AI) has become one of the major technological innovations in modern education. This article explores the role of AI in personalizing each student's learning. Individual learning is a learning method that adapts each student's learning experience to their needs, abilities and interests. AI enables this personalization of learning to be more efficient and productive. AI plays an important role in personalizing each student's learning. With the ability to analyze data, deliver personalized content, provide better feedback, and predict student progress, AI creates a more adaptive and effective learning environment. However, it should be remembered that the role of the teacher as a learning facilitator is still very important in the use of AI, because human interaction is still an important factor in an effective learning process. The method used in this research is a quantitative method. Researchers conducted a survey using a Google Form which consisted of 15 sentences related to the search title. The study found that applying artificial intelligence (AI) to personalize each student's learning produces significant tracking effects in educational settings. Overall, the use of AI in personalized learning has a significant positive impact on education. However, this requires careful oversight and regulation to ensure that this technology is used properly and provides maximum benefit to all students. The limitation of this research is that the researcher only conducted research in schools, and the researcher did not conduct research directly in schools, but distributed a survey link on a Google form which contained statements about the role of artificial intelligence (AI) in individualization. learning of each student.

Understanding Clinical Learning Quality Aspects in Mental Health Nursing Practice Course among Students: A Comprehensive Examination.

There is a growing demand for comprehensive evaluations of the clinical learning quality of nursing education and the necessity to establish robust predictors and mediators for enhancing its outcomes within the context of mental health practice. This study is threefold: 1. Evaluating nursing students' clinical learning quality before and after mental health nursing practice; 2. Establish if the grade of a theoretical course in mental health nursing and the student's perception of their theoretical knowledge level predicts the grade of mental health nursing practice; 3. Explore how model learning opportunities, self-directed learning, safety, and nursing care quality mediate learning environment quality and tutorial strategies quality following mental health nursing practice. Using a before and after the study, 107 undergraduate nursing students at an Israeli university completed a questionnaire and the Clinical Learning Quality Evaluation Index tool to assess their perceptions of clinical learning quality before and after mental health nursing practice. The results showed a decline in students' perceptions of tutorial strategy quality following mental health practical learning in clinical settings, with the theoretical course grade predicting the practical experience grade and underscoring the mediating role of learning opportunities between the learning environment and tutorial strategies. The study's findings emphasize the importance of an adaptive learning environment and a solid theoretical foundation in fostering effective tutorial strategies and enhancing the overall learning outcomes for nursing students in mental health education.

Project VoLL-KI

Abstract“Learning from Learners” (“Von Lernenden Lernen”, “VoLL-KI ” for short) is a is collaborative research project with the goal of creating a practical toolbox of instruments at different levels of abstraction to improve the learning experience and outcomes for students of artificial intelligence. Learning and teaching at tertiary education institutions are currently IT-based but not AI-supported. The VoLL-KI project aims to go this crucial next step. Using AI approaches (e.g., ML, symbolic AI, statistical AI), several educational technologies are developed on the different granularity of the study programs (e.g., course, semester, program). So, dashboards for study planning, recommender systems improving student advisory services and learning material selection, VR-based learning experiences, educational chatbots, and adaptive learning environments are developed. The systems are mostly deployed as prototypes

УПРОВАДЖЕННЯ СИСТЕМ УПРАВЛІННЯ НАВЧАННЯМ З ФУНКЦІЯМИ ГЕНЕРАТИВНОГО ШТУЧНОГО ІНТЕЛЕКТУ В УМОВАХ ПОСТ-ПАНДЕМІЇ

In recent years, the global education system has undergone a transformative shift, relying increasingly on online tools to prove its resilience in the face of various challenges, including epidemiological events. This paper seeks to introduce a Learning Management System (LMS) designed to elevate the delivery of educational content, fostering the training and development of students' knowledge in a dynamic and adaptive learning environment anchored to the connectivity and synchronization needs of smart urban living. The system proposes the integration of a language generation model equipped with Application Programming Interface (API) access. This choice not only streamlines instructor support but also facilitates the creation of courses using an adapted and coherent language. The system goes beyond traditional LMS functionalities by providing suggestions to enhance and diversify lessons. Emphasizing flexibility and adaptability, the proposed LMS caters to various training levels, being able to accommodate the needs of learners across different domains. To identify the appropriate solution, a comparative analysis was conducted, evaluating various platforms based on functionality, intuitive presentation, and customization options. The selected system emerged as the most favorable, offering a robust framework for the development and delivery of educational content. To demonstrate the system's effectiveness, a curriculum was crafted for a specialized field of study - Artificial Intelligence (AI), with a specific focus on the practical application of Machine Learning algorithms. This curriculum incorporates theoretical and practical application components, complemented by a suite of assessment tools and assignments tailored to the proposed subject. The lessons within this curriculum were constructed by drawing insights from various bibliographical sources. What sets this system up-to-date is its integration of generative AI features directly into the LMS, enriching the teaching-learning-evaluating experience.

Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies

AbstractAdaptive learning technologies are closely related to learners' self‐regulatory processes in individual and collaborative learning. This study presents the outcomes of a systematic literature review of empirical evidence on adaptive learning environments to foster self‐regulation and shared regulation of learning in collaborative settings. We provide an overview of what and how adaptive technologies have been used to understand and promote self‐regulated learning in collaborative contexts. A search resulted in 38 papers being analysed. Specifically, we identified the seven main objectives (feedback and scaffolding, self‐regulatory skills and strategies, learning trajectories, collaborative learning processes, adaptation and regulation, self‐assessment, and help‐seeking behaviour) that the adaptive technology research has been focusing on. We also summarize the implications derived from the reviewed papers and frame them within seven thematic areas. Finally, this review stresses that future research should consider developing a converging theoretical framework that would enable concrete monitoring and support for self‐regulation and socially shared regulation of learning. Our findings set a baseline to support the adoption and proliferation of adaptive learning technology within self‐regulated learning research and development. Practitioner notesWhat is already known about this topic By providing personalized and learner‐centric adaptive learning environments (ADLEs), adaptive learning technology can support and foster self‐regulated learning (SRL) practices. It is possible to create a more student‐centred and effective learning environment by combining adaptive learning and collaborative learning. Socially shared regulatory activities can involve planning, monitoring, controlling and reflecting on a group's learning processes. What this paper adds Provides a systematic literature review of empirical evidence on ADLEs, SRL and socially shared regulation of learning (SSRL) in collaborative contexts. Summarizes the insights on (S)SRL through ADLEs in collaborative learning. Identifies challenges and opportunities for ADLEs to support (S)SRL in collaborative learning. Implications for practice and/or policy Learning analytics and educational technology researchers will be able to use the systematic review as a guide for future research. Learning analytics and educational technology practitioners will be able to use the systematic review as a summary of the field's current state.

Investigating popular topics for the integration of the internet of things in physical education: A content analysis in higher education

Background Problems: Advanced technologies, such as the Internet of Things (IoT), have played an increasingly important role in the development of physical education in higher education. Research Objectives: This study aims to conduct a content analysis of the use of IoT technology in physical education in higher education. Methods: Scopus and Web of Science databases were used to collect and analyse data for this study, with a total of 178 articles obtained, consisting of 104 articles (Scopus) and 74 articles (Web of Science). The final data used for this study consisted of 20 articles, as it only targeted the top five topics related to the internet of things (IoT) in physical education after being analysed using ScientoPy. Findings and Results: The analysis identified five main topics related to the application of the internet of things in physical education in higher education, including cloud, artificial intelligence, deep learning, big data, and virtual reality. The implications of these findings for the development of physical education in higher education are discussed in this study, emphasizing the importance of IoT technology integration in creating a more adaptive and responsive learning environment. Conclusion: The contributions of this research include providing deeper insights into how IoT technologies can be applied in physical education in higher education, as well as highlighting the potential to improve the effectiveness of learning and teaching in this area. The conclusions of this study emphasises the importance of continuing to develop innovations in the application of IoT technology in physical education in higher education in order to achieve a more efficient and competitive learning environment.

GRAMMAROUS Revolutionizing English Vocabulary Acquisition for Children through Gamified Learning

Interactive Learning through Gamified Activities: The Grammarous App This paper presents Grammarous, a novel Android application that revolutionizes children's English vocabulary acquisition through interactive and gamified activities. By leveraging cutting-edge technologies and principles of gamification, Grammarous incorporates a diverse range of engaging features tailored to cater to various learning styles, making vocabulary acquisition an enjoyable and highly effective process for young learners. The app employs innovative techniques such as Shake-to-Spell, where children can rearrange scrambled letters into words by shaking their devices, fostering both visual and kinesthetic learning. Other features include Fill-in-the-blanks to reinforce memory and spelling, Spelling Bees for friendly competition, Rearrange Words to stimulate critical thinking, Matching Games for solidifying vocabulary relationships, and Shake Alphabets for letter recognition and mastery. Grammarous leverages Firebase Authentication for secure user management and real-time data retrieval to facilitate personalized learning experiences. By seamlessly integrating education and technology, the app creates an immersive and adaptive learning environment that actively engages children and promotes cognitive development. With its innovative approach to gamifying language learning, Grammarous has the potential to revolutionize the field of educational technology, providing a fun and effective platform for children to acquire essential English vocabulary skills crucial for academic and personal growth. Keywords: android app, vocabulary, gamification, spelling bee, innovative, educational games, language expansion, interactive learning, cognitive development, personalized education

Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling

AbstractWhile the potential of personalized education has long been emphasized, the practical adoption of adaptive learning environments has been relatively slow. Discussion about underlying reasons for this disparity often centers on factors such as usability, the role of teachers, or privacy concerns. Although these considerations are important, I argue that a key factor contributing to this relatively slow progress is the inherent complexity of developing adaptive learning environments. I focus specifically on the modeling techniques that provide the foundation for adaptive behavior. The design of these models presents us with numerous challenges, nuances, and trade-offs. Awareness of these challenges is essential for guiding our efforts, both in the practical development of our systems and in our research endeavors.

Reframing A Self-Organized Learning Environment (SOLE) to Accommodate Indonesian EFL Students’ Learning Loss

In the wake of the COVID-19 lockdown, students have faced heightened learning loss, attributed to factors such as inadequate learning methods, restricted access to education, decreased instructional time, and emotional stressors. Particularly affected is English language learning, prompting the need for effective remedies. This study proposes the implementation of new Self-Organized Learning Environments (SOLEs) as a strategic response to mitigate learning setbacks, especially in post-pandemic contexts. Employing a systematic review of relevant literature and employing established methodologies for position paper composition, this research undertakes a comprehensive analysis of SOLEs' effectiveness in nurturing students who experienced learning loss. Divided into three focal areas, the study explores the evolving new role of teachers within SOLEs, the integration of SOLEs in English as a Foreign Language (EFL) pedagogy, and the utilization of digital applications to enhance language-focused SOLE practices. Through this multifaceted analysis, the study aims to offer innovative insights and actionable recommendations for addressing learning loss and fostering resilient educational strategies in the aftermath of the pandemic.The results of this study contribute valuable knowledge to the ongoing discourse on educational recovery, with implications for educators, policymakers, and stakeholders invested in creating adaptive and effective learning environments.

Building paths to success: a multilevel analysis of the effects of an emotional intelligence development program on the academic achievement of future teachers.

The situation generated by the recent pandemic has had several effects on education, one of them being the necessary but hasty efforts of teachers and students to adapt to the demands of a virtual classroom environment. Thus, it is essential to promote the development of digital competencies in future teachers, enabling them to create effective learning situations in the digital medium. Moreover, the European university curriculum includes a set of specific competencies (specific to each degree) and a series of generic competencies, many of which are related to emotional intelligence. Our work emerges from these specific premises and the more general need to develop emotional skills and learning abilities in virtual environments for future educators. The sample comprised 240 students in higher education, pursuing a bachelor's degree in education at the University of Alicante, Spain (68.3% were female and average age of the participants was 19.43 years SD = 4.127). Using a quasi-experimental design with pretests, posttests, and a control group, we present the effects of an online program aimed at improving the emotional skills and academic achievement of future teachers in higher education. In this study, a 30-h emotional intelligence training program was implemented. Since the student sample was grouped into different classes, we analyzed, using a generalized linear mixed-effects model, whether students who took the program showed a significant improvement in their academic achievement at the end of their studies, compared to those who did not take it. The findings show a significant improvement in the academic achievement at the end of the bachelor's degree studies of the students in the experimental group. These results open up a field of possibilities for the implementation of this type of training using virtual environments, enabling interventions to enhance the emotional development of the teaching staff (and, subsequently, in their students), develop their teaching careers adequately, and employ the ideal strategies to address educational programs. Looking ahead, the integration of emotional development programs that incorporate emerging technology into university curricula could enhance the preparation of educators profoundly and create adaptable learning environments for contemporary students.

Integrating emerging technologies into classrooms: a multimedia perspective

This research aimed to understand how emerging technologies are transforming pedagogical practices in education. The main objective was to analyze the adoption of technologies such as augmented reality, artificial intelligence, and mobile learning in classrooms, assessing their impact on student engagement and motivation. The methodology was based on a literature review, following Gil's guidelines (2002), allowing for a comprehensive analysis of recent studies on the subject. The results showed a significant impact of emerging technologies on education, offering opportunities for more engaging and adaptive learning environments, as discussed by authors like Freitas (2017) and Alves and Nogueira (2019). However, challenges such as resistance to change, resource limitations, and the need for continuous teacher training were identified. The final considerations highlighted the importance of emerging technologies as transformative elements in education, despite the existing challenges. This study emphasizes the need to overcome these obstacles to maximize the benefits of technological innovations in education.

A Novel Neural Ensemble Architecture for On-the-fly Classification of Evolving Text Streams

We study on-the-fly classification of evolving text streams in which the relation between the input data and target labels changes over time—i.e., “concept drift.” These variations decrease the model’s performance, as predictions become less accurate over time and they necessitate a more adaptable system. While most studies focus on concept drift detection and handling with ensemble approaches, the application of neural models in this area is relatively less studied. We introduce Adaptive Neural Ensemble Network ( AdaNEN ), a novel ensemble-based neural approach, capable of handling concept drift in data streams. With our novel architecture, we address some of the problems neural models face when exploited for online adaptive learning environments. Most current studies address concept drift detection and handling in numerical streams, and the evolving text stream classification remains relatively unexplored. We hypothesize that the lack of public and large-scale experimental data could be one reason. To this end, we propose a method based on an existing approach for generating evolving text streams by introducing various types of concept drifts to real-world text datasets. We provide an extensive evaluation of our proposed approach using 12 state-of-the-art baselines and 13 datasets. We first evaluate concept drift handling capability of AdaNEN and the baseline models on evolving numerical streams; this aims to demonstrate the concept drift handling capabilities of our method on a general spectrum and motivate its use in evolving text streams. The models are then evaluated in evolving text stream classification. Our experimental results show that AdaNEN consistently outperforms the existing approaches in terms of predictive performance with conservative efficiency.

Artificial intelligence for human learning: A review of machine learning techniques used in education research and a suggestion of a learning design model

The goal of this research is to (1) identify the status and development of AI and ML-based learning support systems and their impact on human learning, with a specific focus on techniques employed in previous research, and (2) demonstrate the process of designing a learning support system using AI. Artificial intelligence (AI) and machine learning (ML) technologies have received attention in education. The existing research on AI in education is examined, considering the implications of its application in research. Noteworthy ML techniques from the literature are explained, followed by a discussion on leveraging AI and ML technologies to enhance learning support. Additionally, with consideration of both front-end and back-end approaches,a framework for incorporating AI into education is proposed. Subsequently, a learning design model, Self-regulated Learning with AI Assistants (SLAA), is suggested for addressing the objectives of AI-based learning support system design. The categorization of AI and ML techniques in education research reveals nine types, including supervised learning, mining approaches, and Bayesian techniques. The exploration illustrates how these techniques can be employed in designing a learning support system. This paper provides an empirical overview of AI in education, addresses technological and pedagogical considerations for developing personalized and adaptive learning environments, and outlines the challenges and potential future research directions.