A systematic review of artificial intelligence techniques for collaborative learning over the past two decades

As the popularity and accessibility of artificial intelligence (AI) in learning continue to rise, online education has increasingly incorporated AI. It is imperative to investigate how AI in the scholarly community supports the learning of autonomous students. This scoping review investigated articles that employed AI in discussion forums. The objective of this study is to develop a summary that denotes the effective integration of AI in discussion forums, thereby characterizing the overall learning experience. In accordance with the search, appraisal, synthesis, and analysis (SALSA) framework, the preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram was employed to design the article screening and selection process. Scopus was utilized for the literature search, resulting in the identification of twelve articles that met the predetermined inclusion and exclusion criteria. The extracted articles covered the following six applications: intelligent tutoring systems, AI-assisted apps, chatbots, automatic formative assessment, AI teaching assistants, and AI-assisted lifelong learning assistants. Three types of man-machine partnerships related to the activities are discussed: student-AI partnerships, student-AI-student partnerships, and student-AI-teacher partnerships, the latter being the most commonly observed. The generated content was frequently processed through teacher adjustments and checks. Components of community of inquiry theory were adapted as indicators. Cognitive presence was observed to be more predominant than social presence, with a particular emphasis on individual learning achievements. Three distinct perspectives regarding the role of AI in learning were identified. The first perspective posited that AI functioned as a virtual teacher, supplementing the teaching functions of human teachers and delivering information to students. In the second perspective, AI facilitated students in collecting information by enabling interactions with messages or peers. In the third perspective, the significance of the automatic evaluation function, which assessed learning activities and provided feedback to enhance the system while simultaneously implementing immediate changes in the learning process, was acknowledged. Regarding the role of communication, teachers clearly intended for students to acquire knowledge through it. Despite the focus on assisting individual learners, each article encompassed references from a wide range of fields and exhibited substantial diversity overall. Consequently, the domain of discussion forums with AI was depicted through the complex interplay between technology, pedagogy, and the learning environment. This context is known as entangled pedagogies, which emphasize understanding the interconnections among these elements and their reciprocal influences. In summary, AI was predominantly utilized in the form of chatbots and GenAI in discussion forums. Emerging AI roles included virtual teachers, peer/message matching, and automated evaluation. Aggregated information was presented in a manner that supported student learning. AI also served as a conduit between students and teachers.

Artificial Intelligence (AI) in learning and education is not new. However, the rise of the Covid-19 pandemic led to a focus on its role in Learning Management Systems (LMS). This study also examined the role of AI in the LMS with features including Natural Language Processing and Reasoning. The researchers used a self-proposed model and gathered data from the mass communication students in two public sector universities in Pakistan. Results revealed that Artificial Intelligence is integral to Learning Management Systems (LMS) in the selected institutions. Further, the effect of Natural Language Processing (NLP) on Reasoning indicated these two factors as interlinked, improving the quality of LMS. Additionally, Natural Language Processing significantly mediated the effect of AI on the LMS, showing that language processing facilitates LMS usage among students. Finally, the results also indicated the significant mediation effect of Reasoning on the relationship s between AI and the LMS. Overall, the results also remained supported towards the incorporation of Artificial Intelligence (AI) along with Natural Language Processing (NLP) and Reasoning in Learning Management Systems (LMS). The researchers conclude that AI in Learning Management Systems is widely applied in Pakistani institutions. The young students also acknowledge the benefits of AI-enabled LMS that have enhanced their learning experiences and provided them with logical solutions and answers to their complex queries. Further, the study limitations are discussed and highlighted accordingly.

In response to the growing popularity of artificial intelligence (AI) usage in daily life, AI education is increasingly being provided at the K-12 level, with relevant initiatives being launched worldwide. Examining how these programs have been implemented and summarizing useful experiences is thus imperative. Although prior reviews have described the characteristics of AI education programs in publications, the papers reviewed were mostly nonempirical reports, and the analysis typically only involved a descriptive summary. The current review focuses on the most recent empirical studies on AI teaching programs in K-12 contexts through a systematic search of the Web of Science database from 2010 to 2022. To provide a comprehensive overview of the status of AI teaching and learning (T&L), 32 empirical studies were analyzed both descriptively and thematically. We analyzed (1) the research status, (2) the pedagogical design, and (3) the assessments and outcomes of the AI teaching programs. An increasing number of studies have focused on AI education at the K-12 stage, but most of them have a small sample size. Moreover, the data were mostly collected through interviews and self-reports. We reviewed the pedagogical design of AI teaching programs by using Gerlach and Ely’s pedagogical design model. The results comprehensively delineated current AI teaching programs through nine dimensions: learning theory, pedagogical approach, T&L activities, learning content, scale, teaching resources, prior knowledge prerequisite, aims and objectives, assessment, and learning outcome. The results highlighted the positive impact of current AI teaching programs on students’ motivation, engagement, and attitude. However, we observed a lack of sufficient research objectively measuring students’ knowledge acquisition as learning outcomes. Overall, in this paper, we discussed relevant findings in terms of research trends, learning content, teaching units, characteristics of the pedagogical design, and assessment and evaluation by providing illustrations of exemplary designs; we also discussed future directions for research and practice in AI education in the K-12 context.

The use of Artificial Intelligence (AI) in student learning is an increasingly relevant issue in the context of modern education. This article outlines the main issues by explaining the challenges and potential of AI in learning. The purpose of this article is to provide acomprehensive understanding of the role of AI in student learning in Indonesia. The research method used is a literature review, which includes the analysis of various sources and perspectives related to theuse of AI in higher education. The data used in this article is information from various literature sources, including research results, scientific articles, and news related to the implementation of AI in education. Examples of AI applications, such as chatbots for academic guidance, online learning systems, and automated assessments, were obtained from these sources. The results of the data analysis show thatthe use of AI in student learning has great potential to improve the quality of education, but it is necessary to pay attention to ethical challenges, the risk of dependency, and the important role of humaninteraction. Solutions such as AI ethics education, strict regulations, and the integration of AI with human interaction become integral parts of the conclusion of this article. In the era of digital transformation, adeep understanding of the role of AI in education, particularly in the context of chatbots for academic guidance, online learning systems, and automated assessments, is key to enhancing the quality of education and preparing students for a technology-driven future.

Simple SummaryDistinguishing between benign vs. malignant bone lesions is often difficult on imaging. Many bone lesions are infrequent or rarely seen, and often only specialist radiologists have sufficient expertise to provide an accurate diagnosis. In addition, some benign bone tumours may exhibit potentially aggressive features that mimic malignant bone tumours, making the diagnosis even more difficult. The rapid development of artificial intelligence (AI) techniques has led to remarkable progress in image-recognition tasks, including the classification and characterization of various tumours. This study will review the most recent evidence for AI techniques in differentiating bone lesions on various imaging modalities using a systematic approach. Potential clinical applications of AI techniques for clinical diagnosis and management of bone lesions will also be discussed.An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Artificial intelligence (AI) and its impact on society have received a great deal of attention in the past five years since the first Stanford AI100 report. AI already globally impacts individuals in critical and personal ways, and many industries will continue to experience disruptions as the full algorithmic effects are understood. However, with regard to education, adopting in disciplines remains limited largely to Computer Science and Information Technology in postsecondary education. Recent advances with technology are especially promising for their potential to create and scale personalized learning for students, to optimize strategies for learning outcomes, and to increase access to a more diverse population. Research has confirmed that the current use of AI in education (AIEd) leads to positive outcomes, including improved learning outcomes for students, along with increased access, increased retention, lower cost of education, and decreased time to completion. Future uses of AI will include the following: enabling engaging and interactive education anytime and anywhere; personalized AI mentors that will help students identify and reach their goals; and mass-personalization that will allow AI to be tailored to each student’s learning style, level, and needs. Yet with all the potential benefits that AI and machine learning (ML) may provide students, there remains a general reticence to adopt this technology because of misconceptions and perceptions that elementary educators will need expensive equipment, robust support from IT, or to retool and learn programming or coding. As such, this study seeks to identify current perceptions early childhood, and elementary educators in the state of Missouri, USA have towards AI in general; the policies, training and existing resources in districts regarding technology in general and AI in particular; relative comfort with technology and willingness of educators to adopt new technologies for classroom instruction; and perform a needs assessment for necessary infrastructure, including reliable internet access, hardware and software. Results indicate a broad acceptance and willingness to adopt AI in daily activities and classroom instruction, but barriers to entry were identified as lack of resources and training.

Medical imaging and radiation science students' use of artificial intelligence for learning and assessment

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

This study examines the interdisciplinary connections that result from deep learning applications in artificial intelligence (AI) across several fields. A division of machine learning recognized as "deep learning" has proven to be remarkably effective at drawing out intricate patterns and representations from enormous datasets. Deep learning algorithms have made important improvements in fields as well as computer vision, natural language processing, robotics, finance, healthcare, advertising, and more by utilizing deep neural networks. This study’s goal is to evaluate the impact of deep learning in AI across disciplines and to draw attention to its revolutionary potential. The study examines the specific uses of deep learning in several fields and talk about the potential and problems that come with its deployment. The study provides a look at how precise object recognition, scene comprehension, and image production made deep learning transform the computer vision. Deep learning approaches have improved machine translation, sentiment analysis, and dialogue systems in natural language processing. This research study offers insights into the possibility of interdisciplinary cooperation by looking at the uses, difficulties, and future prospects of deep learning in AI. It demonstrates the transformative impact of deep learning in fostering creativity and advancement across a variety of sectors, opening the door for the creation of more intelligent and advanced AI systems.

This study examines students' perceptions of the positive and negative impacts of using Artificial Intelligence (AI) in learning in the digital era. In the digital era, AI is growing rapidly and its potential is expanding in education. Understanding students' perceptions is key to implementing AI effectively and optimally. This study used a qualitative method with a descriptive approach and involved 114 students at two universities in Lampung. Data was collected through an online questionnaire. The results show that most students have a positive perception of the impact of AI in learning. AI helps them focus on learning, understand the material more easily, complete tasks faster, and make learning more interesting. However, some students also have concerns about the negative impacts of AI, such as laziness in learning, dependence on technology, and potential misuse of AI. This study concludes that AI has great potential to improve the quality of student learning. It should be understood that AI cannot replace the role of lecturers, but it can assist lecturers and students in the learning process. It is important to use AI carefully and balance it with conventional learning. The results of this research are expected to contribute to various parties in achieving better educational goals.

The development of digital technology has revolutionized the paradigm of elementary school education. Artificial Intelligence (AI) has become one of the significant breakthroughs in the application of artificial intelligence in the learning process. The purpose of this study is to explore the integration of AI in language learning to improve the digital literacy of elementary school children in the context of educational futurology. Through a literature study and trend analysis approach to identify various forms of AI utilization, such as educational chatbots, NLP (Natural Language Processing)-based applications, and adaptive platforms tailored to children's learning styles. This study discusses the ethical, pedagogical, and cultural challenges in the application of AI, as well as its implications for the development of language skills, children's understanding of literature, and the strengthening of cultural identity. The results of the analysis show that AI has great potential in supporting comprehensive digital literacy, provided that it is designed with a humanistic and contextual approach. The limitations of this study lie in the exploratory and predictive nature of the analysis, the limitations of generalization due to the heterogeneity of AI interventions, and the potential for publication bias that needs to be taken into account. Further long-term empirical research is needed to validate these findings. The practical implications of this research are the importance of synergy between educators, technology developers, and policymakers. Teachers are trained to become active users, technology developers design integrated contextual content, and policymakers develop ethical guidelines and adaptive regulations for inclusive, sustainable, and culturally sensitive AI implementation

This systematic literature review explores the integration of artificial intelligence (AI) technologies such as intelligent tutoring systems (ITS), machine learning, natural language processing, and adaptive learning platforms in university education. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we evaluated peer-reviewed articles, case studies, and government reports from 2015 onwards. The results demonstrate that AI technologies not only enhance personalized learning and educational outcomes but also streamline administrative functions, transforming educational practices. However, challenges such as ethical issues, data privacy, and algorithmic bias remain. The review underscores the importance of theoretical frameworks like constructivist learning theory and the TPACK framework for effective AI integration. Recommendations are provided for educators, administrators, and policymakers to ensure responsible AI use in university settings. This paper offers insights into the current capabilities and future prospects of AI in higher education, promoting ongoing research and strategic implementation.

Along with the development of technology, the use of information and communication technology (ICT) in the world of education is increasingly inevitable. One form of ICT utilization that is currently developing rapidly is the use of artificial intelligence (AI) in the learning process. AI technology has great potential in supporting the teaching and learning process, both in terms of delivering materials, interacting with students, and in terms of assessment and evaluation. In junior high schools, especially at SMPN 3 Taman, learning Indonesian still faces challenges in developing students' writing skills, especially in narrative learning materials, such as making short stories. Learning short stories requires creativity, understanding of text structure, and good language skills. However, this learning process is often hampered by limited teaching resources and less innovative teaching techniques. This study analyzes the effectiveness of using AI media in learning to write short stories at Taman 3 State Junior High School, Sidoarjo Regency. This study uses a qualitative approach with a case study method in several secondary schools that have implemented AI in Indonesian language learning, By using a case study method involving observation, interviews, and document analysis, the results of the study show that AI contributes to increasing student creativity, helping to compile a more systematic storyline, and increasing student confidence in writing. However, the use of AI in learning to write short stories still requires teacher guidance so that it remains in line with educational goals and does not reduce students' natural creativity.

Artificial Intelligence (AI) is increasingly embedded in secondary education, yet its impact on high school students and teachers remains underexplored. This study employed a sequential explanatory mixed-methods design to investigate AI adoption, attitudes, and concerns among 545 students and 24 teachers in Kashmir, India. Quantitative results, grounded in the Technology Acceptance Model and Sociocultural Learning Theory, showed that sociocultural and self-perceived learning emerged as the strongest predictors of positive attitudes, followed by ease of use and usefulness, while ethical concerns had a minor negative influence. Qualitative interviews revealed optimism about AI’s potential but highlighted risks related to plagiarism, over-reliance, and assessment authenticity. Findings point to the need for AI literacy, ethical training, and clear institutional policies to guide responsible integration. This study contributes one of the first comprehensive accounts of adolescent and teacher perspectives on AI in high school learning, offering evidence-based recommendations for pedagogy and policy.

Artificial intelligence is penetrating various fields. The demand for interdisciplinary talent is increasingly important, while interdisciplinary educational activities for high school students are lagging behind. Project‐based learning (PBL) in artificial intelligence (AI) and robotic education activities supported by a robotic sailboat platform, the sailboat test arena (STAr), has been shown to popularise AI and robotic knowledge in young students. In the implementation of the programme, PBL was provided for students, and gamification pedagogy was applied to increase participants' learning motivation and engagement. The results show that the proposed STAr‐based programme is capable of delivering the desired knowledge and skills to students at high school levels. The assessment results suggest that most students achieve learning outcomes on average. Students showed more interest in AI and marine disciplines and were willing to participate in more such educational programs. The findings fill the research gap that few existing education platforms have facilitated the teaching and learning of AI and marine disciplines for high school students.