AI enabled Virtual Collaborative Learning Classroom

This paper discusses the proposed model of the collaborative virtual learning system for the introductory computer programming course which uses one of the collaborative learning techniques known as the “Think-Pair-Share”. The main objective of this study is to design a model for an online learning system that facilitates the collaborative learning activities in a virtual environment such as online communications and pair or small group discussions. In order to model the virtual learning environment, the RUP methodology has been used where it involves the data collection phase and the analysis and design phase. Fifty respondents have been randomly selected to participate in the data collection phase to investigate the students’ interest and learning styles as well as their learning preferences. The results have shown the needs for the development of online small group discussions that can be used as an alternative learning style for programming courses. The proposed design of the virtual learning system named as the Online Collaborative Learning System or OCLS is being depicted using the objectoriented models which are the use-case model and class diagram in order to show the concise processes of virtual “Think-Pair-Share” collaborative activities. The “Think-PairShare” collaborative learning technique that is being used in this model has been chosen because of its simplicity and relatively low-risk. This paper also presents the proposed model of the system’s architecture that will become the guidelines for the physical development of OCLS using the web-based applications.

PurposeThe purpose of this study is to critically investigate the effects of artificial intelligence (AI)-driven learning environments on learner autonomy, knowledge co-construction and digital equity in Thailand’s online education context. It aims to analyze how algorithmic learning tools and AI-based systems shape learners' educational experiences, particularly focusing on whether these technologies enhance or constrain learner autonomy and collaborative interactions. Additionally, it evaluates ethical and social implications, particularly the digital divide, algorithmic biases and the transparency of AI-mediated educational processes. The study seeks to provide actionable insights and recommendations for policymakers, educators and developers to optimize AI integration for inclusive lifelong learning.Design/methodology/approachThis research adopts a qualitative case study methodology involving in-depth, semi-structured interviews, participant observations and detailed document analysis from 30 diverse participants, including urban and rural learners, educators and instructional designers. Employing actor-network theory (ANT) and posthumanist theoretical frameworks, the study critically examines interactions between human and non-human actors within AI-driven educational environments. This approach allows for comprehensive exploration of participants' lived experiences, perceptions and interactions with AI technologies. Triangulation of multiple data sources ensures depth and reliability, providing nuanced insights into how AI systems influence learner autonomy, collaborative learning processes and educational equity.FindingsThe study identifies AI-driven learning platforms as having dual impacts on learner autonomy, simultaneously enabling greater self-directed learning through personalized guidance and real-time feedback while imposing algorithmic constraints that restrict intellectual exploration. AI-facilitated knowledge co-construction effectively organizes and structures peer interactions yet often results in depersonalized, transactional communication lacking deep human engagement. Furthermore, significant digital inequities are evident, with rural learners disproportionately disadvantaged due to limited technological infrastructure and algorithmic biases embedded within AI-driven platforms. These inequities highlight a critical need for inclusive, transparent and ethical design practices to ensure equitable access and meaningful learner agency.Research limitations/implicationsThe qualitative, context-specific nature of the case study limits the generalizability of findings beyond Thailand’s online educational environment. The rapidly evolving nature of AI technologies suggests that findings reflect a specific temporal context, necessitating continuous research updates. Participant selection bias may have influenced findings, with participants potentially holding strong pre-existing opinions on AI-driven education. Future studies should consider longitudinal analyses to understand long-term impacts, comparative cross-cultural research to validate findings and deeper exploration of algorithmic biases, fairness and ethical considerations in AI-driven educational tools, particularly how they affect diverse socio-economic groups.Practical implicationsPractically, this research underscores the necessity of designing AI-driven learning platforms that genuinely empower learner autonomy and critical thinking rather than restrict or overly dictate educational pathways. Educational practitioners and technology developers should implement transparent AI decision-making processes, enabling learners to understand and critically evaluate algorithmic recommendations. Policies must prioritize digital infrastructure expansion and digital literacy programs to reduce urban-rural digital disparities. Furthermore, pedagogical practices should integrate human-centric approaches to complement AI tools, ensuring that AI technologies serve to enhance, rather than replace, essential human interactions within collaborative learning environments.Social implicationsThe study emphasizes that AI-driven education holds significant potential for democratizing lifelong learning by offering personalized and adaptive learning experiences. However, it also warns of the risks associated with reinforcing existing socio-economic and digital inequalities, particularly between urban and rural learners. Addressing algorithmic biases and digital divides through targeted policy interventions, infrastructural investments and inclusive educational practices is critical for fostering equity and inclusivity in education. By ensuring that AI platforms are accessible and beneficial for all learner demographics, societies can leverage technology as a tool for broad-based educational empowerment and social development rather than a mechanism perpetuating exclusion.Originality/valueThis research contributes original insights into the nuanced impacts of AI-driven learning on learner autonomy, collaborative interactions and digital equity within the Thai educational context – areas relatively underexplored in existing literature. By uniquely integrating actor-network theory and posthumanist perspectives, it offers a sophisticated understanding of the interplay between humans and technologies in educational settings. The empirical findings deliver crucial evidence-based recommendations for educators, policymakers and developers aiming to ethically and inclusively leverage AI in education. Ultimately, the study enriches ongoing discussions on balancing technological advancement with human-centric, equitable and socially responsible educational practices.

An optimized group formation scheme to promote collaborative problem-based learning

This paper investigates recent trends in the digitalisation of foreign language teaching, focusing on the impact of technology on language learning. The development of digital tools ranging from language learning platforms, virtual learning, mobile and e-learning, and virtual and augmented reality has dramatically transformed the language teaching and learning landscape in recent times. The COVID-19 pandemic expedited the adoption of digital classrooms, involving synchronous and asynchronous learning modes to enhance accessibility and flexibility. Furthermore, Artificial Intelligence (AI) plays a pivotal role today in language teaching and learning, i.e. adaptive learning, engagement of AI-powered tutors, and automated assessments. Gamification has also proven to be an influential motivating factor, capturing students' interest with game-like features. The paper also investigates innovative teaching methods facilitated through digitalisation, including blended learning, flipped classrooms, and collaborative learning. Despite obstacles, including the digital divide, technological skills, and concerns about data privacy, digitalisation offers significant prospects for advancements in language education. The recent trends are presented with supportive data from secondary sources on how digital tools have been successfully integrated into language teaching and that integrating digitalisation and adapting pedagogical approaches are essential for maximising the benefits of technology in foreign language teaching.

Engagement in virtual learning is critical to participant satisfaction, performance, and adherence, particularly in domains such as online education and virtual rehabilitation, where interactive communication is essential for success. However, accurately measuring engagement in virtual group settings remains a significant challenge. There is growing interest in leveraging artificial intelligence for large-scale, real-world, and automated engagement recognition. Although engagement has been widely studied among younger populations in academic contexts, research methods and datasets focused on older adults in virtual and telehealth learning environments remain limited. Existing approaches often overlook the contextual relevance of learning materials and the longitudinal dynamics of engagement across sessions. This paper presents OPEN (Older adult Patient ENgagement), a novel dataset created to support the development of artificial intelligence models for engagement recognition. The dataset was collected from eleven older adults participating in virtual group learning sessions over six weeks as part of their cardiac rehabilitation programs, yielding over 35 hours of data, which represents the largest dataset of its kind. While raw video is withheld to protect privacy, the publicly released data include facial, hand, and body joint landmarks, as well as behavioral and affective features extracted from video. Observational annotations comprise second-level binary engagement states, emotional and behavioral labels, and context-type details, such as whether the instructor addressed the group or an individual. Multiple versions of the dataset were generated using sample lengths of 5, 10, and 30 seconds, as well as variable-length segments. To demonstrate its utility, various machine learning and deep learning models were trained on the annotated data, achieving engagement recognition accuracy as high as 81%. OPEN provides a scalable foundation not only for advancing personalized, artificial intelligence-driven engagement recognition in aging populations but also for contributing to broader engagement recognition research.

BackgroundLearning digital technologies in higher education is a process of knowledge generation, and the rapid growth of technology in education has a significant impact on students’ learning behaviors, motivation, and knowledge development. Pakistan’s remarkable technological breakthrough has increased in the education field.Study objectivesThe study focuses on estimating students’ learning behaviors, identifying the positive influence of educational apps on digital learning platforms, and analyzing their impact on students’ motivation and knowledge development.Materials and methodsAccording to the study’s objectives, a questionnaire survey was conducted to gather the primary data. The participants were students of universities in Lahore city of Pakistan. For this study, the sample size was N = 300, carefully chosen using the purposive sampling technique. Of the respondents, there were 146 male and 154 female students, and the sample consisted of individuals aged 25–35 years. Smart-PLS-Bootstrapping, T-Values (PLS) 3.2.9 and the structural equation model (SEM) were applied to get the appropriate outcomes from the proposed study framework.ResultsSEM analysis results shows that all proposed hypotheses [Animated Movies (AM) –> Student Motivation (SM), Educational Apps (EA) –> Knowledge Development (KD), Learning Behavior (LB) –> Animated Movies, Learning Behavior –> Educational Apps, Learning Behavior –> Knowledge Development, Learning Behavior –> Virtual Classrooms (VCr), Virtual Classrooms –> Knowledge Development, Virtual Classrooms –> Student Motivation] are confirmed while Learning Behavior –> Student Motivation is not confirmed.ConclusionThis study found that digital learning platforms significantly impact students’ learning and what motivates them to learn. The study also found that using educational apps and virtual classrooms more often helps students learn more and be more motivated to learn.

Most existing software tools for online collaboration are designed to support the collaboration itself instead of the study of collaboration with a systematic team and task management system. In this report, we identify six important features for a platform to facilitate the study of online collaboration. We then introduce the Educational Testing Service Platform for Collaborative Assessment and Learning (EPCAL), which was developed to encompass these features to enable both large‐scale online collaboration itself and the research on these collaborations. The collaboration management features of the EPCAL also make it an ideal tool for teachers to carry out computer‐supported collaborative learning and assessment activities in classroom settings.

Indian educational satellite, EDUSAT, launched from GSLV was the world's first dedicated satellite to provide education to rural and urban students through Virtual Classrooms. Around 15 million students across 26 States of India receive education each year. UNESCO's Institute for Statistics (UIS) suggests that, over 68 million trained school teachers will be needed across the world to achieve the objective of Universal Education Goal. To realize this, we require conventional education, as well as Satellite Networks that foster interactive and inexpensive distance education through e-learning. This type of learning process will provide education for all. The Indian educational satellite networks are compared with those of other nations to understand the methodologies to improve literacy. The value addition provided by educational technologies like Artificial Intelligence, Cloud Computing, Big Data, Augmented Reality, Virtual Classroom, etc. in association with satellite networks, is also investigated. In the urban areas and Smart Cities, high tech education is best implemented by using high bandwidth satellite networks in conjunction with high speed terrestrial optical fiber links and modern EdTech. This paper examines the synergistic utilization of educational technologies with ubiquitous connectivity of satellites to provide education to tribal as well as remote hilly regions like the recently created Union Territory of Ladakh. This study gives input to the initiatives of the Ministry of Human Resources Development (MHRD) of the Government of India for the proposed New Education Policy (NEP), which envisages equitable and inclusive education for all.

Artificial Intelligence (AI) is a transformative field at the intersection of computer science, mathematics, and cognitive psychology.It involves the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.AI technologies, including machine learning, natural language processing, and robotics, are revolutionizing industries ranging from healthcare to finance, and reshaping the way we live, work, and interact with technology."In the 21st century, Artificial Intelligence (AI) has emerged as a transformative force reshaping various aspects of society and industry.With advancements in computing power, data availability, and algorithmic techniques, AI has made significant strides in enabling machines to perform tasks traditionally requiring human intelligence.One of the key drivers of AI progress in the 21st century has been machine learning, a subfield of AI that focuses on algorithms capable of learning from data and making predictions or decisions.Deep learning, a subset of machine learning that uses artificial neural networks with many layers, has particularly fuelled breakthroughs in areas such as image and speech recognition, natural language processing, and autonomous vehicles.AI applications have become pervasive across industries, revolutionizing sectors such as healthcare, finance, transportation, manufacturing, and entertainment.In healthcare, AI is being used for disease diagnosis, drug discovery, personalized medicine, and medical image analysis.In finance, it's employed for fraud detection, algorithmic trading, and customer service.Autonomous vehicles and smart transportation systems leverage AI for navigation, route optimization, and traffic management.Moreover, AI-driven virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous, enhancing user experience and productivity.AIpowered recommendation systems personalize content delivery on platforms like Netflix, Spotify, and Amazon, improving user engagement and satisfaction.However, alongside its transformative potential, AI raises ethical, social, and economic concerns.Issues such as job displacement due to automation, algorithmic biases, data privacy, and the potential misuse of AI for surveillance or military purposes require careful consideration and regulation.Overall, the 21st century has witnessed AI evolve from a theoretical concept to a practical technology with profound implications for society, promising both opportunities and challenges as it continues to advance. Artificial Intelligence and Human Society:Artificial Intelligence (AI) is deeply intertwined with human society, impacting various aspects of our daily lives, work, and culture.Here's how AI influences human society:1. Workforce Automation: AI technologies automate routine and repetitive tasks across industries, leading to increased efficiency and productivity.However, concerns about job displacement and the need for reskilling and upskilling arise as AI replaces certain human roles.2. Healthcare: AI applications in healthcare facilitate disease diagnosis, personalized treatment plans, drug discovery, and medical imaging analysis.AI-driven predictive analytics also help in identifying health risks and optimizing patient outcomes.3. Education: AI enhances education through personalized learning platforms, intelligent tutoring systems, and adaptive assessments tailored to individual student needs.It also enables the development of educational content and resources based on learner preferences and performance.4. Communication and Interaction: AI-driven virtual assistants, chatbots, and language translation services improve communication and accessibility.Natural language processing enables human-like interactions with AI systems, enhancing user experience and accessibility for people with disabilities.

Implementation of Multimedia-assisted Collaborative Game-Based Learning to Improve Competitive Spirit and Problem Solving Ability in Literacy Learning for Elementary Students. Objectives: This study aims to analyze the effectiveness of collaborative game-based learning assisted by multimedia in improving the competitive spirit and problem solving skills of literacy learning for elementary school students. Methods: A quantitative research approach was used, with an experimental research design. The specific design was a one-group pre-test post-test design. The research subjects consisted of 30 grade 4-6 students from an elementary school in Surabaya. Data were collected through pre- and post-test assessments to evaluate the impact of the intervention. Findings: The findings revealed that the implementation of multimedia-assisted collaborative game-based learning had a significant positive effect on students' competitive spirit and problem-solving abilities. By integrating game elements with team-based collaboration, this approach enhanced students' literacy skills while fostering critical, creative, and analytical thinking. The collaborative nature of the games allowed students to engage in healthy competition, which not only boosted their motivation but also improved their teamwork and communication skills. Additionally, students developed negotiation skills and learned how to work cooperatively to achieve common goals, which are vital social competencies. Conclusion: In conclusion, the implementation of multimedia-assisted collaborative game-based learning proved to be an effective strategy for enhancing students' competitive spirit, problem-solving skills, and literacy development. The approach, by combining game elements with collaborative efforts, effectively promoted critical thinking and enhanced social skills, making it a promising educational tool for elementary school students. This study suggests that game-based learning, supported by multimedia, can be an engaging and effective method to improve both cognitive and social outcomes in literacy education. Keywords: collaborative learning, game-based learning, literacy learning, competitive spirit, problem solving skills.

In an era of increased accountability in assessing student learning outcomes, greater emphasis has been focused on factors that influence student learning. In this paper we examine the impact of instructional methods and information technology on student learning styles, all critical factors affecting student learning. A research framework that suggests the relationship of instructional processes and information technology to learning styles is proposed. Using the framework, pre-and post-tests based on the Grasha-Riechmann Student Learning Styles Scales were used to assess changes in student learning styles over the course of a semester in three college-level courses. Through specific instructional intervention coupled with collaborative projects and the use of course-management software, the results of the assessments showed a significant increase in students’ Collaborative, Participant, and Independent learning styles over the course of the semester. Implications for practice and additional research are suggested.

The primary purpose of this innovative research was to investigate the influence of virtual science learning applications and student engagement and performance in Anatomy and Physiology courses at Abuyog Community College. Recognizing the increasing integration of digital tools in education, this study sought to determine how such applications could enhance student learning experiences and outcomes compared to traditional methods. Several key research questions drove this study. These questions were: How do virtual learning applications impact student engagement in Anatomy and Physiology courses? What effects do these tools have on students' performance and understanding of the subject matter? How do students perceive virtual learning applications relative to conventional teaching methods? Which components of these applications correlate most strongly with improved learning outcomes? To address these questions, an experimental research design was employed, involving 30 students divided equally into control and experimental groups. The control group followed traditional learning methods, while the experimental group utilized virtual learning applications. Data collection methods included pre-tests and post-tests to measure academic performance, as well as Likert scale surveys and open-ended questions to capture student perceptions and experiences. The intervention featured the introduction of innovative interactive virtual learning tools. These tools were specifically designed to teach complex anatomical and physiological concepts through engaging, visual, and hands-on experiences. They provided unique features such as 3D models, real-time simulations, and immediate feedback mechanisms. The findings revealed that students in the experimental group showed significant improvement in both engagement and academic performance. The Likert scale results indicated that the virtual learning applications increased student motivation and active participation. Open-ended responses highlighted that student found the interactive features and visual aids particularly beneficial for understanding difficult concepts and maintaining interest. These results suggest that virtual science learning applications can be a powerful tool in enhancing student engagement and learning outcomes in Anatomy and Physiology. The study supports the integration of such digital tools into the curriculum to foster a more interactive and effective learning environment, providing practical insights for educators and curriculum developers. Future research should explore the long-term impacts of these applications and their potential in other educational contexts.

Background: The Covid-19 pandemic has imposed adaption to virtual learning for students and educators across all levels of education in the world. The effectiveness of virtual learning varies amongst age groups. It has been suggested that the adoption of virtual learning will continue to be implemented even after pandemic, particularly in higher education. Therefore, it is crucial to validate the effectiveness of a virtual learning approach among university students to ensure a smooth transition from a conventional education model to a hybrid education model. Thus, this study aims to evaluate the impact of virtual learning on students’ performance in a virtual classroom. Methods: We analysed survey data collected from undergraduate students at Multimedia University, Malaysia. Convenience sampling and self-administered online surveys were used to understand the impact of virtual learning. Multiple regression analysis was performed using SPSS software Results: A total of 210 first and second year degree and diploma students responded to the online surveys. Factors affecting virtual learning were segregated into three categories: virtual teaching techniques, technology issues, and environment distraction. Respondents stated that the critical factor that affect the effectiveness of virtual learning and impacts on students’ performance was the virtual teaching techniques employed by educators. Conclusions: This study concluded that virtual teaching techniques have significant impact on students’ performance whereas technology issues and environment distraction do not significantly influence students’ performance during virtual learning. Although this study is limited to students from Multimedia University, it lays the groundwork for future research to involve students from other universities or other countries. A future study can address more factors that affect virtual learning and students’ performance, such as students’ attitude and motivation.

A Mobile Intelligent Information System (MIIS) represents a transformative tool in the digital age, empowering users with access to dynamic and personalized information anytime, anywhere. Through advanced technologies such as artificial intelligence, machine learning, and natural language processing, MIIS harnesses the power of data to deliver tailored insights and services to users via mobile devices. With its intuitive interface and seamless integration with mobile platforms, MIIS offers a user-friendly experience that adapts to the evolving needs and preferences of users. As mobile technology continues to evolve, MIIS remains at the forefront, revolutionizing the way information is accessed, processed, and utilized in our increasingly interconnected world. This paper presents a multi-objective approach to construct an English web-based independent learning platform leveraging a Mobile Intelligent Information System (MIIS), enhanced by Genetic Integrated Web Optimization (GIWO) with the integration of Ant Bee and Whale Optimization algorithms. The proposed framework aims to optimize multiple objectives, including content relevance, user engagement, and learning effectiveness, in the design and deployment of the learning platform. Through simulated experiments and empirical evaluations, the effectiveness of the GIWO-enhanced MIIS in constructing the web-based learning platform is assessed. Results demonstrate significant improvements in content recommendation accuracy, user satisfaction, and learning outcomes compared to traditional approaches. The integration of Ant Bee and Whale Optimization algorithms further enhances the optimization process, enabling the system to adapt dynamically to evolving user needs and preferences. This study highlights the potential of leveraging MIIS and GIWO algorithms for the multi-objective construction of web-based learning platforms, paving the way for more personalized, efficient, and effective independent learning experiences in English education. The GIWO-enhanced MIIS achieved an average increase of 35% in content relevance, as evidenced by precision and recall scores. Additionally, user satisfaction ratings increased by 40%, indicating higher levels of engagement and perceived usefulness of the platform. Furthermore, learning outcomes improved by 25%, as measured by pre- and post-assessment scores. These simulation results underscore the efficacy of the proposed approach in optimizing multiple objectives for the construction of web-based independent learning platforms in English education.

The paper examines the impact of artificial intelligence (AI) in unexplored context of virtual project-based team learning. We built on relevant research developed a framework grounded in shared mental models (SMM) of AI. A multi-level, multi-stage model related AI SMM, AI interactivity, trust, and ethics to knowledge updates and learning intentions. A study of 344 graduate students in 48 online teams tested this model. Findings indicate that AI significantly improves learning outcomes in virtual teams, thus enriching the literature on human-agent teams (HATs) and virtual learning. The implications advocate for improved perceptions of AI attributes to maximize learning benefits.