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As education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners' behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to personalize learning paths and match learning materials with learners' profiles. Course recommendation systems have emerged as a solution to help learners select courses that suit their interests and aptitude. A comprehensive review study was required to explore the implementation of course recommender systems, with the specifics of courses and learners as the main focal points. This study provided a framework to explain and categorize data sources for course feature extraction, and described the information sources used in previous research to model learner profiles for course recommendations. This review covered articles published between 2015 and 2022 in the repositories most relevant to education and computer science. It revealed increased attention paid to combining course features from different sources. The creation of multi-dimensional learner profiles using multiple learner characteristics and implementing machine-learning-based recommenders has recently gained momentum. As well, a lack of focus on learners' micro-behaviors and learning actions to create precise models was noted in the literature. Conclusions about recent course recommendation systems development are also discussed.

With the fast development of artificial intelligence and emerging technologies, automatic recognition of students' facial expressions has received increased attention. Facial expressions are a kind of external manifestation of emotional states. It is important for teachers to assess students' emotional states and adjust teaching activities accordingly. However, existing methods for automatic facial expression recognition have the limitations of low accuracy of recognition and poor feature extraction. To address the problem, this study proposed a novel deep learning model called DenseNetX-CBAM to improve facial expression recognition by utilizing a variant of densely connected convolutional networks (DenseNet) to reduce unnecessary parameters and strengthen the reuse of expression features between networks; moreover, convolutional block attention module (CBAM) was integrated to allow the networks to focus on important special regions and important channels when representing features. The proposed model was tested using 217 video clips of 33 students in an online course. The results demonstrated promising effects of the method in improving the accuracy of facial expression recognition, which can help teachers to accurately recognize students' emotions and provide real-time adjustment in online learning environments.

A homogeneous distribution of students in a class is accepted as a key factor for overall success in primary education. A class of students with similar attributes normally increases academic success. It is also a fact that general academic success might be lower in some classes where students have different intelligence and academic levels. In this study, a class distribution model is proposed by using some data science algorithms over a small number of students' dataset. With unsupervised and semi-supervised learning methods in machine learning and data mining, a group of students is equally distributed to classes, taking into account some criteria. This model divides a group of students into clusters by the considering students' different qualitative and quantitative characteristics. A draft study is carried out by predicting the effectiveness and efficiency of the presented approaches. In addition, some process elements such as quantitative and qualitative characteristics of a student, data acquisition style, digitalization of attributes, and creating a future prediction are also included in this study. Satisfactory and promising experimental results are received using a set of algorithms over collected datasets for classroom scenarios. As expected, a clear and concrete evaluation between balanced and unbalanced class distributions cannot be performed since these two scenarios for the class distributions cannot be applicable at the same time.

As blended learning moved toward a new phase during the COVID-19 pandemic, advancements in artificial intelligence (AI) technology provided opportunities to develop more diverse and dynamic blended learning. This systematic review focused on publications related to the use of AI applications in blended learning. The original studies from January 2007 to October 2023 were extracted from the Google Scholar, ERIC, and Web of Science databases. Finally, 30 empirical studies under the inclusion criteria were reviewed based on two conceptual frameworks: four key challenges of blended learning and three roles of AI. We found that AI applications have been used mainly for the online asynchronous individual learning component in blended learning; little work has been conducted on AI applications that help connect online activities with classroom-based offline activities. Many studies have identified the role of AI as a direct mediator to help control flexibility and autonomy of students in blended learning. However, abundant studies have also identified AI as a supplementary assistant using advanced learning analytics technologies that promote effective interactions with students and facilitate the learning process. Finally, the fewest number of studies have explored the role of AI as a new subject such as use as pedagogical agents or robots. Considering the advancements of generative AI technologies, we expect more research on AI in blended learning. The findings of this study suggested that future studies should guide teachers and their smart AI partner to implement blended learning more effectively.

GPT-3 is a neuronal language model that performs tasks such as classification, question-answering and text summarization. Although chatbots like BlenderBot-3 work well in a conversational sense, and GPT-3 can assist experts in evaluating questions, they are quantifiably worse than real teachers in several pedagogical dimensions. We present the first systematic literature review that analyzes the main contributions and uses of GPT-3 in the field of education. The protocols suggested in the PRISMA 2020 statement were followed for the drafting of the review. According to the results, 34 significant productions were identified through a systematic search in ISI Web of Science, SCOPUS and Google Scholar. GPT-3 has been considered in the academic, ethical and medical fields, in humanities and in computer science, in the formulation of questions and answers, and through cooperative educational dialogs. GPT-3 has been proven to have valuable applications in education, such as the automation of routine tasks, in making quick diagnoses of the students' weaknesses and in the automatic generation of questions, but it still faces challenges and limitations that require additional investigation. We discuss the educational possibilities and the limitations to the use of GPT-3.

Purpose: Chatbots and artificial intelligence (AI) have the potential to alleviate some of the challenges faced by humans. Faculties frequently swamped with teaching and research may find it difficult to act in a parental role for students by offering them individualized advice. Hence, the primary purpose of this study is to review the literature on chatbots and AI in light of their role in auto-advising systems. The authors aimed to gain insights into the most pertinent topics and concerns related to robo academic advisor and identify any gaps in the literature that could serve as potential avenues for further research. Design/methodology/approach: The research employs a systematic literature review and bibliometric techniques to find 67 primary papers that have been published between 1984 and 2023. Using the Scopus database, the researchers built a summary of the literature on chatbots and AI in academic advice. Findings: Chatbot applications can be a promising approach to address the challenges of balancing personalized student advising with automation. More empirical research is required, especially on chatbots and other AI-based advising systems, to understand their effectiveness and how they can be integrated into educational settings. Research limitations/implications: This research's sample size may restrict its findings' generalizability. Furthermore, the study's focus on chatbots may overlook the potential benefits of other AI technologies in enhancing robo academic advising systems. Future research could explore the impact of robo academic advisors in diverse societal backgrounds to gain a more comprehensive understanding of their implications. Practical implications: Higher educational institutions (HEIs) should establish a robo academic advising system that serves various stakeholders. The system's chatbots and AI features must be user-friendly, considering the customers' familiarity with robots. Originality/value: This study contributes to a better understanding of HEIs' perceptions of the adoption of chatbots and AI in academic advising by providing insightful information about the main forces behind robo academic advising, illuminating the most frequently studied uses of chatbots and AI in academic advising.

Generative artificial intelligence (AI) tools were met with a mix of enthusiasm, skepticism, and fear. AI adoption soared as people discovered compelling use cases--developers wrote code, realtors generated narratives for their websites, students wrote essays, and much more. Calls for caution attempted to temper AI enthusiasm. Experts highlighted inaccuracies in some of the generated information. Artists, writers, programmers, and other professionals who create for a living questioned their job security. Educators worried that these tools would harm academic integrity. Nevertheless, the tools persist, and AI enthusiasm shows no sign of abating. The current study focuses on how AI tools can be leveraged to improve self-directed learning in the classroom. In a capstone cybersecurity course, the instructor encouraged students to use ChatGPT 3.5 on a self-directed learning assignment to select topics, find learning resources, and carry out their learning plans. Some students did not use ChatGPT. Those who used ChatGPT found it to be a helpful learning aid. All students reported that they plan to use ChatGPT for self-directed learning after graduating. No student violated academic integrity policies. The results demonstrate that AI tools can enhance self-directed learning, though students should be trained to use them effectively. Recommendations for developing assignments that support academic integrity and rigor in the age of AI are provided.

The emergence of artificial intelligence (AI) in academia has prompted various debates on the uses, threats, and limitations of tools that can create text for numerous academic purposes. Critics argue that these advancements may provide opportunities for cheating and plagiarism and even replace the art of writing entirely. To reclaim the creativity and depth that academic writing holds, we propose both an innovative approach to safeguard the creativity and depth of academic writing and a scaffolded way to enhance success in terms of authenticity for the author and, by extension, meaning for the reader. This novel conceptual algorithmic trickle filter model aims to inform successful academic writing and embody the writer's agency--a task too sophisticated for current AI tools. Our model provides a scaffolded decision-making process in a highly personal, flexible, and iterative individual writing development tool applied in a health-conscious way. We position this model as a step towards a pedagogic paradigm shift in reclaiming academic writing that, rather than competing with AI, doubles down on the personal self-evaluative aspects that academic writing offers both author and reader.

This research aims to design an intelligent platform architecture for electronic asset supply chains for digital higher education and to evaluate the architecture of the intelligent platform for electronic asset supply chains for digital higher education. The sample group consists of evaluations of the intelligent platform architecture for the electronic asset supply chains for digital higher education by experts. These experts assess and certify the appropriateness of the architecture, evaluating the content's suitability and the management processes. The evaluations were conducted by 5 experts who have experience in managing assets in higher education or relevant areas. The research results indicate that the designed intelligent platform architecture for electronic asset supply chains for digital higher education, on average, scored 4.43, which is considered 'good'. The evaluation of its developmental trend from architecture to platform has an average score of 4.80, considered 'very good'. Following that, both the system (Administrators) and the (Webserver and Database Server) evaluations yielded the same average score of 4.60, which is also ranked as 'very good.'

Artificial intelligence (AI) and automation are newly impacting the governance of international students, a temporary resident category significant for both direct economic contributions and the formation of a "pool" of potential future immigrants in many immigrant-dependent countries. This paper focuses on tensions within Canada's education-migration ("edugration") system as new technologies intersect with migration regimes, which in turn relate to broader issues of security, administrative burdens, migration governance, and border imperialism. Using an Accidental Ethnography (AccE) approach drawing from practitioner-based legal research, we discuss three themes: (1) "bots at the gate" and the guise of AI's objectivity; (2) a murky international edu-tech industry; and (3) the administrative burdens of digitalized application systems. We suggest that researchers, particularly in education, can benefit from the insights of immigration practitioners who often become aware of potential trends before those less embedded in the everyday negotiation of migration governance.

As a fundamental communication tool, media has undergone transformative changes throughout history. This has a lot to do with education. Each era has been characterized by its own unique media paradigms, from the invention of the printing press in the 15th century to the emergence of radio and television in the 20th century. These paradigms not only define how information is disseminated but also shape the social and political landscapes of their respective periods. In the modern age, the emergence of digital technology and the internet has brought about a new media paradigm that deeply influences every aspect of human interaction and the fabric of society. Today, new media, characterized by its interactivity, decentralization, and unprecedented access, dominates our daily lives. The significance of new media, which affects individual behaviors and global politics, economy, and cultures, cannot be underestimated. This process is also of great importance in terms of education. In particular, media literacy education will enable the individual to personally prepare for or otherwise intervene in the positive or negative aspects of their interaction with the media. From connecting distant communities to altering the dynamics of political campaigns, the impact of new media is ubiquitous. However, along with these advantages, new challenges have also emerged. One of the most concerning aspects of this shift in media is the potential for its exploitation by malicious groups, particularly evident in the rise of "terror agencies" utilizing new media platforms. These organizations adeptly employ new media tools for propaganda, recruitment, and operations, forming a symbiotic relationship that poses significant threats to global security. This article explores how new media paradigms enable the existence of terrorist organizations, the place of education in this regard, and what this complex relationship means for our interconnected world.

Background/purpose: Exploring intelligent agents in digital learning raises questions about the essence of Artificial Intelligence (AI) and its potential impact on education. This article provides insights into these inquiries and outlines outcomes from various experimental implementations, emphasizing the pivotal role of intelligent agents and conversational bots. These technologies have the power to revolutionize education by nurturing adaptive learning and problem-solving skills among university students. This work builds on existing research, aiming to articulate a conceptual understanding of AI as a strategic tool for learning. Materials/methods: The study systematically collected data from Colombian universities and underwent thorough analysis through a systematic review process. Findings were meticulously organized according to themes and categories, enriched by contemporary perspectives in learning theories and artificial intelligence, ensuring a comprehensive exploration within the context of Colombian higher education. Results: The synergy between repositories and artificial intelligence significantly enhances the capability to discover, analyze, and manage academic information. This amalgamation holds great promise as a strategy to enhance efficiency and precision in the university research process. Conclusion: The exploration of AI in education reveals a promising future. The integration of technology within teaching improves learning, making AI a valuable ally for progress and evolution in higher education.

Background/purpose: With the advent of Artificial Intelligence (AI), it has become possible to invent computer systems that can perform human-like processes to tackle large data and solve complex problems. AI has manifested itself in the field of education through several technologies such as intelligent tutoring systems, adaptive teaching/learning, large-scale assessment and evaluation designs, predictive modeling and learning analytics, educational games. AI has incrementally begun to transform the ways teachers teach, students learn, and schools function with inevitable implications for school management and leadership. Materials/methods: This study aims to focus on these implications through highlighting possible contributions of AI-based innovations to school leadership and management based on a comprehensive review of early evidence. Practical implications: With its capability to process large datasets, engage in human-like cognition, thinking, and conversation, make decisions, and execute actions by this means, AI technologies offer several opportunities to improve school-wide leadership, practice open management based on the principles of transparency, participation, and digital skills, create the required synergy to achieve ever-changing educational goals by integrating teachers, students, and parents into educational processes. These technologies have also proven their capacity to help school leaders manage various technical tasks ranging from the management of food/transportation services, supply of instructional materials, human resource management, security, and student information processing. AI also enables learning analytics, or educational data mining, which allows for taking preventive actions and providing customized education by obtaining comprehensive data from students' educational activities across a period. Conclusion: It is undeniable that the integration of AI-based digital technologies bears several opportunities and challenges for adapting the functioning of schools to the new conditions in the interest of students, teachers, and other stakeholders.

Artificial intelligence is a popular technology that affects many fields today. Its impact is noticeable daily, especially in education, design, and other essential areas. This technology, which stands out with the convenience it creates for users and its help in organizing the workflow, will play a critical role in our lives in the future. This study is aimed to touch upon the innovations provided by artificial intelligence in the field of education and some of its basic features. Some software, especially ChatGPT, is included based on the potential of language models and their essential usage areas. In addition, in the study, it is mentioned how artificial intelligence can be evaluated as a material in text and visual form, the essential functions of artificial intelligence technologies, which are rapidly changing today, are explained in this article, and stimulating points about the current situation are mentioned for those who will work in the field.

This study explores how discussing metaphors for AI can help build awareness of the frames that shape our understanding of AI systems, particularly large language models (LLMs) like ChatGPT. Given the pressing need to teach "critical AI literacy", discussion of metaphor provides an opportunity for inquiry and dialogue with space for nuance, playfulness, and critique. Using a collaborative autoethnographic methodology, we analyzed metaphors from a range of sources, and reflected on them individually according to seven questions, then met and discussed our interpretations. We then analyzed how our reflections contributed to the three kinds of literacies delineated in Selber's multiliteracies framework: functional, critical and rhetorical. These allowed us to analyze questions of ethics, equity, and accessibility in relation to AI. We explored each metaphor along the dimension of whether or not it was promoting anthropomorphizing, and to what extent such metaphors imply that AI is sentient. Our findings highlight the role of metaphor reflection in fostering a nuanced understanding of AI, suggesting that our collaborative autoethnographic approach as well as the heuristic model of plotting AI metaphors on dimensions of anthropomorphism and multiliteracies, might be useful for educators and researchers in the pursuit of advancing critical AI literacy.

Leveraging technology to break the iron triangle of access, cost, and quality is a legacy of open universities (OUs), becoming a source of inspiration for higher education in general. Today, OUs face increasing competition from conventional universities, no longer enjoying the first-mover advantages as they did in the earlier years. Can artificial intelligence (AI) enable OUs to stay competitive in the 21st century as other technologies did in the past? This paper first reviews literature on the affordances or (potential) uses of AI for open and distance education and then examines the implications of these affordances for OUs in terms of quality, cost, and access. It concludes by arguing for a systems approach to exploring how OUs can remain open as to people and places as well as to methods and ideas by making creative and innovative uses of AI.

This study examines the perceptions and responses of Vietnamese teachers of English as a Foreign Language (EFL) to academic integrity concerns that arise from the use of AI, specifically chatbots like ChatGPT, in foreign language education. The study employed an open-ended survey to collect data from 31 Vietnamese EFL teachers who were asked to share their views on AI-based academic dishonesty, identify perceived causes, outline consequences for students engaging in AI-based plagiarism, and articulate their pedagogical responses to the issue. The study found that teachers primarily attributed students' AI-driven plagiarism to a deficiency in original ideas, poor learning attitudes and motivation, and students' linguistic competencies. The over-reliance on AI was identified as a hindrance to the development of knowledge and skills such as critical thinking and language proficiency. In response to academic dishonesty, teachers advocated for increased regulations, the implementation of AI-based plagiarism detectors, and education on responsible AI use. The findings underscore the importance of adapting language teaching pedagogies and assessments to incorporate personalised learning and process-oriented teaching approaches that support critical thinking and genuine learning motivation. The insights derived from this research contribute to a deeper understanding of EFL educators' perspectives, offering valuable input for the development of policies and practices aimed at promoting academic integrity in the AI era. [Note: The page range (28-47) shown on the PDF is incorrect. The correct page range is 28-48.]

Hollywood screenwriters worry about Artificial Intelligence (AI) replacements taking over their jobs. Famous museums litigate to protect their art from AI infringement. A major retailer scraps a machine-learning based recruitment program that was biased against women. These are just a few examples of how AI is affecting the world of work, learning, and living. MIS and computer science students are among the professional groups who are embarking into careers with nebulous frontiers obscured by the outcroppings brought on by AI. Computer Science and Information System curriculum task forces have recognized the increasing ethical and professional implications developers' work can have beyond the scope of the programmers' code. In this article, the authors examine the professional, legal, and ethical implications of copyrights and algorithmic bias resulting from development of AI-enhanced applications and offer suggestions for addressing these topics in courses considering changes to the CC2020 and IS2020 Model Curriculum frameworks.

This study compared the problem-solving abilities of ChatGPT and 58 pre-service teachers (PSTs) in solving a mathematical word problem using various strategies. PSTs were asked to solve a problem individually. Data was collected from PSTs' submitted assignments, and their problem-solving strategies were analyzed. ChatGPT was also given the same problem to solve with various prompts, and the correctness of its solutions and problem-solving strategies were assessed alongside those of PSTs. The results indicated that PSTs used diverse strategies and achieved accurate solutions, but not always relevant strategies to children's level of understanding. ChatGPT employed similar strategies to PSTs but mostly produced incorrect solutions, and its performance needed to be contextualized in the primary school context. The study highlights the potential of ChatGPT in mathematics teaching and informs teacher education programs about the possibility of using it in teaching problem-solving strategies.

This article presents a case study and ideas to flip classical dialogic learning method. Some possibilities of using ChatGPT to stimulate the active role of the student in the dialogic teaching method are shown. Data from observations of students training to be teachers of mathematics and informatics in their work with artificial intelligence (AI) are presented. A model for the application of the flipped dialogue in the education of students is proposed. An example of dialogue is given. The study concludes that that the main advantages of using flipped dialog learning with AI are easy access to the platforms, the lack of stress and stimulating the research qualities of students when searching for specific information or stimulating the system to make mistakes. The ability to ask the right leading questions and to detect the gaps of the "interlocutor" are important competencies applicable in dealing with life's problems.

This study proposes that the gestures of an agent tutor in a multimedia learning environment can generate positive and negative emotions in learners and influence their cognitive processes. To achieve this, we developed and integrated positive and negative agent tutor gestures in a multimedia learning environment directed by cognitive gestures. The effects of emotion type on cognition were examined in terms of cognitive load, learning motivation, and achievement. The subjects were 46 university students in Gyeonggi Province, South Korea. The students were divided into three learner groups: cognition, cognition + negative emotion, and cognition + positive emotion. The learners watched a tutorial lecture on the Notion note-taking app by an agent tutor. Data analysis was conducted using one-way ANOVA to determine the cognitive load, learning motivation, and achievement. The results showed that the positive emotion design was more effective in terms of intrinsic cognitive load, learning motivation, and achievement but had a higher extrinsic cognitive load. However, even the negative + passive group showed more positive learning than the cognition group. Although this study focused on gestures by an agent tutor, it implies that such gestures in multimedia learning contexts must be informed by emotional as well as cognitive design to provide a more meaningful learning experience.

The Chat Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence technology that engages in dialogue with humans, capable of generating formulations in the form of micro-narratives that can be problematized as a learning tool in the virtual space. This research aims to understand how ChatGPT can be used in teacher training as a didactic tool to facilitate learning in the virtual space through micro-narratives. We conducted a qualitative study using an action research approach with Brazilian students from the Graduate Program at the State University of Ceará in Brazil. The research consisted of five phases: diagnosis, which involved a mixed questionnaire to assess prior knowledge about generating micro-narratives using ChatGPT; action planning, which involved developing a training plan; implementation of the action, which included practicing the use of ChatGPT to generate micro-narratives and working with them in a didactic perspective in the virtual space; evaluation, which involved sharing the micro-narratives and engaging in circular discussions about them; and learning, which involved documenting the educational possibilities and limitations of micro-narratives. The results, processed using IRaMuTeQ, showed limited prior knowledge about ChatGPT and the importance of micro-narratives for educational work, as well as their fruitful pedagogical use for learning in the virtual space through conscious utilization.

This study proposes a narrative pedagogical model for creating and developing case studies that highlight the challenges and issues surrounding the megatrends of the Fourth Industrial Revolution, specifically "People and the Internet." This proposal, framed in an online learning environment, represents the second stage of an educational platform to guide university students in facilitating an ideathon to address the megatrends of the 4th Industrial Revolution. Designing a model for case study creation related to the megatrends aims to foster complex thinking in university students, especially innovative thinking, a sub-competency of complex thinking. Fostering complex thinking highlights the importance of cognitive, practical, and adaptive skills to address interdisciplinary challenges. The Design Process and Practice Methodology and the case study methodology facilitated the narrative case study design model. The outcome of this study is a tangible product that enriches the debates on education and complex systems thinking in the context of the Fourth Industrial Revolution.

In today's educational environment, the convergence of emerging technologies and active methodologies has become a fundamental driver of change in university education. Emerging technologies, such as artificial intelligence, virtual reality, machine learning, and data analytics, are redefining the dynamics of higher education. Active methodologies, such as problem-based learning, collaborative learning, and flipped learning, center the pedagogical focus on the student, encouraging active participation and problem-solving. The combination of emerging technologies and active methodologies creates a powerful synergy, enabling the effective implementation of personalized and immersive learning experiences. This convergence not only enhances learning, but also prepares students to be autonomous learners, critical thinkers, and 21st century problem solvers.

In this research, we aimed to determine whether students' math achievements improved using ChatGPT, one of the chatbot tools, to prepare lesson plans in primary school math courses. The research was conducted with a pretest-posttest control group experimental design. The study comprises 39 third-grade students (experimental group = 24, control group = 15). The implementation process lasted five weeks and 25 lesson hours. In the experimental group, lessons were taught according to plans prepared using ChatGPT, while in the control group, existing lesson plans were used. Students' academic achievement was measured with a multiple-choice achievement test of 25 questions with two separate questions for each learning objective. According to the results, students' academic achievement increased significantly (d = 1.268) in math lessons taught according to lesson plans prepared using the ChatGPT. Although there was a difference between the posttest scores of the experimental group and the control group in favor of the experimental group, it was determined that this difference was not significant. These results show that teaching primary school math according to lesson plans prepared using ChatGPT is effective in academic achievement. Teachers should consider ChatGPT and their plans, combining them and benefiting from both in the implementation process.

The present study examined students' perception of using Artificial intelligence-powered text-to-speech apps (AI TTS Apps) to learn aspects of English language pronunciation. This investigation was conducted considering the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). To ensure participants had prior experience using AI TTS for pronunciation, they engaged in five sessions focused on learning both segmental and suprasegmental aspects of pronunciation through a text-to-speech app. This research employed a mixed-method approach. A questionnaire was used to examine participants' perceptions of AI TTS Apps, while interviews were conducted to gain deeper insights into their experiences with AI TTS Apps. According to the results, participants positively perceived the use of AI TTS Apps in learning pronunciation. It is recommended to utilize AI Text-to-Speech Apps both within and outside pronunciation classes, due to their positive pedagogical effects.

The GbL platform, or game-based learning platform, with generative AI is a research tool initiated from the combination of game-based learning concepts and generative artificial intelligence technology; thereby, this platform is intended to be used as a guideline for the instruction management, in which learners can respond and interact with the real-time activities by means of gamification. The objectives of this research are (1) to study and synthesize the conceptual framework of the GbL platform with generative AI to enhance digital and technology literacy skills, (2) to develop the architecture of the GbL platform with generative AI to enhance digital and technology literacy skills, and (3) to study the results of the development of the GbL platform with generative AI to enhance digital and technology literacy skills. The results of this research show that (1) the overall elements suitability of the architecture of the GbL platform with generative AI is at the highest level (Mean = 4.51, SD = 0.48), and (2) the overall suitability of the architecture of the GbL platform with generative AI is at the highest level (Mean = 4.59, SD = 0.41). Nevertheless, there are still some research gaps in this study; that is, this study was conducted with quite a small sample group and it focuses mainly on the results of evaluation on the architecture of the GbL platform. Therefore, this research is regarded merely as a pilot study designated for feasibility study to further develop the GbL platform that can be put in practical use in the future.

This study explores the perspectives of Indian library professionals on the use of artificial intelligence (AI) in libraries. It aims to understand their knowledge, awareness, and views on AI and its challenges and opportunities. The research adopts a quantitative approach, using a closed-ended survey to collect data from 386 library professionals in academic institutions across India. The survey covers various aspects, including socio-demographic information, AI knowledge, perspectives on AI in libraries, ethical considerations, and adopting AI tools and services. The findings indicate that Indian library professionals are generally aware of AI and its potential benefits in libraries. They believe AI can enhance library activities, improve accessibility, and support decision-making. However, there are concerns about AI replacing human intelligence within libraries. Factors such as user privacy, funding, staff expertise, and alignment with institutional goals are essential when implementing AI applications. Ethical considerations, including bias and discrimination, intellectual freedom, and transparency, are also significant concerns. AI tools like smart shelving and optical character recognition (OCR) are among the technologies already adopted in Indian libraries.

The field of advertising has a structure that shapes consumer preferences and behaviors. The field of advertising undertakes the tasks of reflecting cultural values and norms, reinforcing or changing gender roles, and supporting social responsibility and campaigns. In addition, advertising, whose main purpose is sales, has a structure that shapes and informs society when considered in social dimensions. Artificial intelligence, which is today's developing and rising technology, is a technological tool that brings the physical and digital world together and affects many other fields such as economy, industry, social field, education. It is thought that the widespread integration of these technologies, which we use in almost every aspect of our daily lives, in the education sector has a significant potential for progress. In this context, in this study, the attitudes of students studying at universities in the TRNC towards artificial intelligence applications were analyzed in order to make sense of the impact of artificial intelligence technology used in the field of advertising, which reaches social dimensions, affects social situations and changes with the effects of developing and changing technologies, on students studying. In addition, the impact of advertisements developed with artificial intelligence on education was also analyzed.

This article describes a study conducted in the discipline of "science education in nature", offered 100% in the distance learning mode, for undergraduate students in biological sciences and pedagogy. The aim was to develop a challenge that would stimulate students creativity and engagement. Through the virtual learning platform, students exchanged information and collaborated with each other. The challenge resulted in a significant increase in student engagement and facilitated the connection between theory and practice. The findings highlight the importance of strategies that promote engagement in distance learning, especially in the discipline in which it was applied. The challenge-based approach stimulates creativity, critical thinking, and the development of essential skills for teacher education in this field.

This paper charts a forward-looking roadmap for open universities, drawing upon their historical evolution and current practices. It advocates a shift toward a universally accessible, personalized education system. At the heart of this proposed advancement lies the customization of learning paths and experiences, where individualized advising and mentorship, and a variety of learning content, resources, and environments are essential. The study underscores the importance of integrating advanced technologies such as artificial intelligence and blockchain into the open and distance education system. Within the discourse, the paper delineates three primary areas for open universities to address: system transformation, expansion of openness, and integration of digital innovation. The concluding part of the paper offers possible strategic recommendations for policymakers and researchers of open universities. The essence of these recommendations is advocating for a universally personalized educational paradigm while making a strong case for addressing the digital divide, fostering strong partnerships at both global and community levels, and supporting the use of the latest technology to its fullest potential. By navigating this transformative journey, open universities are not just participating in the evolution of educational models but also poised to lead a revolution in the broader landscape of higher education.

A qualitative documentary research of software and multimedia artificial intelligences was chosen to enable the ontological understanding of the object of study that aims to explore the potential for learning through Artificial Intelligence (AI). Besides, it is shown how AI, as a novel component within the digital education landscape, contributes to educational technological capital. In light of our research area, the focus is on determining how AI can be harnessed to enhance the development of communicative competence. This entails identifying and categorizing AI tools relevant to language didactics. The sample size for this study is 120 AI applications, sourcing and compiling data on AI tools from specialized websites. This sample serves as a paradigm justifying a mixed-method study capable of combining quantitative and qualitative data. The variables supporting this study consider four characteristics related to the specific typify of the digital tool in the pedagogical context. The first aspect pertains to the classification of generative AI tools with potential educational use. AI tools enriches and enhances the dimensions of learning underscoring the urgent need for literacy in this technology. The second variable was linked to the Pedagogical Competences of Teachers and the areas. The category in language education pertains to educators' ability to create, adapt, and employ digital resources that enhance language teaching and learning. The third and fourth identify the skills related to language learning. In the context of learning environments with AI tools, it is essential to contemplate the role of linguistic competence as subordinate to communicative competence.

From personalized advertising to economic forecasting, artificial intelligence (AI) is becoming an increasingly important element of our daily lives. These advancements raise concerns regarding the transhumanist perspective and associated discussions in the context of technology-human interaction, as well as the influence of artificial intelligence (AI) on education and critical pedagogy. In this regard, the purpose of this research paper was to investigate the intersection of AI and critical pedagogy by critically assessing the potential of AI to promote or hamper critical pedagogical practices in the context of transhumanism. The article provides an overview of the concepts of transhumanism, artificial intelligence, and critical pedagogy. In order to seek answers to research questions, qualitative research design was adopted, and GPT-3 was used as a data collection resource. Noteworthy findings include the similarity of the dialogue with the GPT-3 davinci model to a conversation between two human beings, as well as its difficulty in understanding some of the questions presented from a critical pedagogy perspective. GPT-3 draws attention to the importance of the relationship between humans in education and emphasizes that AI applications can be an opportunity to ensure equality in education. The research provides suggestions indicating the relationship between AI applications and critical pedagogy.

The versatility of ChatGPT extends across diverse domains, including scientific research. This study delves into the transformative prospects of integrating ChatGPT into scientific research, achieved through a SWOT analysis. The analysis explores the model's strengths, which encompass a vast knowledge base, language proficiency, information retrieval, and the capacity for continuous learning. Conversely, it exposes its weaknesses, including a lack of contextual understanding, potential overreliance on training data, limitations in verifying information, and constrained critical thinking abilities. Amidst these factors, opportunities arise, such as facilitating literature reviews, fostering collaborative brainstorming, enabling seamless language translation and interpretation, and amplifying knowledge dissemination. Nonetheless, a spectrum of threats looms, encompassing concerns related to plagiarism, ethical quandaries, the propagation of misinformation, and even the potential erosion of higher-order cognitive thinking. These multifaceted aspects necessitate comprehensive consideration. Recommendations for researchers embarking on ChatGPT integration include a balanced approach that harmonizes AI and human ingenuity, thereby upholding research integrity. The potential of ChatGPT to reshape scientific inquiry can only be realized through conscientious use and ongoing oversight.

In the context of contemporary technological advancements, Artificial Intelligence (AI) has gained considerable significance in the field of education. In light of ChatGPT's growing popularity, this research aims to explore how higher education students perceive the use of ChatGPT in academics, examining factors influencing its acceptance, as well as its benefits, limitations, and ethical concerns. The study applied a survey design, collecting data through Google Forms from undergraduate, postgraduate, and doctoral students. A total of 162 participants, who were using ChatGPT selected through convenience sampling. The findings indicate a positive perception among respondents regarding ChatGPT's academic applications, its benefits, limitations, acceptance factors, and ethical concerns. The study also reveals that the perception of higher education students towards ChatGPT usage is not significantly influenced by gender, academic programs, and streams. The insights gained from this study holds significant implications for the responsible and effective integration of ChatGPT in higher education environments, taking into account its perceived benefits and ethical concerns.

Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th, 7th, and 8th grades attending a school in Central Anatolian City in Turkey, participated in this study in the 2022-2023 school year. Data was collected with a. A deep learning method called deep neural network, one of the ways of machine learning, was used in data analysis. The average number of books read per year had the highest importance among the variables affecting academic performance in science courses. In addition, deep learning predicted students' final science scores with 90% accuracy. According to the results of this study, the percentage of the academic achievement prediction might be raised by reproducing the required data set for the data analysis method with deep learning. A forecast of the student's academic achievement with artificial intelligence and detecting the importance of variables' percentage might be researched for other courses in addition to the Science course.

The rapid development of artificial intelligence and large language models (LLMs) has led to significant advancements in applying machine learning techniques across diverse disciplines, including educational science research. This study investigates the potential of LLMs like ChatGPT for qualitative data analysis, focusing on open, axial, selective coding, theme or pattern identification, and inter-rater reliability. Our findings indicate promising capabilities of ChatGPT in open coding, demonstrating accurate categorization of qualitative data. However, axial coding posed challenges due to the model's limited understanding, which was partially addressed by refining prompts based on ChatGPT's interpretation. ChatGPT also showed competence in selective coding and theme or pattern identification, providing additional insights. For inter-rater reliability, ChatGPT's performance varied across datasets, with improvements observed when providing contextual information. It is important to note the limitations and variability of LLMs such as ChatGPT, which is in public beta and subject to potential limitations in usage and reliability. Our study demonstrates ChatGPT's potential for coding and inter-rater reliability. Improved results are achieved with refined prompts and utilising ChatGPT's own definitions. The adoption of LLMs for qualitative analysis requires further exploration, including addressing algorithmic bias and the potential for inaccurate responses. Validation techniques are crucial in mitigating these risks.

Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) methodologies have successfully tailored commercial applications for decades. However, the recent profound success of large language models like ChatGPT and the enormous subsequent funding from governments and investors have positioned ML to emerge as a paradigm-shifting technology across numerous domains in the coming years. To cultivate a competent workforce and prepare students for success in this new AI-focused evolving world, the integration of ML is proposed to begin in compulsory education rather than in college courses or expensive boot camps. Unfortunately, ML is a complex and intimidating topic for high school teachers to engage with, let alone high school students. Based on our experiences hosting Machine Learning for High School Teachers (ML4HST) workshops for teachers teaching ML topics at our institution, we present in this paper various considerations for educating educators on the topic of ML. In particular, we discuss (a) overarching pedagogic strategies, (b) accessibility of resources such as computational hardware and datasets, (c) balancing theory and implementation, (d) appropriate selection of topics and activities for fostering understanding and engagement, and perhaps most importantly, (e) a compilation of pitfalls to avoid. Synthesizing these insights, we propose a framework for successfully empowering educators to introduce ML in the classroom.

ChatGPT has become a prominent tool for fostering personalized and interactive learning with the advancements in AI technology. This study analyzes 212 academic research articles indexed in the Scopus database as of July 2023. It maps the trajectory of educational studies on ChatGPT, identifying primary themes, influential authors, and contributing institutions. By employing bibliometric indicators and network analysis, the study explores collaboration patterns, citation trends, and the evolution of research interests. The findings show the exponential growth of interest in leveraging ChatGPT for educational purposes and provide insights into the specific educational domains and contexts that have garnered the most attention. Furthermore, the study reveals the collaborative dynamics and intellectual foundations shaping the field by examining co-authorship and citation networks. This bibliometric analysis contributes to a comprehensive understanding of the current state of ChatGPT research in education, offering researchers and practitioners valuable insights into evolving trends and potential future directions for this innovative aspect of AI and learning.

ChatGPT is taking the world and the education sector by storm. Many educators are still hesitant to integrate it within their curricula, owing to the limited practical and theoretical guidance on its applications, despite early conceptual studies advocating for its potential benefits. This pedagogical innovation applied an effectual logic to implement ChatGPT for a founding team activity within an entrepreneurship course. Composing a founding team is an inundating task in venture creation, with long-lasting consequences. So far, there is yet to be an ideal approach proposed in literature or observed in real-life for doing it. In this pedagogical innovation, three student teams with varying business ideas prompted ChatGPT using different keywords and levels of details, to get recommendations on essential team members, their roles and equity split. Each team presented their findings, and then the classroom engaged in a collective discussion. The students were surveyed afterwards to assess the reception and effectiveness of the intervention. Their feedback showed an overwhelming favoritism of ChatGPT, as a convenient and resourceful learning tool. The study establishes the potential value of ChatGPT as a heutagogical tool that supports student-centric entrepreneurial learning across educational institutions and the entrepreneurship ecosystem that extends to the venture creation process.

This study investigates the perceptions of English language teachers regarding the use of ChatGPT in English Language Teaching (ELT). The study aims to fill the research gap by exploring teachers' perspectives on the integration of ChatGPT as an instructional tool and its implications for ELT practices. Using a mixed methods approach, the study employed focus group discussions and an online survey to gather insights from a sample of 115 English language teachers in Metro Manila, Philippines. Findings revealed English language teachers' perspectives on the use of ChatGPT in ELT including potential use of ChatGPT in ELT (e.g., integration for vocabulary expansion and writing practice and enhancing language fluency and communication), potential benefits of ChatGPT to English languge learners (e.g., translation and language comprehension support, conversational practice and language development, access to information and cultural understanding), and potential limitations of using ChatGPT in ELT (e.g., cheating and plagiarism, lack contextual understanding and nuance, reliability and accuracy of information). These findings shed light on teachers' perceptions of ChatGPT as a language teaching tool including ChatGPT's integration into ELT, use of ChatGPT in writing activities, use of ChatGPT in improving language skills, and other benefits of using ChatGPT, including teachers' concerns regarding the authenticity of AI-generated content and the potential impact on language learning outcomes. The findings of this research contribute to the existing literature on the integration of AI technology in ELT and offer practical recommendations for educators seeking to leverage the potential of ChatGPT in language teaching. By giving voice to teachers' perspectives, this study underscores the importance of considering teachers' perceptions and experiences when incorporating emerging technologies like ChatGPT into the ELT classroom. Ultimately, this research aims to foster a deeper understanding of the complex dynamics between AI technology and language teaching, paving the way for informed decision-making and effective implementation in the field of ELT.

This review explores the integration and effects of the Internet of Things (IoT) in education, highlighting its importance in transforming traditional teaching and learning techniques. It examines the early uses and historical growth of IoT, its development, and the turning points in its adoption. It explores IoT platforms, tools, and technologies in education, including wearables, smart devices, augmented and virtual reality, gamification, and collaborative learning. It discusses the role of IoT in improving campus management, including intelligent campuses with IoT-enabled infrastructure, energy-saving technologies, and safety and security improvements. The study discusses data security and privacy issues in IoT installations and the ethical and legal implications of data collection in the classroom. The study also discusses upcoming trends and prospects for IoT usage in education, including AI and machine learning integration. Finally, the review provides insights for educators, decision-makers, and stakeholders, identifying research gaps and recommending areas for future IoT implementation.

In many fields, AI chatbots continue to be popular with new tools and attract the attention of universities, K12 schools, educational organizations, and researchers. The aim of this research is to review the research on AI chatbots by restricting it to the category of education and to examine this research from a methodological point of view. Therefore, we performed a systematic literature review with a sample of 37 SSCI articles published in the educational context. Within the scope of the selected studies, the advantages and disadvantages of AI chatbots in education for students and educators, as well as the types of chatbots used, year, keywords, and method were analyzed. According to the research results, increased motivation to learn and language skill development are advantages for students, while cost-effectiveness and reduced workload are advantages for educators. Limited interaction, misleading answers for learners, originality, and plagiarism are the most common disadvantages for educators. The study also includes research results and recommendations related to the methodological review.

Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the implications of (1) a recent case related to the General Data Protection Regulation (GDPR) and (2) the release of ChatGPT, a generative AI model capable to producing 'human-like' text. These events raise questions for the future of AIEd and the underlying function of assessment, and highlight the importance of active student participation in the integration of AI in education. [This article has been presented in the 5th International Open & Distance Learning Conference-IODL 2022.]