Bibliometric Review of Artificial Intelligence in Project-Based Learning: Trends and Gaps in Social Sciences, Arts, and Humanities

Artificial Intelligence and Sustainable Development in Business Management Context – Bibliometric Review

Background Given today's information deluge and the swift strides in artificial intelligence, foundational knowledge is readily accessible online. The willingness to take the initiative to learn is obviously more important. At present, project-based learning is widely promoted in the world, and China is no exception, and since 2016, Zhejiang, China, has promoted STEAM education as a promotion curriculum integration. An important starting point for transforming the way you learn. Although project-based learning is widely carried out, there is a lack of quantitative research on the effective organization and implementation of project-based learning and the corresponding learning effects.Educators build and provide learners with supportive learning resources and tailor guiding issues of project-based learning to provide a path to transition from traditional direct teaching to a project-based active learning approach that encourages students to be proactive and seek resources as needed. Objectives This study will use the database design curriculum as an example to implement project-based learning, build project-based learning elements, and record students' learning activity data in project-based learning activities, which has 45 students in a junior college in Zhejiang in 2023. Methods The study collected data from learning platforms that included various supportive learning resources, including the number of online discussions, the number of tasks completed, video watch time data, chapter learning repetition, the distribution and trend of chapter learning time, teacher surveys, and face-to-face discussions and offline unsupervised learning. Results and Conclusions This lab takes database project-based learning as an example and evaluates whether learners can gain real-world practical experience in new learning methods. The analysis of project-based learning outcomes shows that the project-based active learning method enhances students' awareness of the importance of database design, cultivates enthusiasm, and promotes active learning in learning, better cultivate students who can navigate beyond basic knowledge points to embrace multidimensional inventive learning. Experimental data shows that strong positive correlation between high-quality project-based learning and student enthusiasm.

The integration of Artificial Intelligence (AI) into Project-Based Learning (PBL) and Problem-Based Learning (PBL) represents a transformative shift in contemporary educational practices. By enhancing these student-centered learning approaches, AI offers innovative solutions to challenges such as time management, personalized scaffolding, and real-time feedback. This chapter explores the intersection of AI and PBL/PBL, focusing on how AI technologies can optimize problem-solving, collaboration, and critical thinking in complex learning environments. AI-powered tools provide adaptive learning pathways, automate administrative tasks, and facilitate the analysis of large datasets to improve decision-making and student engagement. Moreover, the chapter delves into ethical considerations, including biases in AI algorithms, data privacy concerns, and the risks of over-reliance on AI systems, emphasizing the importance of transparent, fair, and inclusive AI implementation. By examining both the opportunities and challenges of AI in education, this chapter provides valuable insights for educators, researchers, and policymakers seeking to enhance learning outcomes through AI-driven innovations. The potential for AI to reshape PBL and PBL paradigms in diverse educational contexts is substantial, offering a pathway toward more efficient, equitable, and engaging learning experiences.

A descriptive bibliometric analysis of works on artificial intelligence (AI) in science education is provided in this article to help readers understand the state of the field’s research at the time. This study’s main objective is to give bibliometric data on publications regarding AI in science education printed in periodicals listed in the Scopus database between 2002 and 2023 end of May. The data gathered from publications scanned and published within the study’s parameters was subjected to descriptive bibliometric analysis based on seven categories: number of articles and citations per year, countries with the most publications, most productive author, most significant affiliation, funding institutions, publication source and subject areas. Most of the papers were published between 2016 and 2022. The United States of America, United Kingdom, and China were the top-3 most productive nations, with the United States of America producing the most publications. The number of citations to the publications indexed in Scopus database increased in a progressive way and reached to maximum number in 2022 with 178 citations. Most productive author on this topic was Salles, P. with four publications. Moreover, Carnegie Mellon University, University of Memphis, and University of Southern California have the maximum number of publications as affiliations. The National Science Foundation was the leader funding institution in terms of number of publications produced. In addition, “Proceedings Frontiers in Education Conference Fie” have the highest number of publications by year as a publication source. Distribution of the publications by subject area was analyzed. The subject areas of the publications were computer sciences, social sciences, science education, technology and engineering education respectively. This study presents a vision for future research and provides a global perspective on AI in science education.

English speaking skills are essential skills in the context of 21st century learning. However, many students at the secondary level still have difficulty in developing this skill optimally due to conventional and less contextual learning approaches. Project-Based Learning (PjBL) strategies are seen as one of the innovative methods that can overcome these challenges because they encourage students' active involvement in a meaningful learning process. The purpose of this study was to identify and analyze the effectiveness of PjBL strategies in improving students' English speaking skills. This study used a qualitative approach with a literature study method (library research). Data were collected from various secondary sources, such as scientific journals, proceedings, and relevant research reports in the last five years (2019–2024). Data analysis was carried out through a content analysis approach that aims to identify the most effective themes, patterns, and learning strategies in the context of PjBL. The results showed that PjBL was able to increase students' self-confidence, enrich vocabulary, and develop sentence structures contextually. Projects such as documentary videos, presentations, and role-plays have been shown to improve students' speaking fluency and encourage collaborative learning. In addition, this strategy also forms critical thinking skills and creativity that are very relevant to the needs of the 21st century. Therefore, the PjBL strategy is recommended to be implemented contextually and applicatively in secondary schools.

In this work we consider an open artificial intelligence game as a matter of study within the lectures of artificial intelligence to combat lack of motivation and increase engagement within the classroom. During formation, students in computer science can deal with moderately complex projects, nevertheless, dealing with such problems is relegated to the Degree Final Project. In this investigation we show the procedural steps of how project-based learning combined with game construction can effectively be used to promote engagement in informatic lectures at university. For the task, we build a 2D game engine and propose students to enroll in factitious research teams with the aim of programming intelligent agents that play the game employing artificial intelligence techniques. The intended principal outcome is to show evidence of the application of project-based learning in artificial intelligence within the lectures, and how it can be combined with game construction to increase motivation in the classroom. Project-based learning has the students learn, organize, and solve challenges while students themselves remain their own responsible for the investigation and process of work. We propose to follow a series of sequential phases that conform a set of milestones that incorporate a project-based learning approach to the lectures. Through this work we show that the use of project-based learning combined with game construction provides reliable evidence that a much deeper understanding about artificial intelligence is attained by students participating in the challenge. Student evaluation questionnaires and final grade results attained by students indicate that students remained more engaged during the semester in comparison to previous semesters in which lack of motivation was reported.

Purpose This study aims to analyze academic research on Artificial Intelligence (AI) applications and tools in academic libraries, focusing on publications from the Scopus database between 2014 and 2024. Design/methodology/approach The study adheres to the PRISMA protocol, using VOSviewer, Bibliometrix, and Rstudio’s Biblioshiny function for bibliographic analysis and visualization. Findings The study highlights how the potential of AI in academic libraries may be increased by changing user needs and technical advancements. It comprises four thematic clusters: foundational technologies (machine learning, natural language processing, and automation), emerging innovations (generative AI), user-centric applications (chatbots), and the importance of AI literacy. It also reveals research gaps in automation and strategic AI integration, providing recommendations for improving library services. Research limitations The study is limited to articles published between 2014 and 2024 in the Scopus database, potentially excluding previous foundational work and research from other sources. Practical implications The study offers policymakers and library practitioners insightful information on effectively utilizing AI tools. This may result in overlooking earlier foundational work and research from multiple sources. Originality/value The study discovers the role of artificial intelligence (AI) in modernizing academic libraries, identifying research gaps, and providing strategic insights to improve technology and user experience.

Education is important to form an intelligent and competitive generation. Project Based Learning (PjBL) offers relevant and interactive learning, recognized by Grant (2002) as a student-centered model. PjBL creates a constructivist environment, developing content understanding and skills, such as communication, time management, research and critical thinking. This research focuses on the application of PjBL in chemistry learning, aiming to find out the results of previous research. This research uses Systematic Literature Review (SLR) and bibliometric review as methods. SLR is considered a systematic and transparent method of collecting, synthesizing and assessing study findings on a topic. The process includes planning (research identification), implementation (literature search and selection), and reporting (synthesis of findings). The research question (RQ) focuses on the application of Project Based Learning (PjBL) in chemistry learning. Twenty journals were selected, mostly from Scopus, with criteria ranging from 2015-2020. The analysis showed that 13 journals met the criteria, and a bibliometric review was then carried out on these journals. The research results show that PjBL focuses on skill development, learning innovation, and the impact on student understanding and participation. The research implications highlight the importance of the PjBL model to improve the learning process and better learning tools. The results revealed a wide variety of methods and samples. Six studies focused on the influence of PjBL, critical thinking abilities, psychomotor skills, problem solving, creativity, cognitive achievement, and teamwork. This framework generally involves students' innovative learning and laboratory experiments. Chemistry concepts include analytical, physical, environmental, basic, and organic chemistry. Data collection and analysis tools serve as benchmarks for future research and implications.

Education multiliteracy is an education that emphasizes on increasing the diversity of literacy in all aspects of life. The multiliteracy education program is a program of literacy by using a variety of approaches (arts, culture, environment, technology, race, ethnicity, gender, and others) are relevant to the learners to achieve and or develop literacy competencies and improve the income and quality of life of participants students. The focus of the problem is as follows: How to increase literacy skills of reading advanced literacy students through Project-based multiliteracy learning models in CLC Hikmah District. Ciamis, CLC Al-Ghifari District. Cirebon, and CLC Geger Sunten District. West Bandung? The project-based multiliteracy pre-cooperative learning model is multiliteracy learning with the theme of science and technology pre-cooperative sub-themes using project-based learning strategies. Learners during learning in enhancing the ability of literacy such as reading, writing, and arithmetic in the language of Indonesia to be given charge of pre cooperatives. Wherein the output of this study is the formation of pre-cooperative business groups, in the process of establishing this cooperative course pre herded learners to complete the project in any appropriate syntax learning project-based learning, namely: 1) Determination of project. 2) Designing project completion steps. 3) Compilation of project implementation schedules. 4) Project completion. 5) Report preparation. 6) Evaluate the process and process results. Based on the research results obtained Asymp.sig (2-tailed) = 0,000 <0.05 then Ho is rejected and Ha is accepted meaning there is an average difference between the pre-test and post-test learning outcomes, which means there is an influence on the use of the Project-based pre-cooperative multi-literacy model towards increased literacy skills in reading students.

This study examines the implementation of Project-Based Learning (PjBL) in music education through a bibliometric analysis of Scopus-indexed publications from 2016 to 2025. A total of 479 relevant articles retrieved from the Scopus database were analyzed using VOSviewer to identify research trends, thematic clusters, and the intellectual structure of the field. The analysis focused on keyword co-occurrence, thematic clustering, and publication trends. The results reveal six major thematic clusters: (1) pedagogy and instructional strategies, (2) educational innovation and technology integration, (3) music, creativity, and collaboration, (4) interdisciplinary learning and 21st-century skills, (5) learning evaluation and outcomes, and (6) artificial intelligence and future-oriented music education. Publication trends indicate a substantial increase in research output, particularly after 2022, which coincides with the post-COVID-19 period and the accelerated adoption of digital learning technologies. Overall, this study provides a comprehensive mapping of PjBL research in music education, highlighting both established and emerging research themes and offering valuable implications for curriculum development and future research directions.

Research background: Recent years have introduced a tremendous growth in the application of artificial intelligence (AI). Many organizations use this technological development to enhance operations and facilitate daily tasks. Influencer marketing is a field where the use of AI becomes inevitable. Purpose of the article: The purpose of this paper is to analyze the current state of research on AI in influencer marketing and provide the core guidelines for further exploration of this field. Research background: Recent years have introduced a tremendous growth in the application of artificial intelligence (AI). Many organizations use this technological development to enhance operations and facilitate daily tasks. Influencer marketing is a field where the use of AI becomes inevitable. Purpose of the article: The purpose of this paper is to analyze the current state of research on AI in influencer marketing and provide the core guidelines for further exploration of this field. Methods: Bibliometric analysis of scientific literature is performed based on an article selection procedure encompassing steps indicated in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. The Scopus database was chosen, as it has the relatively widest coverage of the Social Sciences. The search using keywords “artificial intelligence” or “AI” and “influencer marketing” found 79 documents. Findings &amp; value added: The field is rapidly evolving and attracting new researchers. The emerging field of interest has attracted researchers from 28 countries so far, indicating that the domain is still underexplored and requires contributions from scholars. The analysis revealed the need for the clarification of terminology. The terms ‘AI influencer’, ‘AI-generated influencer’, ‘digital influencer’, and ‘virtual influencer’ were used to describe the same phenomenon. Emerging research trends were determined: AI influencer feature determination and analysis; consumer behavioral reactions towards the AI influencers; platforms, media, and networks used by AI influencers; marketing strategies to be used by AI influencers; and suitability and appropriateness of research methods in AI influencer marketing. The main contribution and value-added lie in the determination of underexplored scopes and implications for researchers. The identified research trends would also serve as a checklist for managers choosing to apply an AI influencer in their marketing strategies: the choice of the most suitable platform and determination of the appropriate features of the influencer would set the background for strategy selection and lead to the desired consumer reactions.

This study investigates the impact of integrating Artificial Intelligence (AI) into a Project-Based Learning (PjBL) model to enhance students’ English speaking skills. Recognizing the persistent challenges in providing sufficient speaking practice opportunities in conventional classrooms, this research employed AI-based applications, including Character AI, TikTok, and video editing tools, to create an interactive and personalized speaking environment. This study examines the integration of Artificial Intelligence (AI) into a Project-Based Learning (PjBL) model to enhance students’ English speaking skills. The research was motivated by the limited opportunities for speaking practice in conventional classrooms. AI-based applications such as Character AI, TikTok, and video editing tools were employed to create an interactive and personalized learning environment. A total of 60 students participated in the study, divided into a control group applying conventional PjBL and an experimental group applying AI-assisted PjBLThe study involved 60 students divided into two groups: a control group applying conventional PjBL and an experimental group using AI-assisted PjBL. Data were collected through pre- and post-tests and analyzed using SPSS with descriptive statistics, normality testing, and independent samples t-test. The results revealed that the AI-assisted PjBL group achieved a higher mean speaking score (M = 82.00, SD = 6.103) compared to the control group (M = 72.00, SD = 5.038), with a statistically significant difference (t = 6.922, p &lt; 0.05). These findings indicate that integrating AI in PjBL can significantly improve students’ speaking performance by providing adaptive feedback, increasing learner autonomy, and creating more authentic speaking practice opportunities. However, the study also highlights limitations, including restricted AI platforms and a relatively short experiment duration. The findings suggest that AI-assisted PjBL is a promising approach for enhancing speaking skills in higher education, but it should be complemented with extended practice and teacher facilitation to achieve optimal outcomes.

This study provides a bibliometric analysis of the expanding landscape of artificial intelligence (AI) within the service sector, utilising data from VOSviewer software. The study utilised a network of bibliographic couplings, where nodes symbolised nations involved in the issue. The analysis was also compared between countries along with its strength of research connections, particularly in the tourism and hospitality field. Key findings have shown that the research community is strong and active, with China, the United States and the United Kingdom playing a significant role in innovating to enhance the service sector using AI. These countries have a robust bibliographic coupling, suggesting a substantial amount of interconnected references and research interests. Gursoy, Kautish, and Ting were recognised as the foremost contributors, and areas of corporate management, social science, and computer science have emerged as significant domains in the research of AI and servicescape. The present study provides a comprehensive bibliometric map that clearly shows various platforms, methods, and technologies that shape the AI trend in servicescape, altering how AI aids servicescape.

This chapter explores the intersection of artificial intelligence (AI) and corporate governance (CG), highlighting how AI technologies, such as machine learning and natural language processing, can optimize decision-making, compliance, risk management, and business performance. The study aims to systematically and bibliometric review the existing literature on AI and CG, map current trends, and identify future research agendas in this interdisciplinary field. A dataset of 445 publications was curated using the PRISMA framework, focusing on AI’s application in corporate settings. The study employs bibliometric analysis to explore the contributions of authors, sources, and countries, along with co-citation, thematic map, and keyword co-occurrence analyses to uncover contextual relationships and themes shaping the field. The review identifies a significant increase in publications in recent years, driven by advancements in AI. Key themes include AI-driven decision support systems, ethical considerations, and AI’s role in enhancing corporate transparency and fraud detection. The findings also highlight the diverse academic contributions from scholars in computer science, business ethics, and financial management. AI has the potential to transform CG, but ethical and legislative challenges must be addressed. The study emphasizes the need for multidisciplinary research to explore AI’s practical implications in governance, ensuring responsible and effective implementation. This review provides valuable insights into the emerging context of AI in CG, proposing future research directions to further investigate AI’s impact on governance structures and stakeholder relationships.

The rapid development of artificial intelligence (AI) and its applications is gaining global attention and promises to revolutionise every aspect of human life, including education. AI will transform higher education (HE) institutions through improved adaptation and competitiveness. The application of AI in HE is an emerging research area with limited review. Our paper seeks to provide a comprehensive bibliometric analysis and visualisation of research on AI application in HE in the past two decades. We evaluated 283 articles published by researchers from 59 countries in the Scopus database over the past two decades based on explicit inclusion and exclusion criteria. The study applied various bibliometric indicators and word analysis to examine emergent trends. VOSviewer was used for visualisations to map a knowledge base by uncovering keywords used within AI in HE. The results show the number of AI articles published per year, their geographic distribution, analysis by subject area and keywords, and research trends. Research in AI is interdisciplinary, dominated by computer science and engineering fields. AI research in HE is growing, the first 15 years contributed 22%, and the last five years yielded 78%. Countries with a high investment in research dominated AI research, with China and the United States leading. There is very little research from developing countries. Our paper highlights current and future research directions in AI in education, and its limitations.