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This review article is focused on the following research questions: RQ1: What are the methods used by authors to collect data in order to evaluate one's profile? RQ2: What are the classification algorithms and ranking metrics used to give suggestions to users? RQ3: How effective are these algorithms and metrics identified in RQ2?

There are four major systematic review phases being carried out in this survey, namely the formulation of research questions, conducting the review, which includes the selection of articles and appraising evidence quality, data extraction and narrative data synthesis.

Collecting from primary sources is more personalized and relevant. Embedded skill sets that have a considerable impact on one’s career aspirations could be mined from secondary sources. A hybrid recommender system helped mitigate the limitations of both. The effectiveness of the models depends not only rely on the filtering techniques used but also on the metrics used to measure similarity and the frequency of words or phrases used in a document.

The study benefits internship program coordinators of a university aiming to develop a recommender or matching system platform for their students. The content of the study may shed a light on how university decision-makers can explore options on what are the techniques or algorithms to be integrated. One of the advantages of internship or industrial training programs is that they would help students align them with their career goals. Research studies have discussed other RS filtering techniques apart from the three major filtering techniques.

The outcome of the study, which is a recommendation system to match a student's profile with the knowledge and skills being sought by organizations, may help ease the challenges encountered by both parties. The study benefits internship coordinators of a university who are planning to create a recommendation system, an innovative project to be used in teaching and learning.

Internship programs can help a student grow personally and professionally. A university student looking for internship opportunities can find it a daunting task to undertake, as there is a vast pool of opportunities offered in the market. The confidence levels needed to match their knowledge, skills and career goals with the job descriptions (JDs) could be challenging. The same holds with companies, as finding the right people for the right job is a tough endeavor. The main objective of conducting this study is to identify models implemented in recommendation systems to give and/or rank suggestions given to users.

While surveys regarding recommender systems (RS) exist, there are gaps in the presentation of various data collection methods and the comparison of recommendation filtering techniques used for both primary and secondary sources of data. Most recommendation systems for internship programs are intended for European universities and not much for Southeast Asia. There are also a limited number of comparative studies or systematic review articles related to recommendation systems for internship programs offered in an Southeast Asian landscape. Systematic reviews on the usability of the proposed recommendation systems are also limited. The study presents reviews of articles, from data collection and techniques used to the usability of the proposed recommendation systems, which were presented in the articles being studied.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

The maturity of artificial intelligence technology and the emergence of AI-generated content (AIGC) tools have endowed college students with a human-AIGC tools collaboration learning mode. However, there is still a great controversy about its impact on learning effect. This paper is aimed at investigating the impact of the human-AIGC tools collaboration on the learning effect of college students.

In this paper, a hypothesized model was constructed to investigate the effects of dependence, usage purpose, trust level, frequency, and proficiency of using AIGC tools on the learning effect, respectively. This paper distributed questionnaires through random sampling. Then, the improved Analytic Hierarchy Process (AHP) was used to assign weights and normalize data. Lastly, one-way ANOVA and multiple linear regression analyses were used to measure and analyze variables, revealing the mechanism of influence.

The usage purpose, frequency, and proficiency of using AIGC tools have a significant positive effect on learning. Being clear about the usage purpose of AIGC tools and matching the specific study tasks will enhance the learning effect. College students should organically integrate AIGC tools into each learning process, which is conducive to building a learning flow applicable to oneself, improving efficiency, and then enhancing learning effects. The trust level in AIGC tools is significant, but positively and weakly correlated, indicating that college students need to screen the generated content based on their knowledge system framework and view it dialectically. The dependence on AIGC tools has a negative and significant effect on learning effect. College students are supposed to systematically combine self-reflection and the use of AIGC tools to avoid overdependence on them.

Based on the findings, the learning suggestions for college students in human-machine collaboration in the AIGC era are proposed to provide ideas for the future information-based education system. For further research, scholars can expand on different groups, professions, and fields of study.

Previous studies have focused more on the impact of AIGC on the education system. This paper analyzed the impact of the various factors of using AIGC tools in the learning process on the learning effect from the perspective of college students.

Drawing primarily on the Japanese context, this study aims to highlight this setting to emphasise the potential for tertiary-level self-access language centres to develop lifelong global citizenship, self-reflection and cross-cultural collaboration.

This inquiry calls on the community of practice approach to account for the shared interests motivating lifelong cross-cultural participation, the quality of social engagement between actors, and the material and cognitive tools called upon to realise global citizenship’s shared enterprise.

As argued here, embracing various cultures and inclusive participation can lead to a broader understanding of global citizenship, avoiding narrow-minded views of globalism through shared knowledge and critical practices. Further, self-access provides a cost-effective, technology-mediated alternative to bilateral student mobility, whereby digital community-building occasions cross-cultural practice that may be extended throughout a learner’s life, irrespective of their financial status or place of study.

This study is one of a select few drawing on the community of practice framework within the context of lifelong global citizenship. Nevertheless, such an approach remains primed for future development. With a social constructivist philosophy in view, the authors suggest complementary qualitative research approaches that highlight the socially situated nature of both disciplines.

The principal aim of this research is to acquire a deeper understanding of the opinion held by the training and development (T&D) professionals, regarding the use of artificial intelligence (AI) technology in the area of T&D. Particularly in response to the evolving needs of learners, the research aims to ascertain T&D professionals' perspective on the efficiency of AI in fostering T&D, while understanding the constraints and limitations associated with this technology.

The study is based on qualitative data. With the help of semi-structured interviews, qualitative data has been collected from 21 T&D professionals. Experts working with multinational corporations (MNCs) are selected as a study sample using a convenient sampling technique. Qualitative data were analysed using thematic analysis. Conclusions were drawn based on the results of thematic analysis.

The findings of the study have revealed a notable and rapid evolution in the requirements of learners, particularly during and post-COVID-19 period. AI-based technology has emerged as a significant contributor, offering learners distinct personalised experiences and enhanced convenience. However, the implementation of AI in training remains in its early stages and has not reached widespread adoption. The findings of the study also highlighted various challenges and limitations, while using AI-based technology for training. It has been found that AI complements rather than replaces the role of a physical trainer.

The originality of this study lies in the application of AI-based training for professional learners, from the perspective of the T&D practitioners working with MNCs in Maharashtra, India. Numerous studies that have recently been published, emphasise the areas in which AI technology can transform the T&D industry. Yet, there are currently very less studies that have attempted to understand the evolving needs of learners and support of AI-based training for the same, from the perspective of the T&D professionals working in Maharashtra, India.

This paper aims to explore the digital information repackaging process from the perspective of library website operators. The digital info repackaging process consists of analysis, synthesis, editing and presenting.

Interviews with 17 library website operators regarding their experience in information repackaging. The library website observation also conducted to gather the data on information repackaging.

Librarians who manage library websites repackage digital information using a process of user analysis, content analysis, editing and transforming and translating. Librarians who are in charge of repackaging usually collect, check, group and present information based on the raw data and information. Librarians rarely did criticize or argue the information. They just deliver it in the pact and attractive format. In the future, librarians can add critical comments on their digital information content, such as give recommendation, provide point of view, add some suggestion or make comparison.

This paper can provide an overview of the process of repackaging digital information carried out by university libraries which can be provided by libraries via websites. Apart from that, it is hoped that the presence of this research can help librarians in carrying out the process of repackaging digital information.

This paper can add to the scientific development of information science and library studies related to the field of information repackaging, especially in the repackaging of digital information in creating website content to efficiently meet the information needs of university library users.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

Artificial intelligence (AI) technologies have led to significant transformations across industries and society, including the field of education. The integration of AI in educational settings has the potential to improve students' learning experience and support their individual competencies when paired with non-AI methods. Despite the growing importance of AI in modern education, there remains a noticeable research gap regarding its use in entrepreneurship education and the effects of Chatbots on students' entrepreneurial competencies. To address this gap, an exploratory study was conducted on undergraduate students who were tasked with using ChatGPT to improve their business model canvas.

The chosen methodology aligned with the research purpose, aiming to explore the relationship between Generative AI and competencies. Due to the novel nature of the research problem, an exploratory study was conducted using a mixed methods approach. A survey with open- and closed-ended questions was designed, and statistical and text analyses were performed to interpret data and test identified propositions.

The findings of this study indicate that ChatGPT can enhance the types of students' entrepreneurial competencies considered in this study: spotting opportunities, creativity, vision, valuing ideas and ethical and sustainable thinking. The results show that ChatGPT can be particularly helpful to improve the ability of students of valuing ideas.

Overall, this study highlights the potential of adopting ChatGPT in experiential learning methodologies for enhancing students' entrepreneurial competencies and improving their learning outcomes.

This study proposes an integrated Machine Learning and simulated framework for a personalized learning system. This framework aims to improve the integrity of the provided tasks, adapt to each student individually and ultimately enhance students' academic performance.

This methodology comprises two components. (1) A simulation-based system that utilizes reinforcement algorithms to assign additional questions to students who do not reach pass grade thresholds. (2) A Machine Learning system that uses the data from the system to identify the drivers of passing or failing and predict the likelihood of each student passing or failing based on their engagement with the simulated system.

The results of this study offer preliminary evidence of the effectiveness of the proposed simulation system and indicate that such a system has the potential to foster improvements in learning outcomes.

As with all empirical studies, this research has limitations. A simulation study is an abstraction of reality and may not be completely accurate. Student performance in real-world environments may be higher than estimated in this simulation, reducing the required teacher support.

The developed personalized learning (PL) system demonstrates a strong foundation for improving students' performance, particularly within a blended learning context. The findings indicate that simulated performance using the system exhibited improvement when individual students experienced higher learning benefits tailored to their needs.

The research offers evidence of the effectiveness of personalized learning systems and highlights their capacity to drive improvements in education. The proposed system holds the potential to enhance learning outcomes by tailoring tasks to meet the unique needs of each student.

This study contributes to the growing literature on personalized learning, emphasizing the importance of leveraging machine learning in educational technologies to enable precise predictions of student performance.

Integrating sustainability into university curricula brings diverse challenges and conflicts as separate units vie for ownership of courses and topics. This case study presents a six dimensions sustainability framework developed at The Ohio State University to organize curricula under an inclusive strategy.

An interdisciplinary group of faculty focused on sustainability education engaged in a three-phased process including review of sustainability definitions from diverse disciplines; analysis of key aspects of the definitions in conjunction with course descriptions and learning outcomes; and identification of commonalities across the key aspects. This yielded six foundational dimensions of sustainability which serve as a means to assess curricular contributions across University units and topics. The six dimensions framework has been used in practice in multiple contexts.

The six dimensions framework provides a way to identify and foster diverse sustainability curricula efforts. It has enabled academic units to describe their disciplinary and interdisciplinary perspectives on diverse sustainability topics and the University to advance a broad sustainability vision.

The six dimensions framework provides a novel “big tent” approach to integration of sustainability into higher education curricula. The framework provides guidance about what counts as sustainability while maintaining the breadth that widens participation.