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This article aims to identify and describe the research outcomes of studies that have employed the theoretical framework of lesson study (LS) in initial science teacher preparation programs. The focus is on the impact of LS on preservice teachers’ (PST) pedagogical and content knowledge, beliefs, routines and norms for professional learning and instructional practices.

A systematic approach was employed to compile pertinent literature by initially searching scholarly databases using specific keywords and phrases related to prospective science teacher preparation. Seventeen studies, encompassing both qualitative research and mixed-methods research, met the inclusion criteria and significantly contributed to the study’s findings. The authors independently conducted a coding process, applying a predefined code scheme based on Lewis et al.'s (2019) theoretical framework. The outcomes of the coding process were compared, and reliability tests were conducted to ensure the consistency of the coding.

In preservice science teacher (PSST) education, LS proves transformative, enriching pedagogical and content knowledge, shaping beliefs, fostering collaboration and influencing instructional practices. Its collaborative, reflective and iterative nature significantly contributes to the professional growth of preservice science teachers, preparing them for effective, student-centered teaching practices. Further investigation is warranted in the realm of LS, particularly concerning preservice science teachers and their beliefs.

This literature review on science PSTs is one of the pioneering efforts to employ the professional development framework crafted by Lewis et al. (2019).

This study aims to address the existing knowledge gap by investigating teachers’ adoption of ChatGPT for educational purposes. The study specifically focuses on identifying the factors that motivate and inhibit teachers in adoption of ChatGPT in higher education institutions (HEIs).

This research has used interpretative phenomenological analysis – a qualitative approach. Through in-depth interviews among the teachers, data was collected to identify the motivating and inhibiting factors that impacted teachers’ willingness to adopt ChatGPT. The data was collected from 48 teachers working across HEIs of Uttarakhand region in India.

The analysis revealed seven themes under motivating factors that encourage teachers to adopt ChatGPT for their educational purposes. These include time factor, tool for competitive edge, learning enhancement tool for students, research facilitator, benefits in educational settings, troubleshooter and easy to use. On the other hand, inhibiting factors comprise five themes, which include technical difficulties, limited features for educational and research purposes, tool for handicapping innovation and creativity, lack of personal touch and ethical considerations.

The findings will be valuable for HEIs in establishing policies that promote the appropriate and effective use of ChatGPT. Moreover, the study provides recommendations to ChatGPT solution providers for improving ChatGPT services for effective adoption of ChatGPT among teachers and implementation at HEIs. Further, it contributes to the body of literature by filling a knowledge gap about teacher adoption of ChatGPT in the HEIs. Through qualitative research, the study has pinpointed specific motivating and inhibiting factors that affect teacher adoption of ChatGPT.

Unlike previous studies that primarily explored the potential advantages and drawbacks of ChatGPT in education, this research study delves deeper into the topic. It makes a substantial contribution to our understanding of ChatGPT adoption among teachers by identifying distinct factors that either motivate or inhibit teachers from adopting ChatGPT for job related purposes. The study provides novel insights that were previously mislaid, thereby introducing a fresh perspective to the existing literature

Artificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.

The Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.

Research on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.

This is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.

Cooperative agreements between universities and firms (U–F) have gained prominence. However, the literature on organisational culture and the formation of cooperation agreements is scarce. This study aims to analyse, from the perspective of the managers of small- and medium-sized enterprises (SMEs) and those in charge in universities, the perceptions of the influence of organisational culture on this type of U–F cooperation.

To this end, multiple case studies were adopted, involving cooperation agreements between a Portuguese and eight SMEs incubated in UBImedical. Semi-structured interviews were used to gather information, aiming to understand the meaning, importance and possible obstacles caused by organisational culture in this U–F cooperation agreement.

Content analysis of the results obtained leads to the conclusion that cultural compatibility is a crucial factor for successful U–F cooperation. The exchange of knowledge, mutual trust and flexibility between those involved are identified as key determinants to build shared norms that allow a more productive, assertive union.

The study represents an important tool to support SME managers and those in charge of universities, as the evidence obtained can help them to define policies and actions with regard to the U–F cooperation process. More precisely, these SME and university managers could give more attention to culture in future cooperation agreements.

This study advances understanding of the role of organisational culture in a cooperation agreement since this was a gap identified in the literature on the topic. It also contributes to the existing body of work on U–F cooperation, demonstrating that organisational culture is considered important by partners in these agreements and should be adjusted towards compatible alignment of each party’s expectations.

This study aims to investigate the influence of ChatGPT, an AI-based chatbot, on the digital learning experience of students at Mzumbe University.

This study adopted a qualitative research design to gather in-depth insights from participants. Semi-structured interviews and an analysis of previous chat content were used as primary sources of data. Thematic analysis was used to analyze the qualitative data, allowing for the exploration of participants’ perspectives, experiences and opinions regarding the integration of ChatGPT into the learning process.

The results of the study demonstrated that ChatGPT is widely used in educational contexts and has a positive influence on students’ study habits, academic performance, and understanding of course material. Students appreciated the system’s simplicity, tailored instructions, and the promptness and accuracy of the responses. Despite the possibility of isolated mistakes.

It is important to recognize the limitations of this study. First, the sample size was small, limiting the broad application of the results. Second, this study’s narrow emphasis on students at Mzumbe University limits its applicability in other situations. Furthermore, depending on self-reported experiences, biases, such as individual interpretation or recollection bias, can occur.

Educators can maximize ChatGPT in the classroom by using study insights. Its advantages, such as effectiveness and enhanced performance, highlight the possibility for student-centered learning. Practitioners are guided by their awareness of problems, such as probable errors. Constant updates guarantee ChatGPT’s applicability and provide educators with useful advice.

Peer impact is highlighted in this study concerning social factors on the adoption of AI in education. Resolving issues preserves public confidence. Views influence public opinion and direct policymakers in discussions about safe AI use. It influences public attitudes while navigating the ethical integration of AI.

This study offers insightful information about the impact of ChatGPT on digital learning in Tanzania’s higher education. It makes innovative research contributions that enhance educational practices and emphasizes the advantages, difficulties and demands of responsible usage in the context of AI-based chatbots.

This paper delves into the critical role of women in the domains of architecture, engineering, construction and urban planning, particularly within the context of the United Nations sustainable development goals (SDGs) and the imperative of achieving gender parity. Concentrating on the Gulf Cooperation Council (GCC) countries, this paper explores the profound importance of women as essential contributors to urbanisation and sustainable development. It scrutinizes the existing gender disparities within the architecture, engineering and construction (AEC) industries; evaluates prevailing women empowerment indices in diverse sectors; and presents a strategic framework for harnessing women’s engagement to cultivate inclusive and sustainable urban cultures in the GCC. This conceptual paper introduces an actionable framework that can serve as a guiding agenda for empowering women in the AEC sectors by incorporating their socio-cultural, economic and ecological contributions towards creating sustainable cities.

Centring its focus on Gulf Cities, this paper employs a comprehensive approach to examine the current gender disparities within the architecture, engineering and construction sectors. It probes potential barriers and advocates for leveraging women’s participation to foster inclusive and sustainable urban development in the GCC. The study introduces the Women’s Empowerment Index (WEI) for the Gulf AEC industry elucidating how these measures are translated into a tailored framework.

The paper presents a practical framework that provides actionable guidance for engaging various stakeholders, including governments, academia and industry players, to empower women within the AEC industries. An “Agenda for equitable AEC industries for sustainable urban development: Our Common Gulf Cities” is culminated in a Women Empowerment Index for the AEC Industry (WEI-AEC) designed to serve as a guidance tool to monitor progress within industry, governments and academia.

Future research endeavours could advance the framework by conducting institutional support analyses, multi-stakeholder collaboration studies and practical testing of the framework within real-world scenarios.

The findings of this study can guide and influence a diverse range of initiatives, including policy development, educational strategies, corporate endeavours, awareness campaigns, capacity-building programs, skill enhancement initiatives and knowledge exchange among the Gulf Cooperation Council countries.

This conceptual paper introduces a practical framework that can serve as a roadmap for implementing the study’s potential to shape policies, educational programs and corporate initiatives aimed at advancing both gender equality and the development of sustainable cities.

The aim of this study was to measure the level of challenges faced by novice teachers in South Africa. Findings suggest a need for professional development courses to upskill teachers with effective pedagogies that can incorporate the social and emotional components into teaching and learning.

This study applied a descriptive research methodology by administering a questionnaire to 143 novice teachers. The data analysis technique was the Rasch model.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. The three major challenges identified are uninvolved parents, discipline problems and a lack of guidance and counselling. These challenges can be related to social and emotional learning (SEL) components.

The study used quantitative methods and discovered the challenges that novice teachers face. If the research uses a combination of qualitative methods, it will be possible to better identify the specific causes of the above three challenges of novice teachers.

Due to the complex nature of South African society, many novice teachers are overwhelmed by the challenges they face when entering the profession. These challenges are often embedded in societal risk factors, which complicate the transition from student teacher to novice teacher. The major challenges identified in this study can be related to SEL components, as the challenges are closely linked to the psychological and social backgrounds of the students. Teachers in this study indicated that they found it difficult to deal with these challenges at the beginning of their careers.

By identifying the challenges facing new teachers in South Africa, they will be better prepared for their work in schools. Therefore, they will improve the above situation to continue developing professionally.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. Teachers in this study indicated that they found it difficult to deal with these challenges in the beginning of their careers. Professional development courses are suggested to help teachers deal with issues such as discipline, uninvolved parents and guidance and counselling effectively. Higher education programmes should also include these topics in their curricula for student teachers. A greater emphasis on training teachers in their pastoral roles is suggested.

The challenges facing 21st-century society are becoming increasingly complex, requiring the development of new citizen competencies. This study aims to validate an educational model focused on developing complex thinking in higher education students. Current educational models lack future-ready competencies, necessitating the emergence of new models to guide future generations toward the common good.

This was an adaptation of the causal-layered analysis (CLA) applied to 415 participants from higher education institutions in Mexico, Panama and Spain. Sessions were designed to present the proposed educational model and explore participants’ perceptions of its significance and contributions to future education.

Key findings include the following: participants perceived complexity as difficult and challenging; causes of problems were linked to outdated educational models requiring replacement by those that develop students’ competencies; participants envisioned changes that would develop individuals capable of understanding and transforming society; and participants recognized the model’s transformative potential, offering a novel proposal for 21st-century education.

This research sought to gather opinions from different stakeholders using the CLA methodology, providing a deep understanding of participants’ perspectives on the proposed solution.

This study aims to highlight the growth and development of Indo-German collaborative research over the past three decades. Moreover, this study encompasses an in-depth examination of funding acknowledgements to gain valuable insights into the financial support that underpins these collaborative endeavours. Together with this paper, the authors provide an openly accessible data set of Indo-German research papers for further and reproducible research activities (the “Indo-German Literature Dataset”).

The data were retrieved from the Web of Science (WoS) database from the year 1990 till the 30th of November 2022. A total of 36,999 records were retrieved against the used query. Acknowledged entities were extracted using a named entity recognition (NER) model specifically trained for this task. Interrelations between the extracted entities and scientific domains, lengths of acknowledgement texts, number of authors and affiliations, number of citations and gender of the first author, as well as collaboration patterns between Indian and German funders were examined.

The study reveals a consistent and increasing growth in the publication trend over the years. The study brings to light that Physics, Chemistry, Materials Science, Astronomy and Astrophysics and Engineering prominently dominate the Indo-German collaborative research. The USA, followed by England and France, are the most active collaborators in Indian and German research. Largely, research was funded by major German and Indian funding agencies, international corporations and German and American universities. Associations between the first author’s gender and acknowledged entity were observed. Additionally, relations between entity, entity type and scientific domain were discovered.

The study paves the way for enhanced collaboration, optimized resource utilization and societal advantages by offering a profound comprehension of the intricacies inherent in research partnerships between India and Germany. Implementation of the insights gleaned from this study holds the promise of cultivating a more resilient and influential collaborative research ecosystem between the two nations.

The study highlights a deeper understanding of the composition of the Indo-German collaborative research landscape of the past 30 years and its significance in advancing scientific knowledge and fostering international partnerships. Furthermore, the authors provide an open version of the original WoS data set. The Indo-German Literature Data set consists of 22,844 papers from OpenAlex and is available for related studies like literature studies and scientometrics.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.