ChatGPT in Political Education

The purpose of this study was to evaluate the effectiveness of Artificial Intelligence (AI) tools in teaching and learning in higher education institutions in Kenya, specifically focusing on Intelligent Tutoring Systems (ITS), Adaptive Learning Platforms, Virtual Learning Assistants (VLAs), Automated Grading Systems and Learning Analytics Systems (LAS), their accessibility use and its effectiveness in teaching and learning. The study employed a mixed-methods research design, combining both quantitative and qualitative approaches, to gather comprehensive data from faculty members, students, and administrators across 15 selected public and private universities and technical colleges in Kenya. The findings indicated that the accessibility of AI tools in institutions of higher learning in Kenya is significantly limited. A large majority of respondents expressed that AI tools are not readily available, highlighting disparities in access across different departments and projects within institutions. In terms of usage, the integration of AI tools into teaching and learning practices is still in its early stages in most institutions and where they are available they are not always well-integrated with existing curricula, leading to limited and uneven adoption across different disciplines. Despite these challenges, those who have begun using AI tools have reported benefits such as personalized learning, more efficient assessment processes, and enhanced feedback mechanisms, indicating that AI has the potential to transform educational practices if more effectively utilized. Findings further established a significant correlation between AI tools and effective teaching and learning in institutions of higher learning in Kenya (r = .781; p = .000). The study noted that while AI can significantly improve the educational experience, its current impact is constrained by several factors. Faculty members' unfamiliarity with AI, the lack of comprehensive training, and the inadequate integration of AI tools into the curriculum are major barriers to their effective use. However, where AI has been successfully implemented, it has contributed to better learning outcomes, higher student engagement, and more personalized feedback. The study recommended that institutions must invest in infrastructure, ongoing professional development, and curriculum integration, ensuring that AI tools are both accessible and effectively used to enhance teaching and learning outcomes.

This article explores the use of podcasts in the learning and teaching of Politics and International Relations in a UK Higher Education institution. The article has three main sections. The first explores the pedagogic issues associated with the use of podcasts. The second highlights examples of good practice for using podcast material in lectures/seminars. Finally, and perhaps most importantly, it outlines student attitudes to the use of podcasts within learning and teaching.

The global landscape of medical research and writing over the last decade has been evolving rapidly.It got a major boost by the public launch of ChatGPT in November 2022.It has been further accelerated due to the widespread availability and easy access to a variety of artificial intelligence (AI) tools based on 1 Large Language Models (LLMs).The use of AI tools in medical research and writing is on the rise.However, many students, faculty members and researchers particularly in developing countries are still unclear on the transparent and ethical integration of the AI tools in research and writing.We propose a framework for the appropriate integration of AI in medical research and writing in context of a developing country like Pakistan.It highlights responsible and transparent use of AI tools, discuss authorship in the era of AI tools and how to ensure scientific integrity. How AI Tools can Enhance Research and WritingAI tools can help researchers with various steps of research and writing like brainstorming and refining research questions, creating questionnaires, detailed data analysis, creating outlines of the manuscripts and improving the readability by enhancing the language, grammar and syntax of the 2 text.This can potentially improve the efficiency and productivity of the researchers.However, there are some ethical challenges unique to the use of these AI tools that need consideration.It is important to establish and follow a framework for responsible and ethical use of AI in research and writing that promotes transparency, upholds the integrity of the research and maintains public trust in research 3 findings. Brainstorming Ideas and Creating Research QuestionsThe first step in a research project is conceptualizing research goals and brainstorming ideas.Before using AI tools, researchers must understand questions they aim to answer, and the potential impact it can create.This clarity helps in ensuring that AI tools will complement, not dictate, the direction of the research.This step should not be totally delegated to an AI tool.Any LLM can be used to generate and brainstorm a research question using appropriate p ro m p t s .A I t o o l s l i ke " Re s e a rc h k i c k " (https://www.researchkick.com/) are specifically created to brainstorm ideas and refine research questions.

BackgroundModern patient care depends on the continuous improvement of community and clinical pharmacy services, and artificial intelligence (AI) has the potential to play a key role in this evolution. Although AI has been increasingly implemented in various fields of pharmacy, little is known about the knowledge, attitudes, and practices (KAP) of pharmacy students and faculty members towards this technology.ObjectivesThe primary objective of this study was to investigate the KAP of pharmacy students and faculty members regarding AI in six countries in the Middle East as well as to identify the predictive factors behind the understanding of the principles and practical applications of AI in healthcare processes.Material and methodsThis study was a descriptive cross-sectional survey. A total of 875 pharmacy students and faculty members in the faculty of pharmacy in Jordan, Palestine, Lebanon, Egypt, Saudi Arabia, and Libya participated in the study. Data was collected through an online electronic questionnaire. The data collected included information about socio-demographics, understanding of AI basic principles, participants’ attitudes toward AI, the participants’ AI practices.ResultsMost participants (92.6%) reported having heard of AI technology in their practice, but only a small proportion (39.5%) had a good understanding of its concepts. The overall level of knowledge about AI among the study participants was moderate, with the mean knowledge score being 42.3 ± 21.8 out of 100 and students having a significantly higher knowledge score than faculty members. The attitude towards AI among pharmacy students and faculty members was positive, but there were still concerns about the impact of AI on job security and patient safety. Pharmacy students and faculty members had limited experience using AI tools in their practice. The majority of respondents (96.2%) believed that AI could improve patient care and pharmacy services. However, only a minority (18.6%) reported having received education or training on AI technology. High income, a strong educational level and background, and previous experience with technologies were predictors of KAP toward using AI in pharmacy practice. Finally, there was a positive correlation between knowledge about AI and attitudes towards AI as well as a significant positive correlation between AI knowledge and overall KAP scores.ConclusionThe findings suggest that while there is a growing awareness of AI technology among pharmacy professionals in the Middle East and North Africa (MENA) region, there are still significant gaps in understanding and adopting AI in pharmacy Practice.

Intelligent political and ideological education has steadily gained popularity in today's educational landscape due to the rapid advancement of artificial intelligence technology. This research aims to investigate the emerging role of artificial intelligence in political and ideological education. First of all, we clearly define and explain the concept of intelligent ideological and political education, and deeply analyze the basic connotation and characteristics of intelligent ideological and political education including its important value. Secondly,we analyze the application status of artificial intelligence technology in ideological and political education, and give two representative typical cases.At the same time, we also analyzed the innovative practice of artificial intelligence technology from two aspects of moral education and political education.Furthermore, given the difficulties AI technology faces in the context of political and ideological education, we suggest appropriate responses,This study concludes with a discussion on artificial intelligence's development tendency in ideological education going forward.Our prediction is that as artificial intelligence technology advances and becomes more sophisticated, intelligent education will serve as a strong basis for comprehensive innovation in educational methods, content, and management. This will enable the development of exceptional talent who possess a strong sense of national identity, global perspective, and social responsibility.

Wikipedia is a widely used information source among students in higher education institutes but is regarded as less credible among teaching faculty. The present study is an attempt to understand how faculty perceive, use and believe Wikipedia as a credible source of information. The paper draws upon a survey data gathered from 257 university faculty members by employing the systematic random sampling technique. The analysis of collected data suggests that faculty members used Wikipedia for multiple purposes ranging from teaching and research to leisure reading. They regarded Wikipedia as a useful and credible source of information while having a positive belief in the Wikipedia project. Overall, the findings of this study suggest that faculty perceptions have grown more favorable regarding Wikipedia use, usefulness, and quality of information. Hence, our study found that faculty members were trying to integrate Wikipedia into their teaching, learning and research endeavors by allowing students to consult and cite Wikipedia information and enabling them to be critical and proficient users of Wikipedia as a part of their information-seeking, gathering, and sense-making practices. However, there is a need of moving beyond the passive consumption of content to contribute to Wikipedia content by creating new entries, and adding to and editing earlier entries. The present study would enhance our understanding of the richness, usefulness, and credibility of Wikipedia information among university faculty from a developing country’s perspective. Such understanding contributes to illustrating the global acceptance level of Wikipedia in academia as a source of information and knowledge.

In order to solve the existing problems such as the gap in education level, the lack of personalized services, and the blocked educational resources, AI technology can provide the following auxiliary work: the intelligent counselor management system can improve the scientific management level, in-depth study and improve the quality evaluation of ideological and political education in colleges and universities, and the introduction of intelligent tutors to achieve intelligent attendance, Big data promotes the realization of ideological and political personalized education and the ‘‘employment’’ of intelligent robots to innovate human-computer cooperation. The scientific education management and creative teaching mode brought by artificial intelligence provides a powerful condition for the realization of ideological and political personalized education in colleges and universities. However, we should also be alert to the moral, ideological and privacy problems caused by excessive reliance on AI, and face up to the limitations of the special requirements of ideological and political education in colleges and universities on the role of AI, as well as the great room for human educators to learn and improve. Only by correctly understanding the advantages and disadvantages of artificial intelligence on ideological and political education in colleges and universities can we ensure that the innovative role of artificial intelligence in ideological and political education in colleges and universities can be maximized. Therefore, we should optimize the institutional environment for the application of AI in ideological and political education in colleges and universities from the legislative and regulatory levels.

Algorithms and artificial intelligence (AI) tools are crucial for digital literacy and competitiveness in today’s high-tech environment, transforming jobs across various sectors, including the media. The history of journalism is closely linked with technological development; from Gutenberg’s printing press to radio, television, the internet and AI tools. This paper aims to determine the perception of AI in the Croatian media and its alignment with Croatian journalists’ opinions, as well as assess ChatGPT’s effectiveness in sentiment analysis. The insights gained should improve the understanding of the impact of AI on public opinion and its ethical implications for journalism. The research methodology combined AI-driven sentiment analysis (ChatGPT Plus) with qualitative content analysis. A total of 45 articles about AI published in the Croatian media between April and September 2023 were evaluated. Two hypotheses were tested: H1, that ChatGPT’s sentiment analysis matches human assessment, and H2, that the Croatian media generally express a positive view of AI. The results confirmed H1, with ChatGPT’s sentiment analysis corresponding to human assessment in 44 out of 45 cases. H2 was partially corroborated; sentiment distribution pointed to neutral (42%), positive (36%), and negative (22%) views of AI. Positive articles highlighted the benefits of AI in areas such as healthcare, while negative articles raised privacy and employment concerns. The results point to a balanced perspective on AI in the Croatian media, recognising advantages and risks alike. Future research can expand on these findings by examining long term sentiment trends in domestic media and comparing them with global trends. Improved AI tools for independent data collection and more accurate sentiment interpretation can further support this endeavour. Additionally, this paper paves the way for further sentiment analysis of media articles, exploring the implications of AI application on readers’ perception and reception of information.

The rapid development of modern information technology has brought new opportunities both for the ideological and political education of college students, but also brought new challenges for the ideological and political education of college students. In order to deal with the opportunities and challenges, we need to re-examine and construct the system to adapt itself to the development of the ideological and political education of college students, and put the modern educational technology into practice, so as to strengthen the effectiveness of ideological and political education of college students.

The incorporation of Artificial Intelligence (AI) into education marks a significant shift in how students learn, teachers teach, and educational institutions operate. This research delved into the knowledge and views of Library and Information Science (LIS) educators regarding the use of AI in library schools in Rivers State. The study employed a survey approach, combining qualitative and quantitative methods. A total of 44 LIS educators from various institutions in Rivers State participated, selected through random sampling, and data were collected using an online survey. The study found that while many LIS educators are aware of AI and have integrated it into their teaching and research, there remains a considerable gap in formal training and professional development in this area. Despite this, there is a clear understanding among educators of the value of AI in library and information science education, in line with broader trends in education and industry. The research also identified positive attitudes towards AI as a tool to enhance education quality and prepare students for careers in librarianship and information science. However, several barriers hinder the integration of AI into curricula and practices, including lecturer attitudes, credibility of information sources, internet connectivity, negative institutional perceptions, and low lecturer competency in AI. To address these challenges, the study recommends that Nigerian library schools fully implement AI technologies like chatbots, barcodes, RFIDs, and robotics to enhance teaching activities. It also suggests that higher education institutions develop specialized training programs and workshops on AI for library schools, covering both basic and advanced concepts. This would enable educators to effectively integrate AI into their teaching and research practices.

Modern political education faces a number of problems, including the crisis of meaning generation and deconstruction of the political sign, ideological transformations under the influence of digitalization, the gap between curricula and dynamic political reality. From the point of view of the cultural-semiotic approach, the problem of political education lies in the deconstruction of political signs, leading to their meaninglessness. As a result, communication loses its semantic load, despite the increase in the number of information exchanges. Yuri Lotman emphasizes the role of texts as “meaning-generating devices”. In political education, such texts are ideas about the past, present and future. The construction and transmission of these texts are associated with a number of didactic difficulties. In addition, modern political communication is networked and non-linear in nature, which gives rise to new challenges: loss of trust in experts and traditional sources of information, fragmentation of knowledge and values, inability of education to produce a creative audience ready for independent meaning generation. Digitalization and social media play a significant role in political education. Artificial intelligence and algorithms actively influence the agenda and the formation of public opinion, acting as gatekeepers and content makers, and political communication takes the form of digital rituals. The effect of echo chambers and information bubbles contributes to the fragmentation of worldviews, although trends and advertising can create a common communicative experience. Nevertheless, the digital environment forms an unstable and dynamic picture of the political world, which creates a threat of polarization of society and reduces the deliberative potential of democracy. Thus, ideological deconsolidation can be considered as part of political socialization and education. Institutional political education in Russia is implemented at the universal (schools, non-core faculties, etc.) and professional (university political science programs) levels. At the universal level, political education is characterized by isolation from current politics, dogmatism, and lack of reflection, which gives rise to mistrust on the part of students, fragmentation of knowledge, and cognitive poverty. Professional political education is complicated by the imbalance of interests of the state, students, and employers, as well as by the insufficient substantive value of educational programs.

The health sector has benefited in many ways from the data science and artificial intelligence (AI). One of the most promising applications of data science and AI in the healthcare field is sentiment analysis and emotion detection. Sentiment analysis is the automatic categorization of sentiment in a free text, whereas emotional detection categorizes emotion on a human face using a sophisticated image dispensation. This chapter aimed to focus on Sentiment Analysis and Emotion detection applications related to wellbeing healthcare through a systematic review of the recent literature. With the support of AI methods and other mathematical models, sentiment analysis can offer significant assistance to healthcare professionals, especially psychiatrists to understand the mental health and psychological problems of wellbeing. In general, people with certain intolerable problems, serious illnesses, addictions to something, suicide victims, and caregivers use social networks, health websites, and other web portals to share their sentiments. These are important data sources for sentiment analysis related to health. Emotion detection and recognition mechanisms use facial expressions for emotions such as joy, sadness, surprise, and anger, and in addition, capture “micro-expressions” or controlled expression of body language as the main source of data. Analysis outcomes help health professionals to decide when patients need help or need medication. In conclusion, health professionals and community service volunteers or caregivers can use the results of the sentiment and emotion detection analysis to help with wellbeing when they need it. The accuracy of the analysis results can be improved by combining the analysis of human expressions from a variety of forms such as texts, facial expressions, body language, and speech.KeywordsSentiment analysisEmotion detectionAnd recognitionHealthcareAI applicationsData science applications

This research aims to analyze public sentiment towards artificial intelligence (AI) tools via the Twitter platform using the Naive Bayes classifier algorithm. Twitter is a popular social media platform for sharing opinions and thoughts, making it suitable for sentiment analysis. Sentiment analysis is the process of analyzing and understanding opinions, attitudes, or feelings contained in text, such as tweets, product reviews, or other social media posts. The problems discussed in sentiment analysis can vary depending on the context. Tests carried out using the Naïve Bayes Classifier algorithm can conclude that the data collected was 2119. In this research, there are several steps that must be taken to analyzethe data, starting with crawling, labeling, preprocessing, splitting data, dividing test data, and training data, and finally applying the Naïve Bayes Classifier Algorithm. The results of the data analysis were divided into two categories: positive and negative, with 58.41% positive data and 12.43% negative data. In the analysis experiment, the Naïve Bayes accuracy value reached 79.41%, with a precision of 88% and a recall of 88%. The aim of the results of this research is to examine the public's response regarding artificial intelligence tools using the Naïve Bayes Classifier Algorithm to provide better sentiment results. So many see AI as a technology that carries great potential to improve human life. On the other hand, there are concerns about AI's negative impact on employment, privacy, and even its potential to take over human control. Ethical concerns also arise regarding the use of AI in decision-making that can affect human lives without adequate control. So artificial intelligence tools can be accepted by society because they have many benefits. Therefore, sentiment analysis and natural data processing use the Python programming language to categorize user comment data through a breakdown process.

The paper examines the potential of artificial intelligence (AI) in parsing text and conducting sentiment analysis to identify early markers of mental health and neurodegenerative disorders. Through the analysis of textual data, we investigate whether AI can provide a noninvasive, continuous, and objective complement to traditional diagnostic practices. Background: the early detection of mental health (such as depression, anxiety, psychotic disorders, Alzheimer’s disease and dementia) and neurodegenerative disorders (like Parkinson’s disease) remains a critical challenge in clinical practice. Traditional diagnostic methods rely on clinical evaluations that may be subjective and episodic. Recent advancements in AI and natural language processing (NLP) have opened new avenues for precognitive health assessments, suggesting that variations in language and expressed sentiments in written text can serve as potential biomarkers for these conditions. Materials and Methods: the research used a dataset comprising various forms of textual data, including anonymized social media interactions, transcripts from patient interviews, and electronic health records. NLP algorithms were deployed to parse the text, and machine learning models were trained to identify language patterns and sentiment changes. The study also incorporated a sentiment analysis to gauge emotional expression, a key component of mental health diagnostics. Results: the AI models were able to identify language use patterns and sentiment shifts that correlated with clinically validated instances of mental health symptoms and neurodegenerative conditions. Notably, the models detected an increased use of negative a ect words, a higher frequency of first-person singular pronouns, and a decrease in future tense in individuals with depression. For neurode-generative conditions, there was a notable decline in language complexity and semantic coherence over time. Conclusions: the implemented pipeline of AI-parsed text and sentiment analysis appears to be a promising tool for the early detection and ongoing monitoring of mental health and neurodegenerative disorders. However, these methods are supplementary and cannot replace the nuanced clinical evaluation process. Future research must refine the AI algorithms to account for linguistic diversity and context, while also addressing ethical considerations regarding data use and privacy. The integration of AI tools in clinical settings necessitates a multidisciplinary approach, ensuring that technological advancements align with patient-centered care and ethical standards.

INTRODUCTION: To apply artificial intelligence technology to ideological and political education in colleges and universities, as well as to combine artificial intelligence technology with ideological and political education in colleges and universities, it is necessary that wireless networks have complete coverage. OBJECTIVES: How can ideological and political education in universities and colleges be merged with artificial intelligence? How can artificial intelligence be used to support progressive political education at the college level? Starting with these issues, this paper will focus on the development of ideological and political education in colleges and universities as its main research question and refer to artificial intelligence technology as a method of ideological and political education in colleges and universities supported by wireless networks. METHODS: By examining the characteristics of artificial intelligence and ideological education in colleges and universities, and combining the poor immediacy and weak timeliness of information dissemination, as well as the low degree of identification of student groups with the theoretical courses of ideological education in the field of ideological education under the view of artificial intelligence, to explore the path of innovation of ideological education, RESULTS: In order to adapt to the demands of AI technology and improve people's capacity to use AI learning software, we need start with educators and educated individuals. Additionally, the government should encourage the development of artificial intelligence technologies financially and technically. Finally, it examines how civic education in colleges and universities could be improved through the use of artificial intelligence. This would allow civic education to benefit from the advantages of AI technology. CONCLUSION: In order to support the positive and healthy development of political education courses in colleges and universities across the nation, this paper encourages the creation of concepts and methods for teaching politics in higher education institutions.