Exploring the impact of artificial intelligence on teaching and learning in higher education

Integrating generative artificial intelligence in a writing intensive course for undergraduate nursing students

While generative artificial intelligence (AI) has been around for many years, it has only recently become available for use by the public. This powerful resource has changed the landscape for higher education and many instructors fear the negative effects it can have on academic integrity and student creativity in the writing process. However, it is certain that AI is here to stay, and it is crucial that educators embrace this technology and teach students to use this resource carefully and wisely. Communication is an essential component in nursing practice and cultivating competent writing skills is a vital aspect of nursing education. However, nursing students struggle with scholarly writing especially at the undergraduate level. Integrating generative artificial intelligence into a writing intensive course offers a unique approach to aid students in improving their writing. In this pilot project, students were given an assignment to actively engage with generative artificial intelligence and critically analyze the response using current nursing literature to support or refute the output. This assignment was used to springboard class discussion on advantages and disadvantages of using artificial intelligence for scholarly writing. This novel approach has the potential to build confidence and competence in novice writers which supports their success in nursing school and in clinical practice.

Applications of Artificial Intelligence in Medical Education: A Systematic Review

Artificial intelligence (AI) models, like Chat Generative Pre-Trained Transformer (OpenAI, San Francisco, CA), have recently gained significant popularity due to their ability to make autonomous decisions and engage in complex interactions. To fully harness the potential of these learning machines, users must understand their strengths and limitations. As AI tools become increasingly prevalent in our daily lives, it is essential to explore how this technology has been used so far in healthcare and medical education, as well as the areas of medicine where it can be applied. This paper systematically reviews the published literature on the PubMed database from its inception up to June 6, 2024, focusing on studies that used AI at some level in medical education, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Several papers identified where AI was used to generate medical exam questions, produce clinical scripts for diseases, improve the diagnostic and clinical skills of students and clinicians, serve as a learning aid, and automate analysis tasks such as screening residency applications. AI shows promise at various levels and in different areas of medical education, and our paper highlights some of these areas. This review also emphasizes the importance of educators and students understanding AI's principles, capabilities, and limitations before integration. In conclusion, AI has potential in medical education, but more research needs to be done to fully explore additional areas of applications, address the current gaps in knowledge, and its future potential in training healthcare professionals.

The ethics of artificial intelligence use in university libraries in Zimbabwe

Introduction

The emergence of artificial intelligence (AI) has revolutionised higher education teaching and learning. AI has the power to analyse large amounts of data and make intelligent predictions thus changing the whole teaching and learning processes. However, such a rise has led to institutions questioning the morality of these applications. The changes have left librarians and educators worried about the major ethical questions surrounding privacy, equality of information, protection of intellectual property, cheating, misinformation and job security. Libraries have always been concerned about ethics and many go out of their way to make sure communities are educated about the ethical question. However, the emergence of artificial intelligence has caught them unaware.

Methods

This research investigates the preparedness of higher education librarians to support the ethical use of information within the higher and tertiary education fraternity. A qualitative approach was used for this study. Interviews were done with thirty purposively selected librarians and academics from universities in Zimbabwe.

Results

Findings indicated that many university libraries in Zimbabwe are still at the adoption stage of artificial intelligence. It was also found that institutions and libraries are not yet prepared for AI use and are still crafting policies on the use of AI.

Discussion

Libraries seem prepared to adopt AI. They are also prepared to offer training on how to protect intellectual property but have serious challenges in issues of transparency, data security, plagiarism detection and concerns about job losses. However, with no major ethical policies having been crafted on AI use, it becomes challenging for libraries to full adopt its usage.

Generative AI in Higher Education: Balancing Innovation and Integrity

Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI's integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities. Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in GenAI use. By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education.

Impact of artificial intelligence adoption on students' academic performance in open and distance learning: A systematic literature review

The role of artificial intelligence (AI) in education has been extensively studied, focusing on its ability to enhance learning and teaching processes. However, the precise impact of AI adoption on academic performance in open and distance learning (ODL) remains largely unexplored. This systematic literature review critically evaluates AI's impact on academic performance within ODL environments. Drawing from a curated selection of 64 papers from an initial pool of 700, spanning from 2017 to 2023 and sourced from Scopus, Google Scholar, and Web of Science, this study delves into the multifaceted role of AI in enhancing learning outcomes. The meta-analysis reveals a diverse methodological landscape: machine learning methods, employed in 29.69 % of the studies, stand out for their ability to predict academic achievement, which is matched in prevalence by classical statistical methods. Although less common at 3.13 %, hybrid methods are a burgeoning area of research, while a significant 40.63 % of works prioritise nonempirical methods, focusing on theoretical analysis and literature reviews. This investigation highlights the critical factors driving AI adoption in education and its tangible benefits for student performance. It identifies a crucial literature gap: the absence of a process-based framework designed to forecast AI's educational impacts with greater precision, especially across gender and regional lines. By proposing this framework, this study contributes to the academic discourse on AI in education. It underscores the urgent need for structured methodologies to navigate the challenges and opportunities of AI integration. This framework, aligned with UNESCO's 2030 educational objectives, promises to bridge educational divides, ensuring equitable access to quality education across diverse demographics. The findings advocate for future research to design, refine, and test such a framework, paving the way for more inclusive and effective educational technologies in ODL settings.

The Impact of AI Usage on University Students' Willingness for Autonomous Learning

As artificial intelligence (AI) technology becomes increasingly integrated into education, understanding the theoretical mechanisms that drive university students to adopt new learning behaviors through these tools is essential. This study extends the Expectation-Confirmation Model (ECM) by incorporating both cognitive and affective variables to examine students' current AI usage and their future expectations. The model includes intrinsic and extrinsic motivations, focusing on three key factors: positive emotions, digital efficacy, and willingness for autonomous learning. A survey of 721 valid responses revealed that positive emotions, digital efficacy, and satisfaction significantly influence continued AI usage, with positive emotions being particularly critical. Digital efficacy and perceived usefulness also impact satisfaction, but long-term usage intentions are more effectively driven by positive emotions. Furthermore, digital efficacy strongly affects the willingness for autonomous learning. Therefore, higher education institutions should promote AI technology, enhance students' expectation-confirmation levels, and emphasize positive emotional experiences during AI use. Adopting a "human-machine symbiosis" model can foster active learning, personalized learning pathways, and the development of students' digital efficacy and innovation capabilities.

Artificial intelligence and learning environment: Human considerations

Background

Artificial intelligence (AI) has created new opportunities, challenges, and potentials in teaching; however, issues related to the philosophy of using AI technology in learners' learning have not been addressed and have caused some issues and concerns. This issue is due to the research gap in addressing issues related to ethical and human needs, and even values in AI in learning have become more obvious.

Objectives

This study investigates how human‐centered artificial intelligence (HAI) can help learners in a learning environment. In this regard, this article by developing key considerations of HAI in helping students tries to help implement or shift it in the future in learning environments.

Methods

To better understand the key considerations of HAI, qualitative methods and interview techniques were applied in this study. In this regard, 18 samples were interviewed from two groups of experts and faculty members in the fields of technology and computer science and social and humanities sciences. The thematic content analysis method was used to analyse qualitative data.

Results and Conclusions

The results show that AI attempts to integrate ethical and human values in the process of design, development, and research in the fields of recognising and dealing with negative emotions, targeted emotional nature, and access to fairness and justice. It also shows significant promise in understanding feelings and emotions in a learning environment.

Implications

Although AI has been studied in other contexts, HAI has not attracted much attention from researchers. Hence, this study has made worthwhile contributions to the literature as it has specifically focused on HAI in education. In addition, it can resolve some scientific community considerations regarding technological concerns in the field of AI. Furthermore, this article can increase social satisfaction with the use of AI by considering ethical considerations in the learning environment and can particularly benefit researchers, educators, and AI specialists who are involved in the study of HAI applications.

Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework

The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and privacy. Data have been collected from 310 AI users including teachers, researchers, and students. This study finds that users' behavioral intention is positively and significantly associated with trust, social influence, effort expectancy, and performance expectancy. Privacy, on the other hand, has a negative yet significant relationship with behavioral intention unveiling that concerns over privacy can deter users from intending to use AI technologies which is a valuable insight for developers and educators. In determining use behavior, facilitating condition, behavioral intention, and privacy have significant positive impact. This study hasn't found any significant relationship between trust and use behavior elucidating that service providers should have unwavering focus on security measures, credible endorsements, and transparency to build user confidence. In an era dominated by the fourth industrial revolution, this research underscores the pivotal roles of trust and privacy in technology adoption. In addition, this study sheds light on users' perspective to effectively align AI-based technologies with the education system of developing countries. The practical implications encompass insights for service providers, educational institutions, and policymakers, facilitating the smooth adoption of AI technologies in developing countries while emphasizing the importance of trust, privacy, and ongoing refinement.

Impacto de la inteligencia artificial en la toma de decisiones financieras: oportunidades y desafíos para los líderes empresariales

La inteligencia artificial (IA) está transformando las finanzas y los negocios con análisis avanzados, automatización de procesos y predicciones precisas. Aunque mejora la eficiencia y la toma de decisiones, requiere supervisión humana para mantener la ética. La implementación de IA es costosa, demanda capacitación, proporciona mejoras significativas en la gestión de riesgos y en la toma de decisiones financieras, mejora la operación, el servicio al cliente, optimiza carteras e identifica oportunidades de inversión. Sin embargo, surgen desafíos como preocupaciones éticas y de privacidad, la necesidad de interpretar adecuadamente los resultados, asegurar la ciberseguridad y cumplir las regulaciones. La IA automatiza tareas financieras, reduce costos y errores, personaliza servicios al analizar el comportamiento del cliente. No obstante, la dependencia de datos plantea problemas de privacidad y seguridad, los sesgos en los datos pueden afectar la equidad, y complejidad de los algoritmos puede dificultar la transparencia y comprensión de las decisiones automatizadas.

Intelligent Tutoring Systems, Generative Artificial Intelligence (AI), and Healthcare Agents: A Proof of Concept and Dual-Layer Approach

This study introduces a novel methodology for enhancing intelligent tutoring systems (ITS) through the integration of generative artificial intelligence (GenAI) and specialized AI agents. We present a proof of concept (PoC) demo that implements a dual-layer GenAI validation approach that utilizes multiple large language models to ensure the reliability and pedagogical integrity of the AI-generated content. The system features role-specific AI agents, a GenAI-powered scoring mechanism, and an AI mentor that provides periodic guidance. This approach demonstrates capabilities in dynamic scenario generation and real-time adaptability while addressing key challenges in AI-driven education, such as personalization, scalability, and domain-specific knowledge integration. Although exemplified here through a case study in healthcare root cause analysis, the methodology is designed for broad applicability across various fields. Our findings suggest that this approach has significant potential for advancing adaptive learning and personalized instruction while raising important considerations regarding ethical AI application in education. This work provides a foundation for further research into the efficacy and impact of GenAI-enhanced ITS on learning outcomes and instructional design across diverse educational domains.

Evaluating generative AI integration in Saudi Arabian education: a mixed-methods study

Incorporating generative artificial intelligence (GAI) in education has become crucial in contemporary educational environments. This research article thoroughly investigates the ramifications of implementing GAI in the higher education context of Saudi Arabia, employing a blend of quantitative and qualitative research approaches. Survey-based quantitative data reveals a noteworthy correlation between educators' awareness of GAI and the frequency of its application. Notably, around half of the surveyed educators are at stages characterized by understanding and familiarity with GAI integration, indicating a tangible readiness for its adoption. Moreover, the study's quantitative findings underscore the perceived value and ease associated with integrating GAI, thus reinforcing the assumption that educators are motivated and inclined to integrate GAI tools like ChatGPT into their teaching methodologies. In addition to the quantitative analysis, qualitative insights from in-depth interviews with educators unveil a rich tapestry of perspectives. The qualitative data emphasizes GAI's role as a catalyst for collaborative learning, contributing to professional development, and fostering innovative teaching practices.

Exploratory study on student perception on the use of chat AI in process-driven problem-based learning

INTRODUCTION

With the increasing prevalence of artificial intelligence (AI) technology, it is imperative to investigate its influence on education and the resulting impact on student learning outcomes. This includes exploring the potential application of AI in process-driven problem-based learning (PDPBL). This study aimed to investigate the perceptions of students towards the use of ChatGPT) build on GPT-3.5 in PDPBL in the Bachelor of Pharmacy program.

METHODS

Eighteen students with prior experience in traditional PDPBL processes participated in the study, divided into three groups to perform PDPBL sessions with various triggers from pharmaceutical chemistry, pharmaceutics, and clinical pharmacy fields, while utilizing chat AI provided by ChatGPT to assist with data searching and problem-solving. Questionnaires were used to collect data on the impact of ChatGPT on students' satisfaction, engagement, participation, and learning experience during the PBL sessions.

RESULTS

The survey revealed that ChatGPT improved group collaboration and engagement during PDPBL, while increasing motivation and encouraging more questions. Nevertheless, some students encountered difficulties understanding ChatGPT's information and questioned its reliability and credibility. Despite these challenges, most students saw ChatGPT's potential to eventually replace traditional information-seeking methods.

CONCLUSIONS

The study suggests that ChatGPT has the potential to enhance PDPBL in pharmacy education. However, further research is needed to examine the validity and reliability of the information provided by ChatGPT, and its impact on a larger sample size.

Awareness among teaching on AI and ML applications based on fuzzy in education sector at USA

This paper summarises the level of knowledge held by educators in the United States on the use of artificial intelligence and machine learning in the classroom. The education industry seems to have reaped little benefits from the AI & ML industry's growth thus far. In any event, the creation of new ML & AI-based systems is mostly aimed towards areas with higher societal needs, such as medical diagnostics and individual transportation, rather than institutions of higher education. With this analysis, we want to shed some light on the mysterious state of application development in the US education industry. The report was written using a triangulation of research approaches to achieve this objective. First, we surveyed the current state-of-the-art reports from other countries and reviewed the relevant literature on AI & ML applications in the field of education. In the second phase, we analysed, to the extent possible, official documents from the United States education sector that dealt with AI and ML based on fuzzy digitalization initiatives. Third, in order to corroborate and expand upon the impressions received from the relevant literature and the document analysis, 15 guideline-based expert interviews were undertaken. Based on this data, we provide a selection of the AI & ML systems in use in universities and colleges now, analyse the benefits and drawbacks of implementing them, and speculate on their potential future evolution. While it would be a stretch to say that this paper presents a comprehensive overview of the subject, it does provide light on key areas of application and potential future research directions for AI and ML.

The role of an open artificial intelligence platform in modern neurosurgical education: a preliminary study

The use of artificial intelligence in neurosurgical education has been growing in recent times. ChatGPT, a free and easily accessible language model, has been gaining popularity as an alternative education method. It is necessary to explore the potential of this program in neurosurgery education and to evaluate its reliability. This study aimed to show the reliability of ChatGPT by asking various questions to the chat engine, how it can contribute to neurosurgery education by preparing case reports or questions, and its contributions when writing academic articles. The results of the study showed that while ChatGPT provided intriguing and interesting responses, it should not be considered a dependable source of information. The absence of citations for scientific queries raises doubts about the credibility of the answers provided. Therefore, it is not advisable to solely rely on ChatGPT as an educational resource. With further updates and more specific prompts, it may be possible to improve its accuracy. In conclusion, while ChatGPT has potential as an educational tool, its reliability needs to be further evaluated and improved before it can be widely adopted in neurosurgical education.

Exploration on the Operation Status and Optimization Strategy of Networked Teaching of Physical Education Curriculum Based on AI Algorithm

Artificial Intelligence (AI) and networked teaching have made great progress in recent years, especially the application of AI algorithms in teaching, which has been relatively mature. The online teaching of physical education (PE) courses is an important practice of networked teaching, but many colleges and universities ignore the importance of AI when carrying out such teaching work. Many colleges and universities have not played a good role in AI in teaching, which leads to inefficient PE teaching and low learning enthusiasm of students.

A Validation of AI-Enabled Discussion Platform Metrics and Relationships to Student Efforts

Asynchronous discussions are a popular feature in online higher education as they enable instructor-student and student-student interactions at the users' own time and pace. AI-driven discussion platforms are designed to relieve instructors of automatable tasks, e.g., low-stakes grading and post moderation. Our study investigated the validity of an AI-generated score compared to human-driven methods of evaluating student effort and the impact of instructor interaction on students' discussion post quality. A series of within-subjects MANOVAs was conducted on 14,599 discussion posts among over 800 students across four classes to measure post 'curiosity score' (i.e., an AI-generated metric of post quality) and word count. After checking assumptions, one MANOVA was run for each type of instructor interaction: private coaching, public praising, and public featuring. Instructor coaching appears to impact curiosity scores and word count, with later posts being an average of 40 words longer and scoring an average of 15 points higher than the original post that received instructor coaching. AI-driven tools appear to free up time for more creative human interventions, particularly among instructors teaching high-enrollment classes, where a traditional discussion forum is less scalable.

Application of artificial intelligence to the public health education

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

Radiological education in the era of artificial intelligence: A review

In the era of artificial intelligence (AI), a great deal of attention is being paid to AI in radiological practice. There are a large number of AI products on the radiological market based on X-rays, computed tomography, magnetic resonance imaging, and ultrasound. AI will not only change the way of radiological practice but also the way of radiological education. It is still not clearly defined about the exact role AI will play in radiological practice, but it will certainly be consolidated into radiological education in the foreseeable future. However, there are few literatures that have comprehensively summarized the attitudes, opportunities and challenges that AI can pose in the different training phases of radiologists, from university education to continuing education. Herein, we describe medical students' attitudes towards AI, summarize the role of AI in radiological education, and analyze the challenges that AI can pose in radiological education.

Strengthening capacities of multidisciplinary professionals to apply data science in public health: Experience of an international graduate diploma program in Peru

Nowadays it is necessary to strengthen health information systems and data-based solutions. However, there are few graduate training programs in Peru to use tools and methods of data science applied in public health. This article describes the development process and the initial assessment regarding the experience of the participants in an international multidisciplinary diploma in data intelligence for pandemics and epidemics preparedness, which was carried out from January to May 2021. The diploma was structured in 7 modules and 40 Peruvian professionals participated, of which 11 (27.5%) were women, and 16 (40%) came from regions outside of Lima and Callao. We discussed the need to strengthen institutional and health professionals' capacity to adequately manage large volumes of data, information, and knowledge through the application of emerging technologies to optimize data management processes to improve decision-making in health.

Facilitators and Barriers of Artificial Intelligence Applications in Rehabilitation: A Mixed-Method Approach

Artificial intelligence (AI) has been used in physical therapy diagnosis and management for various impairments. Physical therapists (PTs) need to be able to utilize the latest innovative treatment techniques to improve the quality of care. The study aimed to describe PTs' views on AI and investigate multiple factors as indicators of AI knowledge, attitude, and adoption among PTs. Moreover, the study aimed to identify the barriers to using AI in rehabilitation. Two hundred and thirty-six PTs participated voluntarily in the study. A concurrent mixed-method design was used to document PTs' opinions regarding AI deployment in rehabilitation. A self-administered survey consisting of several aspects, including demographic, knowledge, uses, advantages, impacts, and barriers limiting AI utilization in rehabilitation, was used. A total of 63.3% of PTs reported that they had not experienced any kind of AI applications at work. The major factors predicting a higher level of AI knowledge among PTs were being a non-academic worker (OR = 1.77 [95% CI; 1.01 to 3.12], p = 0.04), being a senior PT (OR = 2.44, [95%CI: 1.40 to 4.22], p = 0.002), and having a Master/Doctorate degree (OR = 1.97, [95%CI: 1.11 to 3.50], p = 0.02). However, the cost and resources of AI were the major reported barriers to adopting AI-based technologies. The study highlighted a remarkable dearth of AI knowledge among PTs. AI and advanced knowledge in technology need to be urgently transferred to PTs.

The threat, hype, and promise of artificial intelligence in education

The idea of building intelligent machines has been around for centuries, with a new wave of promising artificial intelligence (AI) in the twenty-first century. Artificial Intelligence in Education (AIED) is a younger phenomenon that has created hype and promises, but also been seen as a threat by critical voices. There have been rich discussions on over-optimism and hype in contemporary AI research. Less has been written about the hyped expectations on AIED and its potential to transform current education. There is huge potential for efficiency and cost reduction, but there is also aspects of quality education and the teacher role. The aim of the study is to identify potential aspects of threat, hype and promise in artificial intelligence for education. A scoping literature review was conducted to gather relevant state-of-the art research in the field of AIED. Main keywords used in the literature search were: artificial intelligence, artificial intelligence in education, AI, AIED, teacher perspective, education, and teacher. Data were analysed with the SWOT-framework as theoretical lens for a thematic analysis. The study identifies a wide variety of strengths, weaknesses, opportunities, and threats for artificial intelligence in education. Findings suggest that there are several important questions to discuss and address in future research, such as: What should the role of the teacher be in education with AI? How does AI align with pedagogical goals and beliefs? And how to handle the potential leak and misuse of user data when AIED systems are developed by for-profit organisations?

Automatic modeling of student characteristics with interaction and physiological data using machine learning: A review

Student characteristics affect their willingness and ability to acquire new knowledge. Assessing and identifying the effects of student characteristics is important for online educational systems. Machine learning (ML) is becoming significant in utilizing learning data for student modeling, decision support systems, adaptive systems, and evaluation systems. The growing need for dynamic assessment of student characteristics in online educational systems has led to application of machine learning methods in modeling the characteristics. Being able to automatically model student characteristics during learning processes is essential for dynamic and continuous adaptation of teaching and learning to each student's needs. This paper provides a review of 8 years (from 2015 to 2022) of literature on the application of machine learning methods for automatic modeling of various student characteristics. The review found six student characteristics that can be modeled automatically and highlighted the data types, collection methods, and machine learning techniques used to model them. Researchers, educators, and online educational systems designers will benefit from this study as it could be used as a guide for decision-making when creating student models for adaptive educational systems. Such systems can detect students' needs during the learning process and adapt the learning interventions based on the detected needs. Moreover, the study revealed the progress made in the application of machine learning for automatic modeling of student characteristics and suggested new future research directions for the field. Therefore, machine learning researchers could benefit from this study as they can further advance this area by investigating new, unexplored techniques and find new ways to improve the accuracy of the created student models.

From Automation to Symmation: Ethnographic Perspectives on What Happens in Front of the Screen

The work of automation in education is not automatic but needs to be ‘done’. Grounded in an ethnographic study which followed a Grade 9/10 class through their daily activities in a ‘regular’ high school for a year, this paper asks how automation is enacted by students and teachers, and what these practices imply for forms of knowledge and relationality. Inspired by feminist technoscience, and drawing on recent work on everyday automation, the paper suggests that the ‘auto-’ of automation in practice is very often more of a ‘sym-’, a ‘with’, in which students and machines co-produce something that looks like automation. Rather than ‘automation’, observing practices in classrooms shows practices of ‘symmation’. The paper elaborates on symmation scenes of realigning, revising and reworking relations. Automation is, in these scenes, deeply embedded in social relations, involving the processing of ability, difference and hierarchy. Rather than the industry hype of automation, these sets of socio-technical practices alert us to the messy, non-linear, contested, warm realities of education (and not just learning) in schools today. The paper identifies specific aspects of how these socio-technical realities impact knowledge and teacher-student relations.

The potential of learning with (and not from) artificial intelligence in education

AI-powered technologies are increasingly being developed for educational purposes to contribute to students' academic performance and overall better learning outcomes. This exploratory review uses the PRISMA approach to describe how the effectiveness of AI-driven technologies is being measured, as well as the roles attributed to teachers, and the theoretical and practical contributions derived from the interventions. Findings from 48 articles highlighted that learning outcomes were more aligned with the optimization of AI systems, mostly nested in a computer science perspective, and did not consider teachers in an active role in the research. Most studies proved to be atheoretical and practical contributions were limited to enhancing the design of the AI system. We discuss the importance of developing complementary research designs for AI-powered tools to be integrated optimally into education.

Robot-Assisted Language Learning: Integrating Artificial Intelligence and Virtual Reality into English Tour Guide Practice

This action research created an application system using robots as a tool for training English-language tour guides. It combined artificial intelligence (AI) and virtual reality (VR) technologies to develop content for tours and a 3D VR environment using the AI Unity plug-in for programming. Students learned to orally interact with the robot and act as a guide to various destinations. The qualitative methods included observation, interviews, and self-reporting of learning outcomes. Two students voluntarily participated in the study. The intervention lasted for ten weeks. The results indicated the teaching effectiveness of robot-assisted language learning (RALL). The students acknowledged the value of RALL and had positive attitudes toward it. The contextualized VR learning environment increased their motivation and engagement in learning, and students perceived that RALL could help develop autonomy, enhance interaction, and provide an active learning experience. The implications of the study are that RALL has potential and that it provides an alternative learning opportunity for students.

Human Intelligence Analysis through Perception of AI in Teaching and Learning

Instructional practices have undergone a drastic change as a result of the development of new educational technology. Artificial intelligence (AI) as a teaching and learning technology will be examined in this theoretical review study. To enhance the quality of teaching and learning, the use of artificial intelligence approaches is being studied. Artificial intelligence integration in educational institutions has been addressed, though. Students' assistance, teaching, learning, and administration are also addressed in the discussion of students' adoption of artificial intelligence. Artificial intelligence has the potential to revolutionize our social interactions and generate new teaching and learning methods that may be evaluated in a variety of contexts. New educational technology can help students and teachers better accomplish and manage their educational objectives. Artificial intelligence algorithms are used in a hybrid teaching mode in this work to examine students' attributes and introduce predictions of future learning success. The teaching process may be carried out in a more efficient manner using the hybrid mode. Educators and scientists alike will benefit from artificial intelligence algorithms that may be used to extract useful information from the vast amounts of data collected on human behavior.

Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model

Social development organizations increasingly employ artificial intelligence (AI)-enabled tools to help team members collaborate effectively and efficiently. These tools are used in various team management tasks and activities. Based on the unified theory of acceptance and use of technology (UTAUT), this study explores various factors influencing employees' use of AI-enabled tools. The study extends the model in two ways: a) by evaluating the impact of these tools on the employees' collaboration and b) by exploring the moderating role of AI aversion. Data were collected through an online survey of employees working with AI-enabled tools. The analysis of the research model was conducted using partial least squares (PLS), with a two-step model - measurement and structural models of assessment. The results revealed that the antecedent variables, such as effort expectancy, performance expectancy, social influence, and facilitating conditions, are positively associated with using AI-enabled tools, which have a positive relationship with collaboration. It also concluded a significant effect of AI aversion in the relationship between performance expectancy and use of technology. These findings imply that organizations should focus on building an environment to adopt AI-enabled tools while also addressing employees' concerns about AI.

Prospects and Challenges of Using Machine Learning for Academic Forecasting

The study examines the prospects and challenges of machine learning (ML) applications in academic forecasting. Predicting academic activities through machine learning algorithms presents an enhanced means to accurately forecast academic events, including the academic performances and the learning style of students. The use of machine learning algorithms such as K-nearest neighbor (KNN), random forest, bagging, artificial neural network (ANN), and Bayesian neural network (BNN) has potentials that are currently being applied in the education sector to predict future events. Many gaps in the traditional forecasting techniques have greatly been bridged by the use of artificial intelligence-based machine learning algorithms thereby aiding timely decision-making by education stakeholders. ML algorithms are deployed by educational institutions to predict students' learning behaviours and academic achievements, thereby giving them the opportunity to detect at-risk students early and then develop strategies to help them overcome their weaknesses. However, despite the benefits associated with the ML approach, there exist some limitations that could affect its correctness or deployment in forecasting academic events, e.g., proneness to errors, data acquisition, and time-consuming issues. Nonetheless, we suggest that machine learning remains one of the promising forecasting technologies with the power to enhance effective academic forecasting that would assist the education industry in planning and making better decisions to enrich the quality of education.

Analysis of the Effect of Artificial Intelligence on Role Cognition in the Education System

Taking the entire education system in Taiyuan City, Shanxi Province, Central China, as an example, this paper uses the questionnaire survey method to analyze the effect of artificial intelligence (AI) on role cognition in the education system. The education system targeted by this questionnaire survey involves 8 categories: preschool education, primary education, secondary education, higher education, adult education, computer network education, enterprise education, and social education; the respondents include 368 teachers, 402 students or learners, 118 school managers, and 124 family members of students or learners in all above education categories. The questionnaire design has a total of 34 question classified into 6 role cognition items, with a 5-level score; a total of 1012 questionnaires were distributed, and 978 were recovered with a recovery rate of 96.64%, in which 957 were valid questionnaires with an effective rate of 97.85%. The study results show that the learning of AI-assisted courses is strongly dependent on course role cognition, and the construction of role cognition is related to the understanding of course content, teaching methods, and activity methods. Therefore, the effect of AI on role cognition in the education system needs to be systematically analyzed from the aspects of function realization form, resource presentation method, supporting hardware form, teacher-student interaction method, and representation method of works. As connecters, teacher's role cognition is limited by the degree of understanding learners, the amount of resources, and data processing capabilities, but the advantage is that they can flexibly monitor and adjust. AI technology is flexible and diverse, it functions in learning and teaching activities in a variety of ways, and there is no agreement on the terminology to describe its role in role recognition. The results of this paper provide a reference for further researches on the effect of AI on role cognition in the education system.

AI-based production and application of English multimode online reading using multi-criteria decision support system

Reading and writing English have greater significance in learning oral English and comprehensive skills. Artificial Intelligence (AI) is important in many aspects of our lives, including education, healthcare, business, and so on. AI has allowed for significant advancements in the educational system. It has quickly risen to the top of the list of the most rapidly expanding educational technology disciplines. Through its creation, AI has contributed to the creation of new educational and knowledge techniques that are currently being researched across a wide range of fields. Chatbots, Robots’ Assistant, Vidreader, Seeing AI, Classcraft, 3D holograms, and other AI-based programmes were developed to assist both teaching staff and students in using and improving the educational system. In the sphere of education, AI is focusing on sentimentalized artificial learning aids and smart instruction systems. The primary goal and objective of the education business is to construct an intelligent education system, which is now possible thanks to the development of teaching assistant robots, smart classrooms based on AI, and English teaching assistance, among other things. Artificial Intelligence techniques may now be employed at all stages of learning to improve the educational system. During the COVID-19 illness, students and teachers took their education and instruction online in a variety of ways. Learning can be done digitally so that folks do not fall behind in their education. The proposed study has considered multi-criteria decision support systems (MCDM) for AI-enabled production and application of English multimode online reading. This study has offered the application of the super decision tool to facilitate the experimental work. As a result of this, researchers will be able to find and design new solutions to the subject.

Research on the Impacts of Cognitive Style and Computational Thinking on College Students in a Visual Artificial Intelligence Course

Visual programming language is a crucial part of learning programming. On this basis, it is essential to use visual programming to lower the learning threshold for students to learn about artificial intelligence (AI) to meet current demands in higher education. Therefore, a 3-h AI course with an RGB-to-HSL learning task was implemented; the results of which were used to analyze university students from two different disciplines. Valid data were collected for 65 students (55 men, 10 women) in the Science (Sci)-student group and 39 students (20 men, 19 women) in the Humanities (Hum)-student group. Independent sample t-tests were conducted to analyze the difference between cognitive styles and computational thinking. No significant differences in either cognitive style or computational thinking ability were found after the AI course, indicating that taking visual AI courses lowers the learning threshold for students and makes it possible for them to take more difficult AI courses, which in turn effectively helping them acquire AI knowledge, which is crucial for cultivating talent in the field of AI.

The Evolving Importance of Artificial Intelligence and Radiology in Medical Trainee Education

Radiology education is understood to be an important component of medical school and resident training, yet lacks a standardization of instruction. The lack of uniformity in both how radiology is taught and learned has afforded opportunities for new technologies to intervene. Now with the integration of artificial intelligence within medicine, it is likely that the current medical trainee curricula will experience the impact it has to offer both for education and medical practice. In this paper, we seek to investigate the landscape of radiologic education within the current medical trainee curricula, and also to understand how artificial intelligence may potentially impact the current and future radiologic education model.

Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision

The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature’s themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

Narratives of epistemic agency in citizen science classification projects: ideals of science and roles of citizens

Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the results of 12 citizen science projects that used machine learning to optimize classification tasks. We examined the distribution of tasks between citizen scientists, experts, and algorithms, and how epistemic agency was enacted in terms of whose knowledge shapes the distribution of tasks, who decides what knowledge is relevant to the classification, and who validates it. In our descriptive results, we found that experts, who include professional scientists and algorithm developers, are involved in every aspect of a project, from annotating or labelling data to giving data to algorithms to train them to make decisions from predictions. Experts also test and validate models to improve their accuracy by scoring their outputs when algorithms fail to make correct decisions. Experts are mostly the humans involved in a loop, but when algorithms encounter problems, citizens are also involved at several stages. In this paper, we present three main examples of citizens-in-the-loop: (a) when algorithms provide incorrect suggestions; (b) when algorithms fail to know how to perform classification; and (c) when algorithms pose queries. We consider the implications of the emphasis on optimization on the ideal of science and the role of citizen scientists from a perspective informed by Science and Technology Studies (STS) and Information Systems (IS). Based on our findings, we conclude that ML in CS classification projects, far from being deterministic in its nature and effects, may be open to question. There is no guarantee that these technologies can replace citizen scientists, nor any guarantee that they can provide citizens with opportunities for more interesting tasks.

Factors Influencing the Acceptance and Use of Internet of Things by Universities

Internet of Things (IoT) is a new concept bringing revolution to higher educational institutions through its usage by providing smart education and better learning outcomes. It has generated new interest and complexities for researchers as well as academicians in higher educational institutions. In this paper, factors influencing the acceptance and usage of IoT in higher educational institutions were developed. Additionally, a model for consenting and using IoT in higher educational institutions was developed. This study laid a foundation for a comprehensive model based on the UTAUT framework. Regression analysis was carried out to obtain the factors that predicts the acceptance and usage of IoT in higher educational institutions. All test results were reliable and valid. The study demonstrates how university administrators can use IoT technologies to improve educational operations and outcomes.

Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education

This exploratory review attempted to gather evidence from the literature by shedding light on the emerging phenomenon of conceptualising the impact of artificial intelligence in education. The review utilised the PRISMA framework to review the analysis and synthesis process encompassing the search, screening, coding, and data analysis strategy of 141 items included in the corpus. Key findings extracted from the review incorporate a taxonomy of artificial intelligence applications with associated teaching and learning practice and a framework for helping teachers to develop and self-reflect on the skills and capabilities envisioned for employing artificial intelligence in education. Implications for ethical use and a set of propositions for enacting teaching and learning using artificial intelligence are demarcated. The findings of this review contribute to developing a better understanding of how artificial intelligence may enhance teachers’ roles as catalysts in designing, visualising, and orchestrating AI-enabled teaching and learning, and this will, in turn, help to proliferate AI-systems that render computational representations based on meaningful data-driven inferences of the pedagogy, domain, and learner models.

Artificial Intelligence and Its Role in Education

The objective of this study is to explore the role of artificial intelligence applications (AIA) in education. AI applications provide the solution in many ways to the exponential rise of modern-day challenges, which create difficulties in access to education and learning. They play a significant role in forming social robots (SR), smart learning (SL), and intelligent tutoring systems (ITS) to name a few. The review indicates that the education sector should also embrace the modern methods of teaching and the necessary technology. Looking into the flow, the education sector organizations need to adopt AI technologies as a necessity of the day and education. The study needs to be tested statistically for better understanding and to make the findings more generalized in the future.

The impact of artificial intelligence on learner-instructor interaction in online learning

Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors' routine tasks, and powering adaptive assessments. However, while the opportunities for AI are promising, the impact of AI systems on the culture of, norms in, and expectations about interactions between students and instructors are still elusive. In online learning, learner-instructor interaction (inter alia, communication, support, and presence) has a profound impact on students' satisfaction and learning outcomes. Thus, identifying how students and instructors perceive the impact of AI systems on their interaction is important to identify any gaps, challenges, or barriers preventing AI systems from achieving their intended potential and risking the safety of these interactions. To address this need for forward-looking decisions, we used Speed Dating with storyboards to analyze the authentic voices of 12 students and 11 instructors on diverse use cases of possible AI systems in online learning. Findings show that participants envision adopting AI systems in online learning can enable personalized learner-instructor interaction at scale but at the risk of violating social boundaries. Although AI systems have been positively recognized for improving the quantity and quality of communication, for providing just-in-time, personalized support for large-scale settings, and for improving the feeling of connection, there were concerns about responsibility, agency, and surveillance issues. These findings have implications for the design of AI systems to ensure explainability, human-in-the-loop, and careful data collection and presentation. Overall, contributions of this study include the design of AI system storyboards which are technically feasible and positively support learner-instructor interaction, capturing students' and instructors' concerns of AI systems through Speed Dating, and suggesting practical implications for maximizing the positive impact of AI systems while minimizing the negative ones.

Features of Mobile Apps for People with Autism in a Post COVID-19 Scenario: Current Status and Recommendations for Apps Using AI

The new 'normal' defined during the COVID-19 pandemic has forced us to re-assess how people with special needs thrive in these unprecedented conditions, such as those with Autism Spectrum Disorder (ASD). These changing/challenging conditions have instigated us to revisit the usage of telehealth services to improve the quality of life for people with ASD. This study aims to identify mobile applications that suit the needs of such individuals. This work focuses on identifying features of a number of highly-rated mobile applications (apps) that are designed to assist people with ASD, specifically those features that use Artificial Intelligence (AI) technologies. In this study, 250 mobile apps have been retrieved using keywords such as autism, autism AI, and autistic. Among 250 apps, 46 were identified after filtering out irrelevant apps based on defined elimination criteria such as ASD common users, medical staff, and non-medically trained people interacting with people with ASD. In order to review common functionalities and features, 25 apps were downloaded and analysed based on eye tracking, facial expression analysis, use of 3D cartoons, haptic feedback, engaging interface, text-to-speech, use of Applied Behaviour Analysis therapy, Augmentative and Alternative Communication techniques, among others were also deconstructed. As a result, software developers and healthcare professionals can consider the identified features in designing future support tools for autistic people. This study hypothesises that by studying these current features, further recommendations of how existing applications for ASD people could be enhanced using AI for (1) progress tracking, (2) personalised content delivery, (3) automated reasoning, (4) image recognition, and (5) Natural Language Processing (NLP). This paper follows the PRISMA methodology, which involves a set of recommendations for reporting systematic reviews and meta-analyses.

Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions

The way people travel, organise their time, and acquire information has changed due to information technologies. Artificial intelligence (AI) and machine learning (ML) are mechanisms that evolved from data management and developing processes. Incorporating these mechanisms into business is a trend many different industries, including education, have identified as game-changers. As a result, education platforms and applications are more closely aligned with learners’ needs and knowledge, making the educational process more efficient. Therefore, AI and ML have great potential in e-learning and higher education institutions (HEI). Thus, the article aims to determine its potential and use areas in higher education based on secondary research and document analysis (literature review), content analysis, and primary research (survey). As referent points for this research, multiple academic, scientific, and commercial sources were used to obtain a broader picture of the research subject. Furthermore, the survey was implemented among students in the Republic of Serbia, with 103 respondents to generate data and information on how much knowledge of AI and ML is held by the student population, mainly to understand both opportunities and challenges involved in AI and ML in HEI. The study addresses critical issues, like common knowledge and stance of research bases regarding AI and ML in HEI; best practices regarding usage of AI and ML in HEI; students’ knowledge of AI and ML; and students’ attitudes regarding AI and ML opportunities and challenges in HEI. In statistical considerations, aiming to evaluate if the indicators were considered reflexive and, in this case, belong to the same theoretical dimension, the Correlation Matrix was presented, followed by the Composite Reliability. Finally, the results were evaluated by regression analysis. The results indicated that AI and ML are essential technologies that enhance learning, primarily through students’ skills, collaborative learning in HEI, and an accessible research environment.

The Beginning of a New Era: Artificial Intelligence in Healthcare

The healthcare sector is considered to be one of the largest and fast-growing industries in the world. Innovations and novel approaches have always remained the prime aims in order to bring massive development. Before the emergence of technology, the healthcare sector was dependent on manpower, which was time-consuming and less accurate with lack of efficiency. With the recent advancements in machine learning, the condition has been steadily revolutionizing. Artificial intelligence (AI) lies in the computer science department, which stresses on the intelligent machines’ creation, that work and react just like human beings. Currently, the applications of AI have been expanding into those fields, which was once thought to be the only domain of human expertise such as healthcare sector. In this review, we have shed light on the present usage of AI in the healthcare sector, such as its working, and the way this system is being implemented in different domains, such as drug discovery, diagnosis of diseases, clinical trials, remote patient monitoring, and nanotechnology. We have also briefly touched upon its applications in other sectors as well. The public opinions have also been analyzed and discussed along with the future prospects. We have discussed the merits, and the other side of AI, i.e. the disadvantages in the last part of the manuscript.

The Effectiveness of Pictorial Aesthetics Based on Multiview Parallel Neural Networks in Art-Oriented Teaching

How to effectively improve the effectiveness of art teaching has always been one of the hot topics concerned by all sectors of society. Especially, in art teaching, situational interaction helps improve the atmosphere of art class. However, there are few attempts to quantitatively evaluate the aesthetics of ink painting. Ink painting expresses images through ink tone and stroke changes, which is significantly different from photos and paintings in visual characteristics, semantic characteristics, and aesthetic standards. For this reason, this study proposes an adaptive computational aesthetic evaluation framework for ink painting based on situational interaction using deep learning techniques. The framework extracts global and local images as multiple input according to the aesthetic criteria of ink painting and designs a model named MVPD-CNN to extract deep aesthetic features; finally, an adaptive deep aesthetic evaluation model is constructed. The experimental results demonstrate that our model has higher aesthetic evaluation performance compared with baseline, and the extracted deep aesthetic features are significantly better than the traditional manual design features, and its adaptive evaluation results reach a Pearson height of 0.823 compared with the manual aesthetic. In addition, art classroom simulation and interference experiments show that our model is highly resistant to interference and more sensitive to the three painting elements of composition, ink color, and texture in specific compositions.