Evolution and Revolution in Artificial Intelligence in Education

Student perceptions of artificial intelligence in Doctor of Physical Therapy Education

PURPOSE

Health profession education is impacted by developments in artificial intelligence (AI). While AI provides useful supplemental material, it is unknown how it will affect learning complex clinical topics. Artificial intelligence has the potential to be a powerful tool when used in tandem with traditional human studying methods. This study aims to understand Doctor of Physical Therapy students' perceptions of AI as a study tool.

METHODS

Students participated in an online pre/post survey evaluating their perceptions of AI's ability to diagnose and treat clinical cases, and two corresponding research activities where students were asked to complete a neurologic case study using classroom notes and a separate case using AI. Descriptive statistics, Wilcoxon signed-rank tests, and effect sizes were calculated.

RESULTS

Twenty-three first year Doctor of Physical Therapy students volunteered to participate. In the post-survey, students' perceptions of AI included increased trust to provide a clinical diagnosis (p = .001; r = 0.66) and a treatment plan (p = .005; r = 0.58), and to consider patient-centered plans of care (p = .036; r = 0.44). There was no significant difference following the research activities about concern handling sensitive information (p = .499), replacing a human clinician (p = .255), or the need for human oversight (p = .833).

CONCLUSION

The use of AI as a study tool is rapidly developing. This study showed that students' perceptions of AI were significantly more positive after an exposure of clinical course application. These results are beneficial to educators to ensure that students are shown the practical applications of AI throughout learning strategies.

How human-AI feedback loops alter human perceptual, emotional and social judgements

Artificial intelligence (AI) technologies are rapidly advancing, enhancing human capabilities across various fields spanning from finance to medicine. Despite their numerous advantages, AI systems can exhibit biased judgements in domains ranging from perception to emotion. Here, in a series of experiments (n = 1,401 participants), we reveal a feedback loop where human-AI interactions alter processes underlying human perceptual, emotional and social judgements, subsequently amplifying biases in humans. This amplification is significantly greater than that observed in interactions between humans, due to both the tendency of AI systems to amplify biases and the way humans perceive AI systems. Participants are often unaware of the extent of the AI's influence, rendering them more susceptible to it. These findings uncover a mechanism wherein AI systems amplify biases, which are further internalized by humans, triggering a snowball effect where small errors in judgement escalate into much larger ones.

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.

Human versus Artificial Intelligence: ChatGPT-4 Outperforming Bing, Bard, ChatGPT-3.5 and Humans in Clinical Chemistry Multiple-Choice Questions

Introduction

Artificial intelligence (AI) chatbots excel in language understanding and generation. These models can transform healthcare education and practice. However, it is important to assess the performance of such AI models in various topics to highlight its strengths and possible limitations. This study aimed to evaluate the performance of ChatGPT (GPT-3.5 and GPT-4), Bing, and Bard compared to human students at a postgraduate master's level in Medical Laboratory Sciences.

Methods

The study design was based on the METRICS checklist for the design and reporting of AI-based studies in healthcare. The study utilized a dataset of 60 Clinical Chemistry multiple-choice questions (MCQs) initially conceived for assessing 20 MSc students. The revised Bloom's taxonomy was used as the framework for classifying the MCQs into four cognitive categories: Remember, Understand, Analyze, and Apply. A modified version of the CLEAR tool was used for the assessment of the quality of AI-generated content, with Cohen's κ for inter-rater agreement.

Results

Compared to the mean students' score which was 0.68±0.23, GPT-4 scored 0.90 ± 0.30, followed by Bing (0.77 ± 0.43), GPT-3.5 (0.73 ± 0.45), and Bard (0.67 ± 0.48). Statistically significant better performance was noted in lower cognitive domains (Remember and Understand) in GPT-3.5 (P=0.041), GPT-4 (P=0.003), and Bard (P=0.017) compared to the higher cognitive domains (Apply and Analyze). The CLEAR scores indicated that ChatGPT-4 performance was "Excellent" compared to the "Above average" performance of ChatGPT-3.5, Bing, and Bard.

Discussion

The findings indicated that ChatGPT-4 excelled in the Clinical Chemistry exam, while ChatGPT-3.5, Bing, and Bard were above average. Given that the MCQs were directed to postgraduate students with a high degree of specialization, the performance of these AI chatbots was remarkable. Due to the risk of academic dishonesty and possible dependence on these AI models, the appropriateness of MCQs as an assessment tool in higher education should be re-evaluated.

Investigating the Impact of the AI-Supported 5E (AI-s5E) Instructional Model on Spatial Ability

Improving students' spatial abilities is an important goal in education. Spatial ability is a skill needed in many fields, such as science, mathematics, engineering, and architecture. Since this ability can be improved through training, this study adopted a quasi-experimental design to investigate the effects of an artificial intelligence-supported 5E (AI-s5E) instructional model on students' spatial visualization, spatial relationships, and spatial orientation performances that explain their spatial abilities. A total of 43 students from two classes at a secondary school in western Turkey were recruited to participate in this study. One of the classes was the experimental group (f = 23), which adopted the AI-s5E approach, and the other class was the control group (f = 20), which adopted the traditional teaching model. The results showed that the integration of the AI-s5E instructional approach into education improved students' spatial abilities and sub-dimensions. In light of the findings, it can be recommended that AI applications, which have a positive and significant impact on spatial skills, can be integrated into teachers' lessons and even included in curriculum programs.

Factors influencing pre-service special education teachers' intention toward AI in education: Digital literacy, teacher self-efficacy, perceived ease of use, and perceived usefulness

The use of artificial intelligence in education (AIEd) has become increasingly significant globally. In China, there is a lack of research examining the behavioral intention toward AIEd among pre-service special education (SPED) teachers in terms of digital literacy and teacher self-efficacy. Building on the technology acceptance model, our study evaluated the aspects influencing pre-service special education teachers' intention toward AI in education. Data was gathered from 274 pre-service SPED teachers studying at a Chinese public normal university of special education and analyzed using structural equation modeling (SEM). The results show that digital literacy is associated with the perceived usefulness and ease of use of AIEd, which influences SPED teachers' intention to use AIEd. Additionally, digital literacy significantly impacts the self-efficacy of SPED teachers. Given these results, AI designers in special education should comprehend the effectiveness and usability of AIEd for fostering behavioral intention formation. Simultaneously, special educational programs that identify key content and activities for digital literacy training should be developed, and educators should attempt to execute the relevant pre-service training to enhance the intention of pre-service SPED teachers toward AIEd.

Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities

Artificial intelligence (AI) has emerged as a powerful tool in healthcare significantly impacting practices from diagnostics to treatment delivery and patient management. This article examines the progress of AI in healthcare, starting from the field's inception in the 1960s to present-day innovative applications in areas such as precision medicine, robotic surgery, and drug development. In addition, the impact of the COVID-19 pandemic on the acceleration of the use of AI in technologies such as telemedicine and chatbots to enhance accessibility and improve medical education is also explored. Looking forward, the paper speculates on the promising future of AI in healthcare while critically addressing the ethical and societal considerations that accompany the integration of AI technologies. Furthermore, the potential to mitigate health disparities and the ethical implications surrounding data usage and patient privacy are discussed, emphasizing the need for evolving guidelines to govern AI's application in healthcare.

The Use of ChatGPT for Education Modules on Integrated Pharmacotherapy of Infectious Disease: Educators' Perspectives

BACKGROUND

Artificial Intelligence (AI) plays an important role in many fields, including medical education, practice, and research. Many medical educators started using ChatGPT at the end of 2022 for many purposes.

OBJECTIVE

The aim of this study was to explore the potential uses, benefits, and risks of using ChatGPT in education modules on integrated pharmacotherapy of infectious disease.

METHODS

A content analysis was conducted to investigate the applications of ChatGPT in education modules on integrated pharmacotherapy of infectious disease. Questions pertaining to curriculum development, syllabus design, lecture note preparation, and examination construction were posed during data collection. Three experienced professors rated the appropriateness and precision of the answers provided by ChatGPT. The consensus rating was considered. The professors also discussed the prospective applications, benefits, and risks of ChatGPT in this educational setting.

RESULTS

ChatGPT demonstrated the ability to contribute to various aspects of curriculum design, with ratings ranging from 50% to 92% for appropriateness and accuracy. However, there were limitations and risks associated with its use, including incomplete syllabi, the absence of essential learning objectives, and the inability to design valid questionnaires and qualitative studies. It was suggested that educators use ChatGPT as a resource rather than relying primarily on its output. There are recommendations for effectively incorporating ChatGPT into the curriculum of the education modules on integrated pharmacotherapy of infectious disease.

CONCLUSIONS

Medical and health sciences educators can use ChatGPT as a guide in many aspects related to the development of the curriculum of the education modules on integrated pharmacotherapy of infectious disease, syllabus design, lecture notes preparation, and examination preparation with caution.

Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students

Introduction

This mixed-methods study evaluates the impact of AI-assisted language learning on Chinese English as a Foreign Language (EFL) students' writing skills and writing motivation. As artificial intelligence (AI) becomes more prevalent in educational settings, understanding its effects on language learning outcomes is crucial.

Methods

The study employs a comprehensive approach, combining quantitative and qualitative methods. The quantitative phase utilizes a pre-test and post-test design to assess writing skills. Fifty EFL students, matched for proficiency, are randomly assigned to experimental (AI-assisted instruction via ChatGPT) or control (traditional instruction) groups. Writing samples are evaluated using established scoring rubrics. Concurrently, semi-structured interviews are conducted with a subset of participants to explore writing motivation and experiences with AI-assisted learning.

Results

Quantitative analysis reveals significant improvements in both writing skills and motivation among students who received AI-assisted instruction compared to the control group. The experimental group demonstrates enhanced proficiency in various aspects of writing, including organization, coherence, grammar, and vocabulary. Qualitative findings showcase diverse perspectives, ranging from recognition of AI's innovative instructional role and its positive influence on writing skills and motivation to concerns about contextual accuracy and over-reliance. Participants also reflect on the long-term impact and sustainability of AI-assisted instruction, emphasizing the need for ongoing development and adaptation of AI tools.

Discussion

The nuanced findings offer a comprehensive understanding of AI's transformative potential in education. These insights have practical implications for practitioners and researchers, emphasizing the benefits, challenges, and the evolving nature of AI's role in language instruction.

Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning

Introduction

This mixed methods study examines the effects of AI-mediated language instruction on English learning achievement, L2 motivation, and self-regulated learning among English as a Foreign Language (EFL) learners. It addresses the increasing interest in AI-driven educational technologies and their potential to revolutionize language instruction.

Methods

Two intact classes, consisting of a total of 60 university students, participated in this study. The experimental group received AI-mediated instruction, while the control group received traditional language instruction. Pre-tests and post-tests were administered to evaluate English learning achievement across various domains, including grammar, vocabulary, reading comprehension, and writing skills. Additionally, self-report questionnaires were employed to assess L2 motivation and self-regulated learning.

Results

Quantitative analysis revealed that the experimental group achieved significantly higher English learning outcomes in all assessed areas compared to the control group. Furthermore, they exhibited greater L2 motivation and more extensive utilization of self-regulated learning strategies. These results suggest that AI-mediated instruction positively impacts English learning achievement, L2 motivation, and self-regulated learning.

Discussion

Qualitative analysis of semi-structured interviews with 14 students from the experimental group shed light on the transformative effects of the AI platform. It was found to enhance engagement and offer personalized learning experiences, ultimately boosting motivation and fostering self-regulated learning. These findings emphasize the potential of AI-mediated language instruction to improve language learning outcomes, motivate learners, and promote autonomy.

Conclusion

This study contributes to evidence-based language pedagogy, offering valuable insights to educators and researchers interested in incorporating AI-powered platforms into language classrooms. The results support the notion that AI-mediated language instruction holds promise in revolutionizing language learning, and it highlights the positive impact of AI-driven educational technologies in the realm of language education.

Machine learning-augmented fluid dynamics simulations for micromixer educational module

Micromixers play an imperative role in chemical and biomedical systems. Designing compact micromixers for laminar flows owning a low Reynolds number is more challenging than flows with higher turbulence. Machine learning models can enable the optimization of the designs and capabilities of microfluidic systems by receiving input from a training library and producing algorithms that can predict the outcomes prior to the fabrication process to minimize development cost and time. Here, an educational interactive microfluidic module is developed to enable the design of compact and efficient micromixers at low Reynolds regimes for Newtonian and non-Newtonian fluids. The optimization of Newtonian fluids designs was based on a machine learning model, which was trained by simulating and calculating the mixing index of 1890 different micromixer designs. This approach utilized a combination of six design parameters and the results as an input data set to a two-layer deep neural network with 100 nodes in each hidden layer. A trained model was achieved with R2 = 0.9543 that can be used to predict the mixing index and find the optimal parameters needed to design micromixers. Non-Newtonian fluid cases were also optimized using 56700 simulated designs with eight varying input parameters, reduced to 1890 designs, and then trained using the same deep neural network used for Newtonian fluids to obtain R2 = 0.9063. The framework was subsequently used as an interactive educational module, demonstrating a well-structured integration of technology-based modules such as using artificial intelligence in the engineering curriculum, which can highly contribute to engineering education.

An application of self-assessment of students in mathematics with intelligent decision systems: questionnaire, design and implementation at digital education

During the last decade an eruptive increase in the demand for intelligent m-learning environments has been observed since instructors in the online academic procedures need to ensure reliability. The research for decision systems seemed inevitable for flexible and effective learning in all levels of education. The prediction of the performance of students during their final exams is considered as a difficult task. In this paper, an application is presented, contributing to an accurate prediction which would assist educators and learning experts in the extraction of useful knowledge for designing learning interventions with enhanced outcomes.

The role of digital technologies in supporting quality improvement in Australian early childhood education and care settings

This national study explored the role of digital technologies in early childhood education and care settings and whether they could contribute to quality improvement as reported by educators and assessors of quality in Australia. In this paper, data from Stage 2 of the Quality Improvement Research Project were used, which comprised 60 Quality Improvement Plans from educators linked with 60 Assessment and Rating reports from the assessors who visited early childhood centres as part of the administration of the National Quality Standards by each of Australia's State and Territory jurisdictions. Bronfenbrenner's ecological systems theory ( Bronfenbrenner, U. (1995). Developmental ecology through space and time: A future perspective. In P. Moen, G. H. Elder, Jr., & K. Lüscher (Eds.), Examining lives in context: Perspectives on the ecology of human development (pp. 619-647). American Psychological Association. 10.1037/10176-018; Bronfenbrenner & Ceci, Bronfenbrenner and Ceci, Psychological Review 101:568-586, 1994) was adopted to facilitate a systemic and dynamic view on the use of digital technologies in these 60 ECEC settings. References (e.g. comments/ suggestions/ examples) made by the educators about the implementation of digital technologies were counted and thematically analysed. Results revealed the strong role new technologies (e.g. documentation and management platforms, tablets, apps, etc.) play in the majority of ECEC settings and especially in relation to three of the seven Quality Areas: Educational programme and practice (Quality Area 1); Collaborative partnerships with families and communities (Quality Area 6) and Governance and leadership (Quality Area 7). Future directions for research are suggested and implications for embracing a more holistic, integrated and broad view on the use of digital technologies are discussed.

Using MDS preference plot as visual analytics of data: A machine learning approach

The primary purpose of this paper is to advocate the use of multidimensional scaling (MDS) preference plot to study relationships among variables and individual differences in these variables. MDS preference plot is not a new visual technique; nevertheless, its application to visualize individual differences in variables for high-dimensional data is rare, particularly in education and social sciences. We illustrated its application using a real example in an educational setting. The results indicate that the MDS preference plot is a viable visualization technique for data mining and analytics. Traditional statistical methods, such as the analysis of variance, can be used to further support the visual analysis results.

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?

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.

Artificial intelligence-enabled prediction model of student academic performance in online engineering education

Online education has been facing difficulty in predicting the academic performance of students due to the lack of usage of learning process, summative data and a precise prediction of quantitative relations between variables and achievements. To address these two obstacles, this study develops an artificial intelligence-enabled prediction model for student academic performance based on students’ learning process and summative data. The prediction criteria are first predefined to characterize and convert the learning data in an online engineering course. An evolutionary computation technique is then used to explore the best prediction model for the student academic performance. The model is validated using another online course that applies the same pedagogy and technology. Satisfactory agreements are obtained between the course outputs and model prediction results. The main findings indicate that the dominant variables in academic performance are the knowledge acquisition, the participation in class and the summative performance. The prerequisite knowledge tends not to play a key role in academic performance. Based on the results, pedagogical and analytical implications are provided. The proposed evolutionary computation-enabled prediction method is found to be a viable tool to evaluate the learning performance of students in online courses. Furthermore, the reported genetic programming model provides an acceptable prediction performance compared to other powerful artificial intelligence methods.

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.

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.

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.

Students' multimodal knowledge sharing in school: Spatial repertoires and semiotic assemblages

In a world 'flooded' with data, students in school need adequate tools as Visual Analytics (VA), that easily process mass data, give support in drawing advanced conclusions and help to make informed predictions in relation to societal circumstances. Methods for how the students' insights may be reformulated and presented in 'appropriate' modes are required as well. Therefore, the aim in this study is to analyse elementary school students' practices of communicating visual discoveries, their insights, as the final stage in the knowledge-building process with an VA-application for interactive data visualization. A design-based intervention study is conducted in one social science classroom to explore modes for students presentation of insights, constructed from the interactive data visualizations. Video captures are used to document 30 students' multifaceted presentations. The analyses are based on concepts from Pennycook (2018) and Deleuze and Guattari (1987). To account for how different modes interact, when students present their findings, one significant empirical sequence is described in detail. The emerging communicative dimensions (visual-, bodily- and verbal-) are embedded within broad spatial repertoires distributing flexible semiotic assemblages. These assemblages provide an incentive for the possibilities of teachers' assessments of their students' knowledge outcomes.

Comparative study on intelligent education research among countries based on bibliographic coupling analysis

Purpose

The continuous development of information technology leads to intelligent education research. In the context of internationalisation, the study aims to understand the relevant research status worldwide, research similarities and differences that need to be discovered, and research frontiers that need to be explored.

Design/methodology/approach

Web of Science (WoS) core collection was used as the data source, descriptive statistical analysis, geographic data visualisation and coupling analysis are used to reveal coupling relationships, present a cooperative situation and discover research frontiers.

Findings

Intelligent education research has been widely carried out in countries around the world, and there is extensive scientific research cooperation. According to coupling analysis results, the coupling strength of bibliographic between countries has been continuously improved, while the coupling strength of keywords has remained balanced, and there is standardisation and diversity of research.

Research limitations/implications

The weakness of the research lies in the limitations of the data sources. Important research achievements on a certain topic in many non-English speaking countries are usually published in native journals. In the future research direction, more coupling analysis objects can be carried out, such as focussing on authors and institutions.

Practical implications

Through the coupling analysis of country bibliographic and keywords, it reveals the consistency and divergence of intelligent education research between different countries at different time spans.

Originality/value

Design and implement country bibliographic coupling (CBC) and country keyword coupling (CKC) strength indicators to calculate the strength of coupling between countries.

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.

The Teaching Design Methods Under Educational Psychology Based on Deep Learning and Artificial Intelligence

This study aims to evaluate the practical application value of the teaching method under the guidance of educational psychology and artificial intelligence (AI) design, taking the deep learning theory as the basis of teaching design. The research objects of this study involve all the teachers, students, and students' parents of Ningbo Middle School. The questionnaires are developed to survey the changes in the performance of students before and after the implementation of the teaching design and the satisfaction of all teachers, students, and parents to different teaching methods by comparing the two results and the satisfaction ratings. All objects in this study volunteer to participate in the questionnaire survey. The results suggest the following: (1) the effective return rates of the questionnaires to teachers, students, and parents are 97, 99, and 95%, respectively, before implementation; whereas those after implementation are 98, 99, and 99%, respectively. Comparison of the two return results suggests that there was no significant difference statistically (P > 0.05). (2) Proportion of scoring results before and after implementation is given as follows: the proportions of levels A, B, C, and D are 35, 40, 15, and 10% before implementation, respectively; while those after implementation are 47, 36, 12, and 5%, respectively. After the implementation, the proportion of level A is obviously higher than that before the implementation, and the proportions of other levels decreased in contrast to those before the implementation, showing statistically obvious differences (P < 0.05). (3) The change in the performance of each subject after 1 year implementation is significantly higher than that before the implementation, and the change in the average performance of each subject shows an upward trend. In summary, (1) the comparison on the effective return rate of the satisfaction survey questionnaire proves the feasibility of its scoring results. (2) The comparison of the survey scoring results shows that people are more satisfied with the new educational design teaching method. (3) The comparison of the change in the performance of each subject before and after the implementation indirectly reflects the drawbacks of partial subject education, indicating that the school should pay the same equal attention to every subject. (4) Due to various objective and subjective factors, the results of this study may be different from the actual situation slightly, and its accuracy has to be further explored in the future.

Artificial intelligence in education: Addressing ethical challenges in K-12 settings

Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. Applications of AI transform the tools of education. AI has a variety of educational applications, such as personalized learning platforms to promote students' learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners' behaviors. Despite the potential benefits of AI to support students' learning experiences and teachers' practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts. The ethical challenges of AI in education must be identified and introduced to teachers and students. To address these issues, this paper (1) briefly defines AI through the concepts of machine learning and algorithms; (2) introduces applications of AI in educational settings and benefits of AI systems to support students' learning processes; (3) describes ethical challenges and dilemmas of using AI in education; and (4) addresses the teaching and understanding of AI by providing recommended instructional resources from two providers-i.e., the Massachusetts Institute of Technology's (MIT) Media Lab and Code.org. The article aims to help practitioners reap the benefits and navigate ethical challenges of integrating AI in K-12 classrooms, while also introducing instructional resources that teachers can use to advance K-12 students' understanding of AI and ethics.

Anatomy 3.0: Rediscovering Theatrum Anatomicum in the wake of Covid-19

The Covid-19 pandemic has challenged medical educators internationally to confront the challenges of adapting their present educational activities to a rapidly evolving digital world. In this article, the authors use anatomy education as proxy to reflect on and remap the past, present, and future of medical education in the face of these disruptions. Inspired by the historical Theatrum Anatomicum (Anatomy 1.0), the authors argue replacing current anatomy dissection laboratory (Anatomy 2.0) with a prototype anatomy studio (Anatomy 3.0). In this studio, anatomists are web-performers who not only collaborate with other foundational science educators to devise meaningful and interactive content but who also partner with actors, directors, web-designers, computer engineers, information technologists, and visual artists to master online interactions and processes in order to optimize students' engagement and learning. This anatomy studio also offers students opportunities to create their own online content and thus reposition themselves digitally, a step into developing a new competency of stage presence within medical education. So restructured, Anatomy 3.0 will prepare students with the skills to navigate an emergent era of tele and digital medicine as well as help to foreshadow forthcoming changes in medical education.

Identification and evaluation of technology trends in K-12 education from 2011 to 2021

Educational technologies have captured the attention of researchers, policy makers, and parents. Each year, considerable effort and money are invested into new technologies, hoping to find the next effective learning tool. However, technology changes rapidly and little attention is paid to the changes after they occur. This paper provides an overall picture of the changing trends in educational technology by analyzing the Horizon Reports' predictions of the most influential educational technologies from 2011 to 2021, identifying larger trends across these yearly predictions, and by using bibliometric analysis to evaluate the accuracy of the identified trends. The results suggest that mobile and analytics technologies trended consistently across the period, there was a trend towards maker technologies and games in the early part of the decade, and emerging technologies (e.g., VR, AI) are predicted to trend in the future. Overall, the specific technologies focused on by the HRs' predictions and by educational researchers' publications seem to coincide with the availability of consumer grade technologies, suggesting that the marketplace and technology industry is driving trends (cf., pedagogy or theory).

Analysis of Worldwide Research Trends on the Impact of Artificial Intelligence in Education

In today’s world, artificial intelligence (AI) and human intelligence coexist, and no field is free from the impact of AI. At present, education cannot be discussed without mentioning AI, which has an omnidirectional impact on all its areas, including the purpose, content, method, and evaluation system. This study aimed to explore the future direction of education by examining the current impact and predicting future impacts of AI. It also examined research trends and collaboration status by country through network analysis, topic modeling and global research trends in AI in education (AIED), by applying the Latent Dirichlet Allocation algorithm. Over the past 20 years, the number of papers on AIED has steadily increased, with a dramatic rise since 2015. The research can be broadly classified into eight topics, including “changes in the content of teaching and learning.” Using a linear regression model, three hot topics, two cold topics and trend changes for each research topic were identified. The study found that AIED research should be more thematically diversified and in-depth; this directly applies AI algorithms and technologies to education, which should be further promoted. This study provides a reference for exploring the direction of future AIED research.

Data-Related Ethics Issues in Technologies for Informal Professional Learning

Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community’s work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. We describe the encountered data-related ethics issues. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning – to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner’s context. Such data may be private, proprietary, or both. Secondly, manual tracking comes with high learner control over data. Thirdly, learning is not necessarily a shared goal in informal professional learning settings. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn’t been collected for the purposes of learning. These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. As an outlook of this paper, we connect these challenges to ongoing research directions within AIED – natural language processing, socio-technical design, and scenario-based data collection - that might be leveraged and aimed towards addressing data-related ethics challenges.

Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

Applying Artificial Intelligence in Physical Education and Future Perspectives

Artificial intelligence (AI) is gradually influencing every aspect of everyday life, including education. AI can also provide special support to learners through academic sustainability or discontinuation predictions. While AI research remains in its early stages, we must examine how it evolves and exerts its potential over time. By utilizing AI in physical education (PE), we can increase its potential use in sports applications, and enact changes upon the nature of PE, its visualization, and repeatability. Based on the concept of AI and related research areas, this study explores its principles and use in PE, and presents a focused, in-depth analysis of the areas of PE technology where AI could be applied—customized PE classes, knowledge provision, learner evaluation, and learner counseling methods. Our findings highlight the expertise required for future PE teachers in applying AI. Regarding practice implications, this study addresses the topic of AI innovations affecting all life domains, including PE; it highlights AI applications’ relevance to PE technology, based on existing research; it proposes that the implications of AI for PE may apply to other educational domains; and finally, it contributes to existing literature and also shares future research prospects regarding AI applications in education and sports.

An Extended Theory of Planned Behavior for the Modelling of Chinese Secondary School Students’ Intention to Learn Artificial Intelligence

Artificial Intelligence (AI) is currently changing how people live and work. Its importance has prompted educators to begin teaching AI in secondary schools. This study examined how Chinese secondary school students’ intention to learn AI were associated with eight other relevant psychological factors. Five hundred and forty-five secondary school students who have completed at least one cycle of AI course were recruited to participate in this study. Based on the theory of planned behavior, the students’ AI literacy, subjective norms, and anxiety were identified as background factors. These background factors were hypothesized to influence the students’ attitudes towards AI, their perceived behavioral control, and their intention to learn AI. To provide more nuanced understanding, the students’ attitude towards AI was further delineated as constituted by their perception of the usefulness of AI, the potential of AI technology to promote social good, and their attitude towards using AI technology. Similarly, the perceived behavioral control was operationalized as students’ confidence towards learning AI knowledge and optimistic outlook of an AI infused world. Relationships between the factors were theoretically illustrated as a model that depicts how students’ intention to learn AI was constituted. Two research questions were then formulated. Confirmatory factor analysis was employed to validate that multi-factor survey, followed by structural equational modelling to ascertain the significant associations between the factors. The confirmatory factor analysis supports the construct validity of the questionnaire. Twenty-five out of the thirty-three hypotheses were supported through structural equation modelling. The model helps researchers and educators to understand the factors that shape students’ intention to learn AI. These factors should be considered for the design of AI curriculum.

A Review of Training and Guidance Systems in Medical Surgery

In this paper, a map of the state of the art of recent medical simulators that provide evaluation and guidance for surgical procedures is performed. The systems are reviewed and compared from the viewpoint of the used technology, force feedback, learning evaluation, didactic and visual aid, guidance, data collection and storage, and type of solution (commercial or non-commercial). The works’ assessment was made to identify if—(1) current applications can provide assistance and track performance in training, and (2) virtual environments are more suitable for practicing than physical applications. Automatic analysis of the papers was performed to minimize subjective bias. It was found that some works limit themselves to recording the session data to evaluate them internally, while others assess it and provide immediate user feedback. However, it was found that few works are currently implementing guidance, aid during sessions, and assessment. Current trends suggest that the evaluation process’s automation could reduce the workload of experts and let them focus on improving the curriculum covered in medical education. Lastly, this paper also draws several conclusions, observations per area, and suggestions for future work.

Out of the laboratory and into the classroom: the future of artificial intelligence in education

Like previous educational technologies, artificial intelligence in education (AIEd) threatens to disrupt the status quo, with proponents highlighting the potential for efficiency and democratization, and skeptics warning of industrialization and alienation. However, unlike frequently discussed applications of AI in autonomous vehicles, military and cybersecurity concerns, and healthcare, AI’s impacts on education policy and practice have not yet captured the public’s attention. This paper, therefore, evaluates the status of AIEd, with special attention to intelligent tutoring systems and anthropomorphized artificial educational agents. I discuss AIEd’s purported capacities, including the abilities to simulate teachers, provide robust student differentiation, and even foster socio-emotional engagement. Next, to situate developmental pathways for AIEd going forward, I contrast sociotechnical possibilities and risks through two idealized futures. Finally, I consider a recent proposal to use peer review as a gatekeeping strategy to prevent harmful research. This proposal serves as a jumping off point for recommendations to AIEd stakeholders towards improving their engagement with socially responsible research and implementation of AI in educational systems.

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

Learner-Context Modelling: A Bayesian Approach

The following paper is a proof-of-concept demonstration of a novel Bayesian model for making inferences about individual learners and the context in which they are learning. This model has implications for both efforts to create rich open leaner models, develop automated personalization and increase the breadth of adaptive responses that machines are capable of. The purpose of the following work is to demonstrate, using both simulated data and a benchmark dataset, that the model can perform comparably to commonly used models. Since the model has fewer parameters and a flexible interpretation, comparable performance opens the possibility of utilizing it to extend automation greater variety of learning environments and use cases.

Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education

In science, technology, engineering, arts, and mathematics (STEAM) education, artificial intelligence (AI) analytics are useful as educational scaffolds to educe (draw out) the students’ AI-Thinking skills in the form of AI-assisted human-centric reasoning for the development of knowledge and competencies. This paper demonstrates how STEAM learners, rather than computer scientists, can use AI to predictively simulate how concrete mixture inputs might affect the output of compressive strength under different conditions (e.g., lack of water and/or cement, or different concrete compressive strengths required for art creations). To help STEAM learners envision how AI can assist them in human-centric reasoning, two AI-based approaches will be illustrated: first, a Naïve Bayes approach for supervised machine-learning of the dataset, which assumes no direct relations between the mixture components; and second, a semi-supervised Bayesian approach to machine-learn the same dataset for possible relations between the mixture components. These AI-based approaches enable controlled experiments to be conducted in-silico, where selected parameters could be held constant, while others could be changed to simulate hypothetical “what-if” scenarios. In applying AI to think discursively, AI-Thinking can be educed from the STEAM learners, thereby improving their AI literacy, which in turn enables them to ask better questions to solve problems.

Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review

BACKGROUND

Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education.

OBJECTIVE

This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education.

METHODS

Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively.

RESULTS

A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students' learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications.

CONCLUSIONS

The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students' learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.