Artificial Intelligence Course Research Articles

The Development and Validation of the Learning Engagement Scale for High School Students' Artificial Intelligence Courses

The Development and Validation of the Learning Engagement Scale for High School Students' Artificial Intelligence Courses

Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students

Artificial Intelligence (AI) is permeating almost every area of society, reshaping how many people, including youth, navigate the world. Despite the increased presence of AI, most people lack a baseline knowledge of how AI works. Moreover, social barriers often hinder equal access to AI courses, perpetuating disparities in participation in the field. To address this, it is crucial to design AI curricula that are effective, inclusive, and relevant, especially to learners from backgrounds that are historically excluded from working in tech. In this paper, we present AI for Wellbeing, a curriculum where students explore conversational AI and the ethical considerations around using it to promote wellbeing. We specifically designed content, educator materials, and educational technologies to meet the interests and needs of students and educators from diverse backgrounds. We piloted AI for Wellbeing in a 5-day virtual workshop with middle school teachers and students. Then, using a mixed-methods approach, we analyzed students' work and teachers' feedback. Our results suggest that the curriculum content and design effectively engaged students, enabling them to implement meaningful AI projects for wellbeing. We hope that the design of this curriculum and insights from our evaluation will inspire future efforts to create culturally relevant K-12 AI curricula.

Thoughts on the Teaching Reformation of Artificial Intelligence Courses

Thoughts on the Teaching Reformation of Artificial Intelligence Courses

An exploration of pedagogical approaches in teaching artificial intelligence courses: Experience from undergraduates students of Bangladesh

The increasing demand for skilled professionals in Artificial Intelligence (AI) within the Fourth Industrial Revolution (4IR) has led to the establishment of AI courses in universities worldwide. This study investigated effective pedagogical approaches in an AI course through in-depth interviews with eighteen undergraduate students in Bangladesh. Grounded theory methodology was employed, resulting in the development of five thematic categories: the significance of feedback, combination of theory and practice, promotion of self-paced learning, connection to real-life problems, and integration with future education and emerging technologies. The findings highlight the importance of these pedagogical approaches in enhancing students' learning outcomes. The study's implications include informing the design of professional development programs for AI teachers to improve instructional methods and learning outcomes. The proposed “Holistic Framework for Teaching and Learning in Emerging AI Courses” combines these five categories to provide practical guidance for instructors in the global context of AI education.

Attitude, Awareness, and Understanding of Artificial Intelligence AI among Medical and Dental Students in Jordan: A Cross-Sectional Study

BACKGROUND: The rapid growth of AI has transformed various sectors, including healthcare. AI aids medical diagnoses and treatment planning, but challenges such as privacy and ethical concerns exist. Understanding AI is crucial for medical and dental students in Jordan to enhance patient care. This study examines students' attitudes, awareness, and understanding of AI to inform effective education and better healthcare practices. METHODS: A cross-sectional study was conducted in Jordan among medical and dental students from Nov 2022 to Mar 2023. Data were analyzed using descriptive statistics, Mann-Whitney U test, Kruskal-Wallis tests, and Spearman's correlation. Ethical approval was obtained, and SPSS was used for analysis. RESULTS: The study involved 800 respondents, predominantly female (54.6%) and with an average age of 19.81 years. Most participants were medical students (64.6%), had a national diploma (75.0%), and were in their first or second year of education (73.1%). The majority were single (97.9%), and 75.1% resided in the central region. In terms of technology background, 38.8% had family members in the technology sector, and half used electronic devices for 3-6 hours daily. Over 75% used electronic devices for studying, while 44.1% had taken technology courses, with only 23.5% having specifically studied Artificial Intelligence (AI). Additionally, 46.0% had prior research experience, but only 18.0% had participated in AI-related research. Participants rated their overall technology knowledge at 2.91 ± 1.06 and AI knowledge at 2.43 ± 1.21. The main sources of AI information were social media (35.0%). Regarding AI applications, 28.0% believed it would enhance medical diagnosis, while 24.0% expressed concerns about hacking and cybersecurity. Only 10.0% planned to consistently incorporate AI into their future medical practice. Statistical tests revealed significant differences in awareness of AI based on study field (p-value = 0.034), income (p-value = 0.028), previous technology involvement (p-value = 0.004), ICDL course participation (p-value = 0.02), AI course participation (p-value < 0.001), and programming language learning (p-value = 0.001). Similar variations were observed in understanding AI basic principles, with additional significance related to research course participation (p-value < 0.001). Attitudes toward AI differed only among those with previous technology course involvement (p-value = 0.037). Spearman's correlation indicated a moderate positive association between awareness of AI in medicine and dentistry and understanding of AI basic principles (r: 0.491, p < 0.001). CONCLUSION: The younger generation of medical and dental professionals’ views AI as a collaborator in their practice. Strong interest and active engagement in AI-related courses highlight the importance of integrating AI education into medical curricula for innovative healthcare practices.

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are taught during AI courses. The separate explanation of these core research areas is common, but the emergence of federated learning has triggered their combined usage. This paper describes a practical scenario in the energy domain where these technologies can be used together to provide a sustainable energy solution for predicting wind turbine active power production. The projects in the AI course were assigned prior to the step-by-step learning of MASs and ML. These concepts were applied using a wind turbine energy dataset collected in Turkey to predict the power production of wind turbines. The observed performance improvements, achieved by applying various agent architectures and data partitioning scenarios, indicate that boosting methods such as LightGBM yield better results even when the settings are modified. Additionally, a questionnaire about the assignments was filled out by the student groups to assess the impact of learning MASs and ML through project-based education. The application of MASs and ML in a hybrid way proves valuable for learning core concepts related to AI education, as evidenced by feedback from students.

Impact of Artificial Intelligence based Learning Process on Students' Tendency to Involve in Independent Research at the Higher Secondary School

The integration of artificial intelligence (AI) has become increasingly essential in implementing innovative instructional approaches at Higher Secondary School (HSS). The demand for advanced education that integrates intelligent technologies to improve the overall educational experience is growing. Individuals that actively study artificial intelligence are more equipped to face future difficulties related to technology, society, and the environment. The Knowledge and Attitudes Survey (KAS) lacks empirical evidence regarding the motivation levels of students in their involvement with the field of AI. The main purpose of this study was to examine how students' self-confidence, peer opinions, and personal beliefs affect their willingness to participate in AI education. A comprehensive longitudinal survey covered a total of 707 students who entered in higher secondary school. The survey examined the subjective norms, intentions, and knowledge of the participants, as well as their acquaintance with AI, their attitude towards acquiring knowledge of AI, their inclination towards AI-based learning, and their self-assurance in studying AI. The participants displayed a primarily optimistic perspective on AI education, with 78.1% expressing positivity. Moreover, an overwhelming majority of 73.1 percent of participants exhibited assurance in their capacity to gain knowledge in the realm of artificial intelligence. The data indicate that a substantial majority of respondents, precisely 82%, recognized the influence of different elements, such as peer pressure, instructors, and families, on their choice to participate in AI courses. The results indicate that students' perception of social norms significantly impacts their motivation to study artificial intelligence. Students must demonstrate a heightened openness to AI learning, especially if they perceive support and approval from their teachers and peers. The desire of pupils to participate in AI learning was discovered to be impacted by their viewpoint, self-assurance, and perceived proficiency in the subject matter. Various tactics can be employed to boost students' motivation to engage in the study of AI at a HSS, as indicated by the research results. These tactics encompass the dissemination of knowledge regarding the importance of AI education, fostering favorable views towards the subject, and nurturing students' self-perception of their abilities and control. This study offers significant insights for government authorities, educators, and other stakeholders in KAS and similar educational settings.

Picture Perfect: The Status of Image Quality in Prostate MRI.

Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

An artificial intelligence course for chemical engineers

Artificial intelligence and machine learning are revolutionising fields of science and engineering. In recent years, process engineering has widely benefited from this novel modelling and optimisation approach. The open literature can offer several examples of their applications to chemical engineering problems. Increasing investments are devoted to these techniques from different industrial areas, but insufficient information on a structured course covering these topics in a chemical engineering curriculum could be found. The course in this paper intends to reduce this gap. We introduce one of the first courses on artificial intelligence applications in a chemical engineering curriculum. The course targets Master's students with a chemical engineering background and insufficient knowledge of statistical approaches. It covers the main aspects by utilising frontal lectures and hands-on exercises with active learning methods. This paper shows the methodology we adapted to introduce students to machine learning techniques and how they responded to each class. The student performances for each test are shown, as well as the survey results based on student feedback and suggestions. This work contains essential guidelines for educators who will provide an artificial intelligence course in a chemical engineering curriculum.

AI Course Design Planning Framework: Developing Domain-Specific AI Education Courses

The use of artificial intelligence (AI) is becoming increasingly important in various domains, making education about AI a necessity. The interdisciplinary nature of AI and the relevance of AI in various fields require that university instructors and course developers integrate AI topics into the classroom and create so-called domain-specific AI courses. In this paper, we introduce the “AI Course Design Planning Framework” as a course planning framework to structure the development of domain-specific AI courses at the university level. The tool evolves non-specific course planning frameworks to address the context of domain-specific AI education. Following a design-based research approach, we evaluated a first prototype of the tool with instructors in the field of AI education who are developing domain-specific courses in this area. The results of our evaluation indicate that the tool allows instructors to create domain-specific AI courses in an efficient and comprehensible way. In general, instructors rated the tool as useful and user-friendly and made recommendations to improve its usability. Future research will focus on testing the application of the tool for domain-specific AI course developments in different domain contexts and examine the influence of using the tool on AI course quality and learning outcomes.

The impact of artificial intelligence on modern education: prospects and challenges

This paper examines the intricate links between artificial intelligence (AI) and education, delving into both theoretical and practical aspects while evaluating possible ramifications for labor market dynamics, professional activity, and wider educational paradigms. Our research methodology involved analyzing relevant scientific literature, classifying data, consulting with subject matter experts, and synthesizing the results. Our research suggests that AI has the ability to greatly improve pedagogical processes, personalize learning experiences to meet individual student needs, and successfully address the time and financial limitations that are inherent in traditional educational models. However, our study also reveals challenges related to data confidentiality, potential plagiarism and fraud associated with AI use, and socioeconomic disparities resulting from unequal technology access. Additionally, we identified a significant gap in current AI usage standards legislation. It is essential for researchers, educators, and policymakers to recognize the potential risks of AI implementation in educational settings and proactively develop strategies that prioritize ethics, safety, and effectiveness. With labor market trends favoring specialists knowledgeable in utilizing AI tools, a consequent change in curricula is expected. In response to our findings, we recommend the creation of new academic disciplines that concentrate on the cultivation of AI expertise; the establishment of comprehensive national AI strategies; the crafting of retraining roadmaps for those who may be affected by AI automation; the inclusion of online AI courses in existing educational programs; and the promotion of grant funding for future AI research. Our future research will concentrate on reducing the potential negative impacts of integrating AI into educational systems.

An Empirical Investigation of the Impact of Artificial Intelligence on Accounting Practice in Nigeria

This study carried out an empirical investigation on the impact of artificial intelligence on accounting practice in Nigeria. The study adopted a survey research design. Data for the study were extracted from one hundred and forty-eight (148) respondents extracted from selected organizations in Abuja. A questionnaire was used as the primary instrument for data collection. The data were analyzed with the aid of frequency tables and percentages and the hypotheses were tested with probability values extracted from the regression output. The major findings of the study revealed that expert systems and neural networks contribute positively and significantly to accounting practice in Nigeria. The implication of this finding is that any accountant that does not arm their skills with the knowledge of artificial intelligence software, may become irrelevant in the nearest future. It is therefore the recommendation of this study that accountants should attend seminars and workshops on modern accounting methodical practices in order to further their knowledge of and proficiency with artificial intelligence technologies, which will increase their dynamism in functional accounting activities and secondly; to prepare upcoming accountants for what lies ahead in the business world, the government should incorporate artificial intelligence courses into accounting at the undergraduate and graduate levels in tertiary institutions.

Readiness Towards Artificial Intelligence Among Undergraduate Medical Students in Malaysia

Artificial intelligence (AI) technology is growing at a fast pace and permeates many aspects of people’s daily lives. Medical students’ inclination towards AI in the medical field increases the probability of successful AI adoption and its value in the medical field. This study was conducted to evaluate medical AI readiness among undergraduate medical students. A cross-sectional study was conducted from March 2022 to April 2022 in a private medical institution in Malaysia. A non-probability purposive sampling method was used to enroll students and a questionnaire was distributed online via Google Forms. The questionnaire, captioned “Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS)”, was used for data collection. The analysis included frequency tables, percentages, standard deviation, unpaired t-test, and analysis of variance (ANOVA) test. Out of 105 participants, 67.62% scored 53 to 83, followed by 24.76%, who scored 84 to 114, and 7.62%, who scored 22 to 52 on the medical AI readiness scale. The mean of the total score of medical AI readiness obtained was 75.04. There were significant correlations between age and study year with the ability, vision, and ethics domains of medical AI readiness. A significant association was observed between previous training with all four domains of medical AI readiness. Policymakers and the educational sector should set up more AI training centers to provide and introduce basic courses on AI. More AI courses should be provided to younger populations to engage in AI digital information earlier, thus enabling them to acquire more confidence in interacting with AI technology in the future.

Exploring Social Biases of Large Language Models in a College Artificial Intelligence Course

Large neural network-based language models play an increasingly important role in contemporary AI. Although these models demonstrate sophisticated text generation capabilities, they have also been shown to reproduce harmful social biases contained in their training data. This paper presents a project that guides students through an exploration of social biases in large language models. As a final project for an intermediate college course in Artificial Intelligence, students developed a bias probe task for a previously-unstudied aspect of sociolinguistic or sociocultural bias they were interested in exploring. Through the process of constructing a dataset and evaluation metric to measure bias, students mastered key technical concepts, including how to run contemporary neural networks for natural language processing tasks; construct datasets and evaluation metrics; and analyze experimental results. Students reported their findings in an in-class presentation and a final report, recounting patterns of predictions that surprised, unsettled, and sparked interest in advocating for technology that reflects a more diverse set of backgrounds and experiences. Through this project, students engage with and even contribute to a growing body of scholarly work on social biases in large language models.

Develop AI Teaching and Learning Resources for Compulsory Education in China

Artificial intelligence course has been required to take for compulsory education students in China. However, not all teachers and schools are fully prepared and ready. This is partially because of the lack of adequate teaching and learning resources, which requires a major expenditure of time and effort for schools and teachers to design and develop. To meet the challenge of lacking appropriate resources in teaching and learning AI from grade 1 to grade 9, we developed AI knowledge structure and instructional resources based on Chinese national curriculum for information science and technology. Our comprehensive AI syllabus contains 90 core concepts, 63 learning indicators, and 27 teaching and learning resources, which have been implemented. The resources have been taken as model courses in teacher training programs and an exemplary course has been implemented in primary schools that verified the effectiveness of our resources.

Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development

Several radiology artificial intelligence (AI) courses are offered by a variety of institutions and educators. The major radiology societies have developed AI curricula focused on basic AI principles and practices. However, a specific AI curriculum focused on pediatric radiology is needed to offer targeted education material on AI model development and performance evaluation. There are inherent differences between pediatric and adult practice patterns, which may hinder the application of adult AI models in pediatric cohorts. Such differences include the different imaging modality utilization, imaging acquisition parameters, lower radiation doses, the rapid growth of children and changes in their body composition, and the presence of unique pathologies and diseases, which differ in prevalence from adults. Thus, to enhance radiologists’ knowledge of the applications of AI models in pediatric patients, curricula should be structured keeping in mind the unique pediatric setting and its challenges, along with methods to overcome these challenges, and pediatric-specific data governance and ethical considerations. In this report, the authors highlight the salient aspects of pediatric radiology that are necessary for AI education in the pediatric setting, including the challenges for research investigation and clinical implementation.

Research Status and Challenges on the Sustainable Development of Artificial Intelligence Courses from a Global Perspective

The widespread application of artificial intelligence technology in various fields has made the sustainable development of artificial intelligence courses an important direction in the field of artificial intelligence education and teaching. Therefore, it is particularly important to conduct an in-depth analysis of the current research status of “artificial intelligence courses” from a global perspective. Firstly, this article clarifies the three stages of slow development, rapid development, and mature development of artificial intelligence curriculum research through the number and distribution years of the literature. It also conducts a co-authorship analysis on the distribution of countries, institutions, and authors of artificial intelligence curriculum research and identifies countries, institutions, and core authors that have made greater contributions to artificial intelligence curriculum research. Secondly, due to the involvement of artificial intelligence in many different fields of knowledge, an analysis is conducted on the journals that published papers on artificial intelligence courses. Finally, based on the analysis of keyword density and time span, the current research hotspots of artificial intelligence courses are summarized: artificial intelligence technology empowerment courses, two education directions at different stages of artificial intelligence courses, and teaching forms in the field of artificial intelligence courses. The current research trend of artificial intelligence courses is analyzed from three aspects: teaching format, teaching content, and teaching objects. This article provides a theoretical reference value and practical basis for future research and development in the field of artificial intelligence courses, while also providing experience for the efficient and sustainable development of artificial intelligence courses to a certain extent.

A Review of Gamification Teaching Based on Creative Thinking Development

The development of artificial intelligence in education has ushered in a new spring as the country pays more and more attention to basic education curriculum and innovative talents. The development of innovative talents depends on the cultivation of ability, and the core of the cultivation of ability is the development of thinking, so it is especially important to cultivate students' innovative thinking for solving the problems of artificial intelligence applications in life. The artificial intelligence course is not only a technical course, but also involves several disciplines. Through the learning of artificial intelligence course, students can be exposed to more comprehensive multidisciplinary knowledge and find the optimal solution for existing problems, which can stimulate students' innovative thinking. This paper reviews the application of innovative thinking in game teaching method.

Preparing Pharmacists for The Era of AI-driven Pharmacological Sciences: An Opinion Article

Due to its rapid development, artificial intelligence (AI) has gained a lot of popularity in many sectors, including pharmacological sciences. In pharmacological sciences, AI has been applied in various areas, from AI-driven drug discovery and genomic sequencing to the development of personalized medicine. Subsequently, the need for pharmacists with adequate AI-related skills and knowledge increases. However, the current traditional curriculum of pharmacy education does not cover AI courses. To elaborate on the current developments in AI-driven pharmacology and present the discussion of the possibility of whether AI-related courses should be included in the current curriculum. We have gathered 32 references on AI-driven pharmacological applications, pharmacy curricula, and the future trends of pharmacological sciences. The literature is obtained from online databases such as Scopus, PubMed, IEEE, and Google Scholar. Based on the literature reviews, we presented the discussion and scientific-based opinion on the need for AI-related courses in the school of pharmacy. We believe that AI will be the basis of future pharmacological development. Hence, we consider that it is the right time to include AI-related courses in the student's curriculum to prepare future-proof pharmacists.

Effect of Artificial Intelligence Course in Nursing on Students' Medical Artificial Intelligence Readiness: A Comparative Quasi-Experimental Study.

It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. This study examined the impact of an AI course in the nursing curriculum on students' medical AI readiness. This comparative quasi-experimental study was conducted with a total of 300 3rd-year nursing students, 129 in the control group and 171 in the experimental group. Students in the experimental group received 28 hours of AI training. The students in the control group were not given any training. Data were collected by a socio-demographic form and the Medical Artificial Intelligence Readiness Scale. An AI course should be included in the nursing curriculum, according to 67.8% of students in the experimental group and 57.4% of students in the control group. The mean score of the experimental group on medical AI readiness was higher ( P < .05) and the effect size of the course on readiness was -0.29. An AI nursing course positively affects students' readiness for medical AI.