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Item The influence of sociodemographic factors on students’ attitudes toward AI-generated video content creation(Spinger Open, 2023) Pellas, NikolaosArtificial Intelligence (AI) and Machine Learning (ML) technologies offer the potential to support digital content creation and media production, providing opportunities for individuals from diverse sociodemographic backgrounds to engage in creative activities and enhance their multimedia video content. However, less attention has been paid to recent research exploring any possible relationships between AI-generated video creation and the sociodemographic variables of undergraduate students. This study aims to investigate the multifaceted relationship between AI-generated video content and sociodemographics by examining its implications for inclusivity, equity, and representation in the digital media landscape. An empirical study about the use of AI in video content creation was conducted with a diverse cohort of three hundred ninety-eighth undergraduate (n = 398) students. Participants voluntarily took part and were tasked with conceiving and crafting their AI-generated video content. All instruments used were combined into a single web-based self-report questionnaire that was delivered to all participants via email. Key research findings demonstrate that students have a favorable disposition when it comes to incorporating AIsupported learning tasks. The factors fostering this favorable attitude among students include their age, the number of devices they use, the time they dedicate to utilizing technological resources, and their level of experience. Nevertheless, it is the student’s participation in AI training courses that exerts a direct impact on students’ ML attitudes, along with their level of contentment with the reliability of these technologies. This study contributes to a more comprehensive understanding of the transformative power of AI in video content creation and underscores the importance of considering instructional contexts and policies to ensure a fair and equitable digital media platform for students from diverse sociodemographic backgrounds.Item Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions(Spinger Open, 2023) Bhatt, Priya; Sethi, Amanrose; Tasgaonkar, Vaibhav; Shroff, Jugal; Pendharkar, Isha; Desai, Aditya; Sinha, Pratyush; Deshpande, Aditya; Joshi, Gargi; Rahate, Anil; Jain, Priyanka; Walambe, Rahee; Kotecha, Ketan; Jain, N. K.Human behaviour reflects cognitive abilities. Human cognition is fundamentally linked to the different experiences or characteristics of consciousness/emotions, such as joy, grief, anger, etc., which assists in effective communication with others. Detection and differentiation between thoughts, feelings, and behaviours are paramount in learning to control our emotions and respond more effectively in stressful circumstances. The ability to perceive, analyse, process, interpret, remember, and retrieve information while making judgments to respond correctly is referred to as Cognitive Behavior. After making a significant mark in emotion analysis, deception detection is one of the key areas to connect human behaviour, mainly in the forensic domain. Detection of lies, deception, malicious intent, abnormal behaviour, emotions, stress, etc., have significant roles in advanced stages of behavioral science. Artificial Intelligence and Machine learning (AI/ML) has helped a great deal in pattern recognition, data extraction and analysis, and interpretations. The goal of using AI and ML in behavioral sciences is to infer human behaviour, mainly for mental health or forensic investigations. The presented work provides an extensive review of the research on cognitive behaviour analysis. A parametric study is presented based on different physical characteristics, emotional behaviours, data collection sensing mechanisms, unimodal and multimodal datasets, modelling AI/ML methods, challenges, and future research directions.Item The application of AI technologies in STEM education: a systematic review from 2011 to 2021(Spinger Open, 2023) Xu, Weiqi; Ouyang, FanBackground: The application of artificial intelligence (AI) in STEM education (AI-STEM), as an emerging field, is confronted with a challenge of integrating diverse AI techniques and complex educational elements to meet instructional and learning needs. To gain a comprehensive understanding of AI applications in STEM education, this study conducted a systematic review to examine 63 empirical AI-STEM research from 2011 to 2021, grounded upon a general system theory (GST) framework. Results: The results examined the major elements in the AI-STEM system as well as the effects of AI in STEM education. Six categories of AI applications were summarized and the results further showed the distribution relationships of the AI categories with other elements (i.e., information, subject, medium, environment) in AI-STEM. Moreover, the review revealed the educational and technological effects of AI in STEM education. Conclusions: The application of AI technology in STEM education is confronted with the challenge of integrating diverse AI techniques in the complex STEM educational system. Grounded upon a GST framework, this research reviewed the empirical AI-STEM studies from 2011 to 2021 and proposed educational, technological, and theoretical implications to apply AI techniques in STEM education. Overall, the potential of AI technology for enhancing STEM education is fertile ground to be further explored together with studies aimed at investigating the integration of technology and educational system.Item The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?(Spinger Open, 2023) Chan, Cecilia Ka Yuk; K. W. Lee, KatherineThis study aimed to explore the experiences, perceptions, knowledge, concerns, and intentions of Generation Z (Gen Z) students with Generation X (Gen X) and Generation Y (Gen Y) teachers regarding the use of generative AI (GenAI) in higher education. A sample of students and teachers were recruited to investigate the above using a survey consisting of both open and closed questions. The findings showed that Gen Z participants were generally optimistic about the potential benefits of GenAI, including enhanced productivity, efficiency, and personalized learning, and expressed intentions to use GenAI for various educational purposes. Gen X and Gen Y teachers acknowledged the potential benefits of GenAI but expressed heightened concerns about overreliance, ethical and pedagogical implications, emphasizing the need for proper guidelines and policies to ensure responsible use of the technology. The study highlighted the importance of combining technology with traditional teaching methods to provide a more effective learning experience. Implications of the findings include the need to develop evidence-based guidelines and policies for GenAI integration, foster critical thinking and digital literacy skills among students, and promote responsible use of GenAI technologies in higher education.Item Research on interaction of innovation spillovers in the AI, Fin‑Tech, and IoT industries: considering structural changes accelerated by COVID‑19(Spinger Open, 2023) Ho, Chi‑MingThis paper aims to probe the influence of innovation spillovers in the artificial intel‑ ligence (AI) and financial technology (Fin‑tech) industries on the value of the internet of things (IoT) companies. Python was utilized to download public information from Yahoo Finance, and then the GARCH model was used to extract the fluctuations of cross‑industry innovation spillovers. Next, the Fama–French three‑factor model was used to explore the interactive changes between variables. The panel data regression analysis indicates that the more firms accept innovation spillovers from other indus‑ tries, the better the excess return; however, this effect differs because of industrial attributes and the environmental changes induced by COVID‑19. Additionally, this study finds that investing in large‑cap growth stocks of IoT firms is more likely to yield excess returns. Finally, the study yields lessons for policy leverage to accelerate the upgrading and transformation of innovation‑interactive industries by referring to the practices of Singapore and South Korea.Item Integrating artificial intelligence into science lessons: teachers’ experiences and views(Spinger Open, 2023) Park, Joonhyeong; Wee Teo, Tang; Chang, Jina; Huang, Jun Song; Koo, SengmengBackground In the midst of digital transformation, schools are transforming their classrooms as they prepare students for a world increasingly automated by new technologies, including artificial intelligence (AI). During curricular implementation, it has not made sense to teachers to teach AI as a stand-alone subject as it is not a traditional discipline in schools. As such, subject matter teachers may need to take on the responsibility of integrating AI content into discipline-based lessons to help students make connections and see its relevance rather than present AI as separate content. This paper reports on a study that piloted a new lesson package in science classrooms to introduce students to the idea of AI. Specifically, the AI-integrated science lesson package, designed by the research team, provided an extended activity that used the same context as an existing lesson activity. Three science teachers from different schools piloted the lesson package with small groups of students and provided feedback on the materials and implementation. Findings The findings revealed the teachers’ perceptions of integrating AI into science lessons in terms of the connection between AI and science, challenges when implementing the AI lesson package and recommendations on improvements. First, the teachers perceived that AI and science have similarities in developing accurate models with quality data and using simplified reasoning, while they thought that AI and science play complementary roles when solving scientific problems. Second, the teachers thought that the biggest challenge in implementing the lesson package was a lack of confidence in content mastery, while the package would be challenging to get buy-in from teachers regarding curriculum adaptation and targeting the appropriate audience. Considering these challenges, they recommended that comprehensive AI resources be provided to teachers, while this package can be employed for science enrichment programs after-school. Conclusions The study has implications for curriculum writers who design lesson packages that introduce AI in science classrooms and for science teachers who wish to contribute to the development of AI literacy for teachers and the extension of the range of school science and STEM to students.Item Implications of AI innovation on economic growth: a panel data study(Spinger Open, 2023) Tan Gonzales, JuliusItem AI literacy in K‑12: a systematic literature review(Spinger Open, 2023) Casal‑Otero, Lorena; Catala, Alejandro; Fernández‑Morante, Carmen; Taboada, Maria; Cebreiro, BeatrizThe successful irruption of AI-based technology in our daily lives has led to a growing educational, social, and political interest in training citizens in AI. Education systems now need to train students at the K-12 level to live in a society where they must interact with AI. Thus, AI literacy is a pedagogical and cognitive challenge at the K-12 level. This study aimed to understand how AI is being integrated into K-12 education worldwide. We conducted a search pro‑ cess following the systematic literature review method using Scopus. 179 documents were reviewed, and two broad groups of AI literacy approaches were identifed, namely learning experience and theoretical perspective. The frst group covered experiences in learning technical, conceptual and applied skills in a particular domain of interest. The second group revealed that signifcant eforts are being made to design models that frame AI literacy proposals. There were hardly any experiences that assessed whether students understood AI concepts after the learning experience. Little attention has been paid to the undesirable consequences of an indiscriminate and insufciently thought-out application of AI. A competency framework is required to guide the didactic proposals designed by educational institutions and defne a curriculum refecting the sequence and academic continuity, which should be modular, per‑ sonalized and adjusted to the conditions of the schools. Finally, AI literacy can be leveraged to enhance the learning of disciplinary core subjects by integrating AI into the teaching process of those subjects, provided the curriculum is co-designed with teachers.