AI Chatbots hold considerable promise for transforming teaching and learning in higher education, particularly in supporting academic writing.However, their adoption by students is shaped by various perceptual and contextual factors.This study investigates the adoption of AI Chatbots by South African university students, guided by the Innovation Diffusion Theory (IDT).It specifically examines four constructs: Perceived Compatibility, Perceived Relative Advantage, Technology Self-Efficacy, and Perceived Complexity.Using a quantitative research design, data were collected via a structured questionnaire from 224 students across all academic levels at the University of Fort Hare.The sample was purposively selected to reflect a range of experiences with AI technologies.The instrument was grounded in IDT and included validated Likert-scale items measuring each construct.The findings reveal that Perceived Compatibility, Relative Advantage, and Technology Self-Efficacy significantly and positively influence chatbot adoption.Students who viewed chatbots as compatible with academic writing tasks and beneficial in improving writing quality were more likely to use them.Higher levels of technology selfefficacy further strengthened adoption.Conversely, Perceived Complexity was negatively associated with adoption, though this effect was moderate.These results offer practical implications.To support AI integration in higher education, chatbot systems must be user-friendly, contextually relevant, and embedded in digital literacy development.The study contributes empirical evidence from a Global South context, highlighting the intersection of psychological, technological, and contextual factors.It also offers actionable guidance for educators, developers, and policymakers seeking to leverage AI to enhance student engagement and academic performance.

Generative AI assistants are permeating programming classrooms, yet little is known about whether students adopt sound usage habits.This study surveyed 50 undergraduates in Computer Science / Computer Information Systems courses to examine the frequency of 11 recommended AI-support behaviors, such as planning queries, verifying AI-generated output, and integrating AI with personal code.Substantial shortfalls were observed in critical forethought and verification steps, especially among students using AI for simpler tasks.In contrast, students applying AI to more complex assignments demonstrated stronger behaviors, although certain foundational habits remained weak across the board.The paper proposes five low-overhead scaffolds to help instructors close these usage gaps and improve AI literacy in programming education.

Generative Artificial Intelligence (AI) is transforming the economy, labor market, and society.Given the importance of generative AI, Information Systems (IS) faculties need to teach students the fundamental technical concepts of generative AI so that they can have a deeper understanding.This study proposes a technical teaching case of developing and running a reasoning model-powered web app on Google Colab.This paper contributes to the IS education discipline in three areas.First, it demystifies generative AI by presenting the behind-scenes code that powers AI.Second, it introduces reasoning models and explains their behavior.Third, the case can be used as a comprehensive technical introduction to generative AI because it discusses many essential concepts, principles, techniques, and best practices of generative AI.

Large Language Models (LLMs) have opened new frontiers in Computational Social Science (CSS) by enabling the extraction, classification, and analysis of large-scale unstructured text data.This study aims to leverage LLMs to systematically encode theoretical constructs from user-generated content.We propose an LLM-powered construct identification framework that employs LLMs for automated encoding, validated against human-coded benchmarks.The framework was evaluated as a case study in the domain of wearable devices.Two experiments for binary and ternary encoding were tested.For both experiments, the LLM demonstrated high accuracy, precision, and recall in encoding theoretical constructs of user-generated content.The findings emphasize that LLMs can complement traditional methods in CSS, enabling scalable, efficient, and effective analysis of social phenomena across diverse domains.

This study explores how listeners perceive and interpret music created by or with artificial intelligence (AI).Using a custom-designed survey, 57 university students and social media users listened to a series of 12 music tracks (composed by humans, co-created with AI, or fully AI-generated) and attempted to identify the origin of each.Participants also responded to questions about their attitudes toward AI in music.Results revealed that co-created tracks were the most difficult to identify correctly, while AI-only and human-generated tracks had modestly higher identification rates.Musicians and non-musicians showed similar accuracy but differed in confidence and willingness to engage with AI-assisted music.Most participants expressed support for labeling AI-generated music and were cautiously open to AIhuman creative collaboration, though concerns remained regarding authenticity and emotional depth.These findings offer insight into public readiness for AI in the arts and suggest pathways for ethical design, media literacy, and future research in music technology.

Despite widespread facial recognition capabilities in modern surveillance systems and growing state-level restrictions, a content analysis of over 250 K-12 student and staff handbooks revealed none addressed facial recognition or biometric data collection policies or usage.With keyword searches across small, medium, large, rural, suburban, and urban school systems, the analysis showed despite overarching use of surveillance cameras on campuses, in auxiliary locations such as stadiums and fields, and on student transportation vehicles, the public school system handbooks reviewed did not address disclosure of any collection of facial recognition or other biometric data possibilities.These major findings, developed via systematic content analysis, indicate a policy vacuum which points to a systemic problem while providing baseline data for policy reform opportunities at the local, state, and federal levels.This systematic policy void represents a critical gap between widespread surveillance technology deployment and institutional transparency, creating potential legal vulnerabilities and compromising informed consent principles for students and their families as well as staff members.This transparency void suggests potential gaps in institutional oversight of biometric data governance practices leading to cybersecurity vulnerabilities and risks.The study's conclusion points to opportunities for K12 student and staff handbooks to provide disclosure regarding biometric tools used for surveillance, administration, and even instructional purposes.

Generative Artificial Intelligence (AI) model is a revolutionary type of AI capable of producing high quality images based on textual inputs.These models utilize natural language processing (NLP) techniques and computer vision to understand and interpret the textual descriptions and then generate images that align with the given descriptions.This study evaluates four prominent text-to-image generative models-DALL-E, Google Imagen, Stable Diffusion, and GROK AI emphasizing on the text-to-image diffusion models.Using a comprehensive evaluation approach, we employ three mathematical formulas the Frchet Inception Distance (FID), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) to assess image quality and realism across datasets collected from these AI platforms.Additionally, human evaluations are conducted to compare the perceptual impact of AI-generated images with mathematical metrics.Our findings highlight the varying degrees of perceived realism among different image generative With AI models, DALL-E and Imagen generally being perceived as more realistic than Stable Diffusion and GROK.As examined, human evaluation is the current gold standard in text-to-image evaluation; however, mathematical based metrics also have promise and value.Our findings contribute to the advancement of text-to-image synthesis and our results provide support for FID as the gold standard evaluation method as it most closely represented human evaluation.

Generative artificial intelligence (AI) is forecasted to have more of an impact on some occupations and industries than others.We examine the impact that AI will have on musicians and singers.This analysis provides an interesting example as musicians typically engage in physical activities when playing, performing, and use creativity when writing songs.We contribute to the existing literature by linking AI to both an occupational analysis and delving into the emotional aspects AI might not be able to provide for musicians.We explore the pros and cons of using AI for the creation of AI generated music.Further, this paper explores music creation to focus on production and performance.Additional consideration is provided regarding the legal and ethical considerations of AI for musicians.

This paper discusses the expansion and proliferation of programming languages that have been developed for data science.The paper gives background information on the technological development that led to the growth and expansion of data science languages.It then explains the common features among these languages that distinguish them from general-purpose programming languages.The paper is intended for professionals experienced in working with general-purpose programming languages and may want to enhance their working knowledge of Data Science Programming Languages.The paper further explains the main features that Data Science programming language offers, which may not be readily available in earlier programming languages .