Artificial intelligence (AI) text detection tools are considered a means of preserving the integrity of scholarly publication by identifying whether a text is written by humans or generated by AI. This study evaluates three popular tools (GPTZero, ZeroGPT, and DetectGPT) through two experiments: first, distinguishing human-written abstracts from those generated by ChatGPT o1 and Gemini 2.0 Pro Experimental; second, evaluating AI-assisted abstracts where the original text has been enhanced by these large language models (LLMs) to improve readability. Results reveal notable trade-offs in accuracy and bias, disproportionately affecting non-native speakers and certain disciplines. This study highlights the limitations of detection-focused approaches and advocates a shift toward ethical, responsible, and transparent use of LLMs in scholarly publication.
Generative AI’s rapid diffusion demands precise, up-to-date measures of AI literacy. This study develops and validates the Multidimensional AI Literacy Competency Scale (MAIL-CS), designed specifically for the GenAI era. Using a large sample of Chinese university students (N=850) and a split-sample design, we conducted EFA and CFA to establish a robust four-factor structure—Foundational Knowledge & Ethics, Operational Skills, Critical Evaluation, and Application & Innovation. The best-fitting model showed strong fit indices, and the 32-item scale demonstrated high internal consistency (Cronbach’s α and McDonald’s ω ≥ .82 subscales; α=.91, ω=.92 total). Convergent validity was supported by positive correlations with digital literacy and critical thinking; discriminant validity was evidenced by negligible relations with Big Five traits. MAIL-CS offers educators, researchers, and policymakers a reliable instrument to diagnose competency gaps, evaluate interventions, and inform curriculum and strategy. Validation in a non-Western context provides a foundation for cross-cultural assessment and future invariance testing.
Background Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. Methodology A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. Results Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. Conclusion There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.
Non-experts have long made important contributions to machine learning (ML) by contributing training data, and recent work has shown that non-experts can also help with feature engineering by suggesting novel predictive features. However, non-experts have only contributed features to prediction tasks already posed by experienced ML practitioners. Here we study how non-experts can design prediction tasks themselves, what types of tasks non-experts will design, and whether predictive models can be automatically trained on data sourced for their tasks. We use a crowdsourcing platform where non-experts design predictive tasks that are then categorized and ranked by the crowd. Crowdsourced data are collected for top-ranked tasks and predictive models are then trained and evaluated automatically using those data. We show that individuals without ML experience can collectively construct useful datasets and that predictive models can be learned on these datasets, but challenges remain. The prediction tasks designed by non-experts covered a broad range of domains, from politics and current events to health behavior, demographics, and more. Proper instructions are crucial for non-experts, so we also conducted a randomized trial to understand how different instructions may influence the types of prediction tasks being proposed. In general, understanding better how non-experts can contribute to ML can further leverage advances in Automatic machine learning and has important implications as ML continues to drive workplace automation.
Computational thinking (CT) is increasingly recognized globally as a fundamental skill for the 21st century, yet its implementation in computer science (CS) education often is still limited to coding activities (Bers, 2018). It is essential to reframe CT as a cognitive approach that supports computational inquiry, problem-solving, and ethical AI engagement, starting from early education using developmentally appropriate tools like ScratchJr (Papadakis, 2020). In the age of artificial intelligence and rapid technological advancements, CS education faces a critical need to evolve beyond traditional approaches.