Jul 2026· International Journal of Scientific Research and Modern Technology· pp. 398· 0 citations· 7 references
TL;DR
The sections that follow trace the historical development of AI within computer science, review the principal technique families and their applications, and close with an original discussion of cross-cutting patterns, ethical obligations, and likely future directions.
Abstract
Artificial intelligence has altered the way computer science research is planned, carried out, and judged. What began in the early twentieth century as a body of formal theory about computation and reasoning matured, over several decades, into a practical discipline: early decision trees and rule-based systems prepared the ground for the statistical learning algorithms in use today (Taulli T). Machine learning, and within it deep learning, now anchors work in natural language processing, computer vision, and robotics, where multi-layered neural networks extract structure from data volumes no manual analysis could cover (Aslay F). Two consequences of this shift frame the present review. The first is methodological. Learning-based models let researchers process large datasets quickly and surface regularities that complexity previously hid from view; in areas ranging from medical diagnostics to autonomous systems, this has translated into measurable gains in accuracy and speed of discovery. The second consequence is organizational. Because AI methods travel well across domains, computer scientists increasingly work alongside specialists in health, finance, and environmental science. These collaborations do more than export algorithms; they feed domain requirements back into algorithm design, producing tools that answer to real problems such as climate modeling and public health surveillance. Integration of this kind is not free of cost. It brings technical hurdles — data quality, reproducibility, computational expense — and it raises ethical questions about bias, transparency, and accountability that the discipline is still learning to answer. Frameworks that establish ethical standards are needed both to preserve public trust and to comply with emerging regulation. The sections that follow trace the historical development of AI within computer science, review the principal technique families and their applications, and close with an original discussion of cross-cutting patterns, ethical obligations, and likely future directions.
Artificial intelligence (AI) is a multidisciplinary field concerned with the development of systems and AI-powered applications capable of performing tasks that normally require human intelligence, including natural language processing and recognition, and decision-making. AI has evolved rapidly and has increasingly influenced various sectors, including higher education and scientific research. In recent years, AI-powered tools have begun to reshape key stages of the research process, notably literature review, data collection, and data analysis. Despite this growing influence, the integration of AI into scientific research remains underexplored in the Moroccan academic sphere. This practice-oriented paper explores a wide range of AI-powered applications and how they can be effectively utilized to enhance research productivity and efficiency across different stages of the research process. This paper offers significantly crucial insights into how scientific research can be streamlined through the utilization of several AI-powered applications while maintaining the academically and methodologically agreed-upon standards and ethics.
M. Sabour· American Journal of Educatio...· 0 citations
Artificial Intelligence (AI) is a multidisciplinary field focused on designing and developing intelligent systems capable of performing tasks that typically require human intelligence. It encompasses the creation of smart computer programs that can perceive, reason, learn, and make decisions. Although AI draws inspiration from the study of human intelligence, it is not restricted to biologically inspired methods or processes. Despite the absence of a universally accepted definition, AI is generally recognized as the study of computational techniques that enable machines to perceive their environment, reason logically, and take appropriate actions. In the modern digital era, the volume of data generated by humans and machines has increased dramatically, surpassing the capacity of individuals to efficiently process, analyze, and make informed decisions based on such information. AI addresses this challenge by enabling computers to learn from data, identify patterns, and support intelligent decision-making. As a result, artificial intelligence has become a fundamental technology driving automation, data analysis, and advanced computing applications across numerous industries. This paper presents an overview of artificial intelligence by discussing its fundamental concepts, definitions, historical evolution, key characteristics, major applications, recent advancements, and notable achievements.
Mahendra Manilal Patel, Brijesh J. Gameti, Darshanaben D. Pandya· International Journal of Sci...· 0 citations
A survey of the past and future of AI Scientists: machines capable of automating science, which have the potential to transform science and create a new form of science that will create a new form of science and transform the world.
The advancement of AI in science raises broader questions concerning the division of cognitive labour between human researchers and machines, and a typology of AI systems used in research is proposed, ranging from specialised scientific AI through scientific AI assistants and agents to hybrid experimental systems that combine computation and physical experimentation.
P. Jedlička· Teorie vědy / Theory of Scie...· 0 citations
This article examines the emerging paradigm of agentic AI for scientific discovery, traces the conceptual shift from tools to agents, lays out a six-stage workflow spanning literature synthesis to manuscript generation, and reviews practical systems in chemistry, equation discovery, materials science, and general machine learning research.
Alexander Taktakidze· Longevity Horizon· 0 citations
Artificial intelligence and computer science will converge to produce the next set of emerging technologies. Artificial intelligence enables machines to learn, make intelligent decisions, and adapt, but it is built on the foundations of computer science, including algorithms, data structures, optimization, complexity theory, and computing platforms. This theoretical review examines the role of these foundations in modern artificial intelligence systems and their contributions to the development of scalable, explainable, efficient, and deployable technologies. The paper discusses the technical foundations of machine learning, deep learning, graph-based intelligence, neuro-symbolic systems, explainable artificial intelligence, and edge intelligence in terms of algorithmic reasoning, data structures, learning optimization, computational efficiency, and computational infrastructure. It also discusses the role of integrating artificial intelligence and computer science across major application areas, including smart health, cybersecurity, robotics, natural language processing, Internet of Things systems, edge computing, and sustainable digital infrastructure. To improve the paper's conceptual framework, it proposes an Algorithm-to-Intelligence Integration Framework that connects computer science foundations, artificial intelligence paradigms, system requirements, application domains, and future technologies. The survey finds that intelligent systems should, in the future, combine adaptive learning with robust computational design to achieve responsible, secure, sustainable, and deployable technological advancement.
Smitha Rajagopal, S. Nirkhi, Bharati S. Pochal et al.· Future Technology· 0 citations