Future Directions of Informatics Research: An Introduction to Artificial Intelligence (AI)-Based Research Opportunities
Keywords:
Generative Artificial Intelligence, Software engineering, Explainable AI (XAI), Autonomous System, Bibliometric Analysis, Cyber SecurityAbstract
Today, Artificial Intelligence (AI) is no longer merely a supporting computing technology but has become a central catalyst for digital evolution in the field of informatics. While specific AI algorithms are highly capable of executing focused instructions, this scientific field is undergoing a crucial shift toward Generative AI, multimodal capabilities, and the automation of software creation. This massive change ultimately creates significant gaps in conventional research paradigms. This research focuses on systematically mapping and identifying emerging study focuses, essential technological prospects, and the probability of socio-technical obstacles in the field of AI-integrated informatics for the next ten years. This investigation applies an in-depth literature study approach combined with a bibliometric evaluation of various peer-reviewed scientific publications from 2022 to 2026. This process summarizes various thematic groups spanning the disciplines of software engineering, data science, and cybersecurity. The evaluation successfully identified three crucial research directions: (1) AI-accelerated software development, which can increase code writing speed by up to 45%, but also increases the risk of security vulnerabilities by 18%; (2) autonomous database structures capable of autonomously managing search indexing; and (3) the application of Explainable AI (XAI), which functions to reduce the level of opacity in neural networks by presenting decision-making processes that are transparently understandable to human users. This exploration confirms the massive shift from AI-powered informatics systems to fully autonomous and self-healing infrastructure. However, the lack of standard guidelines regarding data management and the ethics of AI utilization remains the biggest obstacle today. Therefore, further studies must focus on designing security measurement standards for computational code produced by Large Language Models (LLMs), along with expanding the implementation of XAI in real time for critical facilities.
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