Autonomous military aircraft are a leading element in the development of autonomous technology. To minimize risk, however, traditional military development test and evaluation must further embrace the software engineering concept of verification and validation.

The previous issue of IT Professional saw a set of five articles published as part of the special issue of Generative AI in Higher Education. This second special issue further examines how generative and agentic artificial intelligence (AI) are reshaping higher education and the need for institutions to rethink assessment, pedagogy, governance, and AI literacy. This introduction provides an overview of five articles that address these challenges through AI-aware assessment, responsible academic innovation, process-centered learning, ethical AI adoption, and the use of AI agents to support student learning.

Some maintain the metaverse is everywhere, and we are already deeply immersed, as it continues to excite the senses via the growing integration of avatars, holograms, shared virtual experience, and extended reality. At the same time, the field of low code/no code is blossoming. This article examines how these two fields, both fueled by generative artificial intelligence, are coevolving.

As we rush headlong into generative artificial intelligence (AI), which agentic AI is rapidly overtaking, we must pay attention to history’s lessons. This article examines what history has to offer regarding the scope, data integrity, security, and talent development of generative AI.

Entering its 25th year, IT Professional faces various new challenges posed by the very technology upon which it reports. On one hand, generative artificial intelligence (GenAI) is poised to significantly assist authors, reviewers, and editors in easing their publishing tasks. On the other hand, existing limitations and the ubiquitous nature of GenAI threaten to weaken scientific publication by replacing originality and innovation with shrewd prompting. This may blur the line that distinguishes true professionalism from mere hucksterism. With an emphasis on the positive, this article probes this ticklish question, whose answer is only slowly evolving. To conclude, we offer a use case that involves editing a scientific magazine.

The process of extraction, transform, and load (ETL) is multifaceted, incorporating many often-laborious batch processing techniques to 1) extract data from disparate multimedia sources, 2) transform these data in accordance with a known data architecture or schema, and 3) seamlessly load the properly conditioned data into a structured repository, which can take many forms. Applied agentic artificial intelligence offers the potential for ETL to be fully automated with often missing semantic analysis elements, performing accurately in real time, with human intervention required only to resolve potential conflicts as they arise.

The explosion of online learning and support platforms has given learners unprecedented access to education. This article highlights why I, as a technology professional, chose the IEEE Learning Network over other prominent platforms.

IT professionals must lead a necessary bottom-up initiative to ensure their constituents are sufficiently protected in this age of rampant and still maturing AI.

While not without detractors, the perception of a skills gap remains prevalent among business leaders. After analysis of some salient trends, this article offers some modest near-term initiatives to address immediate skills gap concerns.

This article proposes a bottom-up approach to the first course in computing, in contrast to the conventional top-down approach of teaching programming in a high-level language first, describing details of the course and its benefits.

This report presents insights from interviews with four experts on the challenges and advantages of implementing crowdsourcing strategies in academic research and other areas. Questions on privacy protection, deepfaking, and other topics were addressed.

Interactive artificial intelligence empowering education requires that teachers and their students alike be prepared for unpredicted levels of critical and mathematical thinking.

Is IT bringing humanity headlong to the brink of extinction, or does technology offer profound solutions to stave off existential doom? The elusive answer has increasingly become the topic of ongoing intellectual speculation, both pro and con. Artificial intelligence (AI) has experienced disappointing initiatives since it was first proposed in 1950. In 2023, AI exploded into popular culture. Then, Open AI unveiled the Q-Star mathematical algorithm in late 2023. This introduced the ability for generative AI to reason numerically, dramatically augmenting large language models. This rudimentary breakthrough likely created upheaval at Open AI, possibly even redimensioning the ongoing debate. In the meantime, as humankind approaches the second quarter of the 21st century, further insights may arise through evaluating the dramatic paradigm shifts brought to bear through IT in the preceding 25 years.

Generative artificial intelligence (GAI) stands poised to eliminate whole occupations. In the future, ongoing education will be required for upskilling as new, unimagined jobs evolve. AI-driven trends are accelerating. They are wake-up calls for academic reform.

The analysis of software for software vulnerability has matured over the years. In the beginning, linear approaches were tried to isolate known vulnerabilities via after-the-fact static analysis. As neural nets and machine learning became useful, more dynamic approaches were applied, It will be interesting to see what the new wave of Generative Artificial intelligence can contribute to the realm of assessing software vulnerabilities.

Technological advances, slow curricula evolution, and other elements contributed to shortfalls in the 21st-century preparation of potential computer science students. It becomes necessary to reevaluate how computer science is treated as a teachable subject.

While we applaud the promise and value of generative artificial intelligence and Chat Generative Pretrained Transformer–like tools, leveraging their potential and values heavily depends on what we use them for and how, while also acknowledging and addressing their limits, limitations, and concerns.

Regional centers (RCs) of major university systems characteristically lack the accreditation to create new courses that satisfy local workforce trends. Instead, any RC must rely on courses developed elsewhere under proper academic oversight. It can be challenging to justify, much less attract, such courses for myriad reasons. To help solve this dilemma, a knowledge graph (KG) was created using the NEO4J Graph database version 1.5.7 to assist a new University System of Maryland in Southern Maryland RC in relating its course selection process to existing pipelines. These pipelines, relying on existing articulation agreements, offer opportunities to expand additional curricula via new partnerships.