Strengthening the ubiquity and accuracy of attribution—the foundational principal of academic research—in all usage of Generative Artificial Intelligence (GenAI) will be key in maximizing its ethical and accurate usage, and thus the breadth of its potential, in academia and industry, alike. The internet has promulgated the experience of achieving instant user gratification in accomplishing a task. The authors have referred to this as “internet time” in Higher Education Computer Science and Cybersecurity publications for twenty years. They have taught over sixty courses forty of which were intensive, one-month courses offered online. Teaching students to work on publication-worthy one-month projects was only made possible by virtualization, first in virtual labs and then in cloud infrastructures. Emphasizing documentation and taking time to provide citations in assignments became a daily task. Offering both undergraduate and graduate students an opportunity to learn to publish course projects produced remarkable results. Avoiding plagiarism and giving attribution to team member contributions and ideas offered by other researchers reinforced attribution in the creative process. Learning to freely share creative ideas became the norm. Helping others to succeed became the rule. Systematically inserting collaborative projects into every course fostered the need for recognizing the contributions of all. Mentoring students and teaching them how to write a publishable paper produced twenty-one publications with three in professional journals.

The availability of multiple GenAI platforms has now increased the tendency toward misusing the artificial intelligence (AI) tools by plagiarizing and neglecting attribution by the undisciplined individual. The initial design of Large Language Models (LLMs) and GenAI platforms to include unauthenticated content, without giving its source provenance has promoted even more careless regard by the user. Since GenAI platforms only rarely give a citation on data sources many users represent the query response as their own creative product. Instead of mentoring students to learn how to give proper attribution some universities simply forbid the use of GenAI product.

Many software developers are adopting “vibe coding” practices wherein they partner with GenAI in writing code for software applications. This is a powerful tool, but presents challenges for debugging and maintaining the codebase. Here, too, attribution will be a key mitigator. Today, optimizing for this GenAI output is highly dependent upon effective query authoring. Even apart from the technical development of the user, query optimization increases GenAI-driven productivity according to several studies by 30%. The authors have found that, in industry, this increase can be in excess of 200% for a skilled programmer. Effective query authoring becomes mandatory. A symbiotic flow of query-answer sequence is the remarkable result. Hence, Gartner posits that future companies can only be successful if they master the use of GenAI in corporate processes. The authors have a companion presentation in this conference on illustrating query optimization.

This paper suggests that by mentoring students and employees in rigorous attribution and query optimization—and by requiring acknowledgement of usage of GenAI—students and employees, alike, will better integrate an ethical and productive tool into educational and work processes.

Keywords: AI, attribution, citation, GenAI, plagiarism, productivity, provenance, publication, query.