AI Adoption, Ethical Software Testing, and Academic Research Excellence in State-Owned Universities in Delta State

Abstract

This study investigated the effect of AI adoption, ethical software testing, and academic research excellence in four (4) state-owned universities in Delta State: Delta State University, Abraka; Southern Delta University, Ozoro; Dennis Osadebay University, and University of Delta, Agbor using the multi-stage sampling technique. Data were collected through a structured questionnaire designed in line with the study objectives. Meanwhile, both descriptive and inferential statistics were employed for data analysis. A total of 180 respondents were drawn from the sampled universities. However, 165 copies of questionnaire were filled properly. The study reported that, ChatGPT and Quillbot were the most regularly used AI tools. Again, paraphrasing/theft through paraphrasing, data falsification, conflict of authorship, data privacy breaches and algorithmic bias are the key ethical issues impending AI usage and ethical software testing.  Additionally, ethical awareness (β = 0.416, t-value=6.721, and p=0.000) is the strongest ethical AI tool testing predictor, next to institutional policies (β = 0.339, t-value= 5.840 and p-value=0.000) and access to AI training (β = 0.270, t-value= 0.270 and p-value =0.000). Lastly, the result affirmed that junior (graduate assistant  and assistant lecturers) and mid-level academic staff  (lecturer 11 and lecturer 1) are more willing to use AI tools in research activities than upper level academic staff  (Senior lecturer to Professors). Thus, the study concludes that strict adherence to ethical standards and software testing protocols are essential for achieving academic excellence. Consequently, Nigerian university managements and the National Universities Commission (NUC) need to develop clear, concise, and African context-specific institutional policies and structured ethical software testing frameworks targeted at validating AI solution before deployment and at the same time provide rank-sensitive training programs for all academic staff.